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Sample records for adaptive neural-network-based flight

  1. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    NASA Astrophysics Data System (ADS)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  2. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  3. Design of an adaptive neural network based power system stabilizer.

    PubMed

    Liu, Wenxin; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2003-01-01

    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness. PMID:12850048

  4. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

  5. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  6. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    NASA Technical Reports Server (NTRS)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  7. Adaptive neural-network-based control of robotic manipulators

    NASA Astrophysics Data System (ADS)

    Mitchell, Kyle; Dagli, Cihan H.

    2001-03-01

    Robotic manipulators are beginning to be seen doing more tasks in our environment. Classical controls engineers have long known how to control these automated hands. They have failed to address the continued control of these devices after parts of the control infrastructure have failed. A failed motor or actuator in a manipulator decreases its range of motion and changes its control structure. Most failures however do not render the manipulator useless. This paper will discuss the use of a neural network to actively update the controller design as portions of a manipulator fail. Actuators can become stuck and later free themselves. Motors can lose range of motion or stop completely. Connecting arms can become bent or entangled. Results will be presented on the ability to maintain functionality through a variety of failure modes. The neural network is constructed and tested in a Matlab environment. This allows testing of several neural network techniques such as back propagation and temporal processing without the need to continually reconfigure target hardware. In this paper we will demonstrate that a modified ensemble of back propagation experts can be trained to control a robotic manipulator without the need to calculate the inverse kinematics equations. Further individual experts can be retrained online to allow for adaptive control through changing dynamics. This allows for manipulators to remain in service through failures in the manipulator infrastructure without the need for human intervention into control equations.

  8. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    NASA Technical Reports Server (NTRS)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  9. A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

    NASA Technical Reports Server (NTRS)

    Gupta, Pramod; Schumann, Johann

    2004-01-01

    High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

  10. Adaptive critic neural network-based object grasping control using a three-finger gripper.

    PubMed

    Jagannathan, S; Galan, Gustavo

    2004-03-01

    Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has to be secured accurately and considerably fast without damaging it. Since the gripper, contact dynamics, and the object properties are not typically known beforehand, an adaptive critic neural network (NN)-based hybrid position/force control scheme is introduced. The feedforward action generating NN in the adaptive critic NN controller compensates the nonlinear gripper and contact dynamics. The learning of the action generating NN is performed on-line based on a critic NN output signal. The controller ensures that a three-finger gripper tracks a desired trajectory while applying desired forces on the object for manipulation. Novel NN weight tuning updates are derived for the action generating and critic NNs so that Lyapunov-based stability analysis can be shown. Simulation results demonstrate that the proposed scheme successfully allows fingers of a gripper to secure objects without the knowledge of the underlying gripper and contact dynamics of the object compared to conventional schemes.

  11. Reducing interferences in wireless communication systems by mobile agents with recurrent neural networks-based adaptive channel equalization

    NASA Astrophysics Data System (ADS)

    Beritelli, Francesco; Capizzi, Giacomo; Lo Sciuto, Grazia; Napoli, Christian; Tramontana, Emiliano; Woźniak, Marcin

    2015-09-01

    Solving channel equalization problem in communication systems is based on adaptive filtering algorithms. Today, Mobile Agents (MAs) with Recurrent Neural Networks (RNNs) can be also adopted for effective interference reduction in modern wireless communication systems (WCSs). In this paper MAs with RNNs are proposed as novel computing algorithms for reducing interferences in WCSs performing an adaptive channel equalization. The method to provide it is so called MAs-RNNs. We perform the implementation of this new paradigm for interferences reduction. Simulations results and evaluations demonstrates the effectiveness of this approach and as better transmission performance in wireless communication network can be achieved by using the MAs-RNNs based adaptive filtering algorithm.

  12. Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

    PubMed

    Hwang, Chih-Lyang; Jan, Chau

    2016-02-01

    At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

  13. Verification and Validation of Adaptive and Intelligent Systems with Flight Test Results

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Larson, Richard R.

    2009-01-01

    F-15 IFCS project goals are: a) Demonstrate Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions [A] & [B] failures. b) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs with a Pilot in the Loop. Gen II objectives include; a) Implement and Fly a Direct Adaptive Neural Network Based Flight Controller; b) Demonstrate the Ability of the System to Adapt to Simulated System Failures: 1) Suppress Transients Associated with Failure; 2) Re-Establish Sufficient Control and Handling of Vehicle for Safe Recovery. c) Provide Flight Experience for Development of Verification and Validation Processes for Flight Critical Neural Network Software.

  14. Neural network based architectures for aerospace applications

    NASA Technical Reports Server (NTRS)

    Ricart, Richard

    1987-01-01

    A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.

  15. Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems.

    PubMed

    Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

    2016-01-01

    This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.

  16. Neural network based temporal video segmentation.

    PubMed

    Cao, X; Suganthan, P N

    2002-01-01

    The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences. PMID:12370954

  17. Convolutional Neural Network Based dem Super Resolution

    NASA Astrophysics Data System (ADS)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

  18. Flight Results of the NF-15B Intelligent Flight Control System (IFCS) Aircraft with Adaptation to a Longitudinally Destabilized Plant

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2008-01-01

    Adaptive flight control systems have the potential to be resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. The goal for the adaptive system is to provide an increase in survivability in the event that these extreme changes occur. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane. The adaptive element was incorporated into a dynamic inversion controller with explicit reference model-following. As a test the system was subjected to an abrupt change in plant stability simulating a destabilizing failure. Flight evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to stabilize the vehicle and reestablish good onboard reference model-following. Flight evaluation with the simulated destabilizing failure and adaptation engaged showed improvement in the vehicle stability margins. The convergent properties of this initial system warrant additional improvement since continued maneuvering caused continued adaptation change. Compared to the non-adaptive system the adaptive system provided better closed-loop behavior with improved matching of the onboard reference model. A detailed discussion of the flight results is presented.

  19. Neural Network Based System for Equipment Startup Surveillance

    1996-12-18

    NEBSESS is a system for equipment surveillance and fault detection which relies on a neural-network based means for diagnosing disturbances during startup and for automatically actuating the Sequential Probability Ratio Test (SPRT) as a signal validation means during steady-state operation.

  20. Neural Network Based Intelligent Sootblowing System

    SciTech Connect

    Mark Rhode

    2005-04-01

    , particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  1. Design development of a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Lembeck, Michael F.

    1992-01-01

    This paper identifies the requirements and describes an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is demonstrated for its ability to identify faults in low frequency waveforms.

  2. Neural Network Based Montioring and Control of Fluidized Bed.

    SciTech Connect

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  3. Analog neural network-based helicopter gearbox health monitoring system.

    PubMed

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  4. Adaptive nonlinear flight control

    NASA Astrophysics Data System (ADS)

    Rysdyk, Rolf Theoduor

    1998-08-01

    Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator

  5. Implementation of an Adaptive Controller System from Concept to Flight Test

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

    2009-01-01

    The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) is used to test and develop these algorithms. Modifications to this airplane include adding canards and changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals include demonstration of revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions and advancement of neural-network-based flight control technology for new aerospace system designs. This report presents an overview of the processes utilized to develop adaptive controller algorithms during a flight-test program, including a description of initial adaptive controller concepts and a discussion of modeling formulation and performance testing. Design finalization led to integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness; these are also discussed.

  6. Neural network based analysis for chemical sensor arrays

    SciTech Connect

    Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1995-04-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. In this paper, we examine the effectiveness of using artificial neural networks for real-time data analysis of a sensor array. Analyzing the sensor data in parallel may allow for rapid identification of contaminants in the field without requiring highly selective individual sensors. We use a prototype sensor array which consists of nine tin-oxide Taguchi-type sensors, a temperature sensor, and a humidity sensor. We illustrate that by using neural network based analysis of the sensor data, the selectivity of the sensor array may be significantly improved, especially when some (or all) the sensors are not highly selective.

  7. Neural network based feed-forward high density associative memory

    NASA Technical Reports Server (NTRS)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  8. Implementation of an Adaptive Controller System from Concept to Flight Test

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.; Burken, John J.; Butler, Bradley S.

    2009-01-01

    The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) was used for these algorithms. This airplane has been modified by the addition of canards and by changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals included demonstration of revolutionary control approaches that can efficiently optimize aircraft performance for both normal and failure conditions, and to advance neural-network-based flight control technology for new aerospace systems designs. Before the NF-15B IFCS airplane was certified for flight test, however, certain processes needed to be completed. This paper presents an overview of these processes, including a description of the initial adaptive controller concepts followed by a discussion of modeling formulation and performance testing. Upon design finalization, the next steps are: integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness.

  9. Flight Test Results from the NF-15B Intelligent Flight Control System (IFCS) Project with Adaptation to a Simulated Stabilator Failure

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.; Williams-Hayes, Peggy S.

    2007-01-01

    Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

  10. Flight Test Results from the NF-15B Intelligent Flight Control System (IFCS) Project with Adaptation to a Simulated Stabilator Failure

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.; Williams-Hayes, Peggy S.

    2010-01-01

    Adaptive flight control systems have the potential to be more resilient to extreme changes in airplane behavior. Extreme changes could be a result of a system failure or of damage to the airplane. A direct adaptive neural-network-based flight control system was developed for the National Aeronautics and Space Administration NF-15B Intelligent Flight Control System airplane and subjected to an inflight simulation of a failed (frozen) (unmovable) stabilator. Formation flight handling qualities evaluations were performed with and without neural network adaptation. The results of these flight tests are presented. Comparison with simulation predictions and analysis of the performance of the adaptation system are discussed. The performance of the adaptation system is assessed in terms of its ability to decouple the roll and pitch response and reestablish good onboard model tracking. Flight evaluation with the simulated stabilator failure and adaptation engaged showed that there was generally improvement in the pitch response; however, a tendency for roll pilot-induced oscillation was experienced. A detailed discussion of the cause of the mixed results is presented.

  11. Neural Network Based Intrusion Detection System for Critical Infrastructures

    SciTech Connect

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  12. Neural Network Based Representation of UH-60A Pilot and Hub Accelerations

    NASA Technical Reports Server (NTRS)

    Kottapalli, Sesi

    2000-01-01

    Neural network relationships between the full-scale, experimental hub accelerations and the corresponding pilot floor vertical vibration are studied. The present physics-based, quantitative effort represents an initial systematic study on the UH-60A Black Hawk hub accelerations. The NASA/Army UH-60A Airloads Program flight test database was used. A 'maneuver-effect-factor (MEF)', derived using the roll-angle and the pitch-rate, was used. Three neural network based representation-cases were considered. The pilot floor vertical vibration was considered in the first case and the hub accelerations were separately considered in the second case. The third case considered both the hub accelerations and the pilot floor vertical vibration. Neither the advance ratio nor the gross weight alone could be used to predict the pilot floor vertical vibration. However, the advance ratio and the gross weight together could be used to predict the pilot floor vertical vibration over the entire flight envelope. The hub accelerations data were modeled and found to be of very acceptable quality. The hub accelerations alone could not be used to predict the pilot floor vertical vibration. Thus, the hub accelerations alone do not drive the pilot floor vertical vibration. However, the hub accelerations, along with either the advance ratio or the gross weight or both, could be used to satisfactorily predict the pilot floor vertical vibration. The hub accelerations are clearly a factor in determining the pilot floor vertical vibration.

  13. Flight Test Comparison of Different Adaptive Augmentations for Fault Tolerant Control Laws for a Modified F-15

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Hanson, Curtis E.; Lee, James A.; Kaneshinge, John T.

    2009-01-01

    This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

  14. A neural network-based exploratory learning and motor planning system for co-robots

    PubMed Central

    Galbraith, Byron V.; Guenther, Frank H.; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640

  15. A neural network-based exploratory learning and motor planning system for co-robots.

    PubMed

    Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object. PMID:26257640

  16. A neural network-based exploratory learning and motor planning system for co-robots.

    PubMed

    Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  17. Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

    NASA Astrophysics Data System (ADS)

    Zhang, Li

    With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman

  18. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    PubMed

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction.

  19. Dynamic neural networks based on-line identification and control of high performance motor drives

    NASA Technical Reports Server (NTRS)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  20. Neural Networks Based Approach to Enhance Space Hardware Reliability

    NASA Technical Reports Server (NTRS)

    Zebulum, Ricardo S.; Thakoor, Anilkumar; Lu, Thomas; Franco, Lauro; Lin, Tsung Han; McClure, S. S.

    2011-01-01

    This paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.

  1. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    NASA Astrophysics Data System (ADS)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2016-09-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  2. Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

    NASA Astrophysics Data System (ADS)

    Cuadra, Lucas; Alexandre, Enrique; Gil-Pita, Roberto; Vicen-Bueno, Raúl; Álvarez, Lorena

    2009-12-01

    Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.

  3. Flight Approach to Adaptive Control Research

    NASA Technical Reports Server (NTRS)

    Pavlock, Kate Maureen; Less, James L.; Larson, David Nils

    2011-01-01

    The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The testbed served as a full-scale vehicle to test and validate adaptive flight control research addressing technical challenges involved with reducing risk to enable safe flight in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.

  4. Flight Test Approach to Adaptive Control Research

    NASA Technical Reports Server (NTRS)

    Pavlock, Kate Maureen; Less, James L.; Larson, David Nils

    2011-01-01

    The National Aeronautics and Space Administration s Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The validation of adaptive controls has the potential to enhance safety in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.

  5. Neural network based optimal control of HVAC&R systems

    NASA Astrophysics Data System (ADS)

    Ning, Min

    supervisory controller, a set of five adaptive PI (proportional-integral) controllers are designed for each of the five local control loops of the HVAC&R system. The five controllers are used to track optimal set points and zone air temperature set points. Parameters of these PI controllers are tuned online to reduce tracking errors. The updating rules are derived from Lyapunov stability analysis. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions.

  6. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    SciTech Connect

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  7. Evaluating the performances of statistical and neural network based control charts

    NASA Astrophysics Data System (ADS)

    Teoh, Kok Ban; Ong, Hong Choon

    2015-10-01

    Control chart is used widely in many fields and traditional control chart is no longer adequate in detecting a sudden change in a particular process. So, run rules which are built in into Shewhart X ¯ control chart while Exponential Weighted Moving Average control chart (EWMA), Cumulative Sum control chart (CUSUM) and neural network based control chart are introduced to overcome the limitation regarding to the sensitivity of traditional control chart. In this study, the average run length (ARL) and median run length (MRL) in the shifts in the process mean of control charts mentioned will be computed. We will show that interpretations based only on the ARL can be misleading. Thus, MRL is also used to evaluate the performances of the control charts. From this study, neural network based control chart is found to possess a better performance than run rules of Shewhart X ¯ control chart, EWMA and CUSUM control chart.

  8. Improved Crack Type Classification Neural Network based on Square Sub-images of Pavement Surface

    NASA Astrophysics Data System (ADS)

    Lee, Byoung Jik; Lee, Hosin “David”

    The previous neural network based on the proximity values was developed using rectangular pavement images. However, the proximity value derived from the rectangular image was biased towards transverse cracking. By sectioning the rectangular image into a set of square sub-images, the neural network based on the proximity value became more robust and consistent in determining a crack type. This paper presents an improved neural network to determine a crack type from a pavement surface image based on square sub-images over the neural network trained using rectangular pavement images. The advantage of using square sub-image is demonstrated by using sample images of transverse cracking, longitudinal cracking and alligator cracking.

  9. Hybrid Adaptive Flight Control with Model Inversion Adaptation

    NASA Technical Reports Server (NTRS)

    Nguyen, Nhan

    2011-01-01

    This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.

  10. Rotationally Adaptive Flight Test Surface

    NASA Technical Reports Server (NTRS)

    Barrett, Ron

    1999-01-01

    Research on a new design of flutter exciter vane using adaptive materials was conducted. This novel design is based on all-moving aerodynamic surface technology and consists of a structurally stiff main spar, a series of piezoelectric actuator elements and an aerodynamic shell which is pivoted around the main spar. The work was built upon the current missile-type all-moving surface designs and change them so they are better suited for flutter excitation through the transonic flight regime. The first portion of research will be centered on aerodynamic and structural modeling of the system. USAF DatCom and vortex lattice codes was used to capture the fundamental aerodynamics of the vane. Finite element codes and laminated plate theory and virtual work analyses will be used to structurally model the aerodynamic vane and wing tip. Following the basic modeling, a flutter test vane was designed. Each component within the structure was designed to meet the design loads. After the design loads are met, then the deflections will be maximized and the internal structure will be laid out. In addition to the structure, a basic electrical control network will be designed which will be capable of driving a scaled exciter vane. The third and final stage of main investigation involved the fabrication of a 1/4 scale vane. This scaled vane was used to verify kinematics and structural mechanics theories on all-moving actuation. Following assembly, a series of bench tests was conducted to determine frequency response, electrical characteristics, mechanical and kinematic properties. Test results indicate peak-to-peak deflections of 1.1 deg with a corner frequency of just over 130 Hz.

  11. Research in digital adaptive flight controllers

    NASA Technical Reports Server (NTRS)

    Kaufman, H.

    1976-01-01

    A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Both explicit controllers which directly utilize parameter identification and implicit controllers which do not require identification were considered. Extensive analytical and simulation efforts resulted in the recommendation of two explicit digital adaptive flight controllers. Interface weighted least squares estimation procedures with control logic were developed using either optimal regulator theory or with control logic based upon single stage performance indices.

  12. F-15 IFCS Intelligent Flight Control System

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2008-01-01

    This viewgraph presentation gives a detailed description of the F-15 aircraft, flight tests, aircraft performance and overall advanced neural network based flight control technologies for aerospace systems designs.

  13. Neural-network-based speed controller for induction motors using inverse dynamics model

    NASA Astrophysics Data System (ADS)

    Ahmed, Hassanein S.; Mohamed, Kamel

    2016-08-01

    Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.

  14. Adaptive Flight Control Research at NASA

    NASA Technical Reports Server (NTRS)

    Motter, Mark A.

    2008-01-01

    A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing.

  15. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    NASA Technical Reports Server (NTRS)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  16. Combined imaging and neural-network-based approach in solid waste sorting

    NASA Astrophysics Data System (ADS)

    Alunni, Stefano; Bonifazi, Giuseppe; de Carli, Alessandro; Massacci, Paolo

    2002-04-01

    Solid waste recycling is more and more increasing according to the need to realize dismantled material recovery and to reduce overall environmental pollution. When a recycling strategy is applied sorting strategies have to be developed and implemented. Such an approach ca be considered as the second logical step of the process that is, after that the attributes (physical, chemical, morphological, morphometrical, textural, etc.) of the materials resulting from classical processing (comminution, classification, separation, etc.) are detected and numerically modeled. The resulting feature vector need to be handled by a software architecture performing the required recognition/classification procedure and defining the quality of the investigated products. From the results further feed-back or feed-forward control strategies can be applied in order to improve equipment or processing architectures performances. In this paper are analyzed and described neural network based sorting strategies applied with reference to fluff (light fraction of the materials resulting from car dismantling) recognition.

  17. Gene identification and analysis: an application of neural network-based information fusion

    SciTech Connect

    Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  18. Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

    NASA Astrophysics Data System (ADS)

    Altaisky, Mikhail V.; Zolnikova, Nadezhda N.; Kaputkina, Natalia E.; Krylov, Victor A.; Lozovik, Yurii E.; Dattani, Nikesh S.

    2016-02-01

    We present the results of the simulation of a quantum neural network based on quantum dots using numerical method of path integral calculation. In the proposed implementation of the quantum neural network using an array of single-electron quantum dots with dipole-dipole interaction, the coherence is shown to survive up to 0.1 nanosecond in time and up to the liquid nitrogen temperature of 77K.We study the quantum correlations between the quantum dots by means of calculation of the entanglement of formation in a pair of quantum dots on the GaAs based substrate with dot size of 100 ÷ 101 nanometer and interdot distance of 101 ÷ 102 nanometers order.

  19. A neural network-based power system stabilizer using power flow characteristics

    SciTech Connect

    Park, Y.M.; Choi, M.S.; Lee, K.Y.

    1996-06-01

    A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.

  20. Evolution of an artificial neural network based autonomous land vehicle controller.

    PubMed

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks. PMID:18263046

  1. Back propagation neural network based control for the heating system of a polysilicon reduction furnace.

    PubMed

    Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi

    2013-12-01

    In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal I(d), which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal I(d) the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal I(d) and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.

  2. From Neuromuscular Activation to End-point Locomotion: An Artificial Neural Network-based Technique for Neural Prostheses

    PubMed Central

    Chang, Chia-Lin; Jin, Zhanpeng; Chang, Hou-Cheng; Cheng, Allen C.

    2009-01-01

    Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing non-invasive, closed-loop neuromuscular control strategies for neural prostheses. Our goal was to investigate if an Artificial Neural Network-based (ANN-based) model for closed-loop controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their end-point gait parameters (such as stride length and step width). We recruited 12 persons with SB (5 females and 7 males) and collected their neuromuscular activation and end-point gait parameters during overground walking. Our results show that the proposed ANN-based technique can achieve a highly accurate prediction (e.g., R-values of 0.92 – 0.97, ANN (tansig+tansig) for single composition of data sets) for altered end-point locomotion. Compared to traditional robust regression, this technique can provide up to 80% more accurate prediction. Our results suggest that more precise control of complex neural prostheses during locomotion can be achieved by engaging neuromuscular activity as intrinsic feedback to generate end-point leg movement. This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses. PMID:19389678

  3. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids. PMID:25532191

  4. Neural networks-based damage detection for bridges considering errors in baseline finite element models

    NASA Astrophysics Data System (ADS)

    Lee, Jong Jae; Lee, Jong Won; Yi, Jin Hak; Yun, Chung Bang; Jung, Hie Young

    2005-02-01

    Structural health monitoring has become an important research topic in conjunction with damage assessment and safety evaluation of structures. The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in signal analysis and information processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, a neural networks-based damage detection method using the modal properties is presented, which can effectively consider the modelling errors in the baseline finite element model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modelling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the proposed method. Results of laboratory test on a simply supported bridge model and field test on a bridge with multiple girders confirm the applicability of the present method.

  5. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    USGS Publications Warehouse

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  6. Development and comparison of neural network based soft sensors for online estimation of cement clinker quality.

    PubMed

    Pani, Ajaya Kumar; Vadlamudi, Vamsi Krishna; Mohanta, Hare Krishna

    2013-01-01

    The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models. PMID:22940135

  7. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  8. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    PubMed

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

  9. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    PubMed

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking. PMID:24808521

  10. Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

    NASA Astrophysics Data System (ADS)

    Zhao, Lin; Jia, Yingmin

    2016-06-01

    In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

  11. L(sub 1) Adaptive Flight Control System: Flight Evaluation and Technology Transition

    NASA Technical Reports Server (NTRS)

    Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Isaac; Gregory, Irene M.; Cao, Chengyu

    2010-01-01

    Certification of adaptive control technologies for both manned and unmanned aircraft represent a major challenge for current Verification and Validation techniques. A (missing) key step towards flight certification of adaptive flight control systems is the definition and development of analysis tools and methods to support Verification and Validation for nonlinear systems, similar to the procedures currently used for linear systems. In this paper, we describe and demonstrate the advantages of L(sub l) adaptive control architectures for closing some of the gaps in certification of adaptive flight control systems, which may facilitate the transition of adaptive control into military and commercial aerospace applications. As illustrative examples, we present the results of a piloted simulation evaluation on the NASA AirSTAR flight test vehicle, and results of an extensive flight test program conducted by the Naval Postgraduate School to demonstrate the advantages of L(sub l) adaptive control as a verifiable robust adaptive flight control system.

  12. F-8C adaptive flight control laws

    NASA Technical Reports Server (NTRS)

    Hartmann, G. L.; Harvey, C. A.; Stein, G.; Carlson, D. N.; Hendrick, R. C.

    1977-01-01

    Three candidate digital adaptive control laws were designed for NASA's F-8C digital flyby wire aircraft. Each design used the same control laws but adjusted the gains with a different adaptative algorithm. The three adaptive concepts were: high-gain limit cycle, Liapunov-stable model tracking, and maximum likelihood estimation. Sensors were restricted to conventional inertial instruments (rate gyros and accelerometers) without use of air-data measurements. Performance, growth potential, and computer requirements were used as criteria for selecting the most promising of these candidates for further refinement. The maximum likelihood concept was selected primarily because it offers the greatest potential for identifying several aircraft parameters and hence for improved control performance in future aircraft application. In terms of identification and gain adjustment accuracy, the MLE design is slightly superior to the other two, but this has no significant effects on the control performance achievable with the F-8C aircraft. The maximum likelihood design is recommended for flight test, and several refinements to that design are proposed.

  13. Adaptive Control of Truss Structures for Gossamer Spacecraft

    NASA Technical Reports Server (NTRS)

    Yang Bong-Jun; Calise, anthony J.; Craig, James I.; Whorton, Mark S.

    2007-01-01

    Neural network-based adaptive control is considered for active control of a highly flexible truss structure which may be used to support solar sail membranes. The objective is to suppress unwanted vibrations in SAFE (Solar Array Flight Experiment) boom, a test-bed located at NASA. Compared to previous tests that restrained truss structures in planar motion, full three dimensional motions are tested. Experimental results illustrate the potential of adaptive control in compensating for nonlinear actuation and modeling error, and in rejecting external disturbances.

  14. Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.

    2004-01-01

    A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation

  15. Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

    NASA Astrophysics Data System (ADS)

    Lin, Cheng-Jian; Lee, Chi-Yung

    2010-04-01

    This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.

  16. Full-Scale Flight Research Testbeds: Adaptive and Intelligent Control

    NASA Technical Reports Server (NTRS)

    Pahle, Joe W.

    2008-01-01

    This viewgraph presentation describes the adaptive and intelligent control methods used for aircraft survival. The contents include: 1) Motivation for Adaptive Control; 2) Integrated Resilient Aircraft Control Project; 3) Full-scale Flight Assets in Use for IRAC; 4) NASA NF-15B Tail Number 837; 5) Gen II Direct Adaptive Control Architecture; 6) Limited Authority System; and 7) 837 Flight Experiments. A simulated destabilization failure analysis along with experience and lessons learned are also presented.

  17. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation.

    PubMed

    Lin, Che-Wei; Yang, Ya-Ting C; Wang, Jeen-Shing; Yang, Yi-Ching

    2012-09-01

    This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.

  18. Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

    NASA Technical Reports Server (NTRS)

    Gupta, Pramod; Loparo, Kenneth; Mackall, Dale; Schumann, Johann; Soares, Fola

    2004-01-01

    Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

  19. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    PubMed

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. PMID:22738782

  20. Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.

    PubMed

    Godino-Llorente, J I; Gómez-Vilda, P

    2004-02-01

    It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

  1. Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

    NASA Astrophysics Data System (ADS)

    Valdes, A.; Khorasani, K.

    The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.

  2. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    PubMed

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.

  3. Control Reallocation Strategies for Damage Adaptation in Transport Class Aircraft

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen; Krishnakumar, K.; Limes, Greg; Bryant, Don

    2003-01-01

    This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.

  4. Closing the Certification Gaps in Adaptive Flight Control Software

    NASA Technical Reports Server (NTRS)

    Jacklin, Stephen A.

    2008-01-01

    Over the last five decades, extensive research has been performed to design and develop adaptive control systems for aerospace systems and other applications where the capability to change controller behavior at different operating conditions is highly desirable. Although adaptive flight control has been partially implemented through the use of gain-scheduled control, truly adaptive control systems using learning algorithms and on-line system identification methods have not seen commercial deployment. The reason is that the certification process for adaptive flight control software for use in national air space has not yet been decided. The purpose of this paper is to examine the gaps between the state-of-the-art methodologies used to certify conventional (i.e., non-adaptive) flight control system software and what will likely to be needed to satisfy FAA airworthiness requirements. These gaps include the lack of a certification plan or process guide, the need to develop verification and validation tools and methodologies to analyze adaptive controller stability and convergence, as well as the development of metrics to evaluate adaptive controller performance at off-nominal flight conditions. This paper presents the major certification gap areas, a description of the current state of the verification methodologies, and what further research efforts will likely be needed to close the gaps remaining in current certification practices. It is envisioned that closing the gap will require certain advances in simulation methods, comprehensive methods to determine learning algorithm stability and convergence rates, the development of performance metrics for adaptive controllers, the application of formal software assurance methods, the application of on-line software monitoring tools for adaptive controller health assessment, and the development of a certification case for adaptive system safety of flight.

  5. Data systems and computer science: Neural networks base R/T program overview

    NASA Technical Reports Server (NTRS)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  6. Hybrid adaptive ascent flight control for a flexible launch vehicle

    NASA Astrophysics Data System (ADS)

    Lefevre, Brian D.

    For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the

  7. Neural Network Based Modeling and Analysis of LP Control Surface Allocation

    NASA Technical Reports Server (NTRS)

    Langari, Reza; Krishnakumar, Kalmanje; Gundy-Burlet, Karen

    2003-01-01

    This paper presents an approach to interpretive modeling of LP based control allocation in intelligent flight control. The emphasis is placed on a nonlinear interpretation of the LP allocation process as a static map to support analytical study of the resulting closed loop system, albeit in approximate form. The approach makes use of a bi-layer neural network to capture the essential functioning of the LP allocation process. It is further shown via Lyapunov based analysis that under certain relatively mild conditions the resulting closed loop system is stable. Some preliminary conclusions from a study at Ames are stated and directions for further research are given at the conclusion of the paper.

  8. Methodologies for Adaptive Flight Envelope Estimation and Protection

    NASA Technical Reports Server (NTRS)

    Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine

    2009-01-01

    This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.

  9. F-15 IFCS: Intelligent Flight Control System

    NASA Technical Reports Server (NTRS)

    Bosworth, John

    2007-01-01

    This viewgraph presentation describes the F-15 Intelligent Flight Control System (IFCS). The goals of this project include: 1) Demonstrate revolutionary control approaches that can efficiently optimize aircraft performance in both normal and failure conditions; and 2) Demonstrate advance neural network-based flight control technology for new aerospace systems designs.

  10. Development of soft sensor for neural network based control of distillation column.

    PubMed

    Rani, Asha; Singh, Vijander; Gupta, J R P

    2013-05-01

    The present work is aimed at the design of Levenberg-Marquardt (LM) and adaptive linear network (ADALINE) based soft sensors and their application in inferential control of a multicomponent distillation process. Further the ADALINE sensor is trained online using past measurements, to adapt the changes in the inputs and is termed as dynamic ADALINE (D-ADALINE) sensor. The soft sensors are then used in the control loop to obtain LM based inferential controller (LMIC), ADALINE based inferential controller (ADIC) and D-ADALINE based inferential controller (DADIC) for the process. The performance of dynamic controller is also analyzed for different inputs and sampling intervals. The comparison of results shows the efficient and robust prediction capability of D-ADALINE sensor and hence DADIC proves to be the best controller.

  11. Artificial neural network based hourly load forecasting for decentralized load management

    SciTech Connect

    Mandal, J.K.; Sinha, A.K.

    1995-12-31

    Decentralized load management is an essential part of the power system operation. Forecasting load demand at the substation level is generally more difficult and less accurate compared to forecasting total system load demand. In this paper, Multi-Layered Feed Forward (MLFF) neural network is used to predict the bus-load demand at the substation level. The MLFF network is trained using Back-Propagation (BP) algorithm with adaptive learning technique. The algorithm is tested for two systems having different load patterns.

  12. A Design of Fuzzy Neural Network Based Robust Gain Scheduling Controllers

    NASA Astrophysics Data System (ADS)

    Sato, Yoshishige

    This paper propose robust gain scheduling control design by intelligent control which uses Fuzzy-Neural Network without model. Proposal methods are as follows, To constitute a robust and capable of automatically gain controlling against the conventional fixed PID control system. To build the Neural Network which learns inverse dynamics as feed forward compensation, and to build 2 degrees freedom control which is the feedback compensation. To propose the control system which adaptively adjusts the gain according to the changes of target errors, and to verified the effectiveness of the proposed method.

  13. Flight Test of L1 Adaptive Control Law: Offset Landings and Large Flight Envelope Modeling Work

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Xargay, Enric; Cao, Chengyu; Hovakimyan, Naira

    2011-01-01

    This paper presents new results of a flight test of the L1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems. The flight test was conducted on the subscale turbine powered Generic Transport Model that is an integral part of the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. The results presented include control law evaluation for piloted offset landing tasks as well as results in support of nonlinear aerodynamic modeling and real-time dynamic modeling of the departure-prone edges of the flight envelope.

  14. Adaptive Trajectory Prediction Algorithm for Climbing Flights

    NASA Technical Reports Server (NTRS)

    Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz

    2012-01-01

    Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

  15. Neural network-based visual body weight estimation for drug dosage finding

    NASA Astrophysics Data System (ADS)

    Pfitzner, Christian; May, Stefan; Nüchter, Andreas

    2016-03-01

    Body weight adapted drug dosages are important for emergency treatments: Inaccuracies in body weight estimation may lead to inaccurate drug dosing. This paper describes an improved approach to estimating the body weight of emergency patients in a trauma room, based on images from an RGB-D and a thermal camera. The improvements are specific to several aspects: Fusion of RGB-D and thermal camera eases filtering and segmentation of the patient's body from the background. Robustness and accuracy is gained by an artificial neural network, which considers geometric features from the sensors as input, e.g. the patient's volume, and shape parameters. Preliminary experiments with 69 patients show an accuracy close to 90 percent, with less than 10 percent relative error and the results are compared with the physician's estimate, the patient's statement and an established anthropometric method.

  16. A fast and scalable recurrent neural network based on stochastic meta descent.

    PubMed

    Liu, Zhenzhen; Elhanany, Itamar

    2008-09-01

    This brief presents an efficient and scalable online learning algorithm for recurrent neural networks (RNNs). The approach is based on the real-time recurrent learning (RTRL) algorithm, whereby the sensitivity set of each neuron is reduced to weights associated with either its input or output links. This yields a reduced storage and computational complexity of O(N(2)). Stochastic meta descent (SMD), an adaptive step size scheme for stochastic gradient-descent problems, is employed as means of incorporating curvature information in order to substantially accelerate the learning process. We also introduce a clustered version of our algorithm to further improve its scalability attributes. Despite the dramatic reduction in resource requirements, it is shown through simulation results that the approach outperforms regular RTRL by almost an order of magnitude. Moreover, the scheme lends itself to parallel hardware realization by virtue of the localized property that is inherent to the learning framework. PMID:18779096

  17. An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students.

    PubMed

    Fernández-Alemán, José Luis; López-González, Laura; González-Sequeros, Ofelia; Jayne, Chrisina; López-Jiménez, Juan José; Carrillo-de-Gea, Juan Manuel; Toval, Ambrosio

    2016-04-01

    This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training. PMID:26815339

  18. Is a Complex Neural Network Based Air Quality Prediction Model Better Than a Simple One? A Bayesian Point of View

    NASA Astrophysics Data System (ADS)

    Hoi, K. I.; Yuen, K. V.; Mok, K. M.

    2010-05-01

    In this study the neural network based air quality prediction model was tested in a typical coastal city, Macau, with Latitude 22° 10'N and Longitude 113° 34'E. By using five years of air quality and meteorological data recorded at an ambient air quality monitoring station between 2001 and 2005, it was found that the performance of the ANN model was generally improved by increasing the number of hidden neurons in the training phase. However, the performance of the ANN model was not sensitive to the change in the number of hidden neurons during the prediction phase. Therefore, the improvement in the error statistics for a complex ANN model in the training phase may be only caused by the overfitting of the data. In addition, the posterior PDF of the parameter vector conditional on the training dataset was investigated for different number of hidden neurons. It was found that the parametric space for a simple ANN model was globally identifiable and the Levenberg-Marquardt backpropagation algorithm was able to locate the optimal parameter vector. However, the parameter vector might contain redundant parameters and the parametric space was not globally identifiable when the model class became complex. In addition, the Levenberg-Marquardt backpropagation algorithm was unable to locate the most optimal parameter vector in this situation. Finally, it was concluded that the a more complex MLP model, that fits the data better, is not necessarily better than a simple one.

  19. An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students.

    PubMed

    Fernández-Alemán, José Luis; López-González, Laura; González-Sequeros, Ofelia; Jayne, Chrisina; López-Jiménez, Juan José; Carrillo-de-Gea, Juan Manuel; Toval, Ambrosio

    2016-04-01

    This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training.

  20. Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems.

    PubMed

    Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong

    2014-12-01

    In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach. PMID:25415951

  1. Development of a digital adaptive optimal linear regulator flight controller

    NASA Technical Reports Server (NTRS)

    Berry, P.; Kaufman, H.

    1975-01-01

    Digital adaptive controllers have been proposed as a means for retaining uniform handling qualities over the flight envelope of a high-performance aircraft. Towards such an implementation, an explicit adaptive controller, which makes direct use of online parameter identification, has been developed and applied to the linearized lateral equations of motion for a typical fighter aircraft. The system is composed of an online weighted least-squares parameter identifier, a Kalman state filter, and a model following control law designed using optimal linear regulator theory. Simulation experiments with realistic measurement noise indicate that the proposed adaptive system has the potential for onboard implementation.

  2. ANNSTLF-a neural-network-based electric load forecasting system.

    PubMed

    Khotanzad, A; Afkhami-Rohani, R; Lu, T L; Abaye, A; Davis, M; Maratukulam, D J

    1997-01-01

    A key component of the daily operation and planning activities of an electric utility is short-term load forecasting, i.e., the prediction of hourly loads (demand) for the next hour to several days out. The accuracy of such forecasts has significant economic impact for the utility. This paper describes a load forecasting system known as ANNSTLF (artificial neural-network short-term load forecaster) which has received wide acceptance by the electric utility industry and presently is being used by 32 utilities across the USA and Canada. ANNSTLF can consider the effect of temperature and relative humidity on the load. Besides its load forecasting engine, ANNSTLF contains forecasters that can generate the hourly temperature and relative humidity forecasts needed by the system. ANNSTLF is based on a multiple ANN strategy that captures various trends in the data. Both the first and the second generation of the load forecasting engine are discussed and compared. The building block of the forecasters is a multilayer perceptron trained with the error backpropagation learning rule. An adaptive scheme is employed to adjust the ANN weights during online forecasting. The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database. The results of testing the system on data from ten different utilities are reported.

  3. Neural network based approach for tuning of SNS feedback and feedforward controllers.

    SciTech Connect

    Kwon, S. I.; Prokop, M. S.; Regan, A. H.

    2002-01-01

    The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

  4. Neural network-based motion control of an underactuated wheeled inverted pendulum model.

    PubMed

    Yang, Chenguang; Li, Zhijun; Cui, Rongxin; Xu, Bugong

    2014-11-01

    In this paper, automatic motion control is investigated for one of wheeled inverted pendulum (WIP) models, which have been widely applied for modeling of a large range of two wheeled modern vehicles. First, the underactuated WIP model is decomposed into a fully actuated second order subsystem Σa consisting of planar movement of vehicle forward and yaw angular motions, and a nonactuated first order subsystem Σb of pendulum motion. Due to the unknown dynamics of subsystem Σa and the universal approximation ability of neural network (NN), an adaptive NN scheme has been employed for motion control of subsystem Σa . The model reference approach has been used whereas the reference model is optimized by the finite time linear quadratic regulation technique. The pendulum motion in the passive subsystem Σb is indirectly controlled using the dynamic coupling with planar forward motion of subsystem Σa , such that satisfactory tracking of a set pendulum tilt angle can be guaranteed. Rigours theoretic analysis has been established, and simulation studies have been performed to demonstrate the developed method.

  5. Novel neural networks-based fault tolerant control scheme with fault alarm.

    PubMed

    Shen, Qikun; Jiang, Bin; Shi, Peng; Lim, Cheng-Chew

    2014-11-01

    In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

  6. A Neural-Network-Based Semi-Automated Geospatial Classification Tool

    NASA Astrophysics Data System (ADS)

    Hale, R. G.; Herzfeld, U. C.

    2014-12-01

    North America's largest glacier system, the Bering Bagley Glacier System (BBGS) in Alaska, surged in 2011-2013, as shown by rapid mass transfer, elevation change, and heavy crevassing. Little is known about the physics controlling surge glaciers' semi-cyclic patterns; therefore, it is crucial to collect and analyze as much data as possible so that predictive models can be made. In addition, physical signs frozen in ice in the form of crevasses may help serve as a warning for future surges. The BBGS surge provided an opportunity to develop an automated classification tool for crevasse classification based on imagery collected from small aircraft. The classification allows one to link image classification to geophysical processes associated with ice deformation. The tool uses an approach that employs geostatistical functions and a feed-forward perceptron with error back-propagation. The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network (NN) can recognize. In an application to preform analysis on airborne video graphic data from the surge of the BBGS, an NN was able to distinguish 18 different crevasse classes with 95 percent or higher accuracy, for over 3,000 images. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we designed the tool's semi-automated pre-training algorithm to be adaptable. The tool can be optimized to specific settings and variables of image analysis: (airborne and satellite imagery, different camera types, observation altitude, number and types of classes, and resolution). The generalization of the classification tool brings three important advantages: (1) multiple types of problems in geophysics can be studied, (2) the training process is sufficiently formalized to allow non-experts in neural nets to perform the training process, and (3) the time required to

  7. Flight Test Implementation of a Second Generation Intelligent Flight Control System

    NASA Technical Reports Server (NTRS)

    Williams-Hayes, Peggy S.

    2005-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team was to develop and flight-test control systems that use neural network technology, to optimize the performance of the aircraft under nominal conditions, and to stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The Intelligent Flight Control System team is currently in the process of implementing a second generation control scheme, collectively known as Generation 2 or Gen 2, for flight testing on the NASA F-15 aircraft. This report describes the Gen 2 system as implemented by the team for flight test evaluation. Simulation results are shown which describe the experiment to be performed in flight and highlight the ways in which the Gen 2 system meets the defined objectives.

  8. Stability and Performance Metrics for Adaptive Flight Control

    NASA Technical Reports Server (NTRS)

    Stepanyan, Vahram; Krishnakumar, Kalmanje; Nguyen, Nhan; VanEykeren, Luarens

    2009-01-01

    This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the system's characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the system's input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircraft's input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers.

  9. Adaptive integral dynamic surface control of a hypersonic flight vehicle

    NASA Astrophysics Data System (ADS)

    Aslam Butt, Waseem; Yan, Lin; Amezquita S., Kendrick

    2015-07-01

    In this article, non-linear adaptive dynamic surface air speed and flight path angle control designs are presented for the longitudinal dynamics of a flexible hypersonic flight vehicle. The tracking performance of the control design is enhanced by introducing a novel integral term that caters to avoiding a large initial control signal. To ensure feasibility, the design scheme incorporates magnitude and rate constraints on the actuator commands. The uncertain non-linear functions are approximated by an efficient use of the neural networks to reduce the computational load. A detailed stability analysis shows that all closed-loop signals are uniformly ultimately bounded and the ? tracking performance is guaranteed. The robustness of the design scheme is verified through numerical simulations of the flexible flight vehicle model.

  10. Flight data processing with the F-8 adaptive algorithm

    NASA Technical Reports Server (NTRS)

    Hartmann, G.; Stein, G.; Petersen, K.

    1977-01-01

    An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described

  11. Context-specific adaptation of saccade gain in parabolic flight

    NASA Technical Reports Server (NTRS)

    Shelhamer, Mark; Clendaniel, Richard A.; Roberts, Dale C.

    2002-01-01

    Previous studies established that vestibular reflexes can have two adapted states (e.g., gains) simultaneously, and that a context cue (e.g., vertical eye position) can switch between the two states. Our earlier work demonstrated this phenomenon of context-specific adaptation for saccadic eye movements: we asked for gain decrease in one context state and gain increase in another context state, and then determined if a change in the context state would invoke switching between the adapted states. Horizontal and vertical eye position and head orientation could serve, to varying degrees, as cues for switching between two different saccade gains. In the present study, we asked whether gravity magnitude could serve as a context cue: saccade adaptation was performed during parabolic flight, which provides alternating levels of gravitoinertial force (0 g and 1.8 g). Results were less robust than those from ground experiments, but established that different saccade magnitudes could be associated with different gravity levels.

  12. Astronaut adaptation to 1 G following long duration space flight

    NASA Technical Reports Server (NTRS)

    Walker, John; Greenisen, Michael; Cowell, Lynda L.; Squires, William G.

    1991-01-01

    The paper reviews the results of studies of changes undergone by several physiological systems (including the cardiovascular system, the fluid and electrolyte characteristics, the red blood cells, the bone and the muscle tissues, and the exercise capacity) due to the exposures to microgravity and to the adaptation to 1 G after a long-duration space flight. Special attention is given to the effects of various training protocols and countermeasures used to attenuate the physiological problems encountered upon return from space.

  13. Integrated Neural Flight and Propulsion Control System

    NASA Technical Reports Server (NTRS)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  14. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  15. Flight Validation of a Metrics Driven L(sub 1) Adaptive Control

    NASA Technical Reports Server (NTRS)

    Dobrokhodov, Vladimir; Kitsios, Ioannis; Kaminer, Isaac; Jones, Kevin D.; Xargay, Enric; Hovakimyan, Naira; Cao, Chengyu; Lizarraga, Mariano I.; Gregory, Irene M.

    2008-01-01

    The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf AP with and without adaptive augmentation to recover from control surface failures.

  16. Nutrition and human physiological adaptations to space flight

    NASA Technical Reports Server (NTRS)

    Lane, H. W.; LeBlanc, A. D.; Putcha, L.; Whitson, P. A.

    1993-01-01

    Space flight provides a model for the study of healthy individuals undergoing unique stresses. This review focuses on how physiological adaptations to weightlessness may affect nutrient and food requirements in space. These adaptations include reductions in body water and plasma volume, which affect the renal and cardiovascular systems and thereby fluid and electrolyte requirements. Changes in muscle mass and function may affect requirements for energy, protein and amino acids. Changes in bone mass lead to increased urinary calcium concentrations, which may increase the risk of forming renal stones. Space motion sickness may influence putative changes in gastro-intestinal-hepatic function; neurosensory alterations may affect smell and taste. Some or all of these effects may be ameliorated through the use of specially designed dietary countermeasures.

  17. Flight control system development and flight test experience with the F-111 mission adaptive wing aircraft

    NASA Technical Reports Server (NTRS)

    Larson, R. R.

    1986-01-01

    The wing on the NASA F-111 transonic aircraft technology airplane was modified to provide flexible leading and trailing edge flaps. This wing is known as the mission adaptive wing (MAW) because aerodynamic efficiency can be maintained at all speeds. Unlike a conventional wing, the MAW has no spoilers, external flap hinges, or fairings to break the smooth contour. The leading edge flaps and three-segment trailing edge flaps are controlled by a redundant fly-by-wire control system that features a dual digital primary system architecture providing roll and symmetric commands to the MAW control surfaces. A segregated analog backup system is provided in the event of a primary system failure. This paper discusses the design, development, testing, qualification, and flight test experience of the MAW primary and backup flight control systems.

  18. Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Long, Theresa W.

    1995-01-01

    Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

  19. Launch Vehicle Manual Steering with Adaptive Augmenting Control In-flight Evaluations Using a Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt

    2014-01-01

    An adaptive augmenting control algorithm for the Space Launch System has been developed at the Marshall Space Flight Center as part of the launch vehicles baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a proposed manual steering mode were investigated by giving the pilot trajectory deviation cues and pitch rate command authority.

  20. Flight Test of an L(sub 1) Adaptive Controller on the NASA AirSTAR Flight Test Vehicle

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Xargay, Enric; Cao, Chengyu; Hovakimyan, Naira

    2010-01-01

    This paper presents results of a flight test of the L-1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems. The flight test was conducted on the subscale turbine powered Generic Transport Model that is an integral part of the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. The results presented are for piloted tasks performed during the flight test.

  1. Ambiguous Tilt and Translation Motion Cues after Space Flight and Otolith Assessment during Post-Flight Re-Adaptation

    NASA Technical Reports Server (NTRS)

    Wood, Scott J.; Clarke, A. H.; Harm, D. L.; Rupert, A. H.; Clement, G. R.

    2009-01-01

    Adaptive changes during space flight in how the brain integrates vestibular cues with other sensory information can lead to impaired movement coordination, vertigo, spatial disorientation and perceptual illusions following Gtransitions. These studies are designed to examine both the physiological basis and operational implications for disorientation and tilt-translation disturbances following short duration space flights.

  2. Application of Adaptive Autopilot Designs for an Unmanned Aerial Vehicle

    NASA Technical Reports Server (NTRS)

    Shin, Yoonghyun; Calise, Anthony J.; Motter, Mark A.

    2005-01-01

    This paper summarizes the application of two adaptive approaches to autopilot design, and presents an evaluation and comparison of the two approaches in simulation for an unmanned aerial vehicle. One approach employs two-stage dynamic inversion and the other employs feedback dynamic inversions based on a command augmentation system. Both are augmented with neural network based adaptive elements. The approaches permit adaptation to both parametric uncertainty and unmodeled dynamics, and incorporate a method that permits adaptation during periods of control saturation. Simulation results for an FQM-117B radio controlled miniature aerial vehicle are presented to illustrate the performance of the neural network based adaptation.

  3. Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

    NASA Technical Reports Server (NTRS)

    Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Issac; Kitsios, Ioannis; Cao, Chengyu; Gregory, Irene M.; Valavani, Lena

    2010-01-01

    The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems.

  4. Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Orr, Jeb S.; Miller, Christopher J.; Hanson, Curtis E.

    2014-01-01

    The NASA Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an Adaptive Augmenting Control (AAC) algorithm for launch vehicles that improves robustness and performance by adapting an otherwise welltuned classical control algorithm to unexpected environments or variations in vehicle dynamics. This AAC algorithm is currently part of the baseline design for the SLS Flight Control System (FCS), but prior to this series of research flights it was the only component of the autopilot design that had not been flight tested. The Space Launch System (SLS) flight software prototype, including the adaptive component, was recently tested on a piloted aircraft at Dryden Flight Research Center (DFRC) which has the capability to achieve a high level of dynamic similarity to a launch vehicle. Scenarios for the flight test campaign were designed specifically to evaluate the AAC algorithm to ensure that it is able to achieve the expected performance improvements with no adverse impacts in nominal or nearnominal scenarios. Having completed the recent series of flight characterization experiments on DFRC's F/A-18, the AAC algorithm's capability, robustness, and reproducibility, have been successfully demonstrated. Thus, the entire SLS control architecture has been successfully flight tested in a relevant environment. This has increased NASA's confidence that the autopilot design is ready to fly on the SLS Block I vehicle and will exceed the performance of previous architectures.

  5. A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

    PubMed

    Han, Min; Fan, Jianchao; Wang, Jun

    2011-09-01

    A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays. PMID:21803682

  6. Flight Test of Composite Model Reference Adaptive Control (CMRAC) Augmentation Using NASA AirSTAR Infrastructure

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Gadient, ROss; Lavretsky, Eugene

    2011-01-01

    This paper presents flight test results of a robust linear baseline controller with and without composite adaptive control augmentation. The flight testing was conducted using the NASA Generic Transport Model as part of the Airborne Subscale Transport Aircraft Research system at NASA Langley Research Center.

  7. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

    PubMed Central

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

  8. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    PubMed

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.

  9. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    PubMed

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

  10. F-15 837 IFCS Intelligent Flight Control System Project

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2007-01-01

    This viewgraph presentation reviews the use of Intelligent Flight Control System (IFCS) for the F-15. The goals of the project are: (1) Demonstrate Revolutionary Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions (2) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs. The motivation for the development are to reduce the chance and skill required for survival.

  11. Investigation of the Multiple Model Adaptive Control (MMAC) method for flight control systems

    NASA Technical Reports Server (NTRS)

    1975-01-01

    The application was investigated of control theoretic ideas to the design of flight control systems for the F-8 aircraft. The design of an adaptive control system based upon the so-called multiple model adaptive control (MMAC) method is considered. Progress is reported.

  12. Aeroelastic Deformation: Adaptation of Wind Tunnel Measurement Concepts to Full-Scale Vehicle Flight Testing

    NASA Technical Reports Server (NTRS)

    Burner, Alpheus W.; Lokos, William A.; Barrows, Danny A.

    2005-01-01

    The adaptation of a proven wind tunnel test technique, known as Videogrammetry, to flight testing of full-scale vehicles is presented. A description is presented of the technique used at NASA's Dryden Flight Research Center for the measurement of the change in wing twist and deflection of an F/A-18 research aircraft as a function of both time and aerodynamic load. Requirements for in-flight measurements are compared and contrasted with those for wind tunnel testing. The methodology for the flight-testing technique and differences compared to wind tunnel testing are given. Measurement and operational comparisons to an older in-flight system known as the Flight Deflection Measurement System (FDMS) are presented.

  13. Steerable Adaptive Bullet (StAB) piezoelectric flight control system

    NASA Astrophysics Data System (ADS)

    Barrett, Ron; Barnhart, Ryan; Bramlette, Richard

    2012-04-01

    This paper outlines a new class of piezoelectric flight control actuators which are specifically intended for use in guided hard-launched munitions from under 5.56mm to 40mm in caliber. In March of 2011, US Pat. 7,898,153 was issued, describing this new class of actuators, how they are mounted, laminated, energized and used to control the flight of a wide variety of munitions. This paper is the technical conference paper companion to the Patent. A Low Net Passive Stiffness (LNPS) Post Buckled Precompressed (PBP) piezoelectric actuator element for a 0.40 caliber body, 0.50 caliber round was built and tested. Aerodynamic modeling of the flight control actuator showed that canard deflections of just +/-1° are more than sufficient to provide full flight control against 99% atmospherics to 2km of range while maintaining just 10cm of dispersion with lethal energy pressure levels upon terminal contact. Supersonic wind tunnel testing was conducted as well as a sweep of axial compression. The LNPS/PBP configuration exhibited an amplification factor of 3.8 while maintaining equivalent corner frequencies in excess of 100 Hz and deflection levels of +/-1°. The paper concludes with a fabrication and assembly cost analysis on a mass production scale.

  14. Physiological aeroecology: Anatomical and physiological adaptations for flight

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Flight has evolved independently in birds, bats, and insects and was present in the Mesozoic pterosaurians that have disappeared. Of the roughly 1 million living animal species, more than three-quarters are flying insects. Flying is an extremely successful way of locomotion. At first glance this see...

  15. Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

    NASA Technical Reports Server (NTRS)

    He, Yuning; Lee, Herbert K. H.; Davies, Misty D.

    2012-01-01

    Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.

  16. A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient

    NASA Astrophysics Data System (ADS)

    Sauzède, R.; Claustre, H.; Uitz, J.; Jamet, C.; Dall'Olmo, G.; D'Ortenzio, F.; Gentili, B.; Poteau, A.; Schmechtig, C.

    2016-04-01

    The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean.

  17. A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

    NASA Astrophysics Data System (ADS)

    Agarwal, Smriti; Bisht, Amit Singh; Singh, Dharmendra; Pathak, Nagendra Prasad

    2014-12-01

    Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.

  18. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

    PubMed Central

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S.

    2012-01-01

    Background While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. Methods In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. Results We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. Conclusions A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  19. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects.

    PubMed

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S

    2011-08-01

    BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  20. LaPlace Transform1 Adaptive Control Law in Support of Large Flight Envelope Modeling Work

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Xargay, Enric; Cao, Chengyu; Hovakimyan, Naira

    2011-01-01

    This paper presents results of a flight test of the L1 adaptive control architecture designed to directly compensate for significant uncertain cross-coupling in nonlinear systems. The flight test was conducted on the subscale turbine powered Generic Transport Model that is an integral part of the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. The results presented are in support of nonlinear aerodynamic modeling and instrumentation calibration.

  1. Highly integrated digital electronic control: Digital flight control, aircraft model identification, and adaptive engine control

    NASA Technical Reports Server (NTRS)

    Baer-Riedhart, Jennifer L.; Landy, Robert J.

    1987-01-01

    The highly integrated digital electronic control (HIDEC) program at NASA Ames Research Center, Dryden Flight Research Facility is a multiphase flight research program to quantify the benefits of promising integrated control systems. McDonnell Aircraft Company is the prime contractor, with United Technologies Pratt and Whitney Aircraft, and Lear Siegler Incorporated as major subcontractors. The NASA F-15A testbed aircraft was modified by the HIDEC program by installing a digital electronic flight control system (DEFCS) and replacing the standard F100 (Arab 3) engines with F100 engine model derivative (EMD) engines equipped with digital electronic engine controls (DEEC), and integrating the DEEC's and DEFCS. The modified aircraft provides the capability for testing many integrated control modes involving the flight controls, engine controls, and inlet controls. This paper focuses on the first two phases of the HIDEC program, which are the digital flight control system/aircraft model identification (DEFCS/AMI) phase and the adaptive engine control system (ADECS) phase.

  2. Approach for Structurally Clearing an Adaptive Compliant Trailing Edge Flap for Flight

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Lokos, William A.; Cruz, Josue; Crampton, Glen; Stephens, Craig A.; Kota, Sridhar; Ervin, Gregory; Flick, Pete

    2015-01-01

    The Adaptive Compliant Trailing Edge (ACTE) flap was flown on the National Aeronautics and Space Administration (NASA) Gulfstream GIII testbed at the NASA Armstrong Flight Research Center. This smoothly curving flap replaced the existing Fowler flaps creating a seamless control surface. This compliant structure, developed by FlexSys Inc. in partnership with the Air Force Research Laboratory, supported NASA objectives for airframe structural noise reduction, aerodynamic efficiency, and wing weight reduction through gust load alleviation. A thorough structures airworthiness approach was developed to move this project safely to flight. A combination of industry and NASA standard practice require various structural analyses, ground testing, and health monitoring techniques for showing an airworthy structure. This paper provides an overview of compliant structures design, the structural ground testing leading up to flight, and the flight envelope expansion and monitoring strategy. Flight data will be presented, and lessons learned along the way will be highlighted.

  3. Adaptive support vector regression for UAV flight control.

    PubMed

    Shin, Jongho; Jin Kim, H; Kim, Youdan

    2011-01-01

    This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.

  4. [ROLE OF THE SYMPATHOADRENOMEDULLARY SYSTEM IN FORMATION OF PILOT'S ADAPTATION TO FLIGHT LOADS].

    PubMed

    Sukhoterin, A F; Pashchenko, P S; Plakhov, N N; Zhuravlev, A G

    2015-01-01

    Purpose of the work was to evaluate the sympathoadrenomedullary functions and associated psychophysiological reactions of pilots as a function of flight hours on highly maneuverable aircraft. Volunteers to the investigation were 78 pilots (41 pilots of maneuverable aircraft and 37 pilots of bombers and transporters). Selected methods were to enable comprehensive evaluation of the body functioning against flight loads. Our results evidence that piloting of high maneuverable aircraft but not of bombing and transporting aircrafts activates the sympathoadrenomedullary system significantly. This is particularly common to young pilots with the total flying time less than 1000 hours. Adaptive changes to flight factors were noted to develop with age and experience. PMID:26738308

  5. Flight envelope protection of aircraft using adaptive neural network and online linearisation

    NASA Astrophysics Data System (ADS)

    Shin, Hohyun; Kim, Youdan

    2016-03-01

    Flight envelope protection algorithm is proposed to improve the safety of an aircraft. Flight envelope protection systems find the control inputs to prevent an aircraft from exceeding structure/aerodynamic limits and maximum control surface deflections. The future values of state variables are predicted using the current states and control inputs based on linearised aircraft model. To apply the envelope protection algorithm for the wide envelope of the aircraft, online linearisation is adopted. Finally, the flight envelope protection system is designed using adaptive neural network and least-squares method. Numerical simulations are conducted to verify the performance of the proposed scheme.

  6. A Decentralized Adaptive Approach to Fault Tolerant Flight Control

    NASA Technical Reports Server (NTRS)

    Wu, N. Eva; Nikulin, Vladimir; Heimes, Felix; Shormin, Victor

    2000-01-01

    This paper briefly reports some results of our study on the application of a decentralized adaptive control approach to a 6 DOF nonlinear aircraft model. The simulation results showed the potential of using this approach to achieve fault tolerant control. Based on this observation and some analysis, the paper proposes a multiple channel adaptive control scheme that makes use of the functionally redundant actuating and sensing capabilities in the model, and explains how to implement the scheme to tolerate actuator and sensor failures. The conditions, under which the scheme is applicable, are stated in the paper.

  7. Energetic Metabolism and Biochemical Adaptation: A Bird Flight Muscle Model

    ERIC Educational Resources Information Center

    Rioux, Pierre; Blier, Pierre U.

    2006-01-01

    The main objective of this class experiment is to measure the activity of two metabolic enzymes in crude extract from bird pectoral muscle and to relate the differences to their mode of locomotion and ecology. The laboratory is adapted to stimulate the interest of wildlife management students to biochemistry. The enzymatic activities of cytochrome…

  8. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    NASA Technical Reports Server (NTRS)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  9. Synthesis of ethological studies on behavioural adaptation of the astronaut to space flight conditions

    NASA Astrophysics Data System (ADS)

    Tafforin, Carole

    The motor behaviour of the astronaut as revealed in his movement, posture and orientation is treated as observable evidence of the subject's adaptation to space flight conditions. In addition to the conservative physiological homeostasies, the quantitative description of the astronaut's motor activity in microgravity is postulated in terms of an innovative regulation, within a temporal dynamic. The proposed ethological method consists of first drawing up a specific behavioural repertoire and then of using video recordings of space missions to describe each of the behavioural units observed in the ongoing flux context in which it occurred. Finally the data is quantified into frequencies of occurrence, transition and association and completed with factorial correlation analysis. Comparison of ground training ( g = 1) and space flight ( g = 0) between the first and last day of a mission up to return to Earth gravity simulated by an anti-orthostatic decubitus experiment, reveals the nature of the adaptive strategies implemented. These strategies are evidence of changes in the behavioural repertoire including the search for predominantly visual environmental cues and the progression of motor skill during the flight. The pre-flight period is defined as a phase involving automizing of motor patterns and the post-flight period as rehabituation of strategies which have already been acquired. The phenomena observed are discussed in terms of the new spatial representation and the body image, constructed by the astronaut during his adaptation. They are considered to be optimizing for the subject's relation to his environment.

  10. Nonlinear Adaptive Flight Control for the X-38 Reentry Vehicle

    NASA Astrophysics Data System (ADS)

    Wallner, E. M.; Well, K. H.

    The paper is concerned with designing an attitude control system for the X-38 vehicle for the hypersonic and supersonic region. The design goals are i) good tracking performance such that the vehicle will follow the guidance commands, ii) robust stability and performance in view of uncertain aerodynamic parameters, iii) cross-airframe capability of the control architecture in order to minimize redesign efforts in view of vehicle modifications which might occur during the development process. These goals have been achieved by selecting an inversion based control system design procedure combined with a CMAC neural net for adaptation of the linear PID controller parameters in view of the uncertainties. It is shown that the application of dynamic inversion requires a redefinition of the controlled variables in order to adequately stabilize the closed-loop system. The need for output-redefinition lies in the fact that only two bodyflaps are available for control, which limits the number of controlled variables to two. Simulation results are given to show the efficacy of the control approach.

  11. Approach for Structurally Clearing an Adaptive Compliant Trailing Edge Flap for Flight

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Lokos, William A.; Cruz, Josue; Crampton, Glen; Stephens, Craig A.; Kota, Sridhar; Ervin, Gregory; Flick, Pete

    2015-01-01

    The Adaptive Compliant Trailing Edge (ACTE) flap was flown on the NASA Gulfstream GIII test bed at the NASA Armstrong Flight Research Center. This smoothly curving flap replaced the existing Fowler flaps creating a seamless control surface. This compliant structure, developed by FlexSys Inc. in partnership with Air Force Research Laboratory, supported NASA objectives for airframe structural noise reduction, aerodynamic efficiency, and wing weight reduction through gust load alleviation. A thorough structures airworthiness approach was developed to move this project safely to flight.

  12. Launch Vehicle Manual Steering with Adaptive Augmenting Control In-flight Evaluations of Adverse Interactions Using a Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt; Miller, Chris; Wall, John H.; Vanzwieten, Tannen S.; Gilligan, Eric; Orr, Jeb S.

    2015-01-01

    An adaptive augmenting control algorithm for the Space Launch System has been developed at the Marshall Space Flight Center as part of the launch vehicles baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a proposed manual steering mode were investigated by giving the pilot trajectory deviation cues and pitch rate command authority. Two NASA research pilots flew a total of twenty five constant pitch-rate trajectories using a prototype manual steering mode with and without adaptive control.

  13. Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

    NASA Technical Reports Server (NTRS)

    Thompson, Sarah; Davies, Misty D.; Gundy-Burlet, Karen

    2010-01-01

    Adaptive flight control systems hold tremendous promise for maintaining the safety of a damaged aircraft and its passengers. However, most currently proposed adaptive control methodologies rely on online learning neural networks (OLNNs), which necessarily have the property that the controller is changing during the flight. These changes tend to be highly nonlinear, and difficult or impossible to analyze using standard techniques. In this paper, we approach the problem with a variant of compositional verification. The overall system is broken into components. Undesirable behavior is fed backwards through the system. Components which can be solved using formal methods techniques explicitly for the ranges of safe and unsafe input bounds are treated as white box components. The remaining black box components are analyzed with heuristic techniques that try to predict a range of component inputs that may lead to unsafe behavior. The composition of these component inputs throughout the system leads to overall system test vectors that may elucidate the undesirable behavior

  14. Adaptation of neuromuscular activation patterns during treadmill walking after long-duration space flight

    NASA Astrophysics Data System (ADS)

    Layne, C. S.; Lange, G. W.; Pruett, C. J.; McDonald, P. V.; Merkle, L. A.; Mulavara, A. P.; Smith, S. L.; Kozlovskaya, I. B.; Bloomberg, J. J.

    The precise neuromuscular control needed for optimal locomotion, particularly around heel strike and toe off, is known to be compromised after short duration (8- to 15-day) space flight. We hypothesized here that longer exposure to weightlessness would result in maladaptive neuromuscular activation during postflight treadmill walking. We also hypothesized that space flight would affect the ability of the sensory-motor control system to generate adaptive neuromuscular activation patterns in response to changes in visual target distance during postflight treadmill walking. Seven crewmembers, who completed 3- to 6-month missions, walked on a motorized treadmill while visually fixating on a target placed 30 cm (NEAR) or 2 m (FAR) from the subject's eyes. Electronic foot switch data and surface electromyography were collected from selected muscles of the right lower limb. Results indicate that the phasic features of neuromuscular activation were moderately affected and the relative amplitude of activity in the tibialis anterior and rectus femoris around toe off changed after space flight. Changes also were evident after space flight in how these muscles adapted to the shift in visual target distance.

  15. Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

    PubMed

    Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

    2006-04-01

    This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control

  16. Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

    PubMed

    Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

    2006-04-01

    This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control

  17. Peculiarities of transformation of adaptation level of the astronaut in conditions of long-lasting flight

    NASA Astrophysics Data System (ADS)

    Padashulya, H.; Prisnyakova, L.; Prisnyakov, V.

    Prognostication of the development of adverse factors of psychological processes in the personality of the astronaut who time and again feels transformation of internal structure of his personality is one of cardinal problems of the long-lasting flight Adaptation to changing conditions of long-lasting flight is of particular importance because it has an effect on the efficiency of discharged functions and mutual relations in the team The fact of standard psychological changes emerging in the personality being in the state of structural transformations is the precondition for the possibility of prognostication Age-specific gender and temperamental differences in the personality enable to standardize these changes Examination of the process of transformation of adaptation level of the personality in the varied environment depending on the type of temperament and constituents age and gender is chief object of the report In the report it is shown that in the process of transformation of adaptation parameters - attitude to guillemotleft work guillemotright guillemotleft family guillemotright guillemotleft environment guillemotright and guillemotleft ego guillemotright - the changes can go in two directions - in the direction of increase and decline of indexes The trend of increase enables to accumulate them and form potentiality to reduce or increase the level of personality adaptation There is a hypothesis that the dynamics of the process of transformation of adaptation parameter is shown up in the orientation of increase of

  18. Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

    PubMed

    Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

    2012-08-01

    An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures.

  19. In-flight adaptive performance optimization (APO) control using redundant control effectors of an aircraft

    NASA Technical Reports Server (NTRS)

    Gilyard, Glenn B. (Inventor)

    1999-01-01

    Practical application of real-time (or near real-time) Adaptive Performance Optimization (APO) is provided for a transport aircraft in steady climb, cruise, turn descent or other flight conditions based on measurements and calculations of incremental drag from a forced response maneuver of one or more redundant control effectors defined as those in excess of the minimum set of control effectors required to maintain the steady flight condition in progress. The method comprises the steps of applying excitation in a raised-cosine form over an interval of from 100 to 500 sec. at the rate of 1 to 10 sets/sec of excitation, and data for analysis is gathered in sets of measurements made during the excitation to calculate lift and drag coefficients C.sub.L and C.sub.D from two equations, one for each coefficient. A third equation is an expansion of C.sub.D as a function of parasitic drag, induced drag, Mach and altitude drag effects, and control effector drag, and assumes a quadratic variation of drag with positions .delta..sub.i of redundant control effectors i=1 to n. The third equation is then solved for .delta..sub.iopt the optimal position of redundant control effector i, which is then used to set the control effector i for optimum performance during the remainder of said steady flight or until monitored flight conditions change by some predetermined amount as determined automatically or a predetermined minimum flight time has elapsed.

  20. Design Process of Flight Vehicle Structures for a Common Bulkhead and an MPCV Spacecraft Adapter

    NASA Technical Reports Server (NTRS)

    Aggarwal, Pravin; Hull, Patrick V.

    2015-01-01

    Design and manufacturing space flight vehicle structures is a skillset that has grown considerably at NASA during that last several years. Beginning with the Ares program and followed by the Space Launch System (SLS); in-house designs were produced for both the Upper Stage and the SLS Multipurpose crew vehicle (MPCV) spacecraft adapter. Specifically, critical design review (CDR) level analysis and flight production drawing were produced for the above mentioned hardware. In particular, the experience of this in-house design work led to increased manufacturing infrastructure for both Marshal Space Flight Center (MSFC) and Michoud Assembly Facility (MAF), improved skillsets in both analysis and design, and hands on experience in building and testing (MSA) full scale hardware. The hardware design and development processes from initiation to CDR and finally flight; resulted in many challenges and experiences that produced valuable lessons. This paper builds on these experiences of NASA in recent years on designing and fabricating flight hardware and examines the design/development processes used, as well as the challenges and lessons learned, i.e. from the initial design, loads estimation and mass constraints to structural optimization/affordability to release of production drawing to hardware manufacturing. While there are many documented design processes which a design engineer can follow, these unique experiences can offer insight into designing hardware in current program environments and present solutions to many of the challenges experienced by the engineering team.

  1. Flight test results from a supercritical mission adaptive wing with smooth variable camber

    NASA Technical Reports Server (NTRS)

    Powers, Sheryll Goecke; Webb, Lannie D.; Friend, Edward L.; Lokos, William A.

    1992-01-01

    The mission adaptive wing (MAW) consisted of leading- and trailing-edge variable-camber surfaces that could be deflected in flight to provide a near-ideal wing camber shape for any flight condition. These surfaces featured smooth, flexible upper surfaces and fully enclosed lower surfaces, distinguishing them from conventional flaps that have discontinuous surfaces and exposed or semiexposed mechanisms. Camber shape was controlled by either a manual or automatic flight control system. The wing and aircraft were extensively instrumented to evaluate the local flow characteristics and the total aircraft performance. This paper discusses the interrelationships between the wing pressure, buffet, boundary-layer and flight deflection measurement system analyses and describes the flight maneuvers used to obtain the data. The results are for a wing sweep of 26 deg, a Mach number of 0.85, leading and trailing-edge cambers (delta(sub LE/TE)) of 0/2 and 5/10, and angles of attack from 3.0 deg to 14.0 deg. For the well-behaved flow of the delta(sub LE/TE) = 0/2 camber, a typical cruise camber shape, the local and global data are in good agreement with respect to the flow properties of the wing. For the delta(sub LE/TE) = 5/10 camber, a maneuvering camber shape, the local and global data have similar trends and conclusions, but not the clear-cut agreement observed for cruise camber.

  2. Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

    NASA Technical Reports Server (NTRS)

    Jacklin, Stephen A.; Schumann, Johann; Guenther, Kurt; Bosworth, John

    2006-01-01

    Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

  3. Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)

    NASA Astrophysics Data System (ADS)

    Wade, Robert L.; Walker, Gregory W.

    1996-05-01

    The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.

  4. Flight Test of an Adaptive Controller and Simulated Failure/Damage on the NASA NF-15B

    NASA Technical Reports Server (NTRS)

    Buschbacher, Mark; Maliska, Heather

    2006-01-01

    The method of flight-testing the Intelligent Flight Control System (IFCS) Second Generation (Gen-2) project on the NASA NF-15B is herein described. The Gen-2 project objective includes flight-testing a dynamic inversion controller augmented by a direct adaptive neural network to demonstrate performance improvements in the presence of simulated failure/damage. The Gen-2 objectives as implemented on the NASA NF-15B created challenges for software design, structural loading limitations, and flight test operations. Simulated failure/damage is introduced by modifying control surface commands, therefore requiring structural loads measurements. Flight-testing began with the validation of a structural loads model. Flight-testing of the Gen-2 controller continued, using test maneuvers designed in a sequenced approach. Success would clear the new controller with respect to dynamic response, simulated failure/damage, and with adaptation on and off. A handling qualities evaluation was conducted on the capability of the Gen-2 controller to restore aircraft response in the presence of a simulated failure/damage. Control room monitoring of loads sensors, flight dynamics, and controller adaptation, in addition to postflight data comparison to the simulation, ensured a safe methodology of buildup testing. Flight-testing continued without major incident to accomplish the project objectives, successfully uncovering strengths and weaknesses of the Gen-2 control approach in flight.

  5. Adaptive Failure Compensation for Aircraft Flight Control Using Engine Differentials: Regulation

    NASA Technical Reports Server (NTRS)

    Yu, Liu; Xidong, Tang; Gang, Tao; Joshi, Suresh M.

    2005-01-01

    The problem of using engine thrust differentials to compensate for rudder and aileron failures in aircraft flight control is addressed in this paper in a new framework. A nonlinear aircraft model that incorporates engine di erentials in the dynamic equations is employed and linearized to describe the aircraft s longitudinal and lateral motion. In this model two engine thrusts of an aircraft can be adjusted independently so as to provide the control flexibility for rudder or aileron failure compensation. A direct adaptive compensation scheme for asymptotic regulation is developed to handle uncertain actuator failures in the linearized system. A design condition is specified to characterize the system redundancy needed for failure compensation. The adaptive regulation control scheme is applied to the linearized model of a large transport aircraft in which the longitudinal and lateral motions are coupled as the result of using engine thrust differentials. Simulation results are presented to demonstrate the effectiveness of the adaptive compensation scheme.

  6. Changes in Jump-Down Performance After Space Flight: Short- and Long-Term Adaptation

    NASA Technical Reports Server (NTRS)

    Kofman, I. S.; Reschke, M. F.; Cerisano, J. M.; Fisher, E. A.; Lawrence, E. L.; Peters, B. T.; Bloomberg, J. J.

    2010-01-01

    results demonstrate astronauts adaptive capabilities and full performance recovery within days after flight.

  7. Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen S.; Orr, Jeb S.; Wall, John H.; Gilligan, Eric T.

    2014-01-01

    This paper summarizes the Adaptive Augmenting Control (AAC) flight characterization experiments performed using an F/A-18 (TN 853). AAC was designed and developed specifically for launch vehicles, and is currently part of the baseline autopilot design for NASA's Space Launch System (SLS). The scope covered here includes a brief overview of the algorithm (covered in more detail elsewhere), motivation and benefits of flight testing, top-level SLS flight test objectives, applicability of the F/A-18 as a platform for testing a launch vehicle control design, test cases designed to fully vet the AAC algorithm, flight test results, and conclusions regarding the functionality of AAC. The AAC algorithm developed at Marshall Space Flight Center is a forward loop gain multiplicative adaptive algorithm that modifies the total attitude control system gain in response to sensed model errors or undesirable parasitic mode resonances. The AAC algorithm provides the capability to improve or decrease performance by balancing attitude tracking with the mitigation of parasitic dynamics, such as control-structure interaction or servo-actuator limit cycles. In the case of the latter, if unmodeled or mismodeled parasitic dynamics are present that would otherwise result in a closed-loop instability or near instability, the adaptive controller decreases the total loop gain to reduce the interaction between these dynamics and the controller. This is in contrast to traditional adaptive control logic, which focuses on improving performance by increasing gain. The computationally simple AAC attitude control algorithm has stability properties that are reconcilable in the context of classical frequency-domain criteria (i.e., gain and phase margin). The algorithm assumes that the baseline attitude control design is well-tuned for a nominal trajectory and is designed to adapt only when necessary. Furthermore, the adaptation is attracted to the nominal design and adapts only on an as-needed basis

  8. Adaptation of a Cascade Impactor to Flight Measurement of Droplet Size in Clouds

    NASA Technical Reports Server (NTRS)

    Levine, Joseph; Kleinknecht, Kenneth S.

    1951-01-01

    A cascade impactor, an instrument for obtaining: the size distribution of droplets borne in a low-velocity air stream, was adapted for flight cloud droplet-size studies. The air containing the droplets was slowed down from flight speed by a diffuser to the inlet-air velocity of the impactor. The droplets that enter the impactor impinge on four slides coated with magnesium oxide. Each slide catches a different size range. The relation between the size of droplet impressions and the droplet size was evaluated so that the droplet-size distributions may be found from these slides. The magnesium oxide coating provides a permanent record. of the droplet impression that is not affected by droplet evaporation after the. droplets have impinged.

  9. Dietary and Flight Energetic Adaptations in a Salivary Gland Transcriptome of an Insectivorous Bat

    PubMed Central

    Phillips, Carleton J.; Phillips, Caleb D.; Goecks, Jeremy; Lessa, Enrique P.; Sotero-Caio, Cibele G.; Tandler, Bernard; Gannon, Michael R.; Baker, Robert J.

    2014-01-01

    We hypothesized that evolution of salivary gland secretory proteome has been important in adaptation to insectivory, the most common dietary strategy among Chiroptera. A submandibular salivary gland (SMG) transcriptome was sequenced for the little brown bat, Myotis lucifugus. The likely secretory proteome of 23 genes included seven (RETNLB, PSAP, CLU, APOE, LCN2, C3, CEL) related to M. lucifugus insectivorous diet and metabolism. Six of the secretory proteins probably are endocrine, whereas one (CEL) most likely is exocrine. The encoded proteins are associated with lipid hydrolysis, regulation of lipid metabolism, lipid transport, and insulin resistance. They are capable of processing exogenous lipids for flight metabolism while foraging. Salivary carboxyl ester lipase (CEL) is thought to hydrolyze insect lipophorins, which probably are absorbed across the gastric mucosa during feeding. The other six proteins are predicted either to maintain these lipids at high blood concentrations or to facilitate transport and uptake by flight muscles. Expression of these seven genes and coordinated secretion from a single organ is novel to this insectivorous bat, and apparently has evolved through instances of gene duplication, gene recruitment, and nucleotide selection. Four of the recruited genes are single-copy in the Myotis genome, whereas three have undergone duplication(s) with two of these genes exhibiting evolutionary ‘bursts’ of duplication resulting in multiple paralogs. Evidence for episodic directional selection was found for six of seven genes, reinforcing the conclusion that the recruited genes have important roles in adaptation to insectivory and the metabolic demands of flight. Intragenic frequencies of mobile- element-like sequences differed from frequencies in the whole M. lucifugus genome. Differences among recruited genes imply separate evolutionary trajectories and that adaptation was not a single, coordinated event. PMID:24454705

  10. Complexity and Pilot Workload Metrics for the Evaluation of Adaptive Flight Controls on a Full Scale Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt; Schaefer, Jacob; Burken, John J.; Larson, David; Johnson, Marcus

    2014-01-01

    Flight research has shown the effectiveness of adaptive flight controls for improving aircraft safety and performance in the presence of uncertainties. The National Aeronautics and Space Administration's (NASA)'s Integrated Resilient Aircraft Control (IRAC) project designed and conducted a series of flight experiments to study the impact of variations in adaptive controller design complexity on performance and handling qualities. A novel complexity metric was devised to compare the degrees of simplicity achieved in three variations of a model reference adaptive controller (MRAC) for NASA's F-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Full-Scale Advanced Systems Testbed (Gen-2A) aircraft. The complexity measures of these controllers are also compared to that of an earlier MRAC design for NASA's Intelligent Flight Control System (IFCS) project and flown on a highly modified F-15 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois). Pilot comments during the IRAC research flights pointed to the importance of workload on handling qualities ratings for failure and damage scenarios. Modifications to existing pilot aggressiveness and duty cycle metrics are presented and applied to the IRAC controllers. Finally, while adaptive controllers may alleviate the effects of failures or damage on an aircraft's handling qualities, they also have the potential to introduce annoying changes to the flight dynamics or to the operation of aircraft systems. A nuisance rating scale is presented for the categorization of nuisance side-effects of adaptive controllers.

  11. F-8C adaptive flight control extensions. [for maximum likelihood estimation

    NASA Technical Reports Server (NTRS)

    Stein, G.; Hartmann, G. L.

    1977-01-01

    An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated.

  12. [New methodological approaches in assessing adaptive body reactions in the system of flight medicine expertise].

    PubMed

    Karlov, V N; Balandina, T N

    1995-01-01

    The "cost" of adjustable systemic and cellular reactions in the human body in extreme environments have been assessed from the standpoint of their potentialities for upgrading special methods of evaluating and predicting functional status practiced in flight certification examination. As was stated, the functional reserves of blood circulation should be assessed by chronotropic activity of cardiac regulation in response to hypoxic exposure. The adequacy of adaptive processes on the cellular level can be drawn from the dynamics of malon-dialdehide (the by-product of lipid peroxidation) and plasma monoaminoxidase. The biochemical investigations of peripheral blood and saliva also show promise.

  13. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  14. Sensory-Motor Adaptation to Space Flight: Human Balance Control and Artificial Gravity

    NASA Technical Reports Server (NTRS)

    Paloski, William H.

    2004-01-01

    Gravity, which is sensed directly by the otolith organs and indirectly by proprioceptors and exteroceptors, provides the CNS a fundamental reference for estimating spatial orientation and coordinating movements in the terrestrial environment. The sustained absence of gravity during orbital space flight creates a unique environment that cannot be reproduced on Earth. Loss of this fundamental CNS reference upon insertion into orbit triggers neuro-adaptive processes that optimize performance for the microgravity environment, while its reintroduction upon return to Earth triggers neuro-adaptive processes that return performance to terrestrial norms. Five pioneering symposia on The Role of the Vestibular Organs in the Exploration of Space were convened between 1965 and 1970. These innovative meetings brought together the top physicians, physiologists, and engineers in the vestibular field to discuss and debate the challenges associated with human vestibular system adaptation to the then novel environment of space flight. These highly successful symposia addressed the perplexing problem of how to understand and ameliorate the adverse physiological effects on humans resulting from the reduction of gravitational stimulation of the vestibular receptors in space. The series resumed in 2002 with the Sixth Symposium, which focused on the microgravity environment as an essential tool for the study of fundamental vestibular functions. The three day meeting included presentations on historical perspectives, vestibular neurobiology, neurophysiology, neuroanatomy, neurotransmitter systems, theoretical considerations, spatial orientation, psychophysics, motor integration, adaptation, autonomic function, space motion sickness, clinical issues, countermeasures, and rehabilitation. Scientists and clinicians entered into lively exchanges on how to design and perform mutually productive research and countermeasure development projects in the future. The problems posed by long duration

  15. Flight Test of an Adaptive Configuration Optimization System for Transport Aircraft

    NASA Technical Reports Server (NTRS)

    Gilyard, Glenn B.; Georgie, Jennifer; Barnicki, Joseph S.

    1999-01-01

    A NASA Dryden Flight Research Center program explores the practical application of real-time adaptive configuration optimization for enhanced transport performance on an L-1011 aircraft. This approach is based on calculation of incremental drag from forced-response, symmetric, outboard aileron maneuvers. In real-time operation, the symmetric outboard aileron deflection is directly optimized, and the horizontal stabilator and angle of attack are indirectly optimized. A flight experiment has been conducted from an onboard research engineering test station, and flight research results are presented herein. The optimization system has demonstrated the capability of determining the minimum drag configuration of the aircraft in real time. The drag-minimization algorithm is capable of identifying drag to approximately a one-drag-count level. Optimizing the symmetric outboard aileron position realizes a drag reduction of 2-3 drag counts (approximately 1 percent). Algorithm analysis of maneuvers indicate that two-sided raised-cosine maneuvers improve definition of the symmetric outboard aileron drag effect, thereby improving analysis results and consistency. Ramp maneuvers provide a more even distribution of data collection as a function of excitation deflection than raised-cosine maneuvers provide. A commercial operational system would require airdata calculations and normal output of current inertial navigation systems; engine pressure ratio measurements would be optional.

  16. Adaptation of antennal neurons in moths is associated with cessation of pheromone-mediated upwind flight.

    PubMed Central

    Baker, T C; Hansson, B S; Löfstedt, C; Löfqvist, J

    1988-01-01

    A wind-borne plume of sex pheromone from a female moth or a synthetic source has a fine, filamentous structure that creates steep and rapid fluctuations in concentration for a male moth flying up the plume's axis. The firing rates from single antennal neurons on Agrotis segetum antennae decreased to nearly zero within seconds after the antennae were placed in a pheromone plume 70 cm downwind of a high-concentration source known from previous studies to cause in-flight arrestment of upwind progress. In a separate experiment, the fluctuating output from chilled neurons on Grapholita molesta antennae became attenuated in response to repetitive, experimentally delivered pheromone pulses. The attenuation was correlated with a previously reported higher percentage of in-flight arrestment exhibited by moths flying at cooler compared to warmer temperatures. These results indicate that two peripheral processes related to excessive concentration, complete adaptation of antennal neurons, or merely the attenuation of fluctuations in burst frequency, are important determinants of when upwind progress by a moth flying in a pheromone plume stops and changes to station keeping. Also, adaptation and attenuation may affect the sensation of blend quality by preferentially affecting cells sensitive to the most abundant components in airborne pheromone blends. PMID:3200859

  17. Launch Vehicle Manual Steering with Adaptive Augmenting Control:In-Flight Evaluations of Adverse Interactions Using a Piloted Aircraft

    NASA Technical Reports Server (NTRS)

    Hanson, Curt; Miller, Chris; Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Orr, Jeb S.

    2015-01-01

    An Adaptive Augmenting Control (AAC) algorithm for the Space Launch System (SLS) has been developed at the Marshall Space Flight Center (MSFC) as part of the launch vehicle's baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a potential manual steering mode were also investigated by giving the pilot trajectory deviation cues and pitch rate command authority, which is the subject of this paper. Two NASA research pilots flew a total of 25 constant pitch rate trajectories using a prototype manual steering mode with and without adaptive control, evaluating six different nominal and off-nominal test case scenarios. Pilot comments and PIO ratings were given following each trajectory and correlated with aircraft state data and internal controller signals post-flight.

  18. Behavioural Adaptation to diminished Gravity in Fish - a Parabolic Aircraft Flight Study

    NASA Astrophysics Data System (ADS)

    Forster, A.; Anken, R.; Hilbig, R.

    During the micro gravity phases in the course of parabolic aircraft flights PFs some fish of a given batch were frequently shown to exhibit sensorimotor disorders in terms of revealing so-called looping responses LR or spinning movements SM both forms of motion sickness a kinetosis In order to gain some insights into the time-course of the behavioural adaptation towards diminished gravity in total 272 larval cichlid fish Oreochromis mossambicus were subjected to PFs and their respective behaviour was monitored With the onset of the first parabola P1 15 9 of the animals revealed a kinetotic behaviour whereas kinetoses were shown in 6 5 1 5 and 1 of the animals in P5 P10 and P15 With P20 the animals had adapted completely 0 swimming kinetotically Since the relative decrease of kinetotic animals was especially prominent from P5 to P10 a detailed analysis of the behaviour was undertaken Regarding SM a ratio of 2 9 in P5 decreased to 0 5 in P10 Virtually all individuals showing a SM in P5 had regained a normal behaviour with P10 The SM animals in P10 had all exhibited a normal swimming behaviour in P5 The ratio of LR-fish also decreased from P5 3 6 to P10 1 0 In contrast to the findings regarding SM numerous LM specimens did not regain a normal postural control and only very few animals behaving normally in P5 began to sport a LM behaviour by P10 Summarizing most kinetotic animals rapidly adapted to diminished gravity but few individual fish who swam normally at the beginning of the flights may loose sensorimotor control

  19. Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

    NASA Technical Reports Server (NTRS)

    Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

    2014-01-01

    The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

  20. Improving Sensorimotor Adaptation Following Long Duration Space Flight by Enhancing Vestibular Information Transfer

    NASA Technical Reports Server (NTRS)

    Mulavara, A. P.; Kofman, I. S.; De Dios, Y. E; Galvan, R.; Goel, R.; Miller, C.; Peters, B.; Cohen, H. S.; Jeevarajan, J.; Reschke, M.; Wood, S.; Bergquist, F.; Seidler, R. D.; Bloomberg, J. J.

    2014-01-01

    Crewmember adapted to the microgravity state may need to egress the vehicle within a few minutes for safety and operational reasons after gravitational transitions. The transition from one sensorimotor state to another consists of two main mechanisms: strategic and plastic-adaptive and have been demonstrated in astronauts returning after long duration space flight. Strategic modifications represent "early adaptation" - immediate and transitory changes in control that are employed to deal with short-term changes in the environment. If these modifications are prolonged then plastic-adaptive changes are evoked that modify central nervous system function, automating new behavioral responses. More importantly, this longer term adaptive recovery mechanism was significantly associated with their strategic ability to recover on the first day after return to Earth G. We are developing a method based on stochastic resonance to enhance information transfer by improving the brain's ability to detect vestibular signals (Vestibular Stochastic Resonance, VSR) especially when combined with balance training exercises such as sensorimotor adaptability (SA) training for rapid improvement in functional skill, for standing and mobility. This countermeasure to improve detection of vestibular signals is a stimulus delivery system that is wearable/portable providing low imperceptible levels of white noise based binaural bipolar electrical stimulation of the vestibular system (stochastic vestibular stimulation). To determine efficacy of vestibular stimulation on physiological and perceptual responses during otolith-canal conflicts and dynamic perturbations we have conducted a series of studies: We have shown that imperceptible binaural bipolar electrical stimulation of the vestibular system across the mastoids enhances balance performance in the mediolateral (ML) plane while standing on an unstable surface. We have followed up on the previous study showing VSR stimulation improved balance

  1. [Individual peculiarities of adaptation to long-term space flights: 24-hour heart rhythm monitoring

    NASA Technical Reports Server (NTRS)

    Baevskii, R. M.; Bogomolov, V. V.; Gol'dberger, A. L.; Nikulina, G. A.; Charl'z, D. B.; Goldberger, A. L. (Principal Investigator); Charles, J. B. (Principal Investigator)

    2000-01-01

    Presented are results of studying 24-hr variability of the cardiac rhythm which characterizes individual difference in reactions of two crew members to the same set of stresses during a 115-day MIR mission. Spacelab (USA) cardiorecorders were used. Data of monitoring revealed significantly different baseline health statuses of the cosmonauts. These functional differences were also observed in the mission. In one of the cosmonauts, the cardiac regulation changed over to a more economic functioning with the autonomous balance shifted towards enhanced sympathetic activity. After 2-3 months on mission he had almost recovered pre-launch level of regulation. In the other, the regulatory system was appreciably strained at the beginning of the mission as compared with preflight baseline. Later on, on flight months 2-3, this strain kept growing till a drastic depletion of the functional reserve. On return to Earth, this was manifested by a strong stress reaction with a sharp decline in power of high-frequency and grow in power of very low frequency components of the heart rhythm. The data suggest that adaptation to space flight and reactions in the readaptation period are dependent on initial health status of crew members, and functional reserve.

  2. Development and Flight Testing of an Adaptive Vehicle Health-Monitoring Architecture

    NASA Technical Reports Server (NTRS)

    Woodard, Stanley E.; Coffey, Neil C.; Gonzalez, Guillermo A.; Taylor, B. Douglas; Brett, Rube R.; Woodman, Keith L.; Weathered, Brenton W.; Rollins, Courtney H.

    2002-01-01

    On going development and testing of an adaptable vehicle health-monitoring architecture is presented. The architecture is being developed for a fleet of vehicles. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle, and, a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained expert system. The expert system is parameterized, which makes it adaptable to be trained to both a user's subject reasoning and existing quantitative analytic tools. Communication between all levels is done with wireless radio frequency interfaces. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear. The flight tests were performed to validate the following: the wireless radio frequency communication capabilities of the system, the hardware design, command and control; software operation and, data acquisition, storage and retrieval.

  3. Functional Task Test: 3. Skeletal Muscle Performance Adaptations to Space Flight

    NASA Technical Reports Server (NTRS)

    Ryder, Jeffrey W.; Wickwire, P. J.; Buxton, R. E.; Bloomberg, J. J.; Ploutz-Snyder, L.

    2011-01-01

    The functional task test is a multi-disciplinary study investigating how space-flight induced changes to physiological systems impacts functional task performance. Impairment of neuromuscular function would be expected to negatively affect functional performance of crewmembers following exposure to microgravity. This presentation reports the results for muscle performance testing in crewmembers. Functional task performance will be presented in the abstract "Functional Task Test 1: sensory motor adaptations associated with postflight alternations in astronaut functional task performance." METHODS: Muscle performance measures were obtained in crewmembers before and after short-duration space flight aboard the Space Shuttle and long-duration International Space Station (ISS) missions. The battery of muscle performance tests included leg press and bench press measures of isometric force, isotonic power and total work. Knee extension was used for the measurement of central activation and maximal isometric force. Upper and lower body force steadiness control were measured on the bench press and knee extension machine, respectively. Tests were implemented 60 and 30 days before launch, on landing day (Shuttle crew only), and 6, 10 and 30 days after landing. Seven Space Shuttle crew and four ISS crew have completed the muscle performance testing to date. RESULTS: Preliminary results for Space Shuttle crew reveal significant reductions in the leg press performance metrics of maximal isometric force, power and total work on R+0 (p<0.05). Bench press total work was also significantly impaired, although maximal isometric force and power were not significantly affected. No changes were noted for measurements of central activation or force steadiness. Results for ISS crew were not analyzed due to the current small sample size. DISCUSSION: Significant reductions in lower body muscle performance metrics were observed in returning Shuttle crew and these adaptations are likely

  4. Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

    NASA Technical Reports Server (NTRS)

    Mulavara, A. P.; Seidler, R. D.; Feiveson, A.; Oddsson, L.; Zanello, S.; Oman, C. M.; Ploutz-Snyder, L.; Peters, B.; Cohen, H. S.; Reschke, M.; Wood, S.; Bloomberg, J. J.

    2014-01-01

    Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the re-adapation phase following a return to an earth-gravitational environment. These alterations may disrupt the ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from space flight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual space flight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures that include: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; 3) genotype markers for genetic polymorphisms in Catechol-O-Methyl Transferase, Dopamine Receptor D2, Brain-derived neurotrophic factor and genetic polymorphism of alpha2-adrenergic receptor that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration space flight and an analog bed rest environment. We will be conducting a retrospective study leveraging data already collected from relevant

  5. Stability Assessment and Tuning of an Adaptively Augmented Classical Controller for Launch Vehicle Flight Control

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen; Zhu, J. Jim; Adami, Tony; Berry, Kyle; Grammar, Alex; Orr, Jeb S.; Best, Eric A.

    2014-01-01

    Recently, a robust and practical adaptive control scheme for launch vehicles [ [1] has been introduced. It augments a classical controller with a real-time loop-gain adaptation, and it is therefore called Adaptive Augmentation Control (AAC). The loop-gain will be increased from the nominal design when the tracking error between the (filtered) output and the (filtered) command trajectory is large; whereas it will be decreased when excitation of flex or sloshing modes are detected. There is a need to determine the range and rate of the loop-gain adaptation in order to retain (exponential) stability, which is critical in vehicle operation, and to develop some theoretically based heuristic tuning methods for the adaptive law gain parameters. The classical launch vehicle flight controller design technics are based on gain-scheduling, whereby the launch vehicle dynamics model is linearized at selected operating points along the nominal tracking command trajectory, and Linear Time-Invariant (LTI) controller design techniques are employed to ensure asymptotic stability of the tracking error dynamics, typically by meeting some prescribed Gain Margin (GM) and Phase Margin (PM) specifications. The controller gains at the design points are then scheduled, tuned and sometimes interpolated to achieve good performance and stability robustness under external disturbances (e.g. winds) and structural perturbations (e.g. vehicle modeling errors). While the GM does give a bound for loop-gain variation without losing stability, it is for constant dispersions of the loop-gain because the GM is based on frequency-domain analysis, which is applicable only for LTI systems. The real-time adaptive loop-gain variation of the AAC effectively renders the closed-loop system a time-varying system, for which it is well-known that the LTI system stability criterion is neither necessary nor sufficient when applying to a Linear Time-Varying (LTV) system in a frozen-time fashion. Therefore, a

  6. Development and Flight Testing of an Adaptable Vehicle Health-Monitoring Architecture

    NASA Technical Reports Server (NTRS)

    Woodard, Stanley E.; Coffey, Neil C.; Gonzalez, Guillermo A.; Woodman, Keith L.; Weathered, Brenton W.; Rollins, Courtney H.; Taylor, B. Douglas; Brett, Rube R.

    2003-01-01

    Development and testing of an adaptable wireless health-monitoring architecture for a vehicle fleet is presented. It has three operational levels: one or more remote data acquisition units located throughout the vehicle; a command and control unit located within the vehicle; and a terminal collection unit to collect analysis results from all vehicles. Each level is capable of performing autonomous analysis with a trained adaptable expert system. The remote data acquisition unit has an eight channel programmable digital interface that allows the user discretion for choosing type of sensors; number of sensors, sensor sampling rate, and sampling duration for each sensor. The architecture provides framework for a tributary analysis. All measurements at the lowest operational level are reduced to provide analysis results necessary to gauge changes from established baselines. These are then collected at the next level to identify any global trends or common features from the prior level. This process is repeated until the results are reduced at the highest operational level. In the framework, only analysis results are forwarded to the next level to reduce telemetry congestion. The system's remote data acquisition hardware and non-analysis software have been flight tested on the NASA Langley B757's main landing gear.

  7. Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

    NASA Astrophysics Data System (ADS)

    Shankar, Praveen

    The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network

  8. A Reusable and Adaptable Software Architecture for Embedded Space Flight System: The Core Flight Software System (CFS)

    NASA Technical Reports Server (NTRS)

    Wilmot, Jonathan

    2005-01-01

    The contents include the following: High availability. Hardware is in harsh environment. Flight processor (constraints) very widely due to power and weight constraints. Software must be remotely modifiable and still operate while changes are being made. Many custom one of kind interfaces for one of a kind missions. Sustaining engineering. Price of failure is high, tens to hundreds of millions of dollars.

  9. Implementation of an Adaptive Controller System from Concept to Flight Test

    NASA Technical Reports Server (NTRS)

    Larson, Richard R.; Burken, John J.; Butler, Bradley S.; Yokum, Steve

    2009-01-01

    tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

  10. Evaluation of the Hinge Moment and Normal Force Aerodynamic Loads from a Seamless Adaptive Compliant Trailing Edge Flap in Flight

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Cruz, Josue; Lung, Shun-Fat; Kota, Sridhar; Ervin, Gregory; Lu, Kerr-Jia; Flick, Pete

    2016-01-01

    A seamless adaptive compliant trailing edge (ACTE) flap was demonstrated in flight on a Gulfstream III aircraft at the NASA Armstrong Flight Research Center. The trailing edge flap was deflected between minus 2 deg up and plus 30 deg down in flight. The safety-of-flight parameters for the ACTE flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. The attachment fittings connecting the flap to the aircraft wing rear spar were instrumented with strain gages and calibrated using known loads for measuring hinge moment and normal force loads in flight. The safety-of-flight parameters for the ACTE flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. The attachment fittings connecting the flap to the aircraft wing rear spar were instrumented with strain gages and calibrated using known loads for measuring hinge moment and normal force loads in flight. The interface hardware instrumentation layout and load calibration are discussed. Twenty-one applied calibration test load cases were developed for each individual fitting. The 2-sigma residual errors for the hinge moment was calculated to be 2.4 percent, and for normal force was calculated to be 7.3 percent. The hinge moment and normal force generated by the ACTE flap with a hinge point located at 26-percent wing chord were measured during steady state and symmetric pitch maneuvers. The loads predicted from analysis were compared to the loads observed in flight. The hinge moment loads showed good agreement with the flight loads while the normal force loads calculated from analysis were over-predicted by approximately 20 percent. Normal force and hinge moment loads calculated from the pressure sensors located on the ACTE showed good agreement with the loads calculated from the installed strain gages.

  11. Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study

    PubMed Central

    Liu, Hongbo; Tang, Zhifeng; Yang, Yongli; Weng, Dong; Sun, Gao; Duan, Zhiwen; Chen, Jie

    2009-01-01

    Background Coal workers' pneumoconiosis (CWP) is a preventable, but not fully curable occupational lung disease. More and more coal miners are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control. Methods The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. Sensitivity and ROC analyses were adapted to explain the importance of input variables and the performance of the neural network. The occupational characteristics and the probability values predicted were used to categorize coal miners for their levels of risk for CWP. Results The sensitivity analysis showed that influence of the duration of dust exposure and occupational category on CWP was 65% and 67%, respectively. The area under the ROC in 3 sets was 0.981, 0.969, and 0.992. There were 7959 coal miners with a probability value < 0.001. The average duration of dust exposure was 15.35 years. The average duration of ex-dust exposure was 0.69 years. Of the coal miners, 79.27% worked in helping and mining. Most of the coal miners were born after 1950 and were first exposed to dust after 1970. One hundred forty-four coal miners had a probability value ≥0.1. The average durations of dust exposure and ex-dust exposure were 25.70 and 16.30 years

  12. L1 Adaptive Control Law for Flexible Space Launch Vehicle and Proposed Plan for Flight Test Validation

    NASA Technical Reports Server (NTRS)

    Kharisov, Evgeny; Gregory, Irene M.; Cao, Chengyu; Hovakimyan, Naira

    2008-01-01

    This paper explores application of the L1 adaptive control architecture to a generic flexible Crew Launch Vehicle (CLV). Adaptive control has the potential to improve performance and enhance safety of space vehicles that often operate in very unforgiving and occasionally highly uncertain environments. NASA s development of the next generation space launch vehicles presents an opportunity for adaptive control to contribute to improved performance of this statically unstable vehicle with low damping and low bending frequency flexible dynamics. In this paper, we consider the L1 adaptive output feedback controller to control the low frequency structural modes and propose steps to validate the adaptive controller performance utilizing one of the experimental test flights for the CLV Ares-I Program.

  13. The three-dimensional flight of red-footed boobies: adaptations to foraging in a tropical environment?

    PubMed

    Weimerskirch, H; Le Corre, M; Ropert-Coudert, Y; Kato, A; Marsac, F

    2005-01-01

    In seabirds a broad variety of morphologies, flight styles and feeding methods exist as an adaptation to optimal foraging in contrasted marine environments for a wide variety of prey types. Because of the low productivity of tropical waters it is expected that specific flight and foraging techniques have been selected there, but very few data are available. By using five different types of high-precision miniaturized logger (global positioning systems, accelerometers, time depth recorders, activity recorders, altimeters) we studied the way a seabird is foraging over tropical waters. Red-footed boobies are foraging in the day, never foraging at night, probably as a result of predation risks. They make extensive use of wind conditions, flying preferentially with crosswinds at median speed of 38 km h(-1), reaching highest speeds with tail winds. They spent 66% of the foraging trip in flight, using a flap-glide flight, and gliding 68% of the flight. Travelling at low costs was regularly interrupted by extremely active foraging periods where birds are very frequently touching water for landing, plunge diving or surface diving (30 landings h(-1)). Dives were shallow (maximum 2.4 m) but frequent (4.5 dives h(-1)), most being plunge dives. While chasing for very mobile prey like flying fishes, boobies have adopted a very active and specific hunting behaviour, but the use of wind allows them to reduce travelling cost by their extensive use of gliding. During the foraging and travelling phases birds climb regularly to altitudes of 20-50 m to spot prey or congeners. During the final phase of the flight, they climb to high altitudes, up to 500 m, probably to avoid attacks by frigatebirds along the coasts. This study demonstrates the use by boobies of a series of very specific flight and activity patterns that have probably been selected as adaptations to the conditions of tropical waters.

  14. The three-dimensional flight of red-footed boobies: adaptations to foraging in a tropical environment?

    PubMed

    Weimerskirch, H; Le Corre, M; Ropert-Coudert, Y; Kato, A; Marsac, F

    2005-01-01

    In seabirds a broad variety of morphologies, flight styles and feeding methods exist as an adaptation to optimal foraging in contrasted marine environments for a wide variety of prey types. Because of the low productivity of tropical waters it is expected that specific flight and foraging techniques have been selected there, but very few data are available. By using five different types of high-precision miniaturized logger (global positioning systems, accelerometers, time depth recorders, activity recorders, altimeters) we studied the way a seabird is foraging over tropical waters. Red-footed boobies are foraging in the day, never foraging at night, probably as a result of predation risks. They make extensive use of wind conditions, flying preferentially with crosswinds at median speed of 38 km h(-1), reaching highest speeds with tail winds. They spent 66% of the foraging trip in flight, using a flap-glide flight, and gliding 68% of the flight. Travelling at low costs was regularly interrupted by extremely active foraging periods where birds are very frequently touching water for landing, plunge diving or surface diving (30 landings h(-1)). Dives were shallow (maximum 2.4 m) but frequent (4.5 dives h(-1)), most being plunge dives. While chasing for very mobile prey like flying fishes, boobies have adopted a very active and specific hunting behaviour, but the use of wind allows them to reduce travelling cost by their extensive use of gliding. During the foraging and travelling phases birds climb regularly to altitudes of 20-50 m to spot prey or congeners. During the final phase of the flight, they climb to high altitudes, up to 500 m, probably to avoid attacks by frigatebirds along the coasts. This study demonstrates the use by boobies of a series of very specific flight and activity patterns that have probably been selected as adaptations to the conditions of tropical waters. PMID:15875570

  15. Assessing arboreal adaptations of bird antecedents: testing the ecological setting of the origin of the avian flight stroke.

    PubMed

    Dececchi, T Alexander; Larsson, Hans C E

    2011-01-01

    The origin of avian flight is a classic macroevolutionary transition with research spanning over a century. Two competing models explaining this locomotory transition have been discussed for decades: ground up versus trees down. Although it is impossible to directly test either of these theories, it is possible to test one of the requirements for the trees-down model, that of an arboreal paravian. We test for arboreality in non-avian theropods and early birds with comparisons to extant avian, mammalian, and reptilian scansors and climbers using a comprehensive set of morphological characters. Non-avian theropods, including the small, feathered deinonychosaurs, and Archaeopteryx, consistently and significantly cluster with fully terrestrial extant mammals and ground-based birds, such as ratites. Basal birds, more advanced than Archaeopteryx, cluster with extant perching ground-foraging birds. Evolutionary trends immediately prior to the origin of birds indicate skeletal adaptations opposite that expected for arboreal climbers. Results reject an arboreal capacity for the avian stem lineage, thus lending no support for the trees-down model. Support for a fully terrestrial ecology and origin of the avian flight stroke has broad implications for the origin of powered flight for this clade. A terrestrial origin for the avian flight stroke challenges the need for an intermediate gliding phase, presents the best resolved series of the evolution of vertebrate powered flight, and may differ fundamentally from the origin of bat and pterosaur flight, whose antecedents have been postulated to have been arboreal and gliding.

  16. Assessing Arboreal Adaptations of Bird Antecedents: Testing the Ecological Setting of the Origin of the Avian Flight Stroke

    PubMed Central

    Dececchi, T. Alexander; Larsson, Hans C. E.

    2011-01-01

    The origin of avian flight is a classic macroevolutionary transition with research spanning over a century. Two competing models explaining this locomotory transition have been discussed for decades: ground up versus trees down. Although it is impossible to directly test either of these theories, it is possible to test one of the requirements for the trees-down model, that of an arboreal paravian. We test for arboreality in non-avian theropods and early birds with comparisons to extant avian, mammalian, and reptilian scansors and climbers using a comprehensive set of morphological characters. Non-avian theropods, including the small, feathered deinonychosaurs, and Archaeopteryx, consistently and significantly cluster with fully terrestrial extant mammals and ground-based birds, such as ratites. Basal birds, more advanced than Archaeopteryx, cluster with extant perching ground-foraging birds. Evolutionary trends immediately prior to the origin of birds indicate skeletal adaptations opposite that expected for arboreal climbers. Results reject an arboreal capacity for the avian stem lineage, thus lending no support for the trees-down model. Support for a fully terrestrial ecology and origin of the avian flight stroke has broad implications for the origin of powered flight for this clade. A terrestrial origin for the avian flight stroke challenges the need for an intermediate gliding phase, presents the best resolved series of the evolution of vertebrate powered flight, and may differ fundamentally from the origin of bat and pterosaur flight, whose antecedents have been postulated to have been arboreal and gliding. PMID:21857918

  17. In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

    NASA Technical Reports Server (NTRS)

    Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

    2005-01-01

    In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

  18. Aerodynamic Flight-Test Results for the Adaptive Compliant Trailing Edge

    NASA Technical Reports Server (NTRS)

    Cumming, Stephen B.; Smith, Mark S.; Ali, Aliyah N.; Bui, Trong T.; Ellsworth, Joel C.; Garcia, Christian A.

    2016-01-01

    The aerodynamic effects of compliant flaps installed onto a modified Gulfstream III airplane were investigated. Analyses were performed prior to flight to predict the aerodynamic effects of the flap installation. Flight tests were conducted to gather both structural and aerodynamic data. The airplane was instrumented to collect vehicle aerodynamic data and wing pressure data. A leading-edge stagnation detection system was also installed. The data from these flights were analyzed and compared with predictions. The predictive tools compared well with flight data for small flap deflections, but differences between predictions and flight estimates were greater at larger deflections. This paper describes the methods used to examine the aerodynamics data from the flight tests and provides a discussion of the flight-test results in the areas of vehicle aerodynamics, wing sectional pressure coefficient profiles, and air data.

  19. Space physiology II: adaptation of the central nervous system to space flight--past, current, and future studies.

    PubMed

    Clément, Gilles; Ngo-Anh, Jennifer Thu

    2013-07-01

    Experiments performed in orbit on the central nervous system have focused on the control of posture, eye movements, spatial orientation, as well as cognitive processes, such as three-dimensional visual perception and mental representation of space. Brain activity has also been recorded during and immediately after space flight for evaluating the changes in brain structure activation during tasks involving perception, attention, memory, decision, and action. Recent ground-based studies brought evidence that the inputs from the neurovestibular system also participate in orthostatic intolerance. It is, therefore, important to revisit the flight data of neuroscience studies in the light of new models of integrative physiology. The outcomes of this exercise will increase our knowledge on the adaptation of body functions to changing gravitational environment, vestibular disorders, aging, and our approach towards more effective countermeasures during human space flight and planetary exploration.

  20. A review of adaptive change in musculoskeletal impedance during space flight and associated implications for postflight head movement control

    NASA Technical Reports Server (NTRS)

    McDonald, P. V.; Bloomberg, J. J.; Layne, C. S.

    1997-01-01

    We present a review of converging sources of evidence which suggest that the differences between loading histories experienced in 1-g and weightlessness are sufficient to stimulate adaptation in mechanical impedance of the musculoskeletal system. As a consequence of this adaptive change we argue that we should observe changes in the ability to attenuate force transmission through the musculoskeletal system both during and after space flight. By focusing attention on the relation between human sensorimotor activity and support surfaces, the importance of controlling mechanical energy flow through the musculoskeletal system is demonstrated. The implications of such control are discussed in light of visual-vestibular function in the specific context of head and gaze control during postflight locomotion. Evidence from locomotory biomechanics, visual-vestibular function, ergonomic evaluations of human vibration, and specific investigations of locomotion and head and gaze control after space flight, is considered.

  1. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

    1998-01-01

    A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

  2. Neural network based system for equipment surveillance

    DOEpatents

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  3. Neural network based short term load forecasting

    SciTech Connect

    Lu, C.N.; Wu, H.T. . Dept. of Electrical Engineering); Vemuri, S. . Controls and Composition Div.)

    1993-02-01

    The artificial neural network (ANN) technique for short term load forecasting (STLF) has been proposed by several authors, and gained a lot of attention recently. In order to evaluate ANN as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. This paper makes an attempt to address these issues. The paper presents the results of a study to investigate whether the ANN model is system dependent, and/or case dependent. Data from two utilities were used in modeling and forecasting. In addition, the effectiveness of a next 24 hour ANN model is predicting 24 hour load profile at one time was compared with the traditional next one hour ANN model.

  4. Towards a genetics-based adaptive agent to support flight testing

    NASA Astrophysics Data System (ADS)

    Cribbs, Henry Brown, III

    Although the benefits of aircraft simulation have been known since the late 1960s, simulation almost always entails interaction with a human test pilot. This "pilot-in-the-loop" simulation process provides useful evaluative information to the aircraft designer and provides a training tool to the pilot. Emulation of a pilot during the early phases of the aircraft design process might provide designers a useful evaluative tool. Machine learning might emulate a pilot in a simulated aircraft/cockpit setting. Preliminary work in the application of machine learning techniques, such as reinforcement learning, to aircraft maneuvering have shown promise. These studies used simplified interfaces between machine learning agent and the aircraft simulation. The simulations employed low order equivalent system models. High-fidelity aircraft simulations exist, such as the simulations developed by NASA at its Dryden Flight Research Center. To expand the applicational domain of reinforcement learning to aircraft designs, this study presents a series of experiments that examine a reinforcement learning agent in the role of test pilot. The NASA X-31 and F-106 high-fidelity simulations provide realistic aircraft for the agent to maneuver. The approach of the study is to examine an agent possessing a genetic-based, artificial neural network to approximate long-term, expected cost (Bellman value) in a basic maneuvering task. The experiments evaluate different learning methods based on a common feedback function and an identical task. The learning methods evaluated are: Q-learning, Q(lambda)-learning, SARSA learning, and SARSA(lambda) learning. Experimental results indicate that, while prediction error remain quite high, similar, repeatable behaviors occur in both aircraft. Similar behavior exhibits portability of the agent between aircraft with different handling qualities (dynamics). Besides the adaptive behavior aspects of the study, the genetic algorithm used in the agent is shown to

  5. In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

    NASA Technical Reports Server (NTRS)

    VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

    2015-01-01

    NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

  6. In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

    NASA Technical Reports Server (NTRS)

    Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

    2015-01-01

    NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

  7. Steering by hearing: a bat's acoustic gaze is linked to its flight motor output by a delayed, adaptive linear law.

    PubMed

    Ghose, Kaushik; Moss, Cynthia F

    2006-02-01

    Adaptive behaviors require sensorimotor computations that convert information represented initially in sensory coordinates to commands for action in motor coordinates. Fundamental to these computations is the relationship between the region of the environment sensed by the animal (gaze) and the animal's locomotor plan. Studies of visually guided animals have revealed an anticipatory relationship between gaze direction and the locomotor plan during target-directed locomotion. Here, we study an acoustically guided animal, an echolocating bat, and relate acoustic gaze (direction of the sonar beam) to flight planning as the bat searches for and intercepts insect prey. We show differences in the relationship between gaze and locomotion as the bat progresses through different phases of insect pursuit. We define acoustic gaze angle, theta(gaze), to be the angle between the sonar beam axis and the bat's flight path. We show that there is a strong linear linkage between acoustic gaze angle at time t [theta(gaze)(t)] and flight turn rate at time t + tau into the future [theta(flight) (t + tau)], which can be expressed by the formula theta(flight) (t + tau) = ktheta(gaze)(t). The gain, k, of this linkage depends on the bat's behavioral state, which is indexed by its sonar pulse rate. For high pulse rates, associated with insect attacking behavior, k is twice as high compared with low pulse rates, associated with searching behavior. We suggest that this adjustable linkage between acoustic gaze and motor output in a flying echolocating bat simplifies the transformation of auditory information to flight motor commands.

  8. [Adaptive process in Vietnamese military pilots during the flights on modern Russian aircraft].

    PubMed

    Ushakov, I V; Pham Xuan, Nihn; Bukhtiaiarov, I V; Ushakov, B N

    2013-04-01

    Study on health status of 156 Vietnamese military pilots on Russian modern jet planes (Su-22, Su-27, Su-30, MiG-21B). The results showed that unprofitable factors in working environment (acceleration, radiation, high temperature, humidity, noise) have an impact on the health of pilots during the flight, leading to deterioration of professional health and physiological functions (cardiovascular, respiratory and nervous system) and obesity of pilots after 35 years old. Basing on the studies, we suggested some measures for health protecting, safety of flight and prolonging flight-activity of pilots (training in decompression chamber, vestibular training) and balance in food ration for prevention of professional diseases. PMID:24000611

  9. Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

    NASA Technical Reports Server (NTRS)

    Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

    2010-01-01

    In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

  10. Strain Gage Load Calibration of the Wing Interface Fittings for the Adaptive Compliant Trailing Edge Flap Flight Test

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Holguin, Andrew C.; Cruz, Josue; Lokos, William A.

    2014-01-01

    This is the presentation to follow conference paper of the same name. The adaptive compliant trailing edge (ACTE) flap experiment safety of flight requires that the flap to wing interface loads be sensed and monitored in real time to ensure that the wing structural load limits are not exceeded. This paper discusses the strain gage load calibration testing and load equation derivation methodology for the ACTE interface fittings. Both the left and right wing flap interfaces will be monitored and each contains four uniquely designed and instrumented flap interface fittings. The interface hardware design and instrumentation layout are discussed. Twenty one applied test load cases were developed using the predicted in-flight loads for the ACTE experiment.

  11. An adaptive dual-optimal path-planning technique for unmanned air vehicles with application to solar-regenerative high altitude long endurance flight

    NASA Astrophysics Data System (ADS)

    Whitfield, Clifford A.

    2009-12-01

    A multi-objective technique for Unmanned Air Vehicle (UAV) path and trajectory autonomy generation, through task allocation and sensor fusion has been developed. The Dual-Optimal Path-Planning (D-O.P-P.) Technique generates on-line adaptive flight paths for UAVs based on available flight windows and environmental influenced objectives. The environmental influenced optimal condition, known as the driver' determines the condition, within a downstream virtual window of possible vehicle destinations and orientation built from the UAV kinematics. The intermittent results are pursued by a dynamic optimization technique to determine the flight path. This sequential optimization technique is a multi-objective optimization procedure consisting of two goals, without requiring additional information to combine the conflicting objectives into a single-objective. An example case-study and additional applications are developed and the results are discussed; including the application to the field of Solar Regenerative (SR) High Altitude Long Endurance (HALE) UAV flight. Harnessing solar energy has recently been adapted for use on high altitude UAV platforms. An aircraft that uses solar panels and powered by the sun during the day and through the night by SR systems, in principle could sustain flight for weeks or months. The requirements and limitations of solar powered flight were determined. The SR-HALE UAV platform geometry and flight characteristics were selected from an existing aircraft that has demonstrated the capability for sustained flight through flight tests. The goals were to maintain continual Situational Awareness (SA) over a case-study selected Area of Interest (AOI) and existing UAV power and surveillance systems. This was done for still wind and constant wind conditions at altitude along with variations in latitude. The characteristics of solar flux and the dependence on the surface location and orientation were established along with fixed flight maneuvers for

  12. Development of Micro Air Vehicle Technology With In-Flight Adaptive-Wing Structure

    NASA Technical Reports Server (NTRS)

    Waszak, Martin R. (Technical Monitor); Shkarayev, Sergey; Null, William; Wagner, Matthew

    2004-01-01

    This is a final report on the research studies, "Development of Micro Air Vehicle Technology with In-Flight Adaptrive-Wing Structure". This project involved the development of variable-camber technology to achieve efficient design of micro air vehicles. Specifically, it focused on the following topics: 1) Low Reynolds number wind tunnel testing of cambered-plate wings. 2) Theoretical performance analysis of micro air vehicles. 3) Design of a variable-camber MAV actuated by micro servos. 4) Test flights of a variable-camber MAV.

  13. L(sub 1) Adaptive Control Design for NASA AirSTAR Flight Test Vehicle

    NASA Technical Reports Server (NTRS)

    Gregory, Irene M.; Cao, Chengyu; Hovakimyan, Naira; Zou, Xiaotian

    2009-01-01

    In this paper we present a new L(sub 1) adaptive control architecture that directly compensates for matched as well as unmatched system uncertainty. To evaluate the L(sub 1) adaptive controller, we take advantage of the flexible research environment with rapid prototyping and testing of control laws in the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. We apply the L(sub 1) adaptive control laws to the subscale turbine powered Generic Transport Model. The presented results are from a full nonlinear simulation of the Generic Transport Model and some preliminary pilot evaluations of the L(sub 1) adaptive control law.

  14. Adaptive discrete-time controller design with neural network for hypersonic flight vehicle via back-stepping

    NASA Astrophysics Data System (ADS)

    Xu, Bin; Sun, Fuchun; Yang, Chenguang; Gao, Daoxiang; Ren, Jianxin

    2011-09-01

    In this article, the adaptive neural controller in discrete time is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. The dynamics are decomposed into the altitude subsystem and the velocity subsystem. The altitude subsystem is transformed into the strict-feedback form from which the discrete-time model is derived by the first-order Taylor expansion. The virtual control is designed with nominal feedback and neural network (NN) approximation via back-stepping. Meanwhile, one adaptive NN controller is designed for the velocity subsystem. To avoid the circular construction problem in the practical control, the design of coefficients adopts the upper bound instead of the nominal value. Under the proposed controller, the semiglobal uniform ultimate boundedness stability is guaranteed. The square and step responses are presented in the simulation studies to show the effectiveness of the proposed control approach.

  15. Spatial perception changes associated with space flight: implications for adaptation to altered inertial environments.

    PubMed

    Parker, Donald E

    2003-01-01

    Preparation for extended travel by astronauts within the Solar System, including a possible manned mission to Mars, requires more complete understanding of adaptation to altered inertial environments. Improved understanding is needed to support development and evaluation of interventions to facilitate adaptations during transitions between those environments. Travel to another planet escalates the adaptive challenge because astronauts will experience prolonged exposure to microgravity before encountering a novel gravitational environment. This challenge would have to be met without ground support at the landing site. Evaluation of current adaptive status as well as intervention efficacy can be performed using perceptual, eye movement and postural measures. Due to discrepancies of adaptation magnitude and time-course among these measures, complete understanding of adaptation processes, as well as intervention evaluation, requires examination of all three. Previous research and theory that provide models for comprehending adaptation to altered inertial environments are briefly examined. Reports from astronauts of selected pre- in- and postflight self-motion illusions are described. The currently controversial tilt-translation reinterpretation hypothesis is reviewed and possible resolutions to the controversy are proposed. Finally, based on apparent gaps in our current knowledge, further research is proposed to achieve a more complete understanding of adaptation as well as to develop effective counter-measures.

  16. Spatial perception changes associated with space flight: implications for adaptation to altered inertial environments.

    PubMed

    Parker, Donald E

    2003-01-01

    Preparation for extended travel by astronauts within the Solar System, including a possible manned mission to Mars, requires more complete understanding of adaptation to altered inertial environments. Improved understanding is needed to support development and evaluation of interventions to facilitate adaptations during transitions between those environments. Travel to another planet escalates the adaptive challenge because astronauts will experience prolonged exposure to microgravity before encountering a novel gravitational environment. This challenge would have to be met without ground support at the landing site. Evaluation of current adaptive status as well as intervention efficacy can be performed using perceptual, eye movement and postural measures. Due to discrepancies of adaptation magnitude and time-course among these measures, complete understanding of adaptation processes, as well as intervention evaluation, requires examination of all three. Previous research and theory that provide models for comprehending adaptation to altered inertial environments are briefly examined. Reports from astronauts of selected pre- in- and postflight self-motion illusions are described. The currently controversial tilt-translation reinterpretation hypothesis is reviewed and possible resolutions to the controversy are proposed. Finally, based on apparent gaps in our current knowledge, further research is proposed to achieve a more complete understanding of adaptation as well as to develop effective counter-measures. PMID:15096676

  17. Falcon versus grouse: flight adaptations of a predator and its prey

    USGS Publications Warehouse

    Pennycuick, C.J.; Fuller, M.R.; Oar, J.J.; Kirkpatrick, S.J.

    1994-01-01

    Several falcons were trained to fly along a 500 m course to a lure. The air speeds of the more consistent performers averaged about 1.5 times their calculated minimum power speeds, and occasionally reached 2.1 times the minimum power speed. Wing beat frequencies of all the falcons were above those estimated from earlier field observations, and the same was true of wild Sage Grouse Centrocercus urophasianus, a regular falconer's quarry in the study area. Measurements of grouse killed by falcons showed that their wings were short, with broad slotted tips, whereas the falcons' wings were longer in relation to their body mass, and tapered. The short wings of grouse result in fast flight, high power requirements, and reduced capacity for aerobic flight. Calculations indicated that the grouse should fly faster than the falcons, and had the large amount of flight muscle needed to do so, but that the falcons would be capable of prolonged aerobic flight, whereas the grouse probably would not. We surmise that Sage Grouse cannot fly continuously without incurring an oxygen debt, and are therefore not long-distance migrants, although this limitation is partly due to their large size, and would not apply to smaller galliform birds such as ptarmigan Lagopus spp. The wing action seen in video recordings of the falcons was not consistent with the maintenance of constant circulation. We call it 'chase mode' because it appears to be associated with a high level of muscular exertion, without special regard to fuel economy. It shows features in common with the 'bounding' flight of passerines.

  18. Request for Information Response for the Flight Validation of Adaptive Control to Prevent Loss-of-Control Events. Overview of RFI Responses

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2009-01-01

    Adaptive control should be integrated with a baseline controller and only used when necessary (5 responses). Implementation as an emergency system. Immediately re-stabilize and return to controlled flight. Forced perturbation (excitation) for fine-tuning system a) Check margins; b) Develop requirements for amplitude of excitation. Adaptive system can improve performance by eating into margin constraints imposed on the non-adaptive system. Nonlinear effects due to multi-string voting.

  19. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  20. Investigation of the Multiple Method Adaptive Control (MMAC) method for flight control systems

    NASA Technical Reports Server (NTRS)

    Athans, M.; Baram, Y.; Castanon, D.; Dunn, K. P.; Green, C. S.; Lee, W. H.; Sandell, N. R., Jr.; Willsky, A. S.

    1979-01-01

    The stochastic adaptive control of the NASA F-8C digital-fly-by-wire aircraft using the multiple model adaptive control (MMAC) method is presented. The selection of the performance criteria for the lateral and the longitudinal dynamics, the design of the Kalman filters for different operating conditions, the identification algorithm associated with the MMAC method, the control system design, and simulation results obtained using the real time simulator of the F-8 aircraft at the NASA Langley Research Center are discussed.

  1. In-Flight Suppression of a De-Stabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

    NASA Technical Reports Server (NTRS)

    Wall, John; VanZwieten, Tannen; Giiligan Eric; Miller, Chris; Hanson, Curtis; Orr, Jeb

    2015-01-01

    Adaptive Augmenting Control (AAC) has been developed for NASA's Space Launch System (SLS) family of launch vehicles and implemented as a baseline part of its flight control system (FCS). To raise the technical readiness level of the SLS AAC algorithm, the Launch Vehicle Adaptive Control (LVAC) flight test program was conducted in which the SLS FCS prototype software was employed to control the pitch axis of Dryden's specially outfitted F/A-18, the Full Scale Advanced Systems Test Bed (FAST). This presentation focuses on a set of special test cases which demonstrate the successful mitigation of the unstable coupling of an F/A-18 airframe structural mode with the SLS FCS.

  2. Central Adaptation to Repeated Galvanic Vestibular Stimulation: Implications for Pre-Flight Astronaut Training

    PubMed Central

    Dilda, Valentina; Morris, Tiffany R.; Yungher, Don A.; MacDougall, Hamish G.; Moore, Steven T.

    2014-01-01

    Healthy subjects (N = 10) were exposed to 10-min cumulative pseudorandom bilateral bipolar Galvanic vestibular stimulation (GVS) on a weekly basis for 12 weeks (120 min total exposure). During each trial subjects performed computerized dynamic posturography and eye movements were measured using digital video-oculography. Follow up tests were conducted 6 weeks and 6 months after the 12-week adaptation period. Postural performance was significantly impaired during GVS at first exposure, but recovered to baseline over a period of 7–8 weeks (70–80 min GVS exposure). This postural recovery was maintained 6 months after adaptation. In contrast, the roll vestibulo-ocular reflex response to GVS was not attenuated by repeated exposure. This suggests that GVS adaptation did not occur at the vestibular end-organs or involve changes in low-level (brainstem-mediated) vestibulo-ocular or vestibulo-spinal reflexes. Faced with unreliable vestibular input, the cerebellum reweighted sensory input to emphasize veridical extra-vestibular information, such as somatosensation, vision and visceral stretch receptors, to regain postural function. After a period of recovery subjects exhibited dual adaption and the ability to rapidly switch between the perturbed (GVS) and natural vestibular state for up to 6 months. PMID:25409443

  3. Multivariate Dynamical Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts

    NASA Technical Reports Server (NTRS)

    Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah

    2014-01-01

    The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in intergrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.

  4. Multivariate Dynamic Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts

    NASA Technical Reports Server (NTRS)

    Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah

    2014-01-01

    The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in integrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.

  5. Central adaptation to repeated galvanic vestibular stimulation: implications for pre-flight astronaut training.

    PubMed

    Dilda, Valentina; Morris, Tiffany R; Yungher, Don A; MacDougall, Hamish G; Moore, Steven T

    2014-01-01

    Healthy subjects (N = 10) were exposed to 10-min cumulative pseudorandom bilateral bipolar Galvanic vestibular stimulation (GVS) on a weekly basis for 12 weeks (120 min total exposure). During each trial subjects performed computerized dynamic posturography and eye movements were measured using digital video-oculography. Follow up tests were conducted 6 weeks and 6 months after the 12-week adaptation period. Postural performance was significantly impaired during GVS at first exposure, but recovered to baseline over a period of 7-8 weeks (70-80 min GVS exposure). This postural recovery was maintained 6 months after adaptation. In contrast, the roll vestibulo-ocular reflex response to GVS was not attenuated by repeated exposure. This suggests that GVS adaptation did not occur at the vestibular end-organs or involve changes in low-level (brainstem-mediated) vestibulo-ocular or vestibulo-spinal reflexes. Faced with unreliable vestibular input, the cerebellum reweighted sensory input to emphasize veridical extra-vestibular information, such as somatosensation, vision and visceral stretch receptors, to regain postural function. After a period of recovery subjects exhibited dual adaption and the ability to rapidly switch between the perturbed (GVS) and natural vestibular state for up to 6 months.

  6. Morpho-functional adaptations in the bone tissue under the space flight conditions.

    PubMed

    Rodionova, N V; Oganov, V S

    2001-07-01

    Microgravity in space flight--situation of a maximum deficit of supporting loading on the skeleton and good model for finding-out of osteopenia and osteoporosis development laws, which are wide-spreading now and are "civilization diseases". Most typical for bones in conditions of a microgravitation by changes are: a decrease of intensity growth and osteoplastic processes, osteopenia and osteoporosis, decreasing of a mechanical strength and the risk of breaches arising (Oganov V.S., Schneider V. (1996)). Cytological mechanisms of gravity-dependent reactions in a bone tissue remain in many respects not-clear. By the purpose of our work was the analysis of some ultrastructural changes in bone tissue cells of the monkeys (Macaca mulatta), staying during two weeks onboard the biosatellite BION -11. PMID:12650186

  7. Morpho-functional adaptations in the bone tissue under the space flight conditions.

    PubMed

    Rodionova, N V; Oganov, V S

    2001-07-01

    Microgravity in space flight--situation of a maximum deficit of supporting loading on the skeleton and good model for finding-out of osteopenia and osteoporosis development laws, which are wide-spreading now and are "civilization diseases". Most typical for bones in conditions of a microgravitation by changes are: a decrease of intensity growth and osteoplastic processes, osteopenia and osteoporosis, decreasing of a mechanical strength and the risk of breaches arising (Oganov V.S., Schneider V. (1996)). Cytological mechanisms of gravity-dependent reactions in a bone tissue remain in many respects not-clear. By the purpose of our work was the analysis of some ultrastructural changes in bone tissue cells of the monkeys (Macaca mulatta), staying during two weeks onboard the biosatellite BION -11.

  8. Optimal Control Problem of Feeding Adaptations of Daphnia and Neural Network Simulation

    NASA Astrophysics Data System (ADS)

    Kmet', Tibor; Kmet'ov, Mria

    2010-09-01

    A neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints and open final time. The optimal control problem is transcribed into nonlinear programming problem, which is implemented with adaptive critic neural network [9] and recurrent neural network for solving nonlinear proprojection equations [10]. The proposed simulation methods is illustrated by the optimal control problem of feeding adaptation of filter feeders of Daphnia. Results show that adaptive critic based systematic approach and neural network solving of nonlinear equations hold promise for obtaining the optimal control with control and state constraints and open final time.

  9. Adaptive Responses in Eye-Head-Hand Coordination Following Exposures to a Virtual Environment as a Possible Space Flight Analog

    NASA Technical Reports Server (NTRS)

    Harm, Deborah L.; Taylor, L. C.; Bloomberg, J. J.

    2007-01-01

    Virtual environments (VE) offer unique training opportunities, particularly for training astronauts and preadapting them to the novel sensory conditions of microgravity. Sensorimotor aftereffects of VEs are often quite similar to adaptive sensorimotor responses observed in astronauts during and/or following space flight. The purpose of this research was to compare disturbances in sensorimotor coordination produced by dome virtual environment display and to examine the effects of exposure duration, and repeated exposures to VR systems. The current study examined disturbances in eye-head-hand (EHH) and eye-head coordination. Preliminary results will be presented. Eleven subjects have participated in the study to date. One training session was completed in order to achieve stable performance on the EHH coordination and VE tasks. Three experimental sessions were performed each separated by one day. Subjects performed a navigation and pick and place task in a dome immersive display VE for 30 or 60 min. The subjects were asked to move objects from one set of 15 pedestals to the other set across a virtual square room through a random pathway as quickly and accurately as possible. EHH coordination was measured before, immediately after, and at 1 hr, 2 hr, 4 hr and 6 hr following exposure to VR. EHH coordination was measured as position errors and reaction time in a pointing task that included multiple horizontal and vertical LED targets. Repeated measures ANOVAs were used to analyze the data. In general, we observed significant increases in position errors for both horizontal and vertical targets. The largest decrements were observed immediately following exposure to VR and showed a fairly rapid recovery across test sessions, but not across days. Subjects generally showed faster RTs across days. Individuals recovered from the detrimental effects of exposure to the VE on position errors within 1-2 hours. The fact that subjects did not significantly improve across days

  10. Flight Test of an Intelligent Flight-Control System

    NASA Technical Reports Server (NTRS)

    Davidson, Ron; Bosworth, John T.; Jacobson, Steven R.; Thomson, Michael Pl; Jorgensen, Charles C.

    2003-01-01

    inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS.

  11. Physiological Adaptations and Countermeasures Associated with Long-Duration Space Flights

    NASA Technical Reports Server (NTRS)

    Tipton, C. M.; Hargens, A. R.; Baldwin, K. M.; Schneider, V.; Convertino, V. A.; Kozlovskaya, I.; Wade, Charles E. (Technical Monitor)

    1994-01-01

    On earth, the presence of gravity imposes weight-bearing gradients on tissues which influence the functions of multiple integrative systems. On the other hand, conditions of actual or simulated microgravity can modify and/or nullify these gradients and subsequently alter structure and function. The purpose of this symposium is to discuss the results from short-term Shuttle flights, long term Skylab or Mir missions, or long-term ground based experiments which indicate or suggest that performance has been or could be compromised in space missions of long durations (>one year) or with space tasks (e.g. building space stations) with the goal of identifying countermeasures that could minimize or eliminate the expected anatomical and physiological consequences. After an overview by C. Tipton from the U. Arizona, the countermeasures necessary for the fluid shifts and select functions of the cardiovascular system will be discussed by A. Hargens from NASA Ames Research Center. He will be followed by K. Baldwin of the U. California at Irvine who will discuss the countermeasures needed to prevent the changes that alter the structure, function and control of skeletal muscles. Since changes in bone mass with microgravity are a major concern of NASA, V. Schneider from NASA Headquarters will present data and the countermeasures for bone. Although the results are limited, the changes in the endocrine and immune system deserve mentioning and C. Tipton will assume this responsibility. V. Convertino from the Air Force School of Aerospace Medicine has the challenge of discussing the role, importance, and the specificity of exercise as an effective countermeasure while I. Kozlovskaya from Moscow will elaborate on the Russian experiences with past countermeasures and provide a viewpoint on future ones. After the brief (25 min.) presentations, the speakers will assemble as a panel to discuss the issues raised and the concerns of the audience.

  12. Work, exercise, and space flight. 2: Modification of adaptation by exercise (exercise prescription)

    NASA Technical Reports Server (NTRS)

    Thornton, William

    1989-01-01

    The fundamentals of exercise theory on earth must be rigorously understood and applied to prevent adaptation to long periods of weightlessness. Locomotor activity, not weight, determines the capacity or condition of the largest muscles and bones in the body and usually also determines cardio-respiratory capacity. Absence of this activity results in rapid atrophy of muscle, bone, and cardio-respiratory capacity. Upper body muscle and bone are less affected depending upon the individual's usual, or 1-g, activities. Methodology is available to prevent these changes but space operations demand that it be done in the most efficient fashion, i.e., shortest time. At this point in time we can reasonably select the type of exercise and methods of obtaining it, but additional work in 1-g will be required to optimize the time.

  13. Stability Metrics for Simulation and Flight-Software Assessment and Monitoring of Adaptive Control Assist Compensators

    NASA Technical Reports Server (NTRS)

    Hodel, A. S.; Whorton, Mark; Zhu, J. Jim

    2008-01-01

    Due to a need for improved reliability and performance in aerospace systems, there is increased interest in the use of adaptive control or other nonlinear, time-varying control designs in aerospace vehicles. While such techniques are built on Lyapunov stability theory, they lack an accompanying set of metrics for the assessment of stability margins such as the classical gain and phase margins used in linear time-invariant systems. Such metrics must both be physically meaningful and permit the user to draw conclusions in a straightforward fashion. We present in this paper a roadmap to the development of metrics appropriate to nonlinear, time-varying systems. We also present two case studies in which frozen-time gain and phase margins incorrectly predict stability or instability. We then present a multi-resolution analysis approach that permits on-line real-time stability assessment of nonlinear systems.

  14. Digital adaptive flight control design using single stage model following indices

    NASA Technical Reports Server (NTRS)

    Alag, G.; Kaufman, H.

    1974-01-01

    Simple mechanical linkages are often unable to cope with many control problems associated with high-performance aircraft. This has led to the development of digital fly-by-wire control systems and in particular digital adaptive controllers that can be efficiently adjusted during system operation. To this effect, a control law has been derived based upon the minimization of a single-stage weighted combination of control energy and the squared error between the states of a linear plant and model. This control logic is interfaced with an on-line weighted least-squares estimator and a Kalman state filter. The utility of the resultant control system is illustrated by its application to the linearized dynamics of a typical fighter aircraft.

  15. Strain Gage Load Calibration of the Wing Interface Fittings for the Adaptive Compliant Trailing Edge Flap Flight Test

    NASA Technical Reports Server (NTRS)

    Miller, Eric J.; Holguin, Andrew C.; Cruz, Josue; Lokos, William A.

    2014-01-01

    The safety-of-flight parameters for the Adaptive Compliant Trailing Edge (ACTE) flap experiment require that flap-to-wing interface loads be sensed and monitored in real time to ensure that the structural load limits of the wing are not exceeded. This paper discusses the strain gage load calibration testing and load equation derivation methodology for the ACTE interface fittings. Both the left and right wing flap interfaces were monitored; each contained four uniquely designed and instrumented flap interface fittings. The interface hardware design and instrumentation layout are discussed. Twenty-one applied test load cases were developed using the predicted in-flight loads. Pre-test predictions of strain gage responses were produced using finite element method models of the interface fittings. Predicted and measured test strains are presented. A load testing rig and three hydraulic jacks were used to apply combinations of shear, bending, and axial loads to the interface fittings. Hardware deflections under load were measured using photogrammetry and transducers. Due to deflections in the interface fitting hardware and test rig, finite element model techniques were used to calculate the reaction loads throughout the applied load range, taking into account the elastically-deformed geometry. The primary load equations were selected based on multiple calibration metrics. An independent set of validation cases was used to validate each derived equation. The 2-sigma residual errors for the shear loads were less than eight percent of the full-scale calibration load; the 2-sigma residual errors for the bending moment loads were less than three percent of the full-scale calibration load. The derived load equations for shear, bending, and axial loads are presented, with the calculated errors for both the calibration cases and the independent validation load cases.

  16. Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

    NASA Astrophysics Data System (ADS)

    Williams, Rube B.

    2004-02-01

    Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

  17. Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

    SciTech Connect

    Williams, Rube B.

    2004-02-04

    Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

  18. Auxiliary probe design adaptable to existing probes for remote detection NMR, MRI, and time-of-flight tracing

    NASA Astrophysics Data System (ADS)

    Han, Songi; Granwehr, Josef; Garcia, Sandra; McDonnell, Erin E.; Pines, Alexander

    2006-10-01

    A versatile, detection-only probe design is presented that can be adapted to any existing NMR or MRI probe with the purpose of making the remote detection concept generally applicable. Remote detection suggests freeing the NMR experiment from the confinement of using the same radio frequency (RF) coil and magnetic field for both information encoding and signal detection. Information is stored during the encoding step onto a fluid sensor medium whose magnetization is later measured in a different location. The choice of an RF probe and magnetic field for encoding can be made based solely on the size and characteristics of the sample and the desired information quality without considering detection sensitivity, as this aspect is dealt with by a separate detector. While early experiments required building probes that included two resonant circuits, one for encoding and one for detection, a modular approach with a detection-only probe as presented here can be used along with any existing NMR probe of choice for encoding. The design of two different detection-only probes is presented, one with a saddle coil for milliliter-sized detection volumes, and the other one with a microsolenoid coil for sub-microliter fluid quantities. As example applications, we present time-of-flight (TOF) tracing of hyperpolarized 129Xe spins in a gas mixture through coiled tubing using the microsolenoid coil detector and TOF flow imaging through a nested glass container where the gas flow changes its direction twice between inlet and outlet using the saddle coil detector.

  19. Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data

    NASA Astrophysics Data System (ADS)

    Seitel, Alexander; dos Santos, Thiago R.; Mersmann, Sven; Penne, Jochen; Groch, Anja; Yung, Kwong; Tetzlaff, Ralf; Meinzer, Hans-Peter; Maier-Hein, Lena

    2011-03-01

    Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.

  20. Adaptive Control Strategies for Flexible Robotic Arm

    NASA Technical Reports Server (NTRS)

    Bialasiewicz, Jan T.

    1996-01-01

    The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

  1. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  2. Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

    PubMed Central

    Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

  3. Adaptive neural network motion control of manipulators with experimental evaluations.

    PubMed

    Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

    2014-01-01

    A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

  4. Steering by Hearing: A Bat’s Acoustic Gaze Is Linked to Its Flight Motor Output by a Delayed, Adaptive Linear Law

    PubMed Central

    Ghose, Kaushik; Moss, Cynthia F.

    2012-01-01

    Adaptive behaviors require sensorimotor computations that convert information represented initially in sensory coordinates to commands for action in motor coordinates. Fundamental to these computations is the relationship between the region of the environment sensed by the animal (gaze) and the animal’s locomotor plan. Studies of visually guided animals have revealed an anticipatory relationship between gaze direction and the locomotor plan during target-directed locomotion. Here, we study an acoustically guided animal, an echolocating bat, and relate acoustic gaze (direction of the sonar beam) to flight planning as the bat searches for and intercepts insect prey. We show differences in the relationship between gaze and locomotion as the bat progresses through different phases of insect pursuit. We define acoustic gaze angle, θgaze, to be the angle between the sonar beam axis and the bat’s flight path. We show that there is a strong linear linkage between acoustic gaze angle at time t [θgaze(t)] and flight turn rate at time t + τ into the future [θ̇flight (t + τ)], which can be expressed by the formula θ̇flight (t + τ) = kθgaze(t). The gain, k, of this linkage depends on the bat’s behavioral state, which is indexed by its sonar pulse rate. For high pulse rates, associated with insect attacking behavior, k is twice as high compared with low pulse rates, associated with searching behavior. We suggest that this adjustable linkage between acoustic gaze and motor output in a flying echolocating bat simplifies the transformation of auditory information to flight motor commands. PMID:16467518

  5. Longitudinal Aerodynamic Modeling of the Adaptive Compliant Trailing Edge Flaps on a GIII Airplane and Comparisons to Flight Data

    NASA Technical Reports Server (NTRS)

    Smith, Mark S.; Bui, Trong T.; Garcia, Christian A.; Cumming, Stephen B.

    2016-01-01

    A pair of compliant trailing edge flaps was flown on a modified GIII airplane. Prior to flight test, multiple analysis tools of various levels of complexity were used to predict the aerodynamic effects of the flaps. Vortex lattice, full potential flow, and full Navier-Stokes aerodynamic analysis software programs were used for prediction, in addition to another program that used empirical data. After the flight-test series, lift and pitching moment coefficient increments due to the flaps were estimated from flight data and compared to the results of the predictive tools. The predicted lift increments matched flight data well for all predictive tools for small flap deflections. All tools over-predicted lift increments for large flap deflections. The potential flow and Navier-Stokes programs predicted pitching moment coefficient increments better than the other tools.

  6. Predictive adaptive responses: Condition-dependent impact of adult nutrition and flight in the tropical butterfly Bicyclus anynana.

    PubMed

    Saastamoinen, Marjo; van der Sterren, Dominique; Vastenhout, Nienke; Zwaan, Bas J; Brakefield, Paul M

    2010-12-01

    The experience of environmental stress during development can substantially affect an organism's life history. These effects are often mainly negative, but a growing number of studies suggest that under certain environmental conditions early experience of such stress may yield individuals that are less sensitive to environmental stress later on in life. We used the butterfly Bicyclus anynana to study the effects of limited larval and adult food and forced flight on individual performance measured as reproduction and adult life span. Larvae exposed to food stress showed longer development and produced smaller adults. Thus, they were not able to fully compensate for the food deprivation during development. Females that experienced food stress during development did not increase tolerance for adult food limitation. However, females exposed to food stress during development coped better with forced flight compared with the control group. The apparent absence of costs of flight in poor-quality females may be a by-product of an altered body allocation, as females experiencing both food stress treatments had increased thorax ratios, compared with controls, and increased flight performances. The results reveal an important plasticity component to variation in flight performance and suggest that the cost of flight depends on an individual's internal condition.

  7. Simulation evaluation of a low-altitude helicopter flight guidance system adapted for a helmet-mounted display

    NASA Technical Reports Server (NTRS)

    Swenson, Harry N.; Zelenka, Richard E.; Hardy, Gordon H.; Dearing, Munro G.

    1991-01-01

    A computer aiding concept for low-altitude helicopter flight has been developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generation algorithm based upon dynamic programming, and a helmet-mounted display (HMD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor guidance symbology. The pilot evaluation was conducted at the NASA-Ames Research Center moving base vertical motion simulator (VMS) by pilots representing NASA, the US Army, Air Force, and helicopter industry. The pilot manually tracked the trajectory generated by the algorithm utilizing the HMD symbology. The pilots were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

  8. Miracle Flights

    MedlinePlus

    ... the perfect solution for your needs. Book A Flight Request a flight now Click on the link ... Now Make your donation today Saving Lives One Flight At A Time Miracle Flights provides free flights ...

  9. Quantum neural network based machine translator for Hindi to English.

    PubMed

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. PMID:24977198

  10. Automated neural network-based instrument validation system

    NASA Astrophysics Data System (ADS)

    Xu, Xiao

    2000-10-01

    In a complex control process, instrument calibration is periodically performed to maintain the instruments within the calibration range, which assures proper control and minimizes down time. Instruments are usually calibrated under out-of-service conditions using manual calibration methods, which may cause incorrect calibration or equipment damage. Continuous in-service calibration monitoring of sensors and instruments will reduce unnecessary instrument calibrations, give operators more confidence in instrument measurements, increase plant efficiency or product quality, and minimize the possibility of equipment damage during unnecessary manual calibrations. In this dissertation, an artificial neural network (ANN)-based instrument calibration verification system is designed to achieve the on-line monitoring and verification goal for scheduling maintenance. Since an ANN is a data-driven model, it can learn the relationships among signals without prior knowledge of the physical model or process, which is usually difficult to establish for the complex non-linear systems. Furthermore, the ANNs provide a noise-reduced estimate of the signal measurement. More importantly, since a neural network learns the relationships among signals, it can give an unfaulted estimate of a faulty signal based on information provided by other unfaulted signals; that is, provide a correct estimate of a faulty signal. This ANN-based instrument verification system is capable of detecting small degradations or drifts occurring in instrumentation, and preclude false control actions or system damage caused by instrument degradation. In this dissertation, an automated scheme of neural network construction is developed. Previously, the neural network structure design required extensive knowledge of neural networks. An automated design methodology was developed so that a network structure can be created without expert interaction. This validation system was designed to monitor process sensors plant-wide. Due to the large number of sensors to be monitored and the limited computational capability of an artificial neural network model, a variable grouping process was developed for dividing the sensor variables into small correlated groups which the neural networks can handle. A modification of a statistical method, called Beta method, as well as a principal component analysis (PCA)-based method of estimating the number of neural network hidden nodes was developed. Another development in this dissertation is the sensor fault detection method. The commonly used Sequential Probability Ratio Test (SPRT) continuously measures the likelihood ratio to statistically determine if there is any significant calibration change. This method requires normally distributed signals for correct operation. In practice, the signals deviate from the normal distribution causing problems for the SPRT. A modified SPRT (MSPRT) was developed to suppress the possible, intermittent alarms initiated by spurious spikes in network prediction errors. These methods were applied to data from the Tennessee Valley Authority (TVA) fossil power plant Unit 9 for testing. The results show that the average detectable drift level is about 2.5% for instruments in the boiler system and about 1% in the turbine system of the Unit 9 system. Approximately 74% of the process instruments can be monitored using the methodologies developed in this dissertation.

  11. Neural Network Based Sensory Fusion for Landmark Detection

    NASA Technical Reports Server (NTRS)

    Kumbla, Kishan -K.; Akbarzadeh, Mohammad R.

    1997-01-01

    NASA is planning to send numerous unmanned planetary missions to explore the space. This requires autonomous robotic vehicles which can navigate in an unstructured, unknown, and uncertain environment. Landmark based navigation is a new area of research which differs from the traditional goal-oriented navigation, where a mobile robot starts from an initial point and reaches a destination in accordance with a pre-planned path. The landmark based navigation has the advantage of allowing the robot to find its way without communication with the mission control station and without exact knowledge of its coordinates. Current algorithms based on landmark navigation however pose several constraints. First, they require large memories to store the images. Second, the task of comparing the images using traditional methods is computationally intensive and consequently real-time implementation is difficult. The method proposed here consists of three stages, First stage utilizes a heuristic-based algorithm to identify significant objects. The second stage utilizes a neural network (NN) to efficiently classify images of the identified objects. The third stage combines distance information with the classification results of neural networks for efficient and intelligent navigation.

  12. Learning in neural networks based on a generalized fluctuation theorem

    NASA Astrophysics Data System (ADS)

    Hayakawa, Takashi; Aoyagi, Toshio

    2015-11-01

    Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

  13. Convolution neural-network-based detection of lung structures

    NASA Astrophysics Data System (ADS)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  14. Three neural network based sensor systems for environmental monitoring

    SciTech Connect

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field.

  15. Neural network-based retrieval from software reuse repositories

    NASA Technical Reports Server (NTRS)

    Eichmann, David A.; Srinivas, Kankanahalli

    1992-01-01

    A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.

  16. Neural-network-based satellite tracking for deep space applications

    NASA Astrophysics Data System (ADS)

    Amoozegar, Farid; Ruggier, Charles

    2003-09-01

    NASA has been considering the use of Ka-band for deep space missions primarily for downlink telemetry applications. At such high frequencies, although the link will be expected to improve by a factor of four, the current Deep Space Network (DSN) antennas and transmitters would become less efficient due to higher equipment noise figures and antenna surface errors. Furthermore, the weather effect at Ka-band frequencies will dominate the degradations in link performance and tracking accuracy. At the lower frequencies, such as X-band, conventional CONSCAN or Monopulse tracking techniques can be used without much complexity, however, when utilizing Ka-band frequencies, the tracking of a spacecraft in deep space presents additional challenges. The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions, and examine the trade-off between tracking accuracy and communication link performance.

  17. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  18. Neural network based dynamic controllers for industrial robots.

    PubMed

    Oh, S Y; Shin, W C; Kim, H G

    1995-09-01

    The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

  19. Artificial neural network based permanent magnet DC motor drives

    SciTech Connect

    Hoque, M.A. Zaman, M.R.; Rahman, M.A.

    1995-12-31

    A novel scheme for the speed control of a permanent magnet (PM) dc motor drive incorporating artificial neural network (ANN) is proposed. The drive system includes an ANN speed controller, micro-processor based dc-dc converter and a laboratory PM dc motor. A multi-layer artificial neural network structure with a feedback loop is designed in order to precisely operate the control circuit for the dc-dc converter. The complete drive system is simulated and implemented in real time. Both the simulation and experimental results prove the inherent capability of the ANN which makes it possible to maintain desired speed control in the presence of parameter variations and load disturbances. The performances of the ANN based PM dc drive system are compared with the simulated results of the conventionally controlled drive system. This clearly indicates the better performance of the ANN based PM dc motor drive system, particularly in case of parameter and load variations.

  20. Neural Network-Based Sensor Validation for Turboshaft Engines

    NASA Technical Reports Server (NTRS)

    Moller, James C.; Litt, Jonathan S.; Guo, Ten-Huei

    1998-01-01

    Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

  1. Neural network-based multiple robot simultaneous localization and mapping.

    PubMed

    Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

    2011-12-01

    In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution.

  2. Neural network-based multiple robot simultaneous localization and mapping.

    PubMed

    Saeedi, Sajad; Paull, Liam; Trentini, Michael; Li, Howard

    2011-12-01

    In this paper, a decentralized platform for simultaneous localization and mapping (SLAM) with multiple robots is developed. Each robot performs single robot view-based SLAM using an extended Kalman filter to fuse data from two encoders and a laser ranger. To extend this approach to multiple robot SLAM, a novel occupancy grid map fusion algorithm is proposed. Map fusion is achieved through a multistep process that includes image preprocessing, map learning (clustering) using neural networks, relative orientation extraction using norm histogram cross correlation and a Radon transform, relative translation extraction using matching norm vectors, and then verification of the results. The proposed map learning method is a process based on the self-organizing map. In the learning phase, the obstacles of the map are learned by clustering the occupied cells of the map into clusters. The learning is an unsupervised process which can be done on the fly without any need to have output training patterns. The clusters represent the spatial form of the map and make further analyses of the map easier and faster. Also, clusters can be interpreted as features extracted from the occupancy grid map so the map fusion problem becomes a task of matching features. Results of the experiments from tests performed on a real environment with multiple robots prove the effectiveness of the proposed solution. PMID:22156983

  3. Neural network based satellite tracking for deep space applications

    NASA Technical Reports Server (NTRS)

    Amoozegar, F.; Ruggier, C.

    2003-01-01

    The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

  4. Neural network based detection of hard exudates in retinal images.

    PubMed

    García, María; Sánchez, Clara I; López, María I; Abásolo, Daniel; Hornero, Roberto

    2009-01-01

    Diabetic retinopathy (DR) is an important cause of visual impairment in developed countries. Automatic recognition of DR lesions in fundus images can contribute to the diagnosis of the disease. The aim of this study is to automatically detect one of these lesions, hard exudates (EXs), in order to help ophthalmologists in the diagnosis and follow-up of the disease. We propose an algorithm which includes a neural network (NN) classifier for this task. Three NN classifiers were investigated: multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM). Our database was composed of 117 images with variable colour, brightness, and quality. 50 of them (from DR patients) were used to train the NN classifiers and 67 (40 from DR patients and 27 from healthy retinas) to test the method. Using a lesion-based criterion, we achieved a mean sensitivity (SE(l)) of 88.14% and a mean positive predictive value (PPV(l)) of 80.72% for MLP. With RBF we obtained SE(l)=88.49% and PPV(l)=77.41%, while we reached SE(l)=87.61% and PPV(l)=83.51% using SVM. With an image-based criterion, a mean sensitivity (SE(i)) of 100%, a mean specificity (SP(i)) of 92.59% and a mean accuracy (AC(i)) of 97.01% were obtained with MLP. Using RBF we achieved SE(i)=100%, SP(i)=81.48% and AC(i)=92.54%. With SVM the image-based results were SE(i)=100%, SP(i)=77.78% and AC(i)=91.04%.

  5. A neural network based reputation bootstrapping approach for service selection

    NASA Astrophysics Data System (ADS)

    Wu, Quanwang; Zhu, Qingsheng; Li, Peng

    2015-10-01

    With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

  6. Neural network based speech synthesizer: A preliminary report

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Mcintire, Gary

    1987-01-01

    A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan.

  7. Artificial-neural-network-based failure detection and isolation

    NASA Astrophysics Data System (ADS)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  8. Simulation evaluation of a low-altitude helicopter flight guidance system adapted for a helmet-mounted display

    NASA Technical Reports Server (NTRS)

    Swenson, Harry N.; Zelenka, Richard E.; Hardy, Gordon H.; Dearing, Munro G.

    1992-01-01

    A computer aiding concept for low-altitude helicopter flight was developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generation algorithm based upon dynamic programming and a helmet-mounted display (HMD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor guidance symbology. The trajectory-generation algorithm uses knowledge of the global mission requirements, a digital terrain map, aircraft performance capabilities, and advanced navigation information to determine a trajectory between mission way points that seeks valleys to minimize threat exposure. The pilot evaluation was conducted at NASA ARC moving base Vertical Motion Simulator (VMS) by pilots representing NASA, the U.S. Army, the Air Force, and the helicopter industry. The pilots manually tracked the trajectory generated by the algorithm utilizing the HMD symbology. The pilots were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

  9. Integrated Flight/Structural Mode Control for Very Flexible Aircraft Using L1 Adaptive Output Feedback Controller

    NASA Technical Reports Server (NTRS)

    Che, Jiaxing; Cao, Chengyu; Gregory, Irene M.

    2012-01-01

    This paper explores application of adaptive control architecture to a light, high-aspect ratio, flexible aircraft configuration that exhibits strong rigid body/flexible mode coupling. Specifically, an L(sub 1) adaptive output feedback controller is developed for a semi-span wind tunnel model capable of motion. The wind tunnel mount allows the semi-span model to translate vertically and pitch at the wing root, resulting in better simulation of an aircraft s rigid body motion. The control objective is to design a pitch control with altitude hold while suppressing body freedom flutter. The controller is an output feedback nominal controller (LQG) augmented by an L(sub 1) adaptive loop. A modification to the L(sub 1) output feedback is proposed to make it more suitable for flexible structures. The new control law relaxes the required bounds on the unmatched uncertainty and allows dependence on the state as well as time, i.e. a more general unmatched nonlinearity. The paper presents controller development and simulated performance responses. Simulation is conducted by using full state flexible wing models derived from test data at 10 different dynamic pressure conditions. An L(sub 1) adaptive output feedback controller is designed for a single test point and is then applied to all the test cases. The simulation results show that the L(sub 1) augmented controller can stabilize and meet the performance requirements for all 10 test conditions ranging from 30 psf to 130 psf dynamic pressure.

  10. Flight Wing Surface Pressure and Boundary-Layer Data Report from the F-111 Smooth Variable-Camber Supercritical Mission Adaptive Wing

    NASA Technical Reports Server (NTRS)

    Powers, Sheryll Goecke; Webb, Lannie D.

    1997-01-01

    Flight tests were conducted using the advanced fighter technology integration F-111 (AFTI/F-111) aircraft modified with a variable-sweep supercritical mission adaptive wing (MAW). The MAW leading- and trailing-edge variable-camber surfaces were deflected in flight to provide a near-ideal wing camber shape for the flight condition. The MAW features smooth, flexible upper surfaces and fully enclosed lower surfaces, which distinguishes it from conventional flaps that have discontinuous surfaces and exposed or semi-exposed mechanisms. Upper and lower surface wing pressure distributions were measured along four streamwise rows on the right wing for cruise, maneuvering, and landing configurations. Boundary-layer measurements were obtained near the trailing edge for one of the rows. Cruise and maneuvering wing leading-edge sweeps were 26 deg for Mach numbers less than 1 and 45 deg or 58 deg for Mach numbers greater than 1. The landing wing sweep was 9 deg or 16 deg. Mach numbers ranged from 0.27 to 1.41, angles of attack from 2 deg to 13 deg, and Reynolds number per unit foot from 1.4 x 10(exp 6) to 6.5 x 10(exp 6). Leading-edge cambers ranged from O deg to 20 deg down, and trailing-edge cambers ranged from 1 deg up to 19 deg down. Wing deflection data for a Mach number of 0.85 are shown for three cambers. Wing pressure and boundary-layer data are given. Selected data comparisons are shown. Measured wing coordinates are given for three streamwise semispan locations for cruise camber and one spanwise location for maneuver camber.

  11. Findings on American astronauts bearing on the issue of artificial gravity for future manned space vehicles. [adaptation to weightlessness during manned space flight

    NASA Technical Reports Server (NTRS)

    Berry, C. A.

    1973-01-01

    Findings for American astronauts are reviewed that may indicate some alteration in vestibular response related to exposure to zero gravity. Of 25 individuals participating in Apollo missions 7 through 15, nine have experienced symptomatology that could be related to motion sickness. The apparent divergence between these results and those from the Soviet space program, which initially appears great, may reflect the greater emphasis given by Soviet investigators to vestibular aberrations. Presently the incidence of motion sickness, long known as an indicator of vestibular disturbance, seems too low to warrant any positive statement regarding inclusion of an artificial gravity system in future long term space missions. Where motion sickness has occurred, adaptation to weightlessness has always resulted in abatement of symptoms. In the absence of biomedical justification for incorporating artificial gravity systems in long term space flight vehicles, engineering considerations may dictate the manner in which the final ballot is cast.

  12. Flight control actuation system

    NASA Technical Reports Server (NTRS)

    Wingett, Paul T. (Inventor); Gaines, Louie T. (Inventor); Evans, Paul S. (Inventor); Kern, James I. (Inventor)

    2004-01-01

    A flight control actuation system comprises a controller, electromechanical actuator and a pneumatic actuator. During normal operation, only the electromechanical actuator is needed to operate a flight control surface. When the electromechanical actuator load level exceeds 40 amps positive, the controller activates the pneumatic actuator to offset electromechanical actuator loads to assist the manipulation of flight control surfaces. The assistance from the pneumatic load assist actuator enables the use of an electromechanical actuator that is smaller in size and mass, requires less power, needs less cooling processes, achieves high output forces and adapts to electrical current variations. The flight control actuation system is adapted for aircraft, spacecraft, missiles, and other flight vehicles, especially flight vehicles that are large in size and travel at high velocities.

  13. Flight control actuation system

    NASA Technical Reports Server (NTRS)

    Wingett, Paul T. (Inventor); Gaines, Louie T. (Inventor); Evans, Paul S. (Inventor); Kern, James I. (Inventor)

    2006-01-01

    A flight control actuation system comprises a controller, electromechanical actuator and a pneumatic actuator. During normal operation, only the electromechanical actuator is needed to operate a flight control surface. When the electromechanical actuator load level exceeds 40 amps positive, the controller activates the pneumatic actuator to offset electromechanical actuator loads to assist the manipulation of flight control surfaces. The assistance from the pneumatic load assist actuator enables the use of an electromechanical actuator that is smaller in size and mass, requires less power, needs less cooling processes, achieves high output forces and adapts to electrical current variations. The flight control actuation system is adapted for aircraft, spacecraft, missiles, and other flight vehicles, especially flight vehicles that are large in size and travel at high velocities.

  14. Practical experiences with an adaptive neural network short-term load forecasting system

    SciTech Connect

    Mohammed, O.; Park, D.; Merchant, R.; Dinh, T.; Tong, C.; Azeem, A.; Farah, J.; Drake, C.

    1995-02-01

    An adaptive neural network based short-term electric load forecasting system is presented. The system is developed and implemented for Florida Power and Light Company (FPL). Practical experiences with the system are discussed. The system accounts for seasonal and daily characteristics, as well as abnormal conditions such as cold fronts, heat waves, holidays and other conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism is used to train the neural networks when on-line. The results indicate that the load forecasting system presented gives robust and more accurate forecasts and allows greater adaptability to sudden climatic changes compared with statistical methods. The system is portable and can be modified to suit the requirements of other utility companies.

  15. Adaptive responses in eye-head-hand coordination following exposures to a virtual environment as a possible space flight analog.

    PubMed

    Harm, D L; Taylor, L C; Bloomberg, J J

    2007-07-01

    Virtual reality environments (VRs) offer unique training opportunities, particularly for training astronauts and preadapting them to the novel sensory conditions of microgravity. The purpose of the current research was to compare disturbances in eye-head-hand (EHH) sensorimotor coordination produced by repeated exposures to VR systems. In general, we observed significant increases in position errors for both horizontal and vertical targets. The largest decrements were observed immediately following exposure to VR and showed general recovery within 6 hours across each test session, but not across days. Subjects generally showed faster reaction times across days. These findings provide some direction for developing training schedules for VR users that facilitate adaptation and support the idea that VRs may serve as an analog for sensorimotor effects of spaceflight.

  16. Intelligent flight control systems

    NASA Technical Reports Server (NTRS)

    Stengel, Robert F.

    1993-01-01

    The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

  17. Spacelab 3 flight experiment No. 3AFT23: Autogenic-feedback training as a preventive method for space adaptation syndrome

    NASA Technical Reports Server (NTRS)

    Cowings, Patricia S.; Toscano, William B.; Kamiya, Joe; Miller, Neal E.; Sharp, Joseph C.

    1988-01-01

    Space adaptation syndrome is a motion sickness-like disorder which affects up to 50 percent of all people exposed to microgravity in space. This experiment tested a physiological conditioning procedure (Autogenic-Feedback Training, AFT) as an alternative to pharmacological management. Four astronauts participated as subjects in this experiment. Crewmembers A and B served as treatment subjects. Both received preflight training for control of heart rate, respiration rate, peripheral blood volume, and skin conductance. Crewmembers C and D served as controls (i.e., did not receive training). Crewmember A showed reliable control of his own physiological responses, and a significant increase in motion sickness tolerance after training. Crewmember B, however, demonstrated much less control and only a moderate increase in motion sickness tolerance was observed after training. The inflight symptom reports and physiological data recordings revealed that Crewmember A did not experience any severe symptom episodes during the mission, while Crewmember B reported one severe symptom episode. Both control group subjects, C and D (who took antimotion sickness medication), reported multiple symptom episodes on mission day 0. Both inflight data and crew reports indicate that AFT may be an effective countermeasure. Additional data must be obtained inflight (a total of eight treatment and eight control subjects) before final evaluation of this treatment can be made.

  18. Adaptive neural control of aeroelastic response

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.

    1996-05-01

    The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.

  19. [Intensity of lipid peroxidation and antioxidant protection system indices during re-adaptation period after long-term space flights at the international space station].

    PubMed

    Zhuravleva, O A; Morukov, B V; Markin, A A; Vostrikova, L V; Zabolotskaia, I V

    2011-01-01

    In the blood serum of seventeen members of crews which participated in 14 orbital expeditions to the International Space Station with the duration of 125 to 217 days, during the pre-flight period and on the day of landing on the 1st, 7th and 14th days of the rehabilitation period (RP) the content of LPO products was determined, namely diene conjugates (DC), malon dialdehyde (MDA), shiffbases (SB) and the main lipid oxidant - tocopherol (TP). The group of astronauts who made landing in the Space Shuttle spacecraft (8 persons) and the group of astronauts who accomplished space mission in the Soyus TM spacecraft (9 persons) demonstrated a decrease in DC and MDA levels with a rise in TF concentration in the course of the rehabilitation period. Changes in the group of the American spacecraft astronauts were more pronounced. LPO inhibition during the rehabilitation period is recognized [treated] as an adequate reaction to the stress caused by re-adaptation to the ground conditions. Also are discussed probable mechanisms of intergroup differencies in LPO intensity degree: biomembrane phase state changing under the influence of overloads during de-orbiting and stress response intensity during landing in different types of spacecraft.

  20. Flight experience with a remotely augmented vehicle flight test technique

    NASA Technical Reports Server (NTRS)

    Petersen, K. L.

    1981-01-01

    A flight technique which uses the remotely augmented vehicle (RAV) concept is developed to flight test advanced control law concepts. The design, development and flight test validation of a RAV system mechanized on a digital fly-by-wire aircraft are described, and future applications are discussed. Flight experiments investigate complete inner loop, low sample rate, and adaptive control system mechanisms. The technique, which utilizes a ground-based FORTRAN programmable digital computer and up and down telemetry links is found to provide the flexibility necessary to effectively investigate alternate control law mechanisms in flight.

  1. Toward intelligent flight control

    NASA Technical Reports Server (NTRS)

    Stengel, Robert F.

    1993-01-01

    Flight control systems can benefit by being designed to emulate functions of natural intelligence. Intelligent control functions fall in three categories: declarative, procedural, and reflexive. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are more-or-less spontaneous and are similar to inner-loop control and estimation. Intelligent flight control systems will contain a hierarchy of expert systems, procedural algorithms, and computational neural networks, each expanding on prior functions to improve mission capability to increase the reliability and safety of flight and to ease pilot workload.

  2. Cardiovascular adaptations in weightlessness: The influence of in-flight exercise programs on the cardiovascular adjustments during weightlessness and upon returning to Earth

    NASA Technical Reports Server (NTRS)

    Bennett, C. H.

    1981-01-01

    The effect of in-flight exercise programs on astronauts' cardiovascular adjustments during spaceflight weightlessness and upon return to Earth was studied. Physiological changes in muscle strength and volume, cardiovascular responses during the application of lower body negative pressure, and metabolic activities during pre-flight and flight tests were made on Skylab crewmembers. The successful completion of the Skylab missions showed that man can perform submaximal and maximal aerobic exercise in the weightless enviroment without detrimental trends in any of the physiologic data. Exercise tolerance during flight was unaffected. It was only after return to Earth that a tolerance decrement was noted. The rapid postflight recovery of orthostatic and exercise tolerance following two of the three Skylab missions appeared to be directly related to total in-flight exercise as well as to the graded, regular program of exercise performed during the postflight debriefing period.

  3. Assessment of individual adaptation to microgravity during long term space flight based on stepwise discriminant analysis of heart rate variability parameters

    NASA Astrophysics Data System (ADS)

    Baevsky, Roman M.; Chernikova, Anna G.; Funtova, Irina I.; Tank, Jens

    2011-12-01

    Optimization of the cardiovascular system under conditions of long term space flight is provided by individual changes of autonomic cardiovascular control. Heart rate variability (HRV) analysis is an easy to use method under these extreme conditions. We tested the hypothesis that individual HRV analysis provides important information for crew health monitoring. HRV data from 14 Russian cosmonauts measured during long term space flights are presented (two times before and after flight, monthly in flight). HRV characteristics in the time and in the frequency domain were calculated. Predefined discriminant function equations obtained in reference groups (L1=-0.112*HR-1.006*SI-0.047*pNN50-0.086*HF; L2=0.140*HR-0.165*SI-1.293*pNN50+0.623*HF) were used to define four functional states. (1) Physiological normal, (2) prenosological, (3) premorbid and (4) pathological. Geometric mean values for the ISS cosmonauts based on L1 and L2 remained within normal ranges. A shift from the physiological normal state to the prenosological functional state during space flight was detected. The functional state assessed by HRV improved during space flight if compared to pre-flight and early post-flight functional states. Analysis of individual cosmonauts showed distinct patterns depending on the pre-flight functional state. Using the developed classification a transition process from the state of physiological normal into a prenosological state or premorbid state during different stages of space flight can be detected for individual Russian cosmonauts. Our approach to an estimation of HR regulatory pattern can be useful for prognostic purposes.

  4. Adaptive beam-width control of echolocation sounds by CF-FM bats, Rhinolophus ferrumequinum nippon, during prey-capture flight.

    PubMed

    Matsuta, Naohiro; Hiryu, Shizuko; Fujioka, Emyo; Yamada, Yasufumi; Riquimaroux, Hiroshi; Watanabe, Yoshiaki

    2013-04-01

    The echolocation sounds of Japanese CF-FM bats (Rhinolophus ferrumequinum nippon) were measured while the bats pursued a moth (Goniocraspidum pryeri) in a flight chamber. Using a 31-channel microphone array system, we investigated how CF-FM bats adjust pulse direction and beam width according to prey position. During the search and approach phases, the horizontal and vertical beam widths were ±22±5 and ±13±5 deg, respectively. When bats entered the terminal phase approximately 1 m from a moth, distinctive evasive flight by G. pryeri was sometimes observed. Simultaneously, the bats broadened the beam widths of some emissions in both the horizontal (44% of emitted echolocation pulses) and vertical planes (71%). The expanded beam widths were ±36±7 deg (horizontal) and ±30±9 deg (vertical). When moths began evasive flight, the tracking accuracy decreased compared with that during the approach phase. However, in 97% of emissions during the terminal phase, the beam width was wider than the misalignment (the angular difference between the pulse and target directions). These findings indicate that bats actively adjust their beam width to retain the moving target within a spatial echolocation window during the final capture stages.

  5. Integrated Resilient Aircraft Control Project Full Scale Flight Validation

    NASA Technical Reports Server (NTRS)

    Bosworth, John T.

    2009-01-01

    Objective: Provide validation of adaptive control law concepts through full scale flight evaluation. Technical Approach: a) Engage failure mode - destabilizing or frozen surface. b) Perform formation flight and air-to-air tracking tasks. Evaluate adaptive algorithm: a) Stability metrics. b) Model following metrics. Full scale flight testing provides an ability to validate different adaptive flight control approaches. Full scale flight testing adds credence to NASA's research efforts. A sustained research effort is required to remove the road blocks and provide adaptive control as a viable design solution for increased aircraft resilience.

  6. Orion Abort Flight Test

    NASA Technical Reports Server (NTRS)

    Hayes, Peggy Sue

    2010-01-01

    The purpose of NASA's Constellation project is to create the new generation of spacecraft for human flight to the International Space Station in low-earth orbit, the lunar surface, as well as for use in future deep-space exploration. One portion of the Constellation program was the development of the Orion crew exploration vehicle (CEV) to be used in spaceflight. The Orion spacecraft consists of a crew module, service module, space adapter and launch abort system. The crew module was designed to hold as many as six crew members. The Orion crew exploration vehicle is similar in design to the Apollo space capsules, although larger and more massive. The Flight Test Office is the responsible flight test organization for the launch abort system on the Orion crew exploration vehicle. The Flight Test Office originally proposed six tests that would demonstrate the use of the launch abort system. These flight tests were to be performed at the White Sands Missile Range in New Mexico and were similar in nature to the Apollo Little Joe II tests performed in the 1960s. The first flight test of the launch abort system was a pad abort (PA-1), that took place on 6 May 2010 at the White Sands Missile Range in New Mexico. Primary flight test objectives were to demonstrate the capability of the launch abort system to propel the crew module a safe distance away from a launch vehicle during a pad abort, to demonstrate the stability and control characteristics of the vehicle, and to determine the performance of the motors contained within the launch abort system. The focus of the PA-1 flight test was engineering development and data acquisition, not certification. In this presentation, a high level overview of the PA-1 vehicle is given, along with an overview of the Mobile Operations Facility and information on the White Sands tracking sites for radar & optics. Several lessons learned are presented, including detailed information on the lessons learned in the development of wind

  7. The effects of stress hormones on immune function may be vital for the adaptive reconfiguration of the immune system during fight-or-flight behavior.

    PubMed

    Adamo, Shelley A

    2014-09-01

    Intense, short-term stress (i.e., robust activation of the fight-or-flight response) typically produces a transient decline in resistance to disease in animals across phyla. Chemical mediators of the stress response (e.g., stress hormones) help induce this decline, suggesting that this transient immunosuppression is an evolved response. However, determining the function of stress hormones on immune function is difficult because of their complexity. Nevertheless, evidence suggests that stress hormones help maintain maximal resistance to disease during the physiological changes needed to optimize the body for intense physical activity. Work on insects demonstrates that stress hormones both shunt resources away from the immune system during fight-or-flight responses as well as reconfigure the immune system. Reconfiguring the immune system minimizes the impact of the loss of these resources and reduces the increased costs of some immune functions due to the physiological changes demanded by the fight-or-flight response. For example, during the stress response of the cricket Gryllus texensis, some molecular resources are shunted away from the immune system and toward lipid transport, resulting in a reduction in resistance to disease. However, insects' immune cells (hemocytes) have receptors for octopamine (the insect stress neurohormone). Octopamine increases many hemocyte functions, such as phagocytosis, and these changes would tend to mitigate the decline in immunity due to the loss of molecular resources. Moreover, because the stress response generates oxidative stress, some immune responses are probably more costly when activated during a stress response (e.g., those that produce reactive molecules). Some of these immune responses are depressed during stress in crickets, while others, whose costs are probably not increased during a stress response, are enhanced. Some effects of stress hormones on immune systems may be better understood as examples of reconfiguration

  8. Engineering study for pallet adapting the Apollo laser altimeter and photographic camera system for the Lidar Test Experiment on orbital flight tests 2 and 4

    NASA Technical Reports Server (NTRS)

    Kuebert, E. J.

    1977-01-01

    A Laser Altimeter and Mapping Camera System was included in the Apollo Lunar Orbital Experiment Missions. The backup system, never used in the Apollo Program, is available for use in the Lidar Test Experiments on the STS Orbital Flight Tests 2 and 4. Studies were performed to assess the problem associated with installation and operation of the Mapping Camera System in the STS. They were conducted on the photographic capabilities of the Mapping Camera System, its mechanical and electrical interface with the STS, documentation, operation and survivability in the expected environments, ground support equipment, test and field support.

  9. Flight Planning

    NASA Technical Reports Server (NTRS)

    1991-01-01

    Seagull Technology, Inc., Sunnyvale, CA, produced a computer program under a Langley Research Center Small Business Innovation Research (SBIR) grant called STAFPLAN (Seagull Technology Advanced Flight Plan) that plans optimal trajectory routes for small to medium sized airlines to minimize direct operating costs while complying with various airline operating constraints. STAFPLAN incorporates four input databases, weather, route data, aircraft performance, and flight-specific information (times, payload, crew, fuel cost) to provide the correct amount of fuel optimal cruise altitude, climb and descent points, optimal cruise speed, and flight path.

  10. Experiments on Adaptive Techniques for Host-Based Intrusion Detection

    SciTech Connect

    DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.; THOMAS, EDWARD V.; WUNSCH, DONALD

    2001-09-01

    This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerable preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.

  11. Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

    NASA Technical Reports Server (NTRS)

    Bosworth, John

    2006-01-01

    A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

  12. Understanding Flight

    SciTech Connect

    Anderson, David

    2001-01-31

    Through the years the explanation of flight has become mired in misconceptions that have become dogma. Wolfgang Langewiesche, the author of 'Stick and Rudder' (1944) got it right when he wrote: 'Forget Bernoulli's Theorem'. A wing develops lift by diverting (from above) a lot of air. This is the same way that a propeller produces thrust and a helicopter produces lift. Newton's three laws and a phenomenon called the Coanda effect explain most of it. With an understanding of the real physics of flight, many things become clear. Inverted flight, symmetric wings, and the flight of insects are obvious. It is easy to understand the power curve, high-speed stalls, and the effect of load and altitude on the power requirements for lift. The contribution of wing aspect ratio on the efficiency of a wing, and the true explanation of ground effect will also be discussed.

  13. Cardiovascular function in space flight

    NASA Technical Reports Server (NTRS)

    Nicogossian, A. E.; Charles, J. B.; Bungo, M. W.; Leach-Huntoon, C. S.

    1990-01-01

    Postflight orthostatic intolerance and cardiac hemodynamics associated with manned space flight have been investigated on seven STS missions. Orthostatic heart rates appear to be influenced by the mission duration. The rates increase during the first 7-10 days of flight and recover partially after that. Fluid loading is used as a countermeasure to the postflight orthostatic intolerance. The carotid baroreceptor function shows only slight responsiveness to orthostatic stimulation. Plots of the baroreceptor function are presented. It is concluded that an early adaptation to the space flight conditions involves a fluid shift and that the subsequent alterations in the neutral controlling mechanisms contribute to the orthoststic intolerance.

  14. Flight (Children's Books).

    ERIC Educational Resources Information Center

    Matthews, Susan; Reid, Rebecca; Sylvan, Anne; Woolard, Linda; Freeman, Evelyn B.

    1997-01-01

    Presents brief annotations of 43 children's books, grouped around the theme of flight: flights of imagination, flights across time and around the globe, flights of adventure, and nature's flight. (SR)

  15. The IBEX Flight Segment

    NASA Astrophysics Data System (ADS)

    Scherrer, J.; Carrico, J.; Crock, J.; Cross, W.; Delossantos, A.; Dunn, A.; Dunn, G.; Epperly, M.; Fields, B.; Fowler, E.; Gaio, T.; Gerhardus, J.; Grossman, W.; Hanley, J.; Hautamaki, B.; Hawes, D.; Holemans, W.; Kinaman, S.; Kirn, S.; Loeffler, C.; McComas, D. J.; Osovets, A.; Perry, T.; Peterson, M.; Phillips, M.; Pope, S.; Rahal, G.; Tapley, M.; Tyler, R.; Ungar, B.; Walter, E.; Wesley, S.; Wiegand, T.

    2009-08-01

    IBEX provides the observations needed for detailed modeling and in-depth understanding of the interstellar interaction (McComas et al. in Physics of the Outer Heliosphere, Third Annual IGPP Conference, pp. 162-181, 2004; Space Sci. Rev., 2009a, this issue). From mission design to launch and acquisition, this goal drove all flight system development. This paper describes the management, design, testing and integration of IBEX’s flight system, which successfully launched from Kwajalein Atoll on October 19, 2008. The payload is supported by a simple, Sun-pointing, spin-stabilized spacecraft with no deployables. The spacecraft bus consists of the following subsystems: attitude control, command and data handling, electrical power, hydrazine propulsion, RF, thermal, and structures. A novel 3-step orbit approach was employed to put IBEX in its highly elliptical, 8-day final orbit using a Solid Rocket Motor, which provided large delta-V after IBEX separated from the Pegasus launch vehicle; an adapter cone, which interfaced between the SRM and Pegasus; Motorized Lightbands, which performed separation from the Pegasus, ejection of the adapter cone, and separation of the spent SRM from the spacecraft; a ShockRing isolation system to lower expected launch loads; and the onboard Hydrazine Propulsion System. After orbit raising, IBEX transitioned from commissioning to nominal operations and science acquisition. At every phase of development, the Systems Engineering and Mission Assurance teams supervised the design, testing and integration of all IBEX flight elements.

  16. Active Listening in a Bat Cocktail Party: Adaptive Echolocation and Flight Behaviors of Big Brown Bats, Eptesicus fuscus, Foraging in a Cluttered Acoustic Environment.

    PubMed

    Warnecke, Michaela; Chiu, Chen; Engelberg, Jonathan; Moss, Cynthia F

    2015-09-01

    In their natural environment, big brown bats forage for small insects in open spaces, as well as in vegetation and in the presence of acoustic clutter. While searching and hunting for prey, bats experience sonar interference, not only from densely cluttered environments, but also from calls of conspecifics foraging in close proximity. Previous work has shown that when two bats compete for a single prey item in a relatively open environment, one of the bats may go silent for extended periods of time, which can serve to minimize sonar interference between conspecifics. Additionally, pairs of big brown bats have been shown to adjust frequency characteristics of their vocalizations to avoid acoustic interference in echo processing. In this study, we extended previous work by examining how the presence of conspecifics and environmental clutter influence the bat's echolocation behavior. By recording multichannel audio and video data of bats engaged in insect capture in open and cluttered spaces, we quantified the bats' vocal and flight behaviors. Big brown bats flew individually and in pairs in an open and cluttered room, and the results of this study shed light on the different strategies that this species employs to negotiate a complex and dynamic environment.

  17. Active Listening in a Bat Cocktail Party: Adaptive Echolocation and Flight Behaviors of Big Brown Bats, Eptesicus fuscus, Foraging in a Cluttered Acoustic Environment.

    PubMed

    Warnecke, Michaela; Chiu, Chen; Engelberg, Jonathan; Moss, Cynthia F

    2015-09-01

    In their natural environment, big brown bats forage for small insects in open spaces, as well as in vegetation and in the presence of acoustic clutter. While searching and hunting for prey, bats experience sonar interference, not only from densely cluttered environments, but also from calls of conspecifics foraging in close proximity. Previous work has shown that when two bats compete for a single prey item in a relatively open environment, one of the bats may go silent for extended periods of time, which can serve to minimize sonar interference between conspecifics. Additionally, pairs of big brown bats have been shown to adjust frequency characteristics of their vocalizations to avoid acoustic interference in echo processing. In this study, we extended previous work by examining how the presence of conspecifics and environmental clutter influence the bat's echolocation behavior. By recording multichannel audio and video data of bats engaged in insect capture in open and cluttered spaces, we quantified the bats' vocal and flight behaviors. Big brown bats flew individually and in pairs in an open and cluttered room, and the results of this study shed light on the different strategies that this species employs to negotiate a complex and dynamic environment. PMID:26398707

  18. Do birds sleep in flight?

    NASA Astrophysics Data System (ADS)

    Rattenborg, Niels C.

    2006-09-01

    The following review examines the evidence for sleep in flying birds. The daily need to sleep in most animals has led to the common belief that birds, such as the common swift ( Apus apus), which spend the night on the wing, sleep in flight. The electroencephalogram (EEG) recordings required to detect sleep in flight have not been performed, however, rendering the evidence for sleep in flight circumstantial. The neurophysiology of sleep and flight suggests that some types of sleep might be compatible with flight. As in mammals, birds exhibit two types of sleep, slow-wave sleep (SWS) and rapid eye-movement (REM) sleep. Whereas, SWS can occur in one or both brain hemispheres at a time, REM sleep only occurs bihemispherically. During unihemispheric SWS, the eye connected to the awake hemisphere remains open, a state that may allow birds to visually navigate during sleep in flight. Bihemispheric SWS may also be possible during flight when constant visual monitoring of the environment is unnecessary. Nevertheless, the reduction in muscle tone that usually accompanies REM sleep makes it unlikely that birds enter this state in flight. Upon landing, birds may need to recover the components of sleep that are incompatible with flight. Periods of undisturbed postflight recovery sleep may be essential for maintaining adaptive brain function during wakefulness. The recent miniaturization of EEG recording devices now makes it possible to measure brain activity in flight. Determining if and how birds sleep in flight will contribute to our understanding of a largely unexplored aspect of avian behavior and may also provide insight into the function of sleep.

  19. Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models.

    PubMed

    Yang, Chenguang; Li, Zhijun; Li, Jing

    2013-02-01

    In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly "controls" the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.

  20. Adaptive output feedback control of flexible systems

    NASA Astrophysics Data System (ADS)

    Yang, Bong-Jun

    Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process. Within this context, adaptive compensation for external disturbances is addressed. In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction. While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator. The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in

  1. Space Flight Resource Management for ISS Operations

    NASA Technical Reports Server (NTRS)

    Schmidt, Larry; Slack, Kelley; O'Keefe, William; Huning, Therese; Sipes, Walter; Holland, Albert

    2011-01-01

    This slide presentation reviews the International Space Station (ISS) Operations space flight resource management, which was adapted to the ISS from the shuttle processes. It covers crew training and behavior elements.

  2. Immune response during space flight.

    PubMed

    Criswell-Hudak, B S

    1991-01-01

    The health status of an astronaut prior to and following space flight has been a prime concern of NASA throughout the Apollo series of lunar landings, Skylab, Apollo-Soyuz Test Projects (ASTP), and the new Spacelab-Shuttle missions. Both humoral and cellular immunity has been studied using classical clinical procedures. Serum proteins show fluctuations that can be explained with adaptation to flight. Conversely, cellular immune responses of lymphocytes appear to be depressed in both in vivo as well as in vitro. If this depression in vivo and in vitro is a result of the same cause, then man's adaptation to outer space living will present interesting challenges in the future. Since the cause may be due to reduced gravity, perhaps the designs of the experiments for space flight will offer insights at the cellular levels that will facilitate development of mechanisms for adaptation. Further, if the aging process is viewed as an adaptational concept or model and not as a disease process then perhaps space flight could very easily interact to supply some information on our biological time clocks. PMID:1915698

  3. Optimum Strategies for Selecting Descent Flight-Path Angles

    NASA Technical Reports Server (NTRS)

    Wu, Minghong G. (Inventor); Green, Steven M. (Inventor)

    2016-01-01

    An information processing system and method for adaptively selecting an aircraft descent flight path for an aircraft, are provided. The system receives flight adaptation parameters, including aircraft flight descent time period, aircraft flight descent airspace region, and aircraft flight descent flyability constraints. The system queries a plurality of flight data sources and retrieves flight information including any of winds and temperatures aloft data, airspace/navigation constraints, airspace traffic demand, and airspace arrival delay model. The system calculates a set of candidate descent profiles, each defined by at least one of a flight path angle and a descent rate, and each including an aggregated total fuel consumption value for the aircraft following a calculated trajectory, and a flyability constraints metric for the calculated trajectory. The system selects a best candidate descent profile having the least fuel consumption value while the fly ability constraints metric remains within aircraft flight descent flyability constraints.

  4. Calcium Kinetics During Space Flight

    NASA Technical Reports Server (NTRS)

    Smith, Scott M.; OBrien, K. O.; Abrams, S. A.; Wastney, M. E.

    2005-01-01

    Bone loss during space flight is one of the most critical challenges to astronaut health on space exploration missions. Defining the time course and mechanism of these changes will aid in developing means to counteract bone loss during space flight, and will have relevance for other clinical situations that impair weight-bearing activity. Bone health is a product of the balance between bone formation and bone resorption. Early space research could not clearly identify which of these was the main process altered in bone loss, but identification of the collagen crosslinks in the 1990s made possible a clear understanding that the impact of space flight was greater on bone resorption, with bone formation being unchanged or only slightly decreased. Calcium kinetics data showed that bone resorption was greater during flight than before flight (668 plus or minus 130 vs. 427 plus or minus 153 mg/d, p less than 0.001), and clearly documented that true intestinal calcium absorption was lower during flight than before flight (233 plus or minus 87 vs. 460 plus or minus 47 mg/d, p less than 0.01). Weightlessness had a detrimental effect on the balance in bone turnover: the difference between daily calcium balance during flight (-234 plus or minus 102 mg/d) and calcium balance before flight (63 plus or minus 75 mg/d) approached 300 mg/d (p less than 0.01). These data demonstrate that the bone loss that occurs during space flight is a consequence of increased bone resorption and decreased intestinal calcium absorption. Examining the changes in bone and calcium homeostasis in the initial days and weeks of space flight, as well as at later times on missions longer than 6 months, is critical to understanding the nature of bone adaptation to weightlessness. To increase knowledge of these changes, we studied bone adaptation to space flight on the 16-day Space Shuttle Columbia (STS-107) mission. When the brave and talented crew of Columbia were lost during reentry on the tragic morning

  5. Adaptive Filtering Using Recurrent Neural Networks

    NASA Technical Reports Server (NTRS)

    Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

    2005-01-01

    A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

  6. The flight planning - flight management connection

    NASA Technical Reports Server (NTRS)

    Sorensen, J. A.

    1984-01-01

    Airborne flight management systems are currently being implemented to minimize direct operating costs when flying over a fixed route between a given city pair. Inherent in the design of these systems is that the horizontal flight path and wind and temperature models be defined and input into the airborne computer before flight. The wind/temperature model and horizontal path are products of the flight planning process. Flight planning consists of generating 3-D reference trajectories through a forecast wind field subject to certain ATC and transport operator constraints. The interrelationships between flight management and flight planning are reviewed, and the steps taken during the flight planning process are summarized.

  7. Space flight rehabilitation.

    PubMed

    Payne, Michael W C; Williams, David R; Trudel, Guy

    2007-07-01

    The weightless environment of space imposes specific physiologic adaptations on healthy astronauts. On return to Earth, these adaptations manifest as physical impairments that necessitate a period of rehabilitation. Physiologic changes result from unloading in microgravity and highly correlate with those seen in relatively immobile terrestrial patient populations such as spinal cord, geriatric, or deconditioned bed-rest patients. Major postflight impairments requiring rehabilitation intervention include orthostatic intolerance, bone demineralization, muscular atrophy, and neurovestibular symptoms. Space agencies are preparing for extended-duration missions, including colonization of the moon and interplanetary exploration of Mars. These longer-duration flights will result in more severe and more prolonged disability, potentially beyond the point of safe return to Earth. This paper will review and discuss existing space rehabilitation plans for major postflight impairments. Evidence-based rehabilitation interventions are imperative not only to facilitate return to Earth but also to extend the safe duration of exposure to a physiologically hostile microgravity environment.

  8. Space flight rehabilitation.

    PubMed

    Payne, Michael W C; Williams, David R; Trudel, Guy

    2007-07-01

    The weightless environment of space imposes specific physiologic adaptations on healthy astronauts. On return to Earth, these adaptations manifest as physical impairments that necessitate a period of rehabilitation. Physiologic changes result from unloading in microgravity and highly correlate with those seen in relatively immobile terrestrial patient populations such as spinal cord, geriatric, or deconditioned bed-rest patients. Major postflight impairments requiring rehabilitation intervention include orthostatic intolerance, bone demineralization, muscular atrophy, and neurovestibular symptoms. Space agencies are preparing for extended-duration missions, including colonization of the moon and interplanetary exploration of Mars. These longer-duration flights will result in more severe and more prolonged disability, potentially beyond the point of safe return to Earth. This paper will review and discuss existing space rehabilitation plans for major postflight impairments. Evidence-based rehabilitation interventions are imperative not only to facilitate return to Earth but also to extend the safe duration of exposure to a physiologically hostile microgravity environment. PMID:17167347

  9. Neural network-based recognition of whistlers on spectrograms detected by satellite

    NASA Astrophysics Data System (ADS)

    Conti, Livio

    2016-04-01

    We present a system to automatically recognize and classify the occurrence of whistler waves on spectrograms of electric field measurements performed by satellite. Whistlers - VLF waves generated by lightning, with a specific spectral dispersion relation - can induce precipitation of trapped Van Allen particles and have a role in the chemistry of some atmospheric components (mainly NOx). Moreover, it has also been suggested that the increase of the number of anomalous whistlers (i.e. whistlers with high value of dispersion constant) could be induced by disturbances in the Earth-ionosphere wave-guide, generated by seismo-electromagnetic emissions. On satellite, the recognition of whistlers asks for analyzing high-resolution spectrograms that cannot be downloaded to Earth, due to the limits of data transmission. For this reason, a real time identification and classification must be performed on satellite, by avoiding downloading all the unprocessed data. The procedure that we have developed is based on a Time Delay Neural Network (TDNN). The TDNN, proposed some years ago for speech recognition, can be fruitfully also applied in real-time analysis of electromagnetic spectrograms in order to detect phenomena characterized by a specific shape/signature such as those of the whistler waves. Some studies have been performed by the RNF experiment on board of the DEMETER satellite and our algorithm could be adopted on board of the satellite CSES (China Seismo-Electromagnetic Satellite), launch scheduled by the end of 2016. Moreover, the procedure can be also adopted to automatic analysis of whistlers detected on ground.

  10. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    PubMed Central

    Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  11. Creation and testing of an artificial neural network based carbonate detector for Mars rovers

    NASA Technical Reports Server (NTRS)

    Bornstein, Benjamin; Castano, Rebecca; Gilmore, Martha S.; Merrill, Matthew; Greenwood, James P.

    2005-01-01

    We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

  12. All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

    PubMed

    Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Klein, Jacques-Olivier; Wang, Yu; Zhao, Weisheng

    2016-08-01

    Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work. PMID:27214913

  13. Minimum data requirement for neural networks based on power spectral density analysis.

    PubMed

    Deng, Jiamei; Maass, Bastian; Stobart, Richard

    2012-04-01

    One of the most critical challenges ahead for diesel engines is to identify new techniques for fuel economy improvement without compromising emissions regulations. One technique is the precise control of air/fuel ratio, which requires the measurement of instantaneous fuel consumption. Measurement accuracy and repeatability for fuel rate is the key to successfully controlling the air/fuel ratio and real-time measurement of fuel consumption. The volumetric and gravimetric measurement principles are well-known methods for measurement of fuel consumption in internal combustion engines. However, the fuel flow rate measured by these methods is not suitable for either real-time control or real-time measurement purposes because of the intermittent nature of the measurements. This paper describes a technique that can be used to find the minimum data [consisting of data from just 2.5% of the non-road transient cycle (NRTC)] to solve the problem concerning discontinuous data of fuel flow rate measured using an AVL 733S fuel meter for a medium or heavy-duty diesel engine using neural networks. Only torque and speed are used as the input parameters for the fuel flow rate prediction. Power density analysis is used to find the minimum amount of the data. The results show that the nonlinear autoregressive model with exogenous inputs could predict the particulate matter successfully with R(2) above 0.96 using 2.5% NRTC data with only torque and speed as inputs. PMID:24805042

  14. Neural network based forward prediction of bladder pressure using pudendal nerve electrical activity.

    PubMed

    Geramipour, A; Makki, S; Erfanian, A

    2015-01-01

    Individuals with spinal cord injury or neurological disorders have problems in urinary bladder storage and in voiding function. In these people, the detrusor of bladder contracts at low volume and this causes incontinence. The goal of bladder control is to increase the bladder capacity by electrical stimulation of relative nerves such as pelvic nerves, sacral nerve roots or pudendal nerves. For this purpose, the bladder pressure has to be monitored continuously. In this paper, we propose a method for real-time estimating the bladder pressure using artificial neural network. The method is based upon measurements of electroneurogram (ENG) signal of pudendal nerve. This approach yields synthetic bladder pressure estimates during bladder contraction. The experiments were conducted on three rats. The results show that neural predictor can provide accurate estimation and prediction of bladder pressure with good generalization ability. The average error of 1-second and 5-second ahead prediction of bladder pressure are 9.62% and 10.54%, respectively. PMID:26737354

  15. Extrapolation of Calibration Curve of Hot-wire Spirometer Using a Novel Neural Network Based Approach.

    PubMed

    Ardekani, Mohammad Ali; Nafisi, Vahid Reza; Farhani, Foad

    2012-10-01

    Hot-wire spirometer is a kind of constant temperature anemometer (CTA). The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and cannot be easily extrapolated beyond its calibration range. Therefore, a method for extrapolation of CTA calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network and self-organizing map (SOM) method has been proposed to extrapolate CTA calibration curve for measurement of velocity in the range 0.7-30 m/seconds. Results show that, using this approach for the extrapolation of the CTA calibration curve beyond its upper limit, the standard deviation is about -0.5%, which is acceptable in most cases. Moreover, this approach for the extrapolation of the CTA calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometry applications. Finally, the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%.

  16. Artificial-neural-network-based classification of mammographic microcalcifications using image structure features

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.; Chitre, Yateen S.; Moskowitz, Myron

    1993-07-01

    Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. It is however, difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of `difficult to diagnose' microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 `difficult-to-diagnose' microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of `difficult-to- diagnose' microcalcifications into benign and malignant categories.

  17. Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature

    NASA Astrophysics Data System (ADS)

    Mahesh, C.; Prakash, Satya; Sathiyamoorthy, V.; Gairola, R. M.

    2011-11-01

    An Artificial Neural Network (ANN) based technique is proposed for estimating precipitation over Indian land and oceanic regions [30° S - 40° N and 30° E - 120° E] using Scattering Index (SI) and Polarization Corrected Temperature (PCT) derived from Special Sensor Microwave Imager (SSM/I) measurements. This rainfall retrieval algorithm is designed to estimate rainfall using a combination of SSM/I and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measurements. For training the ANN, SI and PCT (which signify rain signatures in a better way) calculated from SSM/I brightness temperature are considered as inputs and Precipitation Radar (PR) rain rate as output. SI is computed using 19.35 GHz, 22.235 GHz and 85.5 GHz Vertical channels and PCT is computed using 85.5 GHz Vertical and Horizontal channels. Once the training is completed, the independent data sets (which were not included in the training) were used to test the performance of the network. Instantaneous precipitation estimates with independent test data sets are validated with PR surface rain rate measurements. The results are compared with precipitation estimated using power law based (i) global algorithm and (ii) regional algorithm. Overall results show that ANN based present algorithm shows better agreement with PR rain rate. This study is aimed at developing a more accurate operational rainfall retrieval algorithm for Indo-French Megha-Tropiques Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) radiometer.

  18. [Using neural networks based template matching method to obtain redshifts of normal galaxies].

    PubMed

    Xu, Xin; Luo, A-li; Wu, Fu-chao; Zhao, Yong-heng

    2005-06-01

    Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.

  19. Large eddy simulation of extinction and reignition with artificial neural networks based chemical kinetics

    SciTech Connect

    Sen, Baris Ali; Menon, Suresh; Hawkes, Evatt R.

    2010-03-15

    Large eddy simulation (LES) of a non-premixed, temporally evolving, syngas/air flame is performed with special emphasis on speeding-up the sub-grid chemistry computations using an artificial neural networks (ANN) approach. The numerical setup for the LES is identical to a previous direct numerical simulation (DNS) study, which reported considerable local extinction and reignition physics, and hence, offers a challenging test case. The chemical kinetics modeling with ANN is based on a recent approach, and replaces the stiff ODE solver (DI) to predict the species reaction rates in the subgrid linear eddy mixing (LEM) model based LES (LEMLES). In order to provide a comprehensive evaluation of the current approach, additional information on conditional statistics of some of the key species and temperature are extracted from the previous DNS study and are compared with the LEMLES using ANN (ANN-LEMLES, hereafter). The results show that the current approach can detect the correct extinction and reignition physics with reasonable accuracy compared to the DNS. The syngas flame structure and the scalar dissipation rate statistics obtained by the current ANN-LEMLES are provided to further probe the flame physics. It is observed that, in contrast to H{sub 2}, CO exhibits a smooth variation within the region enclosed by the stoichiometric mixture fraction. The probability density functions (PDFs) of the scalar dissipation rates calculated based on the mixture fraction and CO demonstrate that the mean value of the PDF is insensitive to extinction and reignition. However, this is not the case for the scalar dissipation rate calculated by the OH mass fraction. Overall, ANN provides considerable computational speed-up and memory saving compared to DI, and can be used to investigate turbulent flames in a computationally affordable manner. (author)

  20. A radial basis function neural network based on artificial immune systems for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Yan, Qin; Zhong, Yanfei

    2008-12-01

    The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre-defined number of iterations. The classification results are evaluated by comparing with that of the k-means center selection procedures and other results from the literature using remote sensing imagery. It is shown that aiNet-RBF NN algorithm outperform other algorithms and provides an effective option for remote sensing image classification.

  1. Neural network-based model for landslide susceptibility and soil longitudinal profile analyses: Two case studies

    NASA Astrophysics Data System (ADS)

    Farrokhzad, F.; Barari, A.; Choobbasti, A. J.; Ibsen, L. B.

    2011-12-01

    The purpose of this study was to create an empirical model for assessing the landslide risk potential at Savadkouh Azad University, which is located in the rural surroundings of Savadkouh, about 5 km from the city of Pol-Sefid in northern Iran. The soil longitudinal profile of the city of Babol, located 25 km from the Caspian Sea, also was predicted with an artificial neural network (ANN). A multilayer perceptron neural network model was applied to the landslide area and was used to analyze specific elements in the study area that contributed to previous landsliding events. The ANN models were trained with geotechnical data obtained from an investigation of the study area. The quality of the modeling was improved further by the application of some controlling techniques involved in ANN. The observed >90% overall accuracy produced by the ANN technique in both cases is promising for future studies in landslide susceptibility zonation.

  2. Artificial neural network based torque calculation of switched reluctance motor without locking the rotor

    NASA Astrophysics Data System (ADS)

    Kucuk, Fuat; Goto, Hiroki; Guo, Hai-Jiao; Ichinokura, Osamu

    2009-04-01

    Feedback of motor torque is required in most of switched reluctance (SR) motor applications in order to control torque and its ripple. An SR motor shows highly nonlinear property which does not allow calculating torque analytically. Torque can be directly measured by torque sensor, but it inevitably increases the cost and has to be properly mounted on the motor shaft. Instead of torque sensor, finite element analysis (FEA) may be employed for torque calculation. However, motor modeling and calculation takes relatively long time. The results of FEA may also differ from the actual results. The most convenient way seems to calculate torque from the measured values of rotor position, current, and flux linkage while locking the rotor at definite positions. However, this method needs an extra assembly to lock the rotor. In this study, a novel torque calculation based on artificial neural networks (ANNs) is presented. Magnetizing data are collected while a 6/4 SR motor is running. They need to be interpolated for torque calculation. ANN is very strong tool for data interpolation. ANN based torque estimation is verified on the 6/4 SR motor and is compared by FEA based torque estimation to show its validity.

  3. A neural network based virtual screening of cytochrome P450 3A4 inhibitors.

    PubMed

    Molnar, László; Keseru, György M

    2002-02-11

    A virtual screening test to identify potential CP450 3A4 inhibitors has been developed. Molecular structures of inhibitors and non-inhibitors available in the Genetest database were represented using 2D Unity fingerprints and a feedforward neural network was trained to classify molecules regarding their inhibitory activity. Validation tests revealed that our neural net recognizes at least 89% of 3A4 inhibitors and suggest using this methodology in our virtual screening protocol.

  4. Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

    PubMed Central

    Ozekes, Serhat; Gultekin, Selahattin; Tarhan, Nevzat; Hizli Sayar, Gokben; Bayram, Ali

    2015-01-01

    Objective The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). Methods The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. Results The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. Conclusion Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values. PMID:25670947

  5. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S.; Rico-Martinez, R.; Kevrekidis, I.G.

    1993-12-31

    Time-delay mappings constructed using neural networks have proven successful in performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-time systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  6. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S. ); Rico-Martinez, R.; Kevrekidis, I.G. . Dept. of Chemical Engineering)

    1993-01-01

    Time-delay mappings constructed using neural networks have proven successful performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-tune systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  7. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S.; Rico-Martinez, R.; Kevrekidis, I.G.

    1993-06-01

    Time-delay mappings constructed using neural networks have proven successful performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-tune systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  8. Rapid Determination of Bacterial Abundance, Biovolume, Morphology, and Growth by Neural Network-Based Image Analysis

    PubMed Central

    Blackburn, Nicholas; Hagström, Åke; Wikner, Johan; Cuadros-Hansson, Rocio; Bjørnsen, Peter Koefoed

    1998-01-01

    Annual bacterial plankton dynamics at several depths and locations in the Baltic Sea were studied by image analysis. Individual bacteria were classified by using an artificial neural network which also effectively identified nonbacterial objects. Cell counts and frequencies of dividing cells were determined, and the data obtained agreed well with visual observations and previously published values. Cell volumes were measured accurately by comparison with bead standards. The survey included 690 images from a total of 138 samples. Each image contained approximately 200 bacteria. The images were analyzed automatically at a rate of 100 images per h. Bacterial abundance exhibited coherent patterns with time and depth, and there were distinct subsurface peaks in the summer months. Four distinct morphological classes were resolved by the image analyzer, and the dynamics of each could be visualized. The bacterial growth rates estimated from frequencies of dividing cells were different from the bacterial growth rates estimated by the thymidine incorporation method. With minor modifications, the image analysis technique described here can be used to analyze other planktonic classes. PMID:9726867

  9. Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

    USGS Publications Warehouse

    Hong, Y.-S.T.; Rosen, Michael R.; Bhamidimarri, R.

    2003-01-01

    This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP. ?? 2003 Elsevier Science Ltd. All rights reserved.

  10. q-state Potts-glass neural network based on pseudoinverse rule

    SciTech Connect

    Xiong Daxing; Zhao Hong

    2010-08-15

    We study the q-state Potts-glass neural network with the pseudoinverse (PI) rule. Its performance is investigated and compared with that of the counterpart network with the Hebbian rule instead. We find that there exists a critical point of q, i.e., q{sub cr}=14, below which the storage capacity and the retrieval quality can be greatly improved by introducing the PI rule. We show that the dynamics of the neural networks constructed with the two learning rules respectively are quite different; but however, regardless of the learning rules, in the q-state Potts-glass neural networks with q{>=}3 there is a common novel dynamical phase in which the spurious memories are completely suppressed. This property has never been noticed in the symmetric feedback neural networks. Free from the spurious memories implies that the multistate Potts-glass neural networks would not be trapped in the metastable states, which is a favorable property for their applications.

  11. Neural Network Based State of Health Diagnostics for an Automated Radioxenon Sampler/Analyzer

    SciTech Connect

    Keller, Paul E.; Kangas, Lars J.; Hayes, James C.; Schrom, Brian T.; Suarez, Reynold; Hubbard, Charles W.; Heimbigner, Tom R.; McIntyre, Justin I.

    2009-05-13

    Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA’s complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

  12. Neural-network-based signal monitoring in a smart structural system

    NASA Astrophysics Data System (ADS)

    Chen, Stuart S.; Kim, Sungkon

    1994-05-01

    This paper focuses on the signal processing aspect of a smart structure computational support environment for health monitoring, investigating the use of neural networks to identify and locate structural damage in a steel truss structure instrumented with accelerometers and strain gauges. Cracking damage is simulated by introducing sawcuts into the main members of the structure. Results using accelerometer data alone indicate that Quickprop backpropagation neural networks constitute a promising tool for these purposes, although network performance in locating damage should be improved by use of strain data as well.

  13. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

    PubMed

    Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

    2016-01-01

    For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme. PMID:27608018

  14. Short-term load and wind power forecasting using neural network-based prediction intervals.

    PubMed

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  15. Neural network based short-term load forecasting using weather compensation

    SciTech Connect

    Chow, T.W.S.; Leung, C.T.

    1996-11-01

    This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

  16. An implementation of a neural network based load forecasting model for the EMS

    SciTech Connect

    Papalexopoulos, A.D.; Hao, S. ); Peng, T.M. , San Francisco, CA )

    1994-11-01

    This paper presents the development and implementation of an Artificial Neural Network (ANN) based short-term system load forecasting model for the Energy Control Center of the Pacific Gas and Electric Company (PG and E). Insights gained during the development of the model regarding the choice of the input variables and their transformations, the design of the ANN structure, the selection of the training cases and the training process itself will be described in the paper. Attention was paid to model accurately special events, such as holidays, heat-waves, cold snaps and other conditions that disturb the normal pattern of the load.The significant impact of special events on the model's performance was established through testing of an existing load forecasting package that is currently in production use. The new model has been tested under a wide variety of conditions and it is shown in this paper to produce excellent results. Comparison results between the existing, regression based model and the ANN model are very encouraging. The ANN model consistently outperforms the existing model in terms of both, average errors over a long period of time and number of large errors. The ANN model has also been integrated with PG and E's Energy Management System (EMS). It is envisioned that the ANN model will eventually substitute the existing model to support the Company's real-time operations. In the interim both models will be available for production use.

  17. A neural network based technique for short-term forecasting of anomalous load periods

    SciTech Connect

    Lamedica, R.; Prudenzi, A.; Sforna, M.; Caciotta, M.; Cencellli, V.O.1

    1996-11-01

    The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human deficiencies when applied to anomalous load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen`s Self Organizing Map (SOM). The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a back-propagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations.

  18. Neural network based automatic limit prediction and avoidance system and method

    NASA Technical Reports Server (NTRS)

    Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

    2001-01-01

    A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

  19. Further Investigation of Receding Horizion-Based Controllers and Neural Network-Based Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.; Haley, Pamela J. (Technical Monitor)

    2000-01-01

    This report provides a comprehensive summary of the research work performed over the entire duration of the co-operative research agreement between NASA Langley Research Center and Kansas State University. This summary briefly lists the findings and also suggests possible future directions for the continuation of the subject research in the area of Generalized Predictive Control (GPC) and Network Based Generalized Predictive Control (NGPC).

  20. A Proposal for Energy-Efficient Cellular Neural Network Based on Spintronic Devices

    NASA Astrophysics Data System (ADS)

    Pan, Chenyun; Naeemi, Azad

    2016-09-01

    Due to the massive parallel computing capability and outstanding image and signal processing performance, cellular neural network (CNN) is one promising type of non-Boolean computing system that can outperform the traditional digital logic computation and mitigate the physical scaling limit of the conventional CMOS technology. The CNN was originally implemented by VLSI analog technologies with operational amplifiers and operational transconductance amplifiers as neurons and synapses, respectively, which are power and area consuming. In this paper, we propose a hybrid structure to implement the CNN with magnetic components and CMOS peripherals with a complete driving and sensing circuitry. In addition, we propose a digitally programmable magnetic synapse that can achieve both positive and negative values of the templates. After rigorous performance analyses and comparisons, optimal energy is achieved based on various design parameters, including the driving voltage and the CMOS driving size. At a comparable footprint area and operation speed, a spintronic CNN is projected to achieve more than one order of magnitude energy reduction per operation compared to its CMOS counterpart.

  1. Neural network based visualization of collaborations in a citizen science project

    NASA Astrophysics Data System (ADS)

    Morais, Alessandra M. M.; Santos, Rafael D. C.; Raddick, M. Jordan

    2014-05-01

    Citizen science projects are those in which volunteers are asked to collaborate in scientific projects, usually by volunteering idle computer time for distributed data processing efforts or by actively labeling or classifying information - shapes of galaxies, whale sounds, historical records are all examples of citizen science projects in which users access a data collecting system to label or classify images and sounds. In order to be successful, a citizen science project must captivate users and keep them interested on the project and on the science behind it, increasing therefore the time the users spend collaborating with the project. Understanding behavior of citizen scientists and their interaction with the data collection systems may help increase the involvement of the users, categorize them accordingly to different parameters, facilitate their collaboration with the systems, design better user interfaces, and allow better planning and deployment of similar projects and systems. Users behavior can be actively monitored or derived from their interaction with the data collection systems. Records of the interactions can be analyzed using visualization techniques to identify patterns and outliers. In this paper we present some results on the visualization of more than 80 million interactions of almost 150 thousand users with the Galaxy Zoo I citizen science project. Visualization of the attributes extracted from their behaviors was done with a clustering neural network (the Self-Organizing Map) and a selection of icon- and pixel-based techniques. These techniques allows the visual identification of groups of similar behavior in several different ways.

  2. FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

    PubMed

    Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

    2014-01-01

    A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

  3. Real-time flood forecasting by employing artificial neural network based model with zoning matching approach

    NASA Astrophysics Data System (ADS)

    Sulaiman, M.; El-Shafie, A.; Karim, O.; Basri, H.

    2011-10-01

    Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.

  4. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

    NASA Astrophysics Data System (ADS)

    Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

    2016-01-01

    In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

  5. Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems.

    PubMed

    Xu, Hao; Jagannathan, Sarangapani

    2015-03-01

    The stochastic optimal control of nonlinear networked control systems (NNCSs) using neuro-dynamic programming (NDP) over a finite time horizon is a challenging problem due to terminal constraints, system uncertainties, and unknown network imperfections, such as network-induced delays and packet losses. Since the traditional iteration or time-based infinite horizon NDP schemes are unsuitable for NNCS with terminal constraints, a novel time-based NDP scheme is developed to solve finite horizon optimal control of NNCS by mitigating the above-mentioned challenges. First, an online neural network (NN) identifier is introduced to approximate the control coefficient matrix that is subsequently utilized in conjunction with the critic and actor NNs to determine a time-based stochastic optimal control input over finite horizon in a forward-in-time and online manner. Eventually, Lyapunov theory is used to show that all closed-loop signals and NN weights are uniformly ultimately bounded with ultimate bounds being a function of initial conditions and final time. Moreover, the approximated control input converges close to optimal value within finite time. The simulation results are included to show the effectiveness of the proposed scheme. PMID:25720004

  6. Hour-Glass Neural Network Based Daily Money Flow Estimation for Automatic Teller Machines

    NASA Astrophysics Data System (ADS)

    Karungaru, Stephen; Akashi, Takuya; Nakano, Miyoko; Fukumi, Minoru

    Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among many other possible functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (financial institution customers) are many, the financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insufficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a five layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.

  7. Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

    PubMed Central

    Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

    2016-01-01

    For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme. PMID:27608018

  8. Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

    PubMed Central

    Cardoso, L.; Marins, F.; Magalhães, R.; Marins, N.; Oliveira, T.; Vicente, H.; Abelha, A.; Machado, J.; Neves, J.

    2015-01-01

    Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information. PMID:25834836

  9. Abstract computation in schizophrenia detection through artificial neural network based systems.

    PubMed

    Cardoso, L; Marins, F; Magalhães, R; Marins, N; Oliveira, T; Vicente, H; Abelha, A; Machado, J; Neves, J

    2015-01-01

    Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information. PMID:25834836

  10. A neural network-based classification of environment dynamics models for compliant control of manipulation robots.

    PubMed

    Katic, D; Vukobratovic, M

    1998-01-01

    In this paper, a new method for selecting the appropriate compliance control parameters for robot machining tasks based on connectionist classification of unknown dynamic environments, is proposed. The method classifies the type of environment by using multilayer perceptron, and then, determines the control parameters for compliance control using the estimated characteristics. An important feature is that the process of pattern association can work in an on-line mode as a part of selected compliance control algorithm. Convergence process is improved by using evolutionary approach (genetic algorithms) in order to choose the optimal topology of the proposed multilayer perceptron. Compliant motion simulation experiments with robotic arm placed in contact with dynamic environment, described by the stiffness model and by the general impedance model, have been performed in order to verify the proposed approach.

  11. A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application.

    PubMed

    Onumanyi, A J; Onwuka, E N; Aibinu, A M; Ugweje, O C; Salami, M J E

    2014-01-01

    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application.

  12. A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

    NASA Astrophysics Data System (ADS)

    Hsiao, Feng-Hsiag

    2016-10-01

    In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.

  13. Artificial neural network based controller for permanent magnet DC motor drives

    SciTech Connect

    Hoque, M.A.; Zaman, M.R.; Rahman, M.A.

    1995-12-31

    This paper introduces a novel approach of designing a controller using multi-layer feed-forward neural network (FFNN) for the speed control of a permanent magnet (PM) dc motor. Artificial neural network (ANN) controller with its massive parallel properties and learning capabilities offers a promising way to solving the problem of system non-linearity, parameter variations and unexpected load excursions associated with a PM dc motor drive system. Self-tuning technique of the controller in real time is achieved through an improved on-line back-propagation training algorithm based on an output error propagation. The proposed ANN controller is implemented with a PM dc motor drive system in the laboratory. The laboratory test results validate the efficacy of the based controller for a high performance PM dc motor drive.

  14. Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems

    SciTech Connect

    Hoyong Kim; Yunseok Ko; Kyunghee Jung . Dept. of Distribution System)

    1993-07-01

    Neural networks have the capability to map the complex and extremely non-linear relationship between the load levels of zone and system topologies, which is required for feeder reconfiguration in distribution systems. This study is intended to propose the strategies to reconfigure the feeder, by using artificial neural networks with mapping ability. Artificial neural networks determine the appropriate system topology that reduces the power loss according to the variation of load pattern. The control strategy can be easily obtained from the system topology which is provided by artificial neural networks. Artificial neural networks are in groups. The first group estimates the proper load level from the load data of each zone, and the second determines the appropriate system topology from the input load level. In addition, several programs with the training set builder are developed for the design, the training and the accuracy test of artificial neural networks. The authors also evaluate the performance of neural networks designed here, on the test distribution system. Neural networks are implemented in FORTRAN language, and trained on the personal computer COMPAQ 386.

  15. Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

    NASA Astrophysics Data System (ADS)

    Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin

    Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.

  16. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    PubMed Central

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

  17. A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

    PubMed Central

    Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

    2012-01-01

    In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. PMID:23193391

  18. Discriminative boosted forest with convolutional neural network-based patch descriptor for object detection

    NASA Astrophysics Data System (ADS)

    Xiang, Tao; Li, Tao; Ye, Mao; Li, Xudong

    2016-01-01

    Object detection with intraclass variations is challenging. The existing methods have not achieved the optimal combinations of classifiers and features, especially features learned by convolutional neural networks (CNNs). To solve this problem, we propose an object-detection method based on improved random forest and local image patches represented by CNN features. First, we compute CNN-based patch descriptors for each sample by modified CNNs. Then, the random forest is built whose split functions are defined by patch selector and linear projection learned by linear support vector machine. To improve the classification accuracy, the split functions in each depth of the forest make up a local classifier, and all local classifiers are assembled in a layer-wise manner by a boosting algorithm. The main contributions of our approach are summarized as follows: (1) We propose a new local patch descriptor based on CNN features. (2) We define a patch-based split function which is optimized with maximum class-label purity and minimum classification error over the samples of the node. (3) Each local classifier is assembled by minimizing the global classification error. We evaluate the method on three well-known challenging datasets: TUD pedestrians, INRIA pedestrians, and UIUC cars. The experiments demonstrate that our method achieves state-of-the-art or competitive performance.

  19. A neural network based error correction method for radio occultation electron density retrieval

    NASA Astrophysics Data System (ADS)

    Pham, Viet-Cuong; Juang, Jyh-Ching

    2015-12-01

    Abel inversion techniques have been widely employed to retrieve electron density profiles (EDPs) from radio occultation (RO) measurements, which are available by observing Global Navigation Satellite System (GNSS) satellites from low-earth-orbit (LEO) satellites. It is well known that the ordinary Abel inversion might introduce errors in the retrieval of EDPs when the spherical symmetry assumption is violated. The error, however, is case-dependent; therefore it is desirable to associate an error index or correction coefficient with respect to each retrieved EDP. Several error indices have been proposed but they only deal with electron density at the F2 peak and suffer from some drawbacks. In this paper we propose an artificial neural network (ANN) based error correction method for EDPs obtained by the ordinary Abel inversion. The ANN is first trained to learn the relationship between vertical total electron content (TEC) measurements and retrieval errors at the F2 peak, 220 km and 110 km altitudes; correction coefficients are then estimated to correct the retrieved EDPs at these three altitudes. Experiments using the NeQuick2 model and real FORMOSAT-3/COSMIC RO geometry show that the proposed method outperforms existing ones. Real incoherent scatter radar (ISR) measurements at the Jicamarca Radio Observatory and the global TEC map provided by the International GNSS Service (IGS) are also used to valid the proposed method.

  20. Neural network-based distributed attitude coordination control for spacecraft formation flying with input saturation.

    PubMed

    Zou, An-Min; Kumar, Krishna Dev

    2012-07-01

    This brief considers the attitude coordination control problem for spacecraft formation flying when only a subset of the group members has access to the common reference attitude. A quaternion-based distributed attitude coordination control scheme is proposed with consideration of the input saturation and with the aid of the sliding-mode observer, separation principle theorem, Chebyshev neural networks, smooth projection algorithm, and robust control technique. Using graph theory and a Lyapunov-based approach, it is shown that the distributed controller can guarantee the attitude of all spacecraft to converge to a common time-varying reference attitude when the reference attitude is available only to a portion of the group of spacecraft. Numerical simulations are presented to demonstrate the performance of the proposed distributed controller. PMID:24807141

  1. A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature.

    PubMed

    Ferreira, Pedro M; Gomes, João M; Martins, Igor A C; Ruano, António E

    2012-11-12

    Accurate measurements of global solar radiation and atmospheric temperature,as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.

  2. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  3. Feedforward, high density, programmable read only neural network based memory system

    NASA Technical Reports Server (NTRS)

    Daud, Taher; Moopenn, Alex; Lamb, James; Thakoor, Anil; Khanna, Satish

    1988-01-01

    Neural network-inspired, nonvolatile, programmable associative memory using thin-film technology is demonstrated. The details of the architecture, which uses programmable resistive connection matrices in synaptic arrays and current summing and thresholding amplifiers as neurons, are described. Several synapse configurations for a high-density array of a binary connection matrix are also described. Test circuits are evaluated for operational feasibility and to demonstrate the speed of the read operation. The results are discussed to highlight the potential for a read data rate exceeding 10 megabits/sec.

  4. Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations

    NASA Technical Reports Server (NTRS)

    Jani, Yashvant

    1992-01-01

    As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.

  5. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

    PubMed

    Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  6. Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach

    NASA Astrophysics Data System (ADS)

    Cardelli, E.; Faba, A.; Laudani, A.; Lozito, G. M.; Riganti Fulginei, F.; Salvini, A.

    2016-04-01

    This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.

  7. Artificial neural network based fault identification scheme implementation for a three-phase induction motor.

    PubMed

    Kolla, Sri R; Altman, Shawn D

    2007-04-01

    This paper presents results from the implementation and testing of a PC based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three-phase voltages and currents from a 1/3 HP squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure using the JavaNNS program. The trained network is placed in a LabVIEW based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor off. The complete system is successfully tested in real time by creating different faults on the motor.

  8. Controlling basins of attraction in a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Bell, Benjamin; Eilbert, James L.

    1988-01-01

    The size of the basins of attraction around fixed points in recurrent neural nets (NNs) can be modified by a training process. Controlling these attractive regions by presenting training data with various amount of noise added to the prototype signal vectors is discussed. Application of this technique to signal processing results in a classification system whose sensitivity can be controlled. This new technique is applied to the classification of temporal sequences in telemetry data.

  9. Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.

    1996-06-01

    This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.

  10. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

    PubMed Central

    Ferreira, Pedro M.; Gomes, João M.; Martins, Igor A. C.; Ruano, António E.

    2012-01-01

    Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230

  11. Artificial neural networks based subgrid chemistry model for turbulent reactive flow simulations

    NASA Astrophysics Data System (ADS)

    Sen, Baris A.

    Computational analysis of turbulent reactive flow applications requires resolution of the wide range of scales both in time and space from a flow modeling perspective. From a thermo-chemistry point of view, information regarding the radical chemical species is needed in order to capture flame-turbulence interactions accurately. A detailed investigation of all of these processes is time consuming. Thus, there is a need for speeding-up the computations by using the state-of-the art modeling capabilities. This study seeks to answer this problem and focuses in particular on the chemical kinetics calculations. The new approach proposed here is based on incorporating the artificial neural network (ANN) based modeling of the chemical kinetics into the large eddy simulation (LES) of reactive flows. Two separate and new ANN based modeling approaches relevant to the LES are proposed within the thesis work. Here, the first approach depends on employing ANN to predict the species instantaneous reaction rates as a function of the thermochemical state vector ( ẇi = ANN(Yk, T)). The second one is based on using ANN specifically to predict the spatially filtered chemical source terms in the LES modeling as a function of the filtered thermo-chemical state vector and flow quantities ( ẇ¯ i = ANN(Ỹk, T˜, ReDelta, 6Ỹi 6x )). First part of the thesis work dealt with testing different thermo-chemical tabulation techniques that can be used in connection with the ANN approach for the LES. Basically, three distinct methods (and tools) are developed here: thermo-chemical tables based on (i) laminar flames, (ii) laminar flame-vortex interactions (FVI) and (iii) laminar flame-turbulence interactions (FTI). Results based on premixed flame-vortex-turbulence interaction simulations showed that the tables generated based on the second and third approaches are capable of representing the actual thermo-chemical state-space accessed by the LES. Once the tabulation procedure and the ANN training is achieved, ANN is first validated against direct numerical simulation (DNS) of a temporally evolving planar jet flame, which is reported to exhibit complex extinction and re-ignition type of physics. The flame physics are also investigated for this flame, and it is observed that the scalar dissipation rate has a weak sensitivity to extinction and re-ignition, than it is reported in the literature. As a second test case, comparisons against experimental and previous computational data are provided for a practical combustor simulation. Overall for all simulations considered here, the results indicated that the ANN works as a combustion mode independent model and provides fairly accurate results with considerable amount of speed-up and memory savings.

  12. Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach

    PubMed Central

    Narain, Renu; Saxena, Sanjai; Goyal, Achal Kumar

    2016-01-01

    Purpose Currently cardiovascular diseases (CVDs) are the main cause of death worldwide. Disease risk estimates can be used as prognostic information and support for treating CVDs. The commonly used Framingham risk score (FRS) for CVD prediction is outdated for the modern population, so FRS may not be accurate enough. In this paper, a novel CVD prediction system based on machine learning is proposed. Methods This study has been conducted with the data of 689 patients showing symptoms of CVD. Furthermore, the dataset of 5,209 CVD patients of the famous Framingham study has been used for validation purposes. Each patient’s parameters have been analyzed by physicians in order to make a diagnosis. The proposed system uses the quantum neural network for machine learning. This system learns and recognizes the pattern of CVD. The proposed system has been experimentally evaluated and compared with FRS. Results During testing, patients’ data in combination with the doctors’ diagnosis (predictions) are used for evaluation and validation. The proposed system achieved 98.57% accuracy in predicting the CVD risk. The CVD risk predictions by the proposed system, using the dataset of the Framingham study, confirmed the potential risk of death, deaths which actually occurred and had been recorded as due to myocardial infarction and coronary heart disease in the dataset of the Framingham study. The accuracy of the proposed system is significantly higher than FRS and other existing approaches. Conclusion The proposed system will serve as an excellent tool for a medical practitioner in predicting the risk of CVD. This system will be serving as an aid to medical practitioners for planning better medication and treatment strategies. An early diagnosis may be effectively made by using this system. An overall accuracy of 98.57% has been achieved in predicting the risk level. The accuracy is considerably higher compared to the other existing approaches. Thus, this system must be used instead of the well-known FRS. PMID:27486312

  13. Synchronization and stochastic resonance of the small-world neural network based on the CPG.

    PubMed

    Lu, Qiang; Tian, Juan

    2014-06-01

    According to biological knowledge, the central nervous system controls the central pattern generator (CPG) to drive the locomotion. The brain is a complex system consisting of different functions and different interconnections. The topological properties of the brain display features of small-world network. The synchronization and stochastic resonance have important roles in neural information transmission and processing. In order to study the synchronization and stochastic resonance of the brain based on the CPG, we establish the model which shows the relationship between the small-world neural network (SWNN) and the CPG. We analyze the synchronization of the SWNN when the amplitude and frequency of the CPG are changed and the effects on the CPG when the SWNN's parameters are changed. And we also study the stochastic resonance on the SWNN. The main findings include: (1) When the CPG is added into the SWNN, there exists parameters space of the CPG and the SWNN, which can make the synchronization of the SWNN optimum. (2) There exists an optimal noise level at which the resonance factor Q gets its peak value. And the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the noise intensity. The results could have important implications for biological processes which are about interaction between the neural network and the CPG.

  14. Accurate prediction of solvent accessibility using neural networks-based regression.

    PubMed

    Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

    2004-09-01

    Accurate prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins may be used to facilitate protein structure prediction and functional annotation. Toward that goal we developed a novel method for improved prediction of RSAs. Contrary to other machine learning-based methods from the literature, we do not impose a classification problem with arbitrary boundaries between the classes. Instead, we seek a continuous approximation of the real-value RSA using nonlinear regression, with several feed forward and recurrent neural networks, which are then combined into a consensus predictor. A set of 860 protein structures derived from the PFAM database was used for training, whereas validation of the results was carefully performed on several nonredundant control sets comprising a total of 603 structures derived from new Protein Data Bank structures and had no homology to proteins included in the training. Two classes of alternative predictors were developed for comparison with the regression-based approach: one based on the standard classification approach and the other based on a semicontinuous approximation with the so-called thermometer encoding. Furthermore, a weighted approximation, with errors being scaled by the observed levels of variability in RSA for equivalent residues in families of homologous structures, was applied in order to improve the results. The effects of including evolutionary profiles and the growth of sequence databases were assessed. In accord with the observed levels of variability in RSA for different ranges of RSA values, the regression accuracy is higher for buried than for exposed residues, with overall 15.3-15.8% mean absolute errors and correlation coefficients between the predicted and experimental values of 0.64-0.67 on different control sets. The new method outperforms classification-based algorithms when the real value predictions are projected onto two-class classification problems with several commonly used thresholds to separate exposed and buried residues. For example, classification accuracy of about 77% is consistently achieved on all control sets with a threshold of 25% RSA. A web server that enables RSA prediction using the new method and provides customizable graphical representation of the results is available at http://sable.cchmc.org.

  15. Comparative study of neural-network-based learning strategies for collective robotic search problem

    NASA Astrophysics Data System (ADS)

    Zhang, Nian; Novokhodko, Alexander; Wunsch, Donald C., II; Dagli, Cihan H.

    2001-03-01

    One important application of mobile robots is searching a geographical region to locate the origin of a specific sensible phenomenon. A variety of optimization algorithms can be employed to locate the target source which has the maximum intensity of the distribution of illumination function. It is very important to evaluate the performance of those optimization algorithms so that the researchers can adopt the most appropriate optimization approach to save a lot of execution time and cost of both collective robots and human beings. In this paper we provide three different neural network algorithms: steepest ascent algorithm, combined gradient algorithm and stochastic optimization algorithm to solve the collective robotics search problem. Experiments with different pair of number of sources and robots were carried out to investigate the effect of source size and team size on the task performance, as well as the risk of mission failure. The experimental results showed that the performance of steepest ascent method is better than that of combined gradient method, while the stochastic optimization method is better than steepest ascent method.

  16. A neural networks-based hybrid routing protocol for wireless mesh networks.

    PubMed

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

  17. Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller.

    PubMed

    Ding, Zhixia; Shen, Yi

    2016-04-01

    This paper investigates global projective synchronization of nonidentical fractional-order neural networks (FNNs) based on sliding mode control technique. We firstly construct a fractional-order integral sliding surface. Then, according to the sliding mode control theory, we design a sliding mode controller to guarantee the occurrence of the sliding motion. Based on fractional Lyapunov direct methods, system trajectories are driven to the proposed sliding surface and remain on it evermore, and some novel criteria are obtained to realize global projective synchronization of nonidentical FNNs. As the special cases, some sufficient conditions are given to ensure projective synchronization of identical FNNs, complete synchronization of nonidentical FNNs and anti-synchronization of nonidentical FNNs. Finally, one numerical example is given to demonstrate the effectiveness of the obtained results.

  18. Minimum data requirement for neural networks based on power spectral density analysis.

    PubMed

    Deng, Jiamei; Maass, Bastian; Stobart, Richard

    2012-04-01

    One of the most critical challenges ahead for diesel engines is to identify new techniques for fuel economy improvement without compromising emissions regulations. One technique is the precise control of air/fuel ratio, which requires the measurement of instantaneous fuel consumption. Measurement accuracy and repeatability for fuel rate is the key to successfully controlling the air/fuel ratio and real-time measurement of fuel consumption. The volumetric and gravimetric measurement principles are well-known methods for measurement of fuel consumption in internal combustion engines. However, the fuel flow rate measured by these methods is not suitable for either real-time control or real-time measurement purposes because of the intermittent nature of the measurements. This paper describes a technique that can be used to find the minimum data [consisting of data from just 2.5% of the non-road transient cycle (NRTC)] to solve the problem concerning discontinuous data of fuel flow rate measured using an AVL 733S fuel meter for a medium or heavy-duty diesel engine using neural networks. Only torque and speed are used as the input parameters for the fuel flow rate prediction. Power density analysis is used to find the minimum amount of the data. The results show that the nonlinear autoregressive model with exogenous inputs could predict the particulate matter successfully with R(2) above 0.96 using 2.5% NRTC data with only torque and speed as inputs.

  19. AFTI F-111 in flight

    NASA Technical Reports Server (NTRS)

    1986-01-01

    This NASA Ames-Dryden Flight Research Facility photograph shows a modified General Dynamics AFTI/F-111A Aardvark with supercritical mission adaptive wings (MAW) installed. In this photograph the AFTI/F111A is seen banking towards Rodgers Dry Lake and Edwards Air Force Base. With the phasing out of the TACT program came a renewed effort by the Air Force Flight Dynamics Laboratory to extend supercritical wing technology to a higher level of performance. In the early 1980s the supercritical wing on the F-111A aircraft was replaced with a wing built by Boeing Aircraft Company System called a 'mission adaptive wing' (MAW), and a joint NASA and Air Force program called Advanced Fighter Technology Integration (AFTI) was born.

  20. AFTI F-111 in flight

    NASA Technical Reports Server (NTRS)

    1986-01-01

    This NASA Ames-Dryden Flight Research Facility photograph shows a modified General Dynamics AFTI/F-111A Aardvark with supercritical mission adaptive wings (MAW) installed. The Aircraft is in a banking turn towards Rogers Dry Lake and Edwards Air Force Base, California. With the phasing out of the TACT program came a renewed effort by the Air Force Flight Dynamics Laboratory to extend supercritical wing technology to a higher level of performance. In the early 1980s the supercritical wing on the F-111A aircraft was replaced with a wing built by Boeing Aircraft Company System called a 'mission adaptive wing' (MAW), and a joint NASA and Air Force program called Advanced Fighter Technology Integration (AFTI) was born.

  1. Green Flight Challenge

    NASA Video Gallery

    The CAFE Green Flight Challenge sponsored by Google will be held at the CAFE Foundation Flight Test Center at Charles M. Schulz Sonoma County Airport in Santa Rosa, Calif. The Green Flight Challeng...

  2. Fault-Tolerant Flight Computer

    NASA Technical Reports Server (NTRS)

    Chau, Savio

    1996-01-01

    In design concept for adaptive, fault-tolerant flight computer, upon detection of fault in either processor, surviving processor assumes responsibility for both equipment systems. Possible because of cross-strapping between processors, memories, and input/output units. Concept also applicable to other computing systems required to tolerate faults and in which partial loss of processing speed or functionality acceptable price to pay for continued operation in event of faults.

  3. NASA's Flight Opportunities Program

    NASA Video Gallery

    NASA's Flight Opportunities Program is facilitating low-cost access to suborbital space, where researchers can test technologies using commercially developed vehicles. Suborbital flights can quickl...

  4. Cibola flight experiment

    SciTech Connect

    Roussel-Dupre, D.; Caffrey, M. P.

    2004-01-01

    Los Alamos National Laboratory is building the Cibola Flight Experiment (CFE), a reconfigurable processor payload intended for a Low Earth Orbit system. It will survey portions of the VHF and UHF radio spectra. The experiment uses networks of reprogrammable, Field Programmable Gate Arrays (FPGAs) to process the received signals for ionospheric and lightning studies. The objective is to validate the on-orbit use of commercial, reconfigurable FPGA technology utilizing several different single-event upset mitigation schemes. It will also detect and measure impulsive events that occur in a complex background. Surrey Satellite Technology, Ltd (SSTL) is building the small host satellite, CFESat, based upon SSTL's disaster monitoring constellation (DMC) and Topsat mission satellite designs. The CFESat satellite will be launched by the Space Test Program in September 2006 on the US Air Force Evolved Expendable Launch Vehicle (EELV) using the EELV's Secondary Payload Adapter (ESPA) that allows up to six small satellites to be launched as 'piggyback' passengers with larger spacecraft.

  5. Flight Test Series 3: Flight Test Report

    NASA Technical Reports Server (NTRS)

    Marston, Mike; Sternberg, Daniel; Valkov, Steffi

    2015-01-01

    This document is a flight test report from the Operational perspective for Flight Test Series 3, a subpart of the Unmanned Aircraft System (UAS) Integration in the National Airspace System (NAS) project. Flight Test Series 3 testing began on June 15, 2015, and concluded on August 12, 2015. Participants included NASA Ames Research Center, NASA Armstrong Flight Research Center, NASA Glenn Research Center, NASA Langley Research center, General Atomics Aeronautical Systems, Inc., and Honeywell. Key stakeholders analyzed their System Under Test (SUT) in two distinct configurations. Configuration 1, known as Pairwise Encounters, was subdivided into two parts: 1a, involving a low-speed UAS ownship and intruder(s), and 1b, involving a high-speed surrogate ownship and intruder. Configuration 2, known as Full Mission, involved a surrogate ownship, live intruder(s), and integrated virtual traffic. Table 1 is a summary of flights for each configuration, with data collection flights highlighted in green. Section 2 and 3 of this report give an in-depth description of the flight test period, aircraft involved, flight crew, and mission team. Overall, Flight Test 3 gathered excellent data for each SUT. We attribute this successful outcome in large part from the experience that was acquired from the ACAS Xu SS flight test flown in December 2014. Configuration 1 was a tremendous success, thanks to the training, member participation, integration/testing, and in-depth analysis of the flight points. Although Configuration 2 flights were cancelled after 3 data collection flights due to various problems, the lessons learned from this will help the UAS in the NAS project move forward successfully in future flight phases.

  6. Adaptive fuzzy approach to modeling of operational space for autonomous mobile robots

    NASA Astrophysics Data System (ADS)

    Musilek, Petr; Gupta, Madan M.

    1998-10-01

    Robots operating in an unstructured environment need high level of modeling of their operational space in order to plan a suitable path from an initial position to a desired goal. From this perspective, operational space modeling seems to be crucial to ensure a sufficient level of autonomy. In order to compile the information from various sources, we propose a fuzzy approach to evaluate each unit region on a grid map by a certain value of transition cost. This value expresses the cost of movement over the unit region: the higher the value, the more expensive the movement through the region in terms of energy, time, danger, etc. The approach for modeling, proposed in this paper, employs fuzzy granulation of information on various terrain features and their combination based on a fuzzy neural network. In order to adapt to the changing environmental conditions, and to improve the validity of constructed cost maps on-line, the system can be endowed with learning abilities. The learning subsystem would change parameters of the fuzzy neural network based decision system by reinforcements derived from comparisons of the actual cost of transition with the cost obtained from the model.

  7. Flight projects overview

    NASA Technical Reports Server (NTRS)

    Levine, Jack

    1988-01-01

    Information is given in viewgraph form on the activities of the Flight Projects Division of NASA's Office of Aeronautics and Space Technology. Information is given on space research and technology strategy, current space flight experiments, the Long Duration Exposure Facility, the Orbiter Experiment Program, the Lidar In-Space Technology Experiment, the Ion Auxiliary Propulsion System, the Arcjet Flight Experiment, the Telerobotic Intelligent Interface Flight Experiment, the Cryogenic Fluid Management Flight Experiment, the Industry/University In-Space Flight Experiments, and the Aeroassist Flight Experiment.

  8. Visual-Vestibular Responses During Space Flight

    NASA Technical Reports Server (NTRS)

    Reschke, M. F.; Kozlovskaya, I. B.; Paloski, W. H.

    1999-01-01

    Given the documented disruptions that occur in spatial orientation during space flight and the putative sensory-motor information underlying eye and head spatial coding, the primary purpose of this paper is to examine components of the target acquisition system in subjects free to make head and eye movements in three dimensional space both during and following adaptation to long duration space flight. It is also our intention to suggest a simple model of adaptation that has components in common with cerebellar disorders whose neurobiological substrate has been identified.

  9. Design of a Computerised Flight Mill Device to Measure the Flight Potential of Different Insects.

    PubMed

    Martí-Campoy, Antonio; Ávalos, Juan Antonio; Soto, Antonia; Rodríguez-Ballester, Francisco; Martínez-Blay, Victoria; Malumbres, Manuel Pérez

    2016-04-07

    Several insect species pose a serious threat to different plant species, sometimes becoming a pest that produces significant damage to the landscape, biodiversity, and/or the economy. This is the case of Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae), Semanotus laurasii Lucas (Coleoptera: Cerambycidae), and Monochamus galloprovincialis Olivier (Coleoptera: Cerambycidae), which have become serious threats to ornamental and productive trees all over the world such as palm trees, cypresses, and pines. Knowledge about their flight potential is very important for designing and applying measures targeted to reduce the negative effects from these pests. Studying the flight capability and behaviour of some insects is difficult due to their small size and the large area wherein they can fly, so we wondered how we could obtain information about their flight capabilities in a controlled environment. The answer came with the design of flight mills. Relevant data about the flight potential of these insects may be recorded and analysed by means of a flight mill. Once an insect is attached to the flight mill, it is able to fly in a circular direction without hitting walls or objects. By adding sensors to the flight mill, it is possible to record the number of revolutions and flight time. This paper presents a full description of a computer monitored flight mill. The description covers both the mechanical and the electronic parts in detail. The mill was designed to easily adapt to the anatomy of different insects and was successfully tested with individuals from three species R. ferrugineus, S. laurasii, and M. galloprovincialis.

  10. An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

    PubMed

    Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

    2016-05-01

    Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. PMID:26920088

  11. An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

    PubMed

    Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

    2016-05-01

    Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.

  12. Overview of Pre-Flight Physical Training, In-Flight Exercise Countermeasures and the Post-Flight Reconditioning Program for International Space Station Astronauts

    NASA Technical Reports Server (NTRS)

    Kerstman, Eric

    2011-01-01

    International Space Station (ISS) astronauts receive supervised physical training pre-flight, utilize exercise countermeasures in-flight, and participate in a structured reconditioning program post-flight. Despite recent advances in exercise hardware and prescribed exercise countermeasures, ISS crewmembers are still found to have variable levels of deconditioning post-flight. This presentation provides an overview of the astronaut medical certification requirements, pre-flight physical training, in-flight exercise countermeasures, and the post-flight reconditioning program. Astronauts must meet medical certification requirements on selection, annually, and prior to ISS missions. In addition, extensive physical fitness testing and standardized medical assessments are performed on long duration crewmembers pre-flight. Limited physical fitness assessments and medical examinations are performed in-flight to develop exercise countermeasure prescriptions, ensure that the crewmembers are physically capable of performing mission tasks, and monitor astronaut health. Upon mission completion, long duration astronauts must re-adapt to the 1 G environment, and be certified as fit to return to space flight training and active duty. A structured, supervised postflight reconditioning program has been developed to prevent injuries, facilitate re-adaptation to the 1 G environment, and subsequently return astronauts to training and space flight. The NASA reconditioning program is implemented by the Astronaut Strength, Conditioning, and Rehabilitation (ASCR) team and supervised by NASA flight surgeons. This program has evolved over the past 10 years of the International Space Station (ISS) program and has been successful in ensuring that long duration astronauts safely re-adapt to the 1 g environment and return to active duty. Lessons learned from this approach to managing deconditioning can be applied to terrestrial medicine and future exploration space flight missions.

  13. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots.

    PubMed

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models

  14. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

    PubMed Central

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models

  15. Adaptive optimization and control using neural networks

    SciTech Connect

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  16. Cardiovascular adaptation to spaceflight

    NASA Technical Reports Server (NTRS)

    Hargens, A. R.; Watenpaugh, D. E.

    1996-01-01

    This article reviews recent flight and ground-based studies of cardiovascular adaptation to spaceflight. Prominent features of microgravity exposure include loss of gravitational pressures, relatively low venous pressures, headward fluid shifts, plasma volume loss, and postflight orthostatic intolerance and reduced exercise capacity. Many of these short-term responses to microgravity extend themselves during long-duration microgravity exposure and may be explained by altered pressures (blood and tissue) and fluid balance in local tissues nourished by the cardiovascular system. In this regard, it is particularly noteworthy that tissues of the lower body (e.g., foot) are well adapted to local hypertension on Earth, whereas tissues of the upper body (e.g., head) are not as well adapted to increase in local blood pressure. For these and other reasons, countermeasures for long-duration flight should include reestablishment of higher, Earth-like blood pressures in the lower body.

  17. Method and system for determining induction motor speed

    DOEpatents

    Parlos, Alexander G.; Bharadwaj, Raj M.

    2004-03-30

    A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

  18. X-33 Flight Visualization

    NASA Technical Reports Server (NTRS)

    Laue, Jay H.

    1998-01-01

    The X-33 flight visualization effort has resulted in the integration of high-resolution terrain data with vehicle position and attitude data for planned flights of the X-33 vehicle from its launch site at Edwards AFB, California, to landings at Michael Army Air Field, Utah, and Maelstrom AFB, Montana. Video and Web Site representations of these flight visualizations were produced. In addition, a totally new module was developed to control viewpoints in real-time using a joystick input. Efforts have been initiated, and are presently being continued, for real-time flight coverage visualizations using the data streams from the X-33 vehicle flights. The flight visualizations that have resulted thus far give convincing support to the expectation that the flights of the X-33 will be exciting and significant space flight milestones... flights of this nation's one-half scale predecessor to its first single-stage-to-orbit, fully-reusable launch vehicle system.

  19. Poor flight performance in deep-diving cormorants.

    PubMed

    Watanabe, Yuuki Y; Takahashi, Akinori; Sato, Katsufumi; Viviant, Morgane; Bost, Charles-André

    2011-02-01

    Aerial flight and breath-hold diving present conflicting morphological and physiological demands, and hence diving seabirds capable of flight are expected to face evolutionary trade-offs regarding locomotory performances. We tested whether Kerguelen shags Phalacrocorax verrucosus, which are remarkable divers, have poor flight capability using newly developed tags that recorded their flight air speed (the first direct measurement for wild birds) with propeller sensors, flight duration, GPS position and depth during foraging trips. Flight air speed (mean 12.7 m s(-1)) was close to the speed that minimizes power requirement, rather than energy expenditure per distance, when existing aerodynamic models were applied. Flights were short (mean 92 s), with a mean summed duration of only 24 min day(-1). Shags sometimes stayed at the sea surface without diving between flights, even on the way back to the colony, and surface durations increased with the preceding flight durations; these observations suggest that shags rested after flights. Our results indicate that their flight performance is physiologically limited, presumably compromised by their great diving capability (max. depth 94 m, duration 306 s) through their morphological adaptations for diving, including large body mass (enabling a large oxygen store), small flight muscles (to allow for large leg muscles for underwater propulsion) and short wings (to decrease air volume in the feathers and hence buoyancy). The compromise between flight and diving, as well as the local bathymetry, shape the three-dimensional foraging range (<26 km horizontally, <94 m vertically) in this bottom-feeding cormorant. PMID:21228200

  20. IRAC Full-Scale Flight Testbed Capabilities

    NASA Technical Reports Server (NTRS)

    Lee, James A.; Pahle, Joseph; Cogan, Bruce R.; Hanson, Curtis E.; Bosworth, John T.

    2009-01-01

    Overview: Provide validation of adaptive control law concepts through full scale flight evaluation in a representative avionics architecture. Develop an understanding of aircraft dynamics of current vehicles in damaged and upset conditions Real-world conditions include: a) Turbulence, sensor noise, feedback biases; and b) Coupling between pilot and adaptive system. Simulated damage includes 1) "B" matrix (surface) failures; and 2) "A" matrix failures. Evaluate robustness of control systems to anticipated and unanticipated failures.

  1. Flight Test Engineering

    NASA Technical Reports Server (NTRS)

    Pavlock, Kate Maureen

    2013-01-01

    Although the scope of flight test engineering efforts may vary among organizations, all point to a common theme: flight test engineering is an interdisciplinary effort to test an asset in its operational flight environment. Upfront planning where design, implementation, and test efforts are clearly aligned with the flight test objective are keys to success. This chapter provides a top level perspective of flight test engineering for the non-expert. Additional research and reading on the topic is encouraged to develop a deeper understanding of specific considerations involved in each phase of flight test engineering.

  2. F-111 TACT Flight Over the Mojave Desert

    NASA Video Gallery

    Transonic aircraft technology (TACT/F-111A) added an highly efficient supercritical wing and later the third phase applied advanced wing (Mission Adaptive Wing-MAW) flight control technologies and ...

  3. 14 CFR 91.109 - Flight instruction; Simulated instrument flight and certain flight tests.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... 14 Aeronautics and Space 2 2014-01-01 2014-01-01 false Flight instruction; Simulated instrument flight and certain flight tests. 91.109 Section 91.109 Aeronautics and Space FEDERAL AVIATION... OPERATING AND FLIGHT RULES Flight Rules General § 91.109 Flight instruction; Simulated instrument flight...

  4. 14 CFR 91.109 - Flight instruction; Simulated instrument flight and certain flight tests.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... 14 Aeronautics and Space 2 2012-01-01 2012-01-01 false Flight instruction; Simulated instrument flight and certain flight tests. 91.109 Section 91.109 Aeronautics and Space FEDERAL AVIATION... OPERATING AND FLIGHT RULES Flight Rules General § 91.109 Flight instruction; Simulated instrument flight...

  5. 14 CFR 91.109 - Flight instruction; Simulated instrument flight and certain flight tests.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... 14 Aeronautics and Space 2 2013-01-01 2013-01-01 false Flight instruction; Simulated instrument flight and certain flight tests. 91.109 Section 91.109 Aeronautics and Space FEDERAL AVIATION... OPERATING AND FLIGHT RULES Flight Rules General § 91.109 Flight instruction; Simulated instrument flight...

  6. 14 CFR 91.109 - Flight instruction; Simulated instrument flight and certain flight tests.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 14 Aeronautics and Space 2 2011-01-01 2011-01-01 false Flight instruction; Simulated instrument flight and certain flight tests. 91.109 Section 91.109 Aeronautics and Space FEDERAL AVIATION... OPERATING AND FLIGHT RULES Flight Rules General § 91.109 Flight instruction; Simulated instrument flight...

  7. Planning Flight Paths of Autonomous Aerobots

    NASA Technical Reports Server (NTRS)

    Kulczycki, Eric; Elfes, Alberto; Sharma, Shivanjli

    2009-01-01

    Algorithms for planning flight paths of autonomous aerobots (robotic blimps) to be deployed in scientific exploration of remote planets are undergoing development. These algorithms are also adaptable to terrestrial applications involving robotic submarines as well as aerobots and other autonomous aircraft used to acquire scientific data or to perform surveying or monitoring functions.

  8. Integrating Space Flight Resource Management Skills into Technical Lessons for International Space Station Flight Controller Training

    NASA Technical Reports Server (NTRS)

    Baldwin, Evelyn

    2008-01-01

    The Johnson Space Center s (JSC) International Space Station (ISS) Space Flight Resource Management (SFRM) training program is designed to teach the team skills required to be an effective flight controller. It was adapted from the SFRM training given to Shuttle flight controllers to fit the needs of a "24 hours a day/365 days a year" flight controller. More recently, the length reduction of technical training flows for ISS flight controllers impacted the number of opportunities for fully integrated team scenario based training, where most SFRM training occurred. Thus, the ISS SFRM training program is evolving yet again, using a new approach of teaching and evaluating SFRM alongside of technical materials. Because there are very few models in other industries that have successfully tied team and technical skills together, challenges are arising. Despite this, the Mission Operations Directorate of NASA s JSC is committed to implementing this integrated training approach because of the anticipated benefits.

  9. Autonomous Soaring Flight Results

    NASA Technical Reports Server (NTRS)

    Allen, Michael J.

    2006-01-01

    A viewgraph presentation on autonomous soaring flight results for Unmanned Aerial Vehicles (UAV)'s is shown. The topics include: 1) Background; 2) Thermal Soaring Flight Results; 3) Autonomous Dolphin Soaring; and 4) Future Plans.

  10. 'Mighty Eagle' Takes Flight

    NASA Video Gallery

    The "Mighty Eagle," a NASA robotic prototype lander, had a successful first untethered flight Aug. 8 at the Marshall Center. During the 34-second flight, the Mighty Eagle soared and hovered at 30 f...

  11. Electric flight systems, overview

    NASA Technical Reports Server (NTRS)

    Cronin, M. J.

    1982-01-01

    Materials illustrating a presentation on electric flight systems are presented. Fuel consumption, the power plant assembly, flight control technology, electromechanical actuator systems and components of possible power systems are surveyed.

  12. Life-sciences research opportunities in commercial suborbital space flight

    NASA Astrophysics Data System (ADS)

    Shelhamer, Mark

    2014-11-01

    Commercial suborbital space flights will reach altitudes above 100 km, with 3-5 min of weightlessness bracketed by high-g launch and landing phases. The proposed frequency of these flights, and the large passenger population, present interesting opportunities for researchers in the life sciences. The characteristics of suborbital flight are between those of parabolic and orbital flights, opening up new scientific possibilities and easing the burden for obtaining access to 0g. There are several areas where these flights might be used for research in the life sciences: (1) operational research: preparation for “real” space flight, such as rehearsal of medical procedures, (2) applied research-to answer questions relevant to long-term space flight; (3) passenger health and safety-effects on passengers, relevant to screening and training; (4) basic research in physiological mechanisms-to address issues of fundamental science. We describe possible projects in each of these categories. One in particular spans several areas. Based on the anticipated suborbital flight profiles, observations from parabolic flight, and the wide range of fitness and experience levels of suborbital passengers, sensorimotor disturbances such as motion sickness and disorientation are major concerns. Protocols for pre-flight adaptation of sensorimotor responses might help to alleviate some of these problems, based on results from research in the initial flights. This would improve the passenger experience and add to the knowledge base relevant to space flight more generally.

  13. Ornithopter flight stabilization

    NASA Astrophysics Data System (ADS)

    Dietl, John M.; Garcia, Ephrahim

    2007-04-01

    The quasi-steady aerodynamics model and the vehicle dynamics model of ornithopter flight are explained, and numerical methods are described to capture limit cycle behavior in ornithopter flight. The Floquet method is used to determine stability in forward flight, and a linear discrete-time state-space model is developed. This is used to calculate stabilizing and disturbance-rejecting controllers.

  14. In Flight, Online

    ERIC Educational Resources Information Center

    Lucking, Robert A.; Wighting, Mervyn J.; Christmann, Edwin P.

    2005-01-01

    The concept of flight for human beings has always been closely tied to imagination. To fly like a bird requires a mind that also soars. Therefore, good teachers who want to teach the scientific principles of flight recognize that it is helpful to share stories of their search for the keys to flight. The authors share some of these with the reader,…

  15. Surface tension dominates insect flight on fluid interfaces.

    PubMed

    Mukundarajan, Haripriya; Bardon, Thibaut C; Kim, Dong Hyun; Prakash, Manu

    2016-03-01

    Flight on the 2D air-water interface, with body weight supported by surface tension, is a unique locomotion strategy well adapted for the environmental niche on the surface of water. Although previously described in aquatic insects like stoneflies, the biomechanics of interfacial flight has never been analysed. Here, we report interfacial flight as an adapted behaviour in waterlily beetles (Galerucella nymphaeae) which are also dexterous airborne fliers. We present the first quantitative biomechanical model of interfacial flight in insects, uncovering an intricate interplay of capillary, aerodynamic and neuromuscular forces. We show that waterlily beetles use their tarsal claws to attach themselves to the interface, via a fluid contact line pinned at the claw. We investigate the kinematics of interfacial flight trajectories using high-speed imaging and construct a mathematical model describing the flight dynamics. Our results show that non-linear surface tension forces make interfacial flight energetically expensive compared with airborne flight at the relatively high speeds characteristic of waterlily beetles, and cause chaotic dynamics to arise naturally in these regimes. We identify the crucial roles of capillary-gravity wave drag and oscillatory surface tension forces which dominate interfacial flight, showing that the air-water interface presents a radically modified force landscape for flapping wing flight compared with air. PMID:26936640

  16. Surface tension dominates insect flight on fluid interfaces.

    PubMed

    Mukundarajan, Haripriya; Bardon, Thibaut C; Kim, Dong Hyun; Prakash, Manu

    2016-03-01

    Flight on the 2D air-water interface, with body weight supported by surface tension, is a unique locomotion strategy well adapted for the environmental niche on the surface of water. Although previously described in aquatic insects like stoneflies, the biomechanics of interfacial flight has never been analysed. Here, we report interfacial flight as an adapted behaviour in waterlily beetles (Galerucella nymphaeae) which are also dexterous airborne fliers. We present the first quantitative biomechanical model of interfacial flight in insects, uncovering an intricate interplay of capillary, aerodynamic and neuromuscular forces. We show that waterlily beetles use their tarsal claws to attach themselves to the interface, via a fluid contact line pinned at the claw. We investigate the kinematics of interfacial flight trajectories using high-speed imaging and construct a mathematical model describing the flight dynamics. Our results show that non-linear surface tension forces make interfacial flight energetically expensive compared with airborne flight at the relatively high speeds characteristic of waterlily beetles, and cause chaotic dynamics to arise naturally in these regimes. We identify the crucial roles of capillary-gravity wave drag and oscillatory surface tension forces which dominate interfacial flight, showing that the air-water interface presents a radically modified force landscape for flapping wing flight compared with air.

  17. Surface tension dominates insect flight on fluid interfaces

    PubMed Central

    Mukundarajan, Haripriya; Bardon, Thibaut C.; Kim, Dong Hyun; Prakash, Manu

    2016-01-01

    ABSTRACT Flight on the 2D air–water interface, with body weight supported by surface tension, is a unique locomotion strategy well adapted for the environmental niche on the surface of water. Although previously described in aquatic insects like stoneflies, the biomechanics of interfacial flight has never been analysed. Here, we report interfacial flight as an adapted behaviour in waterlily beetles (Galerucella nymphaeae) which are also dexterous airborne fliers. We present the first quantitative biomechanical model of interfacial flight in insects, uncovering an intricate interplay of capillary, aerodynamic and neuromuscular forces. We show that waterlily beetles use their tarsal claws to attach themselves to the interface, via a fluid contact line pinned at the claw. We investigate the kinematics of interfacial flight trajectories using high-speed imaging and construct a mathematical model describing the flight dynamics. Our results show that non-linear surface tension forces make interfacial flight energetically expensive compared with airborne flight at the relatively high speeds characteristic of waterlily beetles, and cause chaotic dynamics to arise naturally in these regimes. We identify the crucial roles of capillary–gravity wave drag and oscillatory surface tension forces which dominate interfacial flight, showing that the air–water interface presents a radically modified force landscape for flapping wing flight compared with air. PMID:26936640

  18. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Technical Reports Server (NTRS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  19. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    NASA Astrophysics Data System (ADS)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-12-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  20. Adaptive Controller Effects on Pilot Behavior

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.

    2014-01-01

    Adaptive control provides robustness and resilience for highly uncertain, and potentially unpredictable, flight dynamics characteristic. Some of the recent flight experiences of pilot-in-the-loop with an adaptive controller have exhibited unpredicted interactions. In retrospect, this is not surprising once it is realized that there are now two adaptive controllers interacting, the software adaptive control system and the pilot. An experiment was conducted to categorize these interactions on the pilot with an adaptive controller during control surface failures. One of the objectives of this experiment was to determine how the adaptation time of the controller affects pilots. The pitch and roll errors, and stick input increased for increasing adaptation time and during the segment when the adaptive controller was adapting. Not surprisingly, altitude, cross track and angle deviations, and vertical velocity also increase during the failure and then slowly return to pre-failure levels. Subjects may change their behavior even as an adaptive controller is adapting with additional stick inputs. Therefore, the adaptive controller should adapt as fast as possible to minimize flight track errors. This will minimize undesirable interactions between the pilot and the adaptive controller and maintain maneuvering precision.

  1. Free Flight Rotorcraft Flight Test Vehicle Technology Development

    NASA Technical Reports Server (NTRS)

    Hodges, W. Todd; Walker, Gregory W.

    1994-01-01

    A rotary wing, unmanned air vehicle (UAV) is being developed as a research tool at the NASA Langley Research Center by the U.S. Army and NASA. This development program is intended to provide the rotorcraft research community an intermediate step between rotorcraft wind tunnel testing and full scale manned flight testing. The technologies under development for this vehicle are: adaptive electronic flight control systems incorporating artificial intelligence (AI) techniques, small-light weight sophisticated sensors, advanced telepresence-telerobotics systems and rotary wing UAV operational procedures. This paper briefly describes the system's requirements and the techniques used to integrate the various technologies to meet these requirements. The paper also discusses the status of the development effort. In addition to the original aeromechanics research mission, the technology development effort has generated a great deal of interest in the UAV community for related spin-off applications, as briefly described at the end of the paper. In some cases the technologies under development in the free flight program are critical to the ability to perform some applications.

  2. Biomechanics of bird flight.

    PubMed

    Tobalske, Bret W

    2007-09-01

    Power output is a unifying theme for bird flight and considerable progress has been accomplished recently in measuring muscular, metabolic and aerodynamic power in birds. The primary flight muscles of birds, the pectoralis and supracoracoideus, are designed for work and power output, with large stress (force per unit cross-sectional area) and strain (relative length change) per contraction. U-shaped curves describe how mechanical power output varies with flight speed, but the specific shapes and characteristic speeds of these curves differ according to morphology and flight style. New measures of induced, profile and parasite power should help to update existing mathematical models of flight. In turn, these improved models may serve to test behavioral and ecological processes. Unlike terrestrial locomotion that is generally characterized by discrete gaits, changes in wing kinematics and aerodynamics across flight speeds are gradual. Take-off flight performance scales with body size, but fully revealing the mechanisms responsible for this pattern awaits new study. Intermittent flight appears to reduce the power cost for flight, as some species flap-glide at slow speeds and flap-bound at fast speeds. It is vital to test the metabolic costs of intermittent flight to understand why some birds use intermittent bounds during slow flight. Maneuvering and stability are critical for flying birds, and design for maneuvering may impinge upon other aspects of flight performance. The tail contributes to lift and drag; it is also integral to maneuvering and stability. Recent studies have revealed that maneuvers are typically initiated during downstroke and involve bilateral asymmetry of force production in the pectoralis. Future study of maneuvering and stability should measure inertial and aerodynamic forces. It is critical for continued progress into the biomechanics of bird flight that experimental designs are developed in an ecological and evolutionary context.

  3. Flight code validation simulator

    SciTech Connect

    Sims, B.A.

    1995-08-01

    An End-To-End Simulation capability for software development and validation of missile flight software on the actual embedded computer has been developed utilizing a 486 PC, i860 DSP coprocessor, embedded flight computer and custom dual port memory interface hardware. This system allows real-time interrupt driven embedded flight software development and checkout. The flight software runs in a Sandia Digital Airborne Computer (SANDAC) and reads and writes actual hardware sensor locations in which IMU (Inertial Measurements Unit) data resides. The simulator provides six degree of freedom real-time dynamic simulation, accurate real-time discrete sensor data and acts on commands and discretes from the flight computer. This system was utilized in the development and validation of the successful premier flight of the Digital Miniature Attitude Reference System (DMARS) in January 1995 at the White Sands Missile Range on a two stage attitude controlled sounding rocket.

  4. Unified powered flight guidance

    NASA Technical Reports Server (NTRS)

    Brand, T. J.; Brown, D. W.; Higgins, J. P.

    1973-01-01

    A complete revision of the orbiter powered flight guidance scheme is presented. A unified approach to powered flight guidance was taken to accommodate all phases of exo-atmospheric orbiter powered flight, from ascent through deorbit. The guidance scheme was changed from the previous modified version of the Lambert Aim Point Maneuver Mode used in Apollo to one that employs linear tangent guidance concepts. This document replaces the previous ascent phase equation document.

  5. Theseus in Flight

    NASA Technical Reports Server (NTRS)

    1996-01-01

    The twin pusher engines of the prototype Theseus research aircraft can be clearly seen in this photo of the aircraft during a 1996 research flight from the Dryden Flight Research Center, Edwards, California. The Theseus aircraft, built and operated by Aurora Flight Sciences Corporation, Manassas, Virginia, was a unique aircraft flown at NASA's Dryden Flight Research Center, Edwards, California, under a cooperative agreement between NASA and Aurora. Dryden hosted the Theseus program, providing hangar space and range safety for flight testing. Aurora Flight Sciences was responsible for the actual flight testing, vehicle flight safety, and operation of the aircraft. The Theseus remotely piloted aircraft flew its maiden flight on May 24, 1996, at Dryden. During its sixth flight on November 12, 1996, Theseus experienced an in-flight structural failure that resulted in the loss of the aircraft. As of the beginning of the year 2000, Aurora had not rebuilt the aircraft. Theseus was built for NASA under an innovative, $4.9 million fixed-price contract by Aurora Flight Sciences Corporation and its partners, West Virginia University, Morgantown, West Virginia, and Fairmont State College, Fairmont, West Virginia. The twin-engine, unpiloted vehicle had a 140-foot wingspan, and was constructed largely of composite materials. Powered by two 80-horsepower, turbocharged piston engines that drove twin 9-foot-diameter propellers, Theseus was designed to fly autonomously at high altitudes, with takeoff and landing under the active control of a ground-based pilot in a ground control station 'cockpit.' With the potential ability to carry 700 pounds of science instruments to altitudes above 60,000 feet for durations of greater than 24 hours, Theseus was intended to support research in areas such as stratospheric ozone depletion and the atmospheric effects of future high-speed civil transport aircraft engines. Instruments carried aboard Theseus also would be able to validate satellite

  6. Theseus in Flight

    NASA Technical Reports Server (NTRS)

    1996-01-01

    The twin pusher propeller-driven engines of the Theseus research aircraft can be clearly seen in this photo, taken during a 1996 research flight at NASA's Dryden Flight Research Center, Edwards, California. The Theseus aircraft, built and operated by Aurora Flight Sciences Corporation, Manassas, Virginia, was a unique aircraft flown at NASA's Dryden Flight Research Center, Edwards, California, under a cooperative agreement between NASA and Aurora. Dryden hosted the Theseus program, providing hangar space and range safety for flight testing. Aurora Flight Sciences was responsible for the actual flight testing, vehicle flight safety, and operation of the aircraft. The Theseus remotely piloted aircraft flew its maiden flight on May 24, 1996, at Dryden. During its sixth flight on November 12, 1996, Theseus experienced an in-flight structural failure that resulted in the loss of the aircraft. As of the beginning of the year 2000, Aurora had not rebuilt the aircraft. Theseus was built for NASA under an innovative, $4.9 million fixed-price contract by Aurora Flight Sciences Corporation and its partners, West Virginia University, Morgantown, West Virginia, and Fairmont State College, Fairmont, West Virginia. The twin-engine, unpiloted vehicle had a 140-foot wingspan, and was constructed largely of composite materials. Powered by two 80-horsepower, turbocharged piston engines that drove twin 9-foot-diameter propellers, Theseus was designed to fly autonomously at high altitudes, with takeoff and landing under the active control of a ground-based pilot in a ground control station 'cockpit.' With the potential ability to carry 700 pounds of science instruments to altitudes above 60,000 feet for durations of greater than 24 hours, Theseus was intended to support research in areas such as stratospheric ozone depletion and the atmospheric effects of future high-speed civil transport aircraft engines. Instruments carried aboard Theseus also would be able to validate satellite

  7. Theseus in Flight

    NASA Technical Reports Server (NTRS)

    1996-01-01

    The Theseus research aircraft in flight over Rogers Dry Lake, Edwards, California, during a 1996 research flight. The Theseus aircraft, built and operated by Aurora Flight Sciences Corporation, Manassas, Virginia, was a unique aircraft flown at NASA's Dryden Flight Research Center, Edwards, California, under a cooperative agreement between NASA and Aurora. Dryden hosted the Theseus program, providing hangar space and range safety for flight testing. Aurora Flight Sciences was responsible for the actual flight testing, vehicle flight safety, and operation of the aircraft. The Theseus remotely piloted aircraft flew its maiden flight on May 24, 1996, at Dryden. During its sixth flight on November 12, 1996, Theseus experienced an in-flight structural failure that resulted in the loss of the aircraft. As of the beginning of the year 2000, Aurora had not rebuilt the aircraft. Theseus was built for NASA under an innovative, $4.9 million fixed-price contract by Aurora Flight Sciences Corporation and its partners, West Virginia University, Morgantown, West Virginia, and Fairmont State College, Fairmont, West Virginia. The twin-engine, unpiloted vehicle had a 140-foot wingspan, and was constructed largely of composite materials. Powered by two 80-horsepower, turbocharged piston engines that drove twin 9-foot-diameter propellers, Theseus was designed to fly autonomously at high altitudes, with takeoff and landing under the active control of a ground-based pilot in a ground control station 'cockpit.' With the potential ability to carry 700 pounds of science instruments to altitudes above 60,000 feet for durations of greater than 24 hours, Theseus was intended to support research in areas such as stratospheric ozone depletion and the atmospheric effects of future high-speed civil transport aircraft engines. Instruments carried aboard Theseus also would be able to validate satellite-based global environmental change measurements. Dryden's Project Manager was John Del Frate.

  8. Theseus in Flight

    NASA Technical Reports Server (NTRS)

    1996-01-01

    The Theseus prototype research aircraft shows off its unique design as it flies low over Rogers Dry Lake during a 1996 test flight from NASA's Dryden Flight Research Center, Edwards, California. The Theseus aircraft, built and operated by Aurora Flight Sciences Corporation, Manassas, Virginia, was a unique aircraft flown at NASA's Dryden Flight Research Center, Edwards, California, under a cooperative agreement between NASA and Aurora. Dryden hosted the Theseus program, providing hangar space and range safety for flight testing. Aurora Flight Sciences was responsible for the actual flight testing, vehicle flight safety, and operation of the aircraft. The Theseus remotely piloted aircraft flew its maiden flight on May 24, 1996, at Dryden. During its sixth flight on November 12, 1996, Theseus experienced an in-flight structural failure that resulted in the loss of the aircraft. As of the beginning of the year 2000, Aurora had not rebuilt the aircraft Theseus was built for NASA under an innovative, $4.9 million fixed-price contract by Aurora Flight Sciences Corporation and its partners, West Virginia University, Morgantown, West Virginia, and Fairmont State College, Fairmont, West Virginia. The twin-engine, unpiloted vehicle had a 140-foot wingspan, and was constructed largely of composite materials. Powered by two 80-horsepower, turbocharged piston engines that drove twin 9-foot-diameter propellers, Theseus was designed to fly autonomously at high altitudes, with takeoff and landing under the active control of a ground-based pilot in a ground control station 'cockpit.' With the potential ability to carry 700 pounds of science instruments to altitudes above 60,000 feet for durations of greater than 24 hours, Theseus was intended to support research in areas such as stratospheric ozone depletion and the atmospheric effects of future high-speed civil transport aircraft engines. Instruments carried aboard Theseus also would be able to validate satellite-based global

  9. Autonomous Flight Safety System

    NASA Technical Reports Server (NTRS)

    Simpson, James

    2010-01-01

    The Autonomous Flight Safety System (AFSS) is an independent self-contained subsystem mounted onboard a launch vehicle. AFSS has been developed by and is owned by the US Government. Autonomously makes flight termination/destruct decisions using configurable software-based rules implemented on redundant flight processors using data from redundant GPS/IMU navigation sensors. AFSS implements rules determined by the appropriate Range Safety officials.

  10. Digital flight control research

    NASA Technical Reports Server (NTRS)

    Potter, J. E.; Stern, R. G.; Smith, T. B.; Sinha, P.

    1974-01-01

    The results of studies which were undertaken to contribute to the design of digital flight control systems, particularly for transport aircraft are presented. In addition to the overall design considerations for a digital flight control system, the following topics are discussed in detail: (1) aircraft attitude reference system design, (2) the digital computer configuration, (3) the design of a typical digital autopilot for transport aircraft, and (4) a hybrid flight simulator.

  11. Bat flight: aerodynamics, kinematics and flight morphology.

    PubMed

    Hedenström, Anders; Johansson, L Christoffer

    2015-03-01

    Bats evolved the ability of powered flight more than 50 million years ago. The modern bat is an efficient flyer and recent research on bat flight has revealed many intriguing facts. By using particle image velocimetry to visualize wake vortices, both the magnitude and time-history of aerodynamic forces can be estimated. At most speeds the downstroke generates both lift and thrust, whereas the function of the upstroke changes with forward flight speed. At hovering and slow speed bats use a leading edge vortex to enhance the lift beyond that allowed by steady aerodynamics and an inverted wing during the upstroke to further aid weight support. The bat wing and its skeleton exhibit many features and control mechanisms that are presumed to improve flight performance. Whereas bats appear aerodynamically less efficient than birds when it comes to cruising flight, they have the edge over birds when it comes to manoeuvring. There is a direct relationship between kinematics and the aerodynamic performance, but there is still a lack of knowledge about how (and if) the bat controls the movements and shape (planform and camber) of the wing. Considering the relatively few bat species whose aerodynamic tracks have been characterized, there is scope for new discoveries and a need to study species representing more extreme positions in the bat morphospace. PMID:25740899

  12. Bat flight: aerodynamics, kinematics and flight morphology.

    PubMed

    Hedenström, Anders; Johansson, L Christoffer

    2015-03-01

    Bats evolved the ability of powered flight more than 50 million years ago. The modern bat is an efficient flyer and recent research on bat flight has revealed many intriguing facts. By using particle image velocimetry to visualize wake vortices, both the magnitude and time-history of aerodynamic forces can be estimated. At most speeds the downstroke generates both lift and thrust, whereas the function of the upstroke changes with forward flight speed. At hovering and slow speed bats use a leading edge vortex to enhance the lift beyond that allowed by steady aerodynamics and an inverted wing during the upstroke to further aid weight support. The bat wing and its skeleton exhibit many features and control mechanisms that are presumed to improve flight performance. Whereas bats appear aerodynamically less efficient than birds when it comes to cruising flight, they have the edge over birds when it comes to manoeuvring. There is a direct relationship between kinematics and the aerodynamic performance, but there is still a lack of knowledge about how (and if) the bat controls the movements and shape (planform and camber) of the wing. Considering the relatively few bat species whose aerodynamic tracks have been characterized, there is scope for new discoveries and a need to study species representing more extreme positions in the bat morphospace.

  13. Adaptive Controller Adaptation Time and Available Control Authority Effects on Piloting

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna; Gregory, Irene

    2013-01-01

    Adaptive control is considered for highly uncertain, and potentially unpredictable, flight dynamics characteristic of adverse conditions. This experiment looked at how adaptive controller adaptation time to recover nominal aircraft dynamics affects pilots and how pilots want information about available control authority transmitted. Results indicate that an adaptive controller that takes three seconds to adapt helped pilots when looking at lateral and longitudinal errors. The controllability ratings improved with the adaptive controller, again the most for the three seconds adaptation time while workload decreased with the adaptive controller. The effects of the displays showing the percentage amount of available safe flight envelope used in the maneuver were dominated by the adaptation time. With the displays, the altitude error increased, controllability slightly decreased, and mental demand increased. Therefore, the displays did require some of the subjects resources but these negatives may be outweighed by pilots having more situation awareness of their aircraft.

  14. Flight research and testing

    NASA Technical Reports Server (NTRS)

    Putnam, Terrill W.; Ayers, Theodore G.

    1988-01-01

    Flight research and testing form a critical link in the aeronautic R and D chain. Brilliant concepts, elegant theories, and even sophisticated ground tests of flight vehicles are not sufficient to prove beyond doubt that an unproven aeronautical concept will actually perform as predicted. Flight research and testing provide the ultimate proof that an idea or concept performs as expected. Ever since the Wright brothers, flight research and testing have been the crucible in which aeronautical concepts have advanced and been proven to the point that engineers and companies have been willing to stake their future to produce and design new aircraft. This is still true today, as shown by the development of the experimental X-30 aerospace plane. The Dryden Flight Research Center (Ames-Dryden) continues to be involved in a number of flight research programs that require understanding and characterization of the total airplane in all the aeronautical disciplines, for example the X-29. Other programs such as the F-14 variable-sweep transition flight experiment have focused on a single concept or discipline. Ames-Dryden also continues to conduct flight and ground based experiments to improve and expand the ability to test and evaluate advanced aeronautical concepts. A review of significant aeronautical flight research programs and experiments is presented to illustrate both the progress made and the challenges to come.

  15. Flight research and testing

    NASA Technical Reports Server (NTRS)

    Putnam, Terrill W.; Ayers, Theodore G.

    1989-01-01

    Flight research and testing form a critical link in the aeronautic research and development chain. Brilliant concepts, elegant theories, and even sophisticated ground tests of flight vehicles are not sufficient to prove beyond a doubt that an unproven aeronautical concept will actually perform as predicted. Flight research and testing provide the ultimate proof that an idea or concept performs as expected. Ever since the Wright brothers, flight research and testing were the crucible in which aeronautical concepts were advanced and proven to the point that engineers and companies are willing to stake their future to produce and design aircraft. This is still true today, as shown by the development of the experimental X-30 aerospace plane. The Dryden Flight Research Center (Ames-Dryden) continues to be involved in a number of flight research programs that require understanding and characterization of the total airplane in all the aeronautical disciplines, for example the X-29. Other programs such as the F-14 variable-sweep transition flight experiment have focused on a single concept or discipline. Ames-Dryden also continues to conduct flight and ground based experiments to improve and expand the ability to test and evaluate advanced aeronautical concepts. A review of significant aeronautical flight research programs and experiments is presented to illustrate both the progress being made and the challenges to come.

  16. Development of flying qualities criteria for single pilot instrument flight operations

    NASA Technical Reports Server (NTRS)

    Bar-Gill, A.; Nixon, W. B.; Miller, G. E.

    1982-01-01

    Flying qualities criteria for Single Pilot Instrument Flight Rule (SPIFR) operations were investigated. The ARA aircraft was modified and adapted for SPIFR operations. Aircraft configurations to be flight-tested were chosen and matched on the ARA in-flight simulator, implementing modern control theory algorithms. Mission planning and experimental matrix design were completed. Microprocessor software for the onboard data acquisition system was debugged and flight-tested. Flight-path reconstruction procedure and the associated FORTRAN program were developed. Algorithms associated with the statistical analysis of flight test results and the SPIFR flying qualities criteria deduction are discussed.

  17. Design of a Computerised Flight Mill Device to Measure the Flight Potential of Different Insects

    PubMed Central

    Martí-Campoy, Antonio; Ávalos, Juan Antonio; Soto, Antonia; Rodríguez-Ballester, Francisco; Martínez-Blay, Victoria; Malumbres, Manuel Pérez

    2016-01-01

    Several insect species pose a serious threat to different plant species, sometimes becoming a pest that produces significant damage to the landscape, biodiversity, and/or the economy. This is the case of Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae), Semanotus laurasii Lucas (Coleoptera: Cerambycidae), and Monochamus galloprovincialis Olivier (Coleoptera: Cerambycidae), which have become serious threats to ornamental and productive trees all over the world such as palm trees, cypresses, and pines. Knowledge about their flight potential is very important for designing and applying measures targeted to reduce the negative effects from these pests. Studying the flight capability and behaviour of some insects is difficult due to their small size and the large area wherein they can fly, so we wondered how we could obtain information about their flight capabilities in a controlled environment. The answer came with the design of flight mills. Relevant data about the flight potential of these insects may be recorded and analysed by means of a flight mill. Once an insect is attached to the flight mill, it is able to fly in a circular direction without hitting walls or objects. By adding sensors to the flight mill, it is possible to record the number of revolutions and flight time. This paper presents a full description of a computer monitored flight mill. The description covers both the mechanical and the electronic parts in detail. The mill was designed to easily adapt to the anatomy of different insects and was successfully tested with individuals from three species R. ferrugineus, S. laurasii, and M. galloprovincialis. PMID:27070600

  18. Design of a Computerised Flight Mill Device to Measure the Flight Potential of Different Insects.

    PubMed

    Martí-Campoy, Antonio; Ávalos, Juan Antonio; Soto, Antonia; Rodríguez-Ballester, Francisco; Martínez-Blay, Victoria; Malumbres, Manuel Pérez

    2016-01-01

    Several insect species pose a serious threat to different plant species, sometimes becoming a pest that produces significant damage to the landscape, biodiversity, and/or the economy. This is the case of Rhynchophorus ferrugineus Olivier (Coleoptera: Dryophthoridae), Semanotus laurasii Lucas (Coleoptera: Cerambycidae), and Monochamus galloprovincialis Olivier (Coleoptera: Cerambycidae), which have become serious threats to ornamental and productive trees all over the world such as palm trees, cypresses, and pines. Knowledge about their flight potential is very important for designing and applying measures targeted to reduce the negative effects from these pests. Studying the flight capability and behaviour of some insects is difficult due to their small size and the large area wherein they can fly, so we wondered how we could obtain information about their flight capabilities in a controlled environment. The answer came with the design of flight mills. Relevant data about the flight potential of these insects may be recorded and analysed by means of a flight mill. Once an insect is attached to the flight mill, it is able to fly in a circular direction without hitting walls or objects. By adding sensors to the flight mill, it is possible to record the number of revolutions and flight time. This paper presents a full description of a computer monitored flight mill. The description covers both the mechanical and the electronic parts in detail. The mill was designed to easily adapt to the anatomy of different insects and was successfully tested with individuals from three species R. ferrugineus, S. laurasii, and M. galloprovincialis. PMID:27070600

  19. Technology review of flight crucial flight controls

    NASA Technical Reports Server (NTRS)

    Rediess, H. A.; Buckley, E. C.

    1984-01-01

    The results of a technology survey in flight crucial flight controls conducted as a data base for planning future research and technology programs are provided. Free world countries were surveyed with primary emphasis on the United States and Western Europe because that is where the most advanced technology resides. The survey includes major contemporary systems on operational aircraft, R&D flight programs, advanced aircraft developments, and major research and technology programs. The survey was not intended to be an in-depth treatment of the technology elements, but rather a study of major trends in systems level technology. The information was collected from open literature, personal communications and a tour of several companies, government organizations and research laboratories in the United States, United Kingdom, France, and the Federal Republic of Germany.

  20. The NASA F-15 Intelligent Flight Control Systems: Generation II

    NASA Technical Reports Server (NTRS)

    Buschbacher, Mark; Bosworth, John

    2006-01-01

    The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.

  1. X-43A Flight Controls

    NASA Technical Reports Server (NTRS)

    Baumann, Ethan

    2006-01-01

    A viewgraph presentation detailing X-43A Flight controls at NASA Dryden Flight Research Center is shown. The topics include: 1) NASA Dryden, Overview and current and recent flight test programs; 2) Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Program, Program Overview and Platform Precision Autopilot; and 3) Hyper-X Program, Program Overview, X-43A Flight Controls and Flight Results.

  2. Somatosensory Substrates of Flight Control in Bats

    PubMed Central

    Marshall, Kara L.; Chadha, Mohit; deSouza, Laura A.; Sterbing-D’Angelo, Susanne J.; Moss, Cynthia F.; Lumpkin, Ellen A.

    2015-01-01

    Summary Flight maneuvers require rapid sensory integration to generate adaptive motor output. Bats achieve remarkable agility with modified forelimbs that serve as airfoils while retaining capacity for object manipulation. Wing sensory inputs provide behaviorally relevant information to guide flight; however, components of wing sensory-motor circuits have not been analyzed. Here, we elucidate the organization of wing innervation in an insectivore, the big brown bat, Eptesicus fuscus. We demonstrate that wing sensory innervation differs from other vertebrate forelimbs, revealing a peripheral basis for the atypical topographic organization reported for bat somatosensory nuclei. Furthermore, the wing is innervated by an unusual complement of sensory neurons poised to report airflow and touch. Finally, we report that cortical neurons encode tactile and airflow inputs with sparse activity patterns. Together, our findings identify neural substrates of somatosensation in the bat wing and imply that evolutionary pressures giving rise to mammalian flight led to unusual sensorimotor projections. PMID:25937277

  3. Somatosensory substrates of flight control in bats.

    PubMed

    Marshall, Kara L; Chadha, Mohit; deSouza, Laura A; Sterbing-D'Angelo, Susanne J; Moss, Cynthia F; Lumpkin, Ellen A

    2015-05-12

    Flight maneuvers require rapid sensory integration to generate adaptive motor output. Bats achieve remarkable agility with modified forelimbs that serve as airfoils while retaining capacity for object manipulation. Wing sensory inputs provide behaviorally relevant information to guide flight; however, components of wing sensory-motor circuits have not been analyzed. Here, we elucidate the organization of wing innervation in an insectivore, the big brown bat, Eptesicus fuscus. We demonstrate that wing sensory innervation differs from other vertebrate forelimbs, revealing a peripheral basis for the atypical topographic organization reported for bat somatosensory nuclei. Furthermore, the wing is innervated by an unusual complement of sensory neurons poised to report airflow and touch. Finally, we report that cortical neurons encode tactile and airflow inputs with sparse activity patterns. Together, our findings identify neural substrates of somatosensation in the bat wing and imply that evolutionary pressures giving rise to mammalian flight led to unusual sensorimotor projections.

  4. Space Flight. Teacher Resources.

    ERIC Educational Resources Information Center

    2001

    This teacher's guide contains information, lesson plans, and diverse student learning activities focusing on space flight. The guide is divided into seven sections: (1) "Drawing Activities" (Future Flight; Space Fun; Mission: Draw); (2) "Geography" (Space Places); (3) "History" (Space and Time); (4) "Information" (Space Transportation System;…

  5. Autonomous Flight Safety System

    NASA Technical Reports Server (NTRS)

    Ferrell, Bob; Santuro, Steve; Simpson, James; Zoerner, Roger; Bull, Barton; Lanzi, Jim

    2004-01-01

    Autonomous Flight Safety System (AFSS) is an independent flight safety system designed for small to medium sized expendable launch vehicles launching from or needing range safety protection while overlying relatively remote locations. AFSS replaces the need for a man-in-the-loop to make decisions for flight termination. AFSS could also serve as the prototype for an autonomous manned flight crew escape advisory system. AFSS utilizes onboard sensors and processors to emulate the human decision-making process using rule-based software logic and can dramatically reduce safety response time during critical launch phases. The Range Safety flight path nominal trajectory, its deviation allowances, limit zones and other flight safety rules are stored in the onboard computers. Position, velocity and attitude data obtained from onboard global positioning system (GPS) and inertial navigation system (INS) sensors are compared with these rules to determine the appropriate action to ensure that people and property are not jeopardized. The final system will be fully redundant and independent with multiple processors, sensors, and dead man switches to prevent inadvertent flight termination. AFSS is currently in Phase III which includes updated algorithms, integrated GPS/INS sensors, large scale simulation testing and initial aircraft flight testing.

  6. Exploring flight crew behaviour

    NASA Technical Reports Server (NTRS)

    Helmreich, R. L.

    1987-01-01

    A programme of research into the determinants of flight crew performance in commercial and military aviation is described, along with limitations and advantages associated with the conduct of research in such settings. Preliminary results indicate significant relationships among personality factors, attitudes regarding flight operations, and crew performance. The potential theoretical and applied utility of the research and directions for further research are discussed.

  7. Electromechanical flight control actuator

    NASA Technical Reports Server (NTRS)

    1979-01-01

    The feasibility of using an electromechanical actuator (EMA) as the primary flight control equipment in aerospace flight is examined. The EMA motor design is presented utilizing improved permanent magnet materials. The necessary equipment to complete a single channel EMA using the single channel power electronics breadboard is reported. The design and development of an improved rotor position sensor/tachometer is investigated.

  8. Nuclear Shuttle in Flight

    NASA Technical Reports Server (NTRS)

    1970-01-01

    This 1970 artist's concept shows a Nuclear Shuttle in flight. As envisioned by Marshall Space Flight Center Program Development engineers, the Nuclear Shuttle would deliver payloads to lunar orbit or other destinations then return to Earth orbit for refueling and additional missions.

  9. Aeroassist Flight Experiment (AFE)

    NASA Technical Reports Server (NTRS)

    Siemers, Paul M., III

    1988-01-01

    Information is given in viewgraph form on the Aeroassist Flight Experiment (AFE), an experiment with the objective of investigating critical vehicle design and environmental technologies applicable to the design of aeroassisted space transfer vehicles. Information is given on design, simulation, flight regime, mission requirements and objectives, instrumentation, and the project schedule.

  10. Java for flight software

    NASA Technical Reports Server (NTRS)

    Benowitz, E.; Niessner, A.

    2003-01-01

    This work involves developing representative mission-critical spacecraft software using the Real-Time Specification for Java (RTSJ). This work currently leverages actual flight software used in the design of actual flight software in the NASA's Deep Space 1 (DSI), which flew in 1998.

  11. Vortex attenuation flight experiments

    NASA Technical Reports Server (NTRS)

    Barber, M. R.; Hastings, E. C., Jr.; Champine, R. A.; Tymczyszyn, J. J.

    1977-01-01

    Flight tests evaluating the effects of altered span loading, turbulence ingestion, combinations of mass and turbulence ingestion, and combinations of altered span loading turbulance ingestion on trailed wake vortex attenuation were conducted. Span loadings were altered in flight by varying the deflections of the inboard and outboard flaps on a B-747 aircraft. Turbulence ingestion was achieved in flight by mounting splines on a C-54G aircraft. Mass and turbulence ingestion was achieved in flight by varying the thrust on the B-747 aircraft. Combinations of altered span loading and turbulence ingestion were achieved in flight by installing a spoiler on a CV-990 aircraft and by deflecting the existing spoilers on a B-747 aircraft. The characteristics of the attenuated and unattenuated vortexes were determined by probing them with smaller aircraft. Acceptable separation distances for encounters with the attenuated and unattenuated vortexes are presented.

  12. Disruption of postural readaptation by inertial stimuli following space flight

    NASA Technical Reports Server (NTRS)

    Black, F. O.; Paloski, W. H.; Reschke, M. F.; Igarashi, M.; Guedry, F.; Anderson, D. J.

    1999-01-01

    Postural instability (relative to pre-flight) has been observed in all shuttle astronauts studied upon return from orbital missions. Postural stability was more closely examined in four shuttle astronaut subjects before and after an 8 day orbital mission. Results of the pre- and post-flight postural stability studies were compared with a larger (n = 34) study of astronauts returning from shuttle missions of similar duration. Results from both studies indicated that inadequate vestibular feedback was the most significant sensory deficit contributing to the postural instability observed post flight. For two of the four IML-1 astronauts, post-flight postural instability and rate of recovery toward their earth-normal performance matched the performance of the larger sample. However, post-flight postural control in one returning astronaut was substantially below mean performance. This individual, who was within normal limits with respect to postural control before the mission, indicated that recovery to pre-flight postural stability was also interrupted by a post-flight pitch plane rotation test. A similar, though less extreme departure from the mean recovery trajectory was present in another astronaut following the same post-flight rotation test. The pitch plane rotation stimuli included otolith stimuli in the form of both transient tangential and constant centripetal linear acceleration components. We inferred from these findings that adaptation on orbit and re-adaptation on earth involved a change in sensorimotor integration of vestibular signals most likely from the otolith organs.

  13. Miscarriage Among Flight Attendants

    PubMed Central

    Grajewski, Barbara; Whelan, Elizabeth A.; Lawson, Christina C.; Hein, Misty J.; Waters, Martha A.; Anderson, Jeri L.; MacDonald, Leslie A.; Mertens, Christopher J.; Tseng, Chih-Yu; Cassinelli, Rick T.; Luo, Lian

    2015-01-01

    Background Cosmic radiation and circadian disruption are potential reproductive hazards for flight attendants. Methods Flight attendants from 3 US airlines in 3 cities were interviewed for pregnancy histories and lifestyle, medical, and occupational covariates. We assessed cosmic radiation and circadian disruption from company records of 2 million individual flights. Using Cox regression models, we compared respondents (1) by levels of flight exposures and (2) to teachers from the same cities, to evaluate whether these exposures were associated with miscarriage. Results Of 2654 women interviewed (2273 flight attendants and 381 teachers), 958 pregnancies among 764 women met study criteria. A hypothetical pregnant flight attendant with median firsttrimester exposures flew 130 hours in 53 flight segments, crossed 34 time zones, and flew 15 hours during her home-base sleep hours (10 pm–8 am), incurring 0.13 mGy absorbed dose (0.36 mSv effective dose) of cosmic radiation. About 2% of flight attendant pregnancies were likely exposed to a solar particle event, but doses varied widely. Analyses suggested that cosmic radiation exposure of 0.1 mGy or more may be associated with increased risk of miscarriage in weeks 9–13 (odds ratio = 1.7 [95% confidence interval = 0.95–3.2]). Risk of a first-trimester miscarriage with 15 hours or more of flying during home-base sleep hours was increased (1.5 [1.1–2.2]), as was risk with high physical job demands (2.5 [1.5–4.2]). Miscarriage risk was not increased among flight attendants compared with teachers. Conclusions Miscarriage was associated with flight attendant work during sleep hours and high physical job demands and may be associated with cosmic radiation exposure. PMID:25563432

  14. Overview With Results and Lessons Learned of the X-43A Mach 10 Flight

    NASA Technical Reports Server (NTRS)

    Marshall, Laurie A.; Bahm, Catherine; Corpening, Griffin P.; Sherrill, Robert

    2005-01-01

    This paper provides an overview of the final flight of the NASA X-43A project. The project consisted of three flights, two planned for Mach 7 and one for Mach 10. The third and final flight, November 16, 2004, was the first Mach 10 flight demonstration of an airframe-integrated, scramjet-powered, hypersonic vehicle. The goals and objectives for the project as well as those for the third flight are presented. The configuration of the Hyper-X stack including the X-43A, Hyper-X launch vehicle, and Hyper-X research vehicle adapter is discussed. The second flight of the X-43A was successfully conducted on March 27, 2004. Mission differences, vehicle modifications and lessons learned from the second flight as they applied to the third flight are also discussed. An overview of flight 3 results is presented.

  15. SLS-1 flight experiments preliminary significant results

    NASA Astrophysics Data System (ADS)

    1992-01-01

    Spacelab Life Sciences-1 (SLS-1) is the first of a series of dedicated life sciences Spacelab missions designed to investigate the mechanisms involved in the physiological adaptation to weightlessness and the subsequent readaptation to 1 gravity (1 G). Hypotheses generated from the physiological effects observed during earlier missions led to the formulation of several integrated experiments to determine the underlying mechanisms responsible for the observed phenomena. The 18 experiments selected for flight on SLS-1 investigated the cardiovascular, cardiopulmonary, regulatory physiology, musculoskeletal, and neuroscience disciplines in both human and rodent subjects. The SLS-1 preliminary results gave insight to the mechanisms involved in the adaptation to the microgravity environment and readaptation when returning to Earth. The experimental results will be used to promote health and safety for future long duration space flights and, as in the past, will be applied to many biomedical problems encountered here on Earth.

  16. Flight effects of fan noise

    NASA Astrophysics Data System (ADS)

    Chestnutt, D.

    1982-09-01

    Simulation of inflight fan noise and flight effects was discussed. The status of the overall program on the flight effects of fan noise was reviewed, and flight to static noise comparisons with the JT15D engine were displayed.

  17. Flight effects of fan noise

    NASA Technical Reports Server (NTRS)

    Chestnutt, D. (Editor)

    1982-01-01

    Simulation of inflight fan noise and flight effects was discussed. The status of the overall program on the flight effects of fan noise was reviewed, and flight to static noise comparisons with the JT15D engine were displayed.

  18. Magnesium and Space Flight

    NASA Technical Reports Server (NTRS)

    Smith, Scott M.; Zwart, Sara R.

    2016-01-01

    Magnesium is an essential nutrient for muscle, cardiovascular, and bone health on Earth, and during space flight. We sought to evaluate magnesium status in astronauts before, during, and after space missions, in 43 astronauts (34 male, 9 female) on 4-6 month space flight missions. We also studied individuals participating in a ground analog of space flight, (head-down tilt bed rest, n=27, 35 +/- 7 y). We evaluated serum concentration and 24-hour urinary excretion of magnesium along with estimates of tissue magnesium status from sublingual cells. Serum magnesium increased late in flight, while urinary magnesium excretion was higher over the course of 180-d space missions. Urinary magnesium increased during flight but decreased significantly at landing. Neither serum nor urinary magnesium changed during bed rest. For flight and bed rest, significant correlations existed between the area under the curve of serum and urinary magnesium and the change in total body bone mineral content. Tissue magnesium concentration was unchanged after flight and bed rest. Increased excretion of magnesium is likely partially from bone and partially from diet, but importantly, it does not come at the expense of muscle tissue stores. While further study is needed to better understand the implications of these findings for longer space exploration missions, magnesium homeostasis and tissue status seem well maintained during 4- to 6-month space missions.

  19. Future Flight Decks

    NASA Technical Reports Server (NTRS)

    Arbuckle, P. Douglas; Abbott, Kathy H.; Abbott, Terence S.; Schutte, Paul C.

    1998-01-01

    The evolution of commercial transport flight deck configurations over the past 20-30 years and expected future developments are described. Key factors in the aviation environment are identified that the authors expect will significantly affect flight deck designers. One of these is the requirement for commercial aviation accident rate reduction, which is probably required if global commercial aviation is to grow as projected. Other factors include the growing incrementalism in flight deck implementation, definition of future airspace operations, and expectations of a future pilot corps that will have grown up with computers. Future flight deck developments are extrapolated from observable factors in the aviation environment, recent research results in the area of pilot-centered flight deck systems, and by considering expected advances in technology that are being driven by other than aviation requirements. The authors hypothesize that revolutionary flight deck configuration changes will be possible with development of human-centered flight deck design methodologies that take full advantage of commercial and/or entertainment-driven technologies.

  20. Ethernet for Space Flight Applications

    NASA Technical Reports Server (NTRS)

    Webb, Evan; Day, John H. (Technical Monitor)

    2002-01-01

    NASA's Goddard Space Flight Center (GSFC) is adapting current data networking technologies to fly on future spaceflight missions. The benefits of using commercially based networking standards and protocols have been widely discussed and are expected to include reduction in overall mission cost, shortened integration and test (I&T) schedules, increased operations flexibility, and hardware and software upgradeability/scalability with developments ongoing in the commercial world. The networking effort is a comprehensive one encompassing missions ranging from small University Explorer (UNEX) class spacecraft to large observatories such as the Next Generation Space Telescope (NGST). Mission aspects such as flight hardware and software, ground station hardware and software, operations, RF communications, and security (physical and electronic) are all being addressed to ensure a complete end-to-end system solution. One of the current networking development efforts at GSFC is the SpaceLAN (Spacecraft Local Area Network) project, development of a space-qualifiable Ethernet network. To this end we have purchased an IEEE 802.3-compatible 10/100/1000 Media Access Control (MAC) layer Intellectual Property (IP) core and are designing a network node interface (NNI) and associated network components such as a switch. These systems will ultimately allow the replacement of the typical MIL-STD-1553/1773 and custom interfaces that inhabit most spacecraft. In this paper we will describe our current Ethernet NNI development along with a novel new space qualified physical layer that will be used in place of the standard interfaces. We will outline our plans for development of space qualified network components that will allow future spacecraft to operate in significant radiation environments while using a single onboard network for reliable commanding and data transfer. There will be a brief discussion of some issues surrounding system implications of a flight Ethernet. Finally, we will

  1. STS-111 Flight Day 3 Highlights

    NASA Astrophysics Data System (ADS)

    2002-06-01

    On Flight Day 3 of STS-111, the crew of Endeavour (Kenneth Cockrell, Commander; Paul Lockhart, Pilot; Franklin Chang-Diaz, Mission Specialist; Philippe Perrin, Mission Specialist) and the Expedition 5 crew (Valery Korzun, Commander; Peggy Whitson, Flight Engineer; Sergei Treschev, Flight Engineer), begin their final approach towards the International Space Station (ISS). From cameras aboard the ISS, live video of Endeavour is shown as it approaches the station. The Orbiter is maneuvered slowly to a position for docking, and cameras from multiple angles show this process. As it is maneuvered, there are clear views of its payload bay, which includes the Leonardo MultiPurpose Logistics Module (MPLM) and the Mobile Base System (MBS), both of which will be installed on the ISS during this mission. In the final stages of the docking procedure there is close-up footage of Endeavour meeting the ISS's Pressurized Mating Adapter 2 on the Destiny Laboratory Module. Inside the ISS, the Expedition 4 crew (Yury Onufrienko, Commander; Daniel Bursch, Flight Engineer; Carl Walz, Flight Engineer), which will be replaced by the Expedition 5 crew, prepares for final docking. Crew members are shown transferring equipment from the Endeavour to the ISS, prior to a replay of the mating of the two crafts. In the replay, the hatch is shown being opened and the two newly arrived crews are greeted with excitement by Expedition 4 crewmembers. The video closes with footage of the Quest airlock used for EVA (extravehicular activity) egress, and the Canadarm 2 robotic arm.

  2. Software for Managing Inventory of Flight Hardware

    NASA Technical Reports Server (NTRS)

    Salisbury, John; Savage, Scott; Thomas, Shirman

    2003-01-01

    The Flight Hardware Support Request System (FHSRS) is a computer program that relieves engineers at Marshall Space Flight Center (MSFC) of most of the non-engineering administrative burden of managing an inventory of flight hardware. The FHSRS can also be adapted to perform similar functions for other organizations. The FHSRS affords a combination of capabilities, including those formerly provided by three separate programs in purchasing, inventorying, and inspecting hardware. The FHSRS provides a Web-based interface with a server computer that supports a relational database of inventory; electronic routing of requests and approvals; and electronic documentation from initial request through implementation of quality criteria, acquisition, receipt, inspection, storage, and final issue of flight materials and components. The database lists both hardware acquired for current projects and residual hardware from previous projects. The increased visibility of residual flight components provided by the FHSRS has dramatically improved the re-utilization of materials in lieu of new procurements, resulting in a cost savings of over $1.7 million. The FHSRS includes subprograms for manipulating the data in the database, informing of the status of a request or an item of hardware, and searching the database on any physical or other technical characteristic of a component or material. The software structure forces normalization of the data to facilitate inquiries and searches for which users have entered mixed or inconsistent values.

  3. Automated flight test management system

    NASA Technical Reports Server (NTRS)

    Hewett, M. D.; Tartt, D. M.; Agarwal, A.

    1991-01-01

    The Phase 1 development of an automated flight test management system (ATMS) as a component of a rapid prototyping flight research facility for artificial intelligence (AI) based flight concepts is discussed. The ATMS provides a flight engineer with a set of tools that assist in flight test planning, monitoring, and simulation. The system is also capable of controlling an aircraft during flight test by performing closed loop guidance functions, range management, and maneuver-quality monitoring. The ATMS is being used as a prototypical system to develop a flight research facility for AI based flight systems concepts at NASA Ames Dryden.

  4. Bat flight and zoonotic viruses

    USGS Publications Warehouse

    O'Shea, Thomas; Cryan, Paul M.; Cunningham, Andrew A.; Fooks, Anthony R.; Hayman, David T.S.; Luis, Angela D.; Peel, Alison J.; Plowright, Raina K.; Wood, James L.N.

    2014-01-01

    Bats are sources of high viral diversity and high-profile zoonotic viruses worldwide. Although apparently not pathogenic in their reservoir hosts, some viruses from bats severely affect other mammals, including humans. Examples include severe acute respiratory syndrome coronaviruses, Ebola and Marburg viruses, and Nipah and Hendra viruses. Factors underlying high viral diversity in bats are the subject of speculation. We hypothesize that flight, a factor common to all bats but to no other mammals, provides an intensive selective force for coexistence with viral parasites through a daily cycle that elevates metabolism and body temperature analogous to the febrile response in other mammals. On an evolutionary scale, this host–virus interaction might have resulted in the large diversity of zoonotic viruses in bats, possibly through bat viruses adapting to be more tolerant of the fever response and less virulent to their natural hosts.

  5. Bat flight and zoonotic viruses.

    PubMed

    O'Shea, Thomas J; Cryan, Paul M; Cunningham, Andrew A; Fooks, Anthony R; Hayman, David T S; Luis, Angela D; Peel, Alison J; Plowright, Raina K; Wood, James L N

    2014-05-01

    Bats are sources of high viral diversity and high-profile zoonotic viruses worldwide. Although apparently not pathogenic in their reservoir hosts, some viruses from bats severely affect other mammals, including humans. Examples include severe acute respiratory syndrome coronaviruses, Ebola and Marburg viruses, and Nipah and Hendra viruses. Factors underlying high viral diversity in bats are the subject of speculation. We hypothesize that flight, a factor common to all bats but to no other mammals, provides an intensive selective force for coexistence with viral parasites through a daily cycle that elevates metabolism and body temperature analogous to the febrile response in other mammals. On an evolutionary scale, this host-virus interaction might have resulted in the large diversity of zoonotic viruses in bats, possibly through bat viruses adapting to be more tolerant of the fever response and less virulent to their natural hosts.

  6. Flight Dynamics Analysis Branch

    NASA Technical Reports Server (NTRS)

    Stengle, Tom; Flores-Amaya, Felipe

    2000-01-01

    This report summarizes the major activities and accomplishments carried out by the Flight Dynamics Analysis Branch (FDAB), Code 572, in support of flight projects and technology development initiatives in fiscal year 2000. The report is intended to serve as a summary of the type of support carried out by the FDAB, as well as a concise reference of key accomplishments and mission experience derived from the various mission support roles. The primary focus of the FDAB is to provide expertise in the disciplines of flight dynamics, spacecraft trajectory, attitude analysis, and attitude determination and control. The FDAB currently provides support for missions and technology development projects involving NASA, government, university, and private industry.

  7. Columbia's first shakedown flight

    NASA Astrophysics Data System (ADS)

    Bell, Peter M.

    The space shuttle orbiter Columbia, first of the planned fleet of spacecraft in the nation's space transportation system, will liftoff on its first orbital shakedown flight on or about the 10th of April 1981. Launch will be from the NASA Kennedy Space Center Launch Complex 39A, no earlier than 45 minutes after sunrise. Crew for the first orbital flight will be John W. Young, commander, veteran of two Gemini and two Apollo space flights, and U.S. Navy Capt. Robert L. Crippen, pilot. Crippen has not flown in space.

  8. Initial Flight Test of the Production Support Flight Control Computers at NASA Dryden Flight Research Center

    NASA Technical Reports Server (NTRS)

    Carter, John; Stephenson, Mark

    1999-01-01

    The NASA Dryden Flight Research Center has completed the initial flight test of a modified set of F/A-18 flight control computers that gives the aircraft a research control law capability. The production support flight control computers (PSFCC) provide an increased capability for flight research in the control law, handling qualities, and flight systems areas. The PSFCC feature a research flight control processor that is "piggybacked" onto the baseline F/A-18 flight control system. This research processor allows for pilot selection of research control law operation in flight. To validate flight operation, a replication of a standard F/A-18 control law was programmed into the research processor and flight-tested over a limited envelope. This paper provides a brief description of the system, summarizes the initial flight test of the PSFCC, and describes future experiments for the PSFCC.

  9. Perspectives on Highly Adaptive or Morphing Aircraft

    NASA Technical Reports Server (NTRS)

    McGowan, Anna-Maria R.; Vicroy, Dan D.; Busan, Ronald C.; Hahn, Andrew S.

    2009-01-01

    The ability to adapt to different flight conditions has been fundamental to aircraft design since the Wright Brothers first flight. Over a hundred years later, unconventional aircraft adaptability, often called aircraft morphing has become a topic of considerable renewed interest. In the past two decades, this interest has been largely fuelled by advancements in multi-functional or smart materials and structures. However, highly adaptive or morphing aircraft is certainly a cross-discipline challenge that stimulates a wide range of design possibilities. This paper will review some of the history of morphing aircraft including recent research programs and discuss some perspectives on this work.

  10. Red blood cell and iron metabolism during space flight

    NASA Technical Reports Server (NTRS)

    Smith, Scott M.

    2002-01-01

    Space flight anemia is a widely recognized phenomenon in astronauts. Reduction in circulating red blood cells and plasma volume results in a 10% to 15% decrement in circulatory volume. This effect appears to be a normal physiologic adaptation to weightlessness and results from the removal of newly released blood cells from the circulation. Iron availability increases, and (in the few subjects studied) iron stores increase during long-duration space flight. The consequences of these changes are not fully understood.

  11. Human Space Flight

    NASA Technical Reports Server (NTRS)

    Woolford, Barbara; Mount, Frances

    2004-01-01

    The first human space flight, in the early 1960s, was aimed primarily at determining whether humans could indeed survive and function in micro-gravity. Would eating and sleeping be possible? What mental and physical tasks could be performed? Subsequent programs increased the complexity of the tasks the crew performed. Table 1 summarizes the history of U.S. space flight, showing the projects, their dates, crew sizes, and mission durations. With over forty years of experience with human space flight, the emphasis now is on how to design space vehicles, habitats, and missions to produce the greatest returns to human knowledge. What are the roles of the humans in space flight in low earth orbit, on the moon, and in exploring Mars?

  12. CID flight/impact

    NASA Technical Reports Server (NTRS)

    Barber, R.

    1986-01-01

    The planned versus the actual results of the controlled impact demonstration of a transport aircraft are discussed. Remote control systems, site selection, manned flight tests, and wreckage distribution are discussed.

  13. SR-71 Flight

    NASA Video Gallery

    Two SR-71A aircraft were loaned from the U.S. Air Force for use for high-speed, high-altitude research at the NASA Dryden Flight Research Center, Edwards, California. One of them was later returned...

  14. Beta experiment flight report

    NASA Technical Reports Server (NTRS)

    1982-01-01

    A focused laser Doppler velocimeter system was developed for the measurement of atmospheric backscatter (beta) from aerosols at infrared wavelengths. The system was flight tested at several different locations and the results of these tests are summarized.

  15. Integrated flight path planning system and flight control system for unmanned helicopters.

    PubMed

    Jan, Shau Shiun; Lin, Yu Hsiang

    2011-01-01

    This paper focuses on the design of an integrated navigation and guidance system for unmanned helicopters. The integrated navigation system comprises two systems: the Flight Path Planning System (FPPS) and the Flight Control System (FCS). The FPPS finds the shortest flight path by the A-Star (A*) algorithm in an adaptive manner for different flight conditions, and the FPPS can add a forbidden zone to stop the unmanned helicopter from crossing over into dangerous areas. In this paper, the FPPS computation time is reduced by the multi-resolution scheme, and the flight path quality is improved by the path smoothing methods. Meanwhile, the FCS includes the fuzzy inference systems (FISs) based on the fuzzy logic. By using expert knowledge and experience to train the FIS, the controller can operate the unmanned helicopter without dynamic models. The integrated system of the FPPS and the FCS is aimed at providing navigation and guidance to the mission destination and it is implemented by coupling the flight simulation software, X-Plane, and the computing software, MATLAB. Simulations are performed and shown in real time three-dimensional animations. Finally, the integrated system is demonstrated to work successfully in controlling the unmanned helicopter to operate in various terrains of a digital elevation model (DEM).

  16. Integrated Flight Path Planning System and Flight Control System for Unmanned Helicopters

    PubMed Central

    Jan, Shau Shiun; Lin, Yu Hsiang

    2011-01-01

    This paper focuses on the design of an integrated navigation and guidance system for unmanned helicopters. The integrated navigation system comprises two systems: the Flight Path Planning System (FPPS) and the Flight Control System (FCS). The FPPS finds the shortest flight path by the A-Star (A*) algorithm in an adaptive manner for different flight conditions, and the FPPS can add a forbidden zone to stop the unmanned helicopter from crossing over into dangerous areas. In this paper, the FPPS computation time is reduced by the multi-resolution scheme, and the flight path quality is improved by the path smoothing methods. Meanwhile, the FCS includes the fuzzy inference systems (FISs) based on the fuzzy logic. By using expert knowledge and experience to train the FIS, the controller can operate the unmanned helicopter without dynamic models. The integrated system of the FPPS and the FCS is aimed at providing navigation and guidance to the mission destination and it is implemented by coupling the flight simulation software, X-Plane, and the computing software, MATLAB. Simulations are performed and shown in real time three-dimensional animations. Finally, the integrated system is demonstrated to work successfully in controlling the unmanned helicopter to operate in various terrains of a digital elevation model (DEM). PMID:22164029

  17. 1999 Flight Mechanics Symposium

    NASA Technical Reports Server (NTRS)

    Lynch, John P. (Editor)

    1999-01-01

    This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on May 18-20, 1999. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers.

  18. The flight robotics laboratory

    NASA Technical Reports Server (NTRS)

    Tobbe, Patrick A.; Williamson, Marlin J.; Glaese, John R.

    1988-01-01

    The Flight Robotics Laboratory of the Marshall Space Flight Center is described in detail. This facility, containing an eight degree of freedom manipulator, precision air bearing floor, teleoperated motion base, reconfigurable operator's console, and VAX 11/750 computer system, provides simulation capability to study human/system interactions of remote systems. The facility hardware, software and subsequent integration of these components into a real time man-in-the-loop simulation for the evaluation of spacecraft contact proximity and dynamics are described.

  19. A benchmark for fault tolerant flight control evaluation

    NASA Astrophysics Data System (ADS)

    Smaili, H.; Breeman, J.; Lombaerts, T.; Stroosma, O.

    2013-12-01

    A large transport aircraft simulation benchmark (REconfigurable COntrol for Vehicle Emergency Return - RECOVER) has been developed within the GARTEUR (Group for Aeronautical Research and Technology in Europe) Flight Mechanics Action Group 16 (FM-AG(16)) on Fault Tolerant Control (2004 2008) for the integrated evaluation of fault detection and identification (FDI) and reconfigurable flight control strategies. The benchmark includes a suitable set of assessment criteria and failure cases, based on reconstructed accident scenarios, to assess the potential of new adaptive control strategies to improve aircraft survivability. The application of reconstruction and modeling techniques, based on accident flight data, has resulted in high-fidelity nonlinear aircraft and fault models to evaluate new Fault Tolerant Flight Control (FTFC) concepts and their real-time performance to accommodate in-flight failures.

  20. Designing Flight Deck Procedures

    NASA Technical Reports Server (NTRS)

    Degani, Asaf; Wiener, Earl

    2005-01-01

    Three reports address the design of flight-deck procedures and various aspects of human interaction with cockpit systems that have direct impact on flight safety. One report, On the Typography of Flight- Deck Documentation, discusses basic research about typography and the kind of information needed by designers of flight deck documentation. Flight crews reading poorly designed documentation may easily overlook a crucial item on the checklist. The report surveys and summarizes the available literature regarding the design and typographical aspects of printed material. It focuses on typographical factors such as proper typefaces, character height, use of lower- and upper-case characters, line length, and spacing. Graphical aspects such as layout, color coding, fonts, and character contrast are discussed; and several cockpit conditions such as lighting levels and glare are addressed, as well as usage factors such as angular alignment, paper quality, and colors. Most of the insights and recommendations discussed in this report are transferable to paperless cockpit systems of the future and computer-based procedure displays (e.g., "electronic flight bag") in aerospace systems and similar systems that are used in other industries such as medical, nuclear systems, maritime operations, and military systems.

  1. Interprofessional Flight Camp.

    PubMed

    Alfes, Celeste M; Rowe, Amanda S

    2016-01-01

    The Dorothy Ebersbach Academic Center for Flight Nursing in Cleveland, OH, holds an annual flight camp designed for master's degree nursing students in the acute care nurse practitioner program, subspecializing in flight nursing at the Frances Payne Bolton School of Nursing at Case Western Reserve University. The weeklong interprofessional training is also open to any health care provider working in an acute care setting and focuses on critical care updates, trauma, and emergency care within the critical care transport environment. This year, 29 graduate nursing students enrolled in a master's degree program from Puerto Rico attended. Although the emergency department in Puerto Rico sees and cares for trauma patients, there is no formal trauma training program. Furthermore, the country only has 1 rotor wing air medical transport service located at the Puerto Rico Medical Center in San Juan. Flight faculty and graduate teaching assistants spent approximately 9 months planning for their participation in our 13th annual flight camp. Students from Puerto Rico were extremely pleased with the learning experiences at camp and expressed particular interest in having more training time within the helicopter flight simulator.

  2. Magnesium and Space Flight

    PubMed Central

    Smith, Scott M.; Zwart, Sara R.

    2015-01-01

    Magnesium is an essential nutrient for muscle, cardiovascular, and bone health on Earth, and during space flight. We sought to evaluate magnesium status in 43 astronauts (34 male, 9 female; 47 ± 5 years old, mean ± SD) before, during, and after 4–6-month space missions. We also studied individuals participating in a ground analog of space flight (head-down-tilt bed rest; n = 27 (17 male, 10 female), 35 ± 7 years old). We evaluated serum concentration and 24-h urinary excretion of magnesium, along with estimates of tissue magnesium status from sublingual cells. Serum magnesium increased late in flight, while urinary magnesium excretion was higher over the course of 180-day space missions. Urinary magnesium increased during flight but decreased significantly at landing. Neither serum nor urinary magnesium changed during bed rest. For flight and bed rest, significant correlations existed between the area under the curve of serum and urinary magnesium and the change in total body bone mineral content. Tissue magnesium concentration was unchanged after flight and bed rest. Increased excretion of magnesium is likely partially from bone and partially from diet, but importantly, it does not come at the expense of muscle tissue stores. While further study is needed to better understand the implications of these findings for longer space exploration missions, magnesium homeostasis and tissue status seem well maintained during 4–6-month space missions. PMID:26670248

  3. Magnesium and Space Flight.

    PubMed

    Smith, Scott M; Zwart, Sara R

    2015-12-08

    Magnesium is an essential nutrient for muscle, cardiovascular, and bone health on Earth, and during space flight. We sought to evaluate magnesium status in 43 astronauts (34 male, 9 female; 47 ± 5 years old, mean ± SD) before, during, and after 4-6-month space missions. We also studied individuals participating in a ground analog of space flight (head-down-tilt bed rest; n = 27 (17 male, 10 female), 35 ± 7 years old). We evaluated serum concentration and 24-h urinary excretion of magnesium, along with estimates of tissue magnesium status from sublingual cells. Serum magnesium increased late in flight, while urinary magnesium excretion was higher over the course of 180-day space missions. Urinary magnesium increased during flight but decreased significantly at landing. Neither serum nor urinary magnesium changed during bed rest. For flight and bed rest, significant correlations existed between the area under the curve of serum and urinary magnesium and the change in total body bone mineral content. Tissue magnesium concentration was unchanged after flight and bed rest. Increased excretion of magnesium is likely partially from bone and partially from diet, but importantly, it does not come at the expense of muscle tissue stores. While further study is needed to better understand the implications of these findings for longer space exploration missions, magnesium homeostasis and tissue status seem well maintained during 4-6-month space missions.

  4. Magnesium and Space Flight.

    PubMed

    Smith, Scott M; Zwart, Sara R

    2015-12-01

    Magnesium is an essential nutrient for muscle, cardiovascular, and bone health on Earth, and during space flight. We sought to evaluate magnesium status in 43 astronauts (34 male, 9 female; 47 ± 5 years old, mean ± SD) before, during, and after 4-6-month space missions. We also studied individuals participating in a ground analog of space flight (head-down-tilt bed rest; n = 27 (17 male, 10 female), 35 ± 7 years old). We evaluated serum concentration and 24-h urinary excretion of magnesium, along with estimates of tissue magnesium status from sublingual cells. Serum magnesium increased late in flight, while urinary magnesium excretion was higher over the course of 180-day space missions. Urinary magnesium increased during flight but decreased significantly at landing. Neither serum nor urinary magnesium changed during bed rest. For flight and bed rest, significant correlations existed between the area under the curve of serum and urinary magnesium and the change in total body bone mineral content. Tissue magnesium concentration was unchanged after flight and bed rest. Increased excretion of magnesium is likely partially from bone and partially from diet, but importantly, it does not come at the expense of muscle tissue stores. While further study is needed to better understand the implications of these findings for longer space exploration missions, magnesium homeostasis and tissue status seem well maintained during 4-6-month space missions. PMID:26670248

  5. Biomedical results of the Space Shuttle orbital flight test program

    NASA Technical Reports Server (NTRS)

    Pool, S. L.; Nicogossian, A.

    1983-01-01

    On July 4, 1982, the Space Shuttle Columbia landed at Edwards Air Force Base, CA, thus successfully completing the fourth and last in a series of Orbital Flight Tests (OFT) of the Space Transportation System (STS). The primary goal of medical operations support for the OFT was to assure the health and well-being of flight personnel during all phases of the mission. To this end, crew health status was evaluated preflight, inflight, and postflight. Biomedical flight test requirements were completed in the following areas: physiological adaptation to microgravity, cabin acoustical noise, cabin atmospheric evaluation, radiation dosimetry, crew exercise equipment evaluation, and a cardiovascular deconditioning countermeasure assessment.

  6. [Biomedical results of the Space Shuttle Orbital Flight Test Program].

    PubMed

    Pool, S L; Nicogossian, A

    1984-01-01

    On July 4, 1982 the Space Shuttle Columbia landed at Edwards Air Force Base, California, thus successfully completing the fourth and last in a series of Orbital Flight Tests (OFT) of the Space Transportation System (STS). The primary goal of medical operation support for the OFT was to assure the health and well-being of flight personnel during all phases of the mission. To this end, the crew health status was evaluated preflight, inflight and postflight. Biomedical flight test requirements were completed in the following areas: physiological adaptation to microgravity, cabin acoustical noise, cabin atmospheric evaluation, radiation dosimetry, crew exercise equipment evaluation and a cardiovascular deconditioning countermeasure assessment.

  7. Biodynamics--the key to flight.

    PubMed

    Stapp, J P

    1986-10-01

    Biodynamics measures the effects of mechanical force on living tissues. The quantitative relations of mechanical stress factors and biological strain responses of the living body provide criteria for limits of injury threshold, reversible injury, permanently disabling injury, and fatal injury. These criteria are guidelines for aerospace design and performance standards involving human survival in the environment of flight. Below these limits, the effects of mechanical force factors on human performance while acutely or chronically exposed to them in aerial or space flight are crucial. Some can be accumulatively disabling; others can be adapted to over a period of time. Extremes of low-frequency vibration cannot be long endured, while sustained zero gravity in space flight produces mild, transient malaise followed by adaptation in several hours. Aerospace flight biodynamics deals with human reactions to absence of gravity; sustained curvilinear acceleration; sustained acceleration and deceleration (launch and reentry in space flight); single impact force (collisions); low-frequency vibration in the whole human body resonance response range; whole-body tumbling and spinning, as in high-altitude free-fall; acoustical range vibrations; explosive blast in air or water; abrupt decompression, as in cabin pressure failure; static forces in tension, compression, torsion and shear. Biodynamic stress analysis takes into account whole-body responses, particular responses of rigid bone, viscous elastic soft tissues, pneumatic and hydraulic effects of gas and fluids in hollow organs, and displacements of solid organs suspended in body cavities. Accurate and comprehensive results require physical measurements, clinical and laboratory studies before and after exposure, subjective reports of trained volunteer subjects, and objective medical and bioengineering evaluation of results.(ABSTRACT TRUNCATED AT 250 WORDS)

  8. Post Flight Dynamic Analysis Simulation

    NASA Technical Reports Server (NTRS)

    Gregory, B. R.

    1970-01-01

    Digital six-degrees-of-freedom, open loop Saturn 5 first stage flight evaluation simulation program obtains post flight simulation of the launch vehicle using actual flight data as input. Results are compared with measured data. For preflight analysis, the program uses predicted flight data as input.

  9. Neural Networks for Flight Control

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles C.

    1996-01-01

    Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

  10. [Adaptation to new environments: microgravity].

    PubMed

    Serova, L V

    2005-01-01

    Review and analysis of the experiments with Wastar rats in microgravity onboard "Cosmos" biosatellites allows to conclude that adaptive potentials of mammals in space flights lasting up to 1/50 th of their life span are enough for rapid elimination of microgravity-induced metabolic and structural alterations on return to Earth, for maintenance of adequate reactions to acute and chronic stressors in the post-flight period, for normal reproductive function and life span. Consideration is given to individual differences in body responses to the micro-g environment.

  11. Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection.

    PubMed

    Carrieri, Arthur H; Copper, Jack; Owens, David J; Roese, Erik S; Bottiger, Jerold R; Everly, Robert D; Hung, Kevin C

    2010-01-20

    An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance. PMID:20090802

  12. A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

    PubMed Central

    Jung, Jun-Young; Heo, Wonho; Yang, Hyundae; Park, Hyunsub

    2015-01-01

    An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases. PMID:26528986

  13. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    PubMed

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process. PMID:26989410

  14. Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA.

    PubMed

    Heddam, Salim

    2014-08-01

    In this study, a comparison between generalized regression neural network (GRNN) and multiple linear regression (MLR) models is given on the effectiveness of modelling dissolved oxygen (DO) concentration in a river. The two models are developed using hourly experimental data collected from the United States Geological Survey (USGS Station No: 421209121463000 [top]) station at the Klamath River at Railroad Bridge at Lake Ewauna. The input variables used for the two models are water, pH, temperature, electrical conductivity, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), the mean absolute error (MAE), Willmott's index of agreement (d), and correlation coefficient (CC) statistics. Of the two approaches employed, the best fit was obtained using the GRNN model with the four input variables used.

  15. Fixed weight Hopfield Neural Network based on optical implementation of all-optical MZI-XNOR logic gate

    NASA Astrophysics Data System (ADS)

    Nugamesh Mutter, Kussay; Mat Jafri, Mohd Zubir; Abdul Aziz, Azlan

    2010-05-01

    Many researches are conducted to improve Hopfield Neural Network (HNN) performance especially for speed and memory capacity in different approaches. However, there is still a significant scope of developing HNN using Optical Logic Gates. We propose here a new model of HNN based on all-optical XNOR logic gates for real time color image recognition. Firstly, we improved HNN toward optimum learning and converging operations. We considered each unipolar image as a set of small blocks of 3-pixels as vectors for HNN. This enables to save large number of images in the net with best reaching into global minima, and because there are only eight fixed states of weights so that only single iteration performed to construct a vector with stable state at minimum energy. HNN is useless in dealing with data not in bipolar representation. Therefore, HNN failed to work with color images. In RGB bands each represents different values of brightness, for d-bit RGB image it is simply consists of d-layers of unipolar. Each layer is as a single unipolar image for HNN. In addition, the weight matrices with stability of unity at the diagonal perform clear converging in comparison with no self-connecting architecture. Synchronously, each matrix-matrix multiplication operation would run optically in the second part, since we propose an array of all-optical XOR gates, which uses Mach-Zehnder Interferometer (MZI) for neurons setup and a controlling system to distribute timely signals with inverting to achieve XNOR function. The primary operation and simulation of the proposal HNN is demonstrated.

  16. Analysis of bioclimatic time series and their neural network-based classification to characterise drought risk patterns in South Italy.

    PubMed

    Incerti, G; Feoli, E; Salvati, L; Brunetti, A; Giovacchini, A

    2007-03-01

    A new approach to characterise geographical areas with a drought risk index (DRI) is suggested, by applying an artificial neural network (ANN) classifier to bioclimatic time series for which operational temporal units (OtUs) are defined. A climatic database, corresponding to a grid of 8 km x 8 km cells covering the Italian peninsula, was considered. Each cell is described by the time series of seven variables recorded from 1989 to 2000. Sixteen cells were selected according to land cover homogeneity and completeness of the time series data. The periodic components of the time series were calculated by means of the fast Fourier transform (FFT) method. Temporal units corresponding to the period of the sinusoidal functions most related to the data were used as OtUs. The ANN for each OtU calculates a DRI value ranging between -1 and 1. The value is interpretable as the proximity of the OtUs to one of two situations corresponding to minimum and maximum drought risk, respectively. The former set (DRI = -1) is represented by an ideal OtU with minimum values of temperatures and evapo-transpiration, and maximum values of rainfall, normalised difference vegetation index (NDVI) and soil water content. The second set (DRI = 1) is represented by the reciprocal OtU to the former one. The classification of the cells based on DRI time profiles showed that, at the scale used in this work, DRI has no dependence on land cover class, but is related to the location of the cells. The methodology was integrated with GIS (geographic information system) software, and used to show the geographic pattern of DRI in a given area at different periods.

  17. Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

    PubMed

    Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim

    2008-10-01

    In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets.

  18. Consistency and complementarity of different coastal ocean observations: A neural network-based analysis for the German Bight

    NASA Astrophysics Data System (ADS)

    Wahle, K.; Stanev, E. V.

    2011-05-01

    HF radar measurements in the German Bight and their consistency with other available observations were analyzed. First, an empirical orthogonal function (EOF) analysis of the radial component of the surface current measured by one radar was performed. Afterwards, Neural Networks (NNs) were trained to now- and forecast the first five EOFs from tide gauge measurements. The inverse problem, i.e., to forecast a sea level from these EOFs was also solved using NNs. For both problems, the influence of wind measurements on the nowcast/forecast accuracy was quantified. The forecast improves if HF radar data are used in combination with wind data. Analysis of the upscaling potential of HF radar measurements demonstrated that information from one radar station in the German Bight is representative of an area larger than the observational domain and could contribute to correcting information from biased observations or numerical models.

  19. A neural-network based estimator to search for primordial non-Gaussianity in Planck CMB maps

    NASA Astrophysics Data System (ADS)

    Novaes, C. P.; Bernui, A.; Ferreira, I. S.; Wuensche, C. A.

    2015-09-01

    We present an upgraded combined estimator, based on Minkowski Functionals and Neural Networks, with excellent performance in detecting primordial non-Gaussianity in simulated maps that also contain a weighted mixture of Galactic contaminations, besides real pixel's noise from Planck cosmic microwave background radiation data. We rigorously test the efficiency of our estimator considering several plausible scenarios for residual non-Gaussianities in the foreground-cleaned Planck maps, with the intuition to optimize the training procedure of the Neural Network to discriminate between contaminations with primordial and secondary non-Gaussian signatures. We look for constraints of primordial local non-Gaussianity at large angular scales in the foreground-cleaned Planck maps. For the SMICA map we found fNL = 33 ± 23, at 1σ confidence level, in excellent agreement with the WMAP-9yr and Planck results. In addition, for the other three Planck maps we obtain similar constraints with values in the interval fNL in [33, 41], concomitant with the fact that these maps manifest distinct features in reported analyses, like having different pixel's noise intensities.

  20. Prediction of Student's Mood during an Online Test Using Formula-based and Neural Network-based Method

    ERIC Educational Resources Information Center

    Moridis, Christos N.; Economides, Anastasios A.

    2009-01-01

    Building computerized mechanisms that will accurately, immediately and continually recognize a learner's affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence…