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1

Stable adaptive fuzzy control of nonlinear systems  

Microsoft Academic Search

A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided,

Li-Xin Wang

1993-01-01

2

Decomposed fuzzy systems and their application in direct adaptive fuzzy control.  

PubMed

In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure. PMID:25222721

Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

2014-10-01

3

Adaptive fuzzy control of the molten steel level in a strip-casting process  

Microsoft Academic Search

This paper presents an adaptive fuzzy controller with nonlinear compensation and a switching control strategy to regulate the molten steel level of a stripcasting system. The proposed controller adopts an adaptive fuzzy control structure which is reported to be superior in performance to neural-network control techniques. The adaptive fuzzy controller is robust due to its fuzzy representation of the controller,

D. Lee; J. S. Lee; T. Kang

1996-01-01

4

Computation of Parametric Adaptive Fuzzy Controller for Wood Drying System  

NASA Astrophysics Data System (ADS)

The paper reports the computation of parametric adaptive fuzzy controller for used to wood drying system. Parametric of adaptive fuzzy controller is control period system. Control period system is how long time need to hoist of temperature drying or humidity drying if the actuator in on-conditions. The parametric is implemented for control system of wood drying process at prototype chamber with solar is source of energy. The actuator of system is heater, damper and sprayer. From result of measurement, that data were doing to analysis statistic to have the parametric. Whenever the parametric want to implemented with mechanism adaptive. Membership Functions of variable control of system to became something is difficult to have effect to temperature and humidity drying. The result of implemented of adaptive fuzzy control is described in graphic typical. The control system is able to adapt change of humidity drying in system schedule of wood drying system.

Situmorang, Zakarias; Wardoyo, Retantyo; Hartati, Sri; Istiyanto, Jazi Eko

2009-08-01

5

Adaptive Fuzzy Control of a Direct Drive Motor: Experimental Aspects  

NASA Technical Reports Server (NTRS)

This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is experimentally verified. The real-time performance is compared with simulation results.

Medina, E.; Akbarzadeh-T, M.-R.; Kim, Y. T.

1998-01-01

6

Adaptive Fuzzy Control of a Direct Drive Motor  

NASA Technical Reports Server (NTRS)

This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is verified by simulation results.

Medina, E.; Kim, Y. T.; Akbaradeh-T., M. -R.

1997-01-01

7

Adaptive learning fuzzy control of a mobile robot.  

National Technical Information Service (NTIS)

In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learn...

A. Tsukada, K. Suzuki, Y. Fujii, Y. Shinohara

1989-01-01

8

Adaptive control of redundant multilink robot using fuzzy logic  

NASA Astrophysics Data System (ADS)

A new approach to fuzzy distance and restriction measures is used to obtain the appropriate orientations of the links for avoiding obstacles in the robot trajectories. This approach eliminates the classical task of solving highly coupled, nonlinear equations describing the ill- posed inverse problems of multilink robot motion at a much less demanding computational time. Such clear advantage of fuzzy logic based adaptive controller are illustrated by simulation results of guidance of a multilink robot in target positioning and trajectories tracking. The simulation results involve a three-link robot arm with capability of moving from one position to any desired position and tracking a defined trajectories accurately. A modified fuzzy rule based distance measure allows the robot to follow trajectories within hitting the obstacles in the path. The simulation results indicate the advantage of fuzzy logic based adaptive controllers in multiple criteria decision-making tasks.

Su, X.; Mitra, Sunanda

1993-12-01

9

Adaptive Fuzzy Control Technology for Automatic Oil Drilling System  

Microsoft Academic Search

The paper proposes an adaptive fuzzy control method for the new oil rigs of ZJ30DB series with AC frequency converters. The assistant motor and its gearing work as executing system. Considering the time-delay, time-variability and nonlinearity of controlled objects, we design a double-loop control system in order to control the drill pressure tending to an expected value. Because it is

Qingjie Zhao; Fasheng Wang; Wei Wang; Hongbin Deng

2007-01-01

10

Evaluation of Adaptive Fuzzy Control Backoff Schemes in Policing Mechanisms  

Microsoft Academic Search

Fuzzy controllers are the most important applications of fuzzy theory, which provides an efficient method to handle inexact information as a basic of reasoning. Fuzzy logic converts knowledge which is expressed in an uncertain form to an exact algorithm. In fuzzy control, the controller can be represented by if-then-else rules. The controller is processing exact input data and is producing

Somchai Lekcharoen; Chanintorn Jittawiriyanukoon

11

Neural and Fuzzy Adaptive Control of Induction Motor Drives  

NASA Astrophysics Data System (ADS)

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller.

Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

2008-06-01

12

Adaptive neuro-fuzzy control of systems with time delay  

Microsoft Academic Search

The authors present an adaptive fuzzy logic controller, which learns about the dynamic of the system under control from an online neural network (NN) identification algorithm. The identification is based on the estimation of parameters of a First-Order-Plus-Dead-Time (FOPDT) model. The outputs of the NN are three parameters: gain, apparent time delay and the dominant time constant. By combining this

H. F. Ho; Y. K. Wong; A. B. Rad

2001-01-01

13

Self-Adaptive Tuning of Fuzzy PID Control of PV Grid-Connected Inverter  

Microsoft Academic Search

The control of photovoltaic grid-connected inverter introduced self-adaptive tuning of fuzzy PID control in this paper, via measured the parameter of object under controls, used fuzzy consequence, comes true to parametric PID real time adjustment, achieved best control effect. By simulated demonstration of Matlab\\/Simulink, self-adaptive tuning of fuzzy PID controlling having improved the PV grid-connected inverter system stability, has improved

Xiaojin Yan; Yan Pan; Jinhao Sun; Yezi Li; Jianling Qi

2009-01-01

14

Fuzzy adaptive proportional-derivative (PD) disturbance accommodation control (DAC) of a bank of heat exchangers  

Microsoft Academic Search

The author shows a controller design in which a fuzzy proportional plus derivative (PD) controller augments an adaptive disturbance accommodating controller (DAC) applied to a bank of heat exchangers. The fuzzy PD uses simple rules to enhance the adaptive DAC to improve setpoint regulation. To illustrate the concept, this method is applied to a simulation of the internal thermal control

Steve Rogers

1991-01-01

15

Fuzzy adaptive process control of resistance spot welding with a current reference model  

Microsoft Academic Search

This paper describes a fuzzy adaptive process control scheme for resistance spot welding and proposes a new method identifying dynamic welding resistance to estimate the spot welding process. An optimal current reference model is also founded, which would modify its output according to the stages in the spot welding process. Then a fuzzy adaptive real time process control system is

Xingqiao Chen; K. Araki

1997-01-01

16

A New Robust Adaptive-Fuzzy Control Method Applied to Quadrotor Helicopter Stabilization  

Microsoft Academic Search

A new method for adaptive-fuzzy control achieves stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance. Techniques traditionally used in adaptive control for robust parameter updates may not be sufficient for fuzzy schemes. In particular, e-modification may result in the fuzzy-membership centers drifting to large values when persistent oscillations are present in the input. These large values

C. Coza; C. J. B. Macnab

2006-01-01

17

Application of Adaptive Neuro Fuzzy Inference System (ANFIS) In Implementing of New CMOS Fuzzy Logic Controller (FLC) Chip  

NASA Astrophysics Data System (ADS)

In this paper, we present away of using Anfis architecture to implement a new fuzzy logic controller chip. Anfis which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data makes fuzzy system to learn. This training is given from a standard response of the system and membership functions are suitably modified. For adaptive Anfis based fuzzy controller and its circuit design, we propose new circuits for implementing each controller block, and illustrate the test results and control surface of Anfis controller along with CMOS fuzzy logic controller using Matlab and Hspice software respectively. For implementing controller according to the Anfis training, we proposed new and improved integrated circuits which consist of Fuzzifier, Min operator and Multiplier/Divider. The control surfaces of controller are obtained by using Anfis training and simulation results of integrated circuits in less than 0.075 mm2 area in 0.35 ?m CMOS standard technology.

Aminifar, S.; Yosefi, Gh.

2007-09-01

18

Torque-ripple minimization in switched reluctance motors using adaptive fuzzy control  

Microsoft Academic Search

An adaptive fuzzy control scheme for the torque-ripple minimization of switched reluctance machines is presented. The fuzzy parameters are initially chosen randomly and then adjusted to optimize the control. The controller produces smooth torque up to the motor base speed. The torque is generated over the maximum positive torque-producing region of a phase. This increases the torque density and avoids

S. Mir; M. E. Elbuluk; I. Husain

1999-01-01

19

Application of adaptive fuzzy control technology to pressure control of a pressurizer  

NASA Astrophysics Data System (ADS)

A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary coolant within allowed range because the sharp change of coolant pressure affects the security of reactor, therefor, the study of pressurizer’s pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for pressure control of a presurizer in a nuclear power plant. The controller can on-line tune fuzzy control rules and parameters by self-learning in the actual control process, which possesses the way of thinking like human to make a decision. The simulation results for a pressurized water reactor plant show that the adaptive fuzzy controller has optimum and intelligent characteristics, which prove the controller is effective.

Yang, Ben-Kun; Bian, Xin-Qian; Guo, Wei-Lai

2005-03-01

20

A New Method for Adaptive Control of Non-Linear Plants Using Type2 Fuzzy Logic and Neural Networks  

Microsoft Academic Search

We describe in this paper adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks. First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A specific non-linear plant

Patricia Melin; Oscar Castillo

2004-01-01

21

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control  

NASA Technical Reports Server (NTRS)

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Kopasakis, George

1997-01-01

22

Adaptive fuzzy-neural-network control for maglev transportation system.  

PubMed

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. PMID:18269938

Wai, Rong-Jong; Lee, Jeng-Dao

2008-01-01

23

Rate-adaptive pacemaker controlled by motion and respiratory rate using neuro-fuzzy algorithm.  

PubMed

Rate-adaptive pacemakers use information from sensors to change the rate of heart stimulation. Until now, fuzzy-pacemaker algorithms have been used to combine inputs from sensors to improve heart rate control, but they have been difficult to implement. In this paper, a pacemaker algorithm which controlled heart rate adaptively by motion and respiratory rate was studied. After chronotropic assessment exercise protocol (CAEP) tests were performed to collect activity and respiratory rate signals, the intrinsic heart rate was inferred from these two signals by a neuro-fuzzy method. For 10 subjects the heart rate inference, using the neuro-fuzzy algorithm, gave 52.4% improved accuracy in comparison with the normal fuzzy table look-up method. The neuro-fuzzy method was applied to a real pacemaker by reduced mapping of the neuro-fuzzy look-up table. PMID:11804178

Shin, J W; Yoon, J H; Yoon, Y R

2001-11-01

24

Real-time neuro-fuzzy systems for adaptive control of musical processes  

NASA Astrophysics Data System (ADS)

We have added a real-time interactive fuzzy reasoning system and neural network simulator to the MAX real-time music programming language. This environment allows us to quickly prototype and experiment with Neural, Fuzzy, and Neuro-Fuzzy systems for control of real- time musical processes. In this paper we introduce our tools and discuss musical contexts that call for the adaptive and generalization capabilities of these systems.

Lee, Michael; Wessel, David

1993-12-01

25

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction  

Microsoft Academic Search

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the

Vincenzo Galdi; Antonio Piccolo; Pierluigi Siano

2008-01-01

26

Generalized projective synchronization of the fractional-order chaotic system using adaptive fuzzy sliding mode control  

NASA Astrophysics Data System (ADS)

An adaptive fuzzy sliding mode strategy is developed for the generalized projective synchronization of a fractional-order chaotic system, where the slave system is not necessarily known in advance. Based on the designed adaptive update laws and the linear feedback method, the adaptive fuzzy sliding controllers are proposed via the fuzzy design, and the strength of the designed controllers can be adaptively adjusted according to the external disturbances. Based on the Lyapunov stability theorem, the stability and the robustness of the controlled system are proved theoretically. Numerical simulations further support the theoretical results of the paper and demonstrate the efficiency of the proposed method. Moreover, it is revealed that the proposed method allows us to manipulate arbitrarily the response dynamics of the slave system by adjusting the desired scaling factor ?i and the desired translating factor ?i, which may be used in a channel-independent chaotic secure communication.

Wang, Li-Ming; Tang, Yong-Guang; Chai, Yong-Quan; Wu, Feng

2014-10-01

27

Predictive Adaptive Control of Nonlinear Multivariable Systems Using Fuzzy CMAC  

Microsoft Academic Search

CMAC computational model that is based on the cerebellum structure is known as a Neural Network with high computation and learning speed. Fuzzy CMAC, by introducing fuzzy reasoning to CMAC, converts it from a black box to a white box whose performance can be interpreted using fuzzy rules. Fuzzy CMAC compared to CMAC has higher approximation and modeling capability and

Kamran Mohajeri; Mohammad Teshnehlab; Mohammad Ali Nekoui; Bijan Moaveni

2008-01-01

28

Adaptive neuro-fuzzy control of ionic polymer metal composite actuators  

NASA Astrophysics Data System (ADS)

An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation.

Thinh, Nguyen Truong; Yang, Young-Soo; Oh, Il-Kwon

2009-06-01

29

Adaptive Fuzzy Decentralized Control for a Class of Uncertain Large-Scale Time-Delay Nonlinear Systems  

Microsoft Academic Search

In this paper, adaptive fuzzy decentralized control is presented for a class of uncertain large-scale time-delay nonlinear systems. By employing the fuzzy logic systems and the technique of delay replacement, the iterative backstep- ping design approach can be carried out, and the common restrictions of the unknown delay signals and the unknown interconnections are removed. Furthermore, the designed fuzzy decentralized

Tao Guo; Jun-ying Zhang

2008-01-01

30

Design of sewage treatment system by applying fuzzy adaptive PID controller  

NASA Astrophysics Data System (ADS)

In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.

Jin, Liang-Ping; Li, Hong-Chan

2013-03-01

31

Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory  

NASA Astrophysics Data System (ADS)

An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H? tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H? tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

Rigatos, Gerasimos G.

2013-10-01

32

Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy  

E-print Network

energy demand. The mathematical modeling and simulation of the photovoltaic system is implementedMaximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS) like ANFIS. This paper presents Maximum Power Point Tracking Control for Photovoltaic System Using

Paris-Sud XI, Université de

33

Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper  

NASA Astrophysics Data System (ADS)

This paper presents a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological (MR) fluid damper in order to validate the effectiveness of the control performance. An interval type 2 fuzzy model is built, and then combined with modified adaptive control to achieve the desired damping force. In the formulation of the new adaptive controller, an enhanced iterative algorithm is integrated with the fuzzy model to decrease the time of calculation (D Wu 2013 IEEE Trans. Fuzzy Syst. 21 80-99) and the control algorithm is synthesized based on the {{H}^{\\infty }} tracking technique. In addition, for the verification of good control performance of the proposed controller, a cylindrical MR damper which can be applied to the vibration control of a washing machine is designed and manufactured. For the operating fluid, a recently developed plate-like particle-based MR fluid is used instead of a conventional MR fluid featuring spherical particles. To highlight the control performance of the proposed controller, two existing adaptive fuzzy control algorithms proposed by other researchers are adopted and altered for a comparative study. It is demonstrated from both simulation and experiment that the proposed new adaptive controller shows better performance of damping force control in terms of response time and tracking accuracy than the existing approaches.

Phu, Do Xuan; Shah, Kruti; Choi, Seung-Bok

2014-06-01

34

Flatness-based embedded adaptive fuzzy control of spark ignited engines  

NASA Astrophysics Data System (ADS)

The paper proposes a differential flatness theory-based adaptive fuzzy controller for spark-ignited (SI) engines. The system's dynamic model is considered to be completely unknown. By applying a change of variables (diffeomorphism) that is based on differential flatness theory the engine's dynamic model is written in the linear canonical (Brunovsky) form. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. These nonlinear terms are approximated with the use of neuro-fuzzy networks while a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. Moreover, using Lyapunov stability analysis it is shown that the adaptive fuzzy control scheme succeeds H? tracking performance, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.

Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

2014-10-01

35

Robust Hinfinity control of multiple time-delay uncertain nonlinear system using fuzzy model and adaptive neural network  

Microsoft Academic Search

In this paper, a robust control method which combines fuzzy model-based control with adaptive neural network control is presented for a class of uncertain nonlinear system with multiple time-delay. The fuzzy T-S model with multiple time-delay is adopted for the approximate modeling of the nonlinear system with some unknown uncertainties, and fuzzy-model-based H? control law is designed by means of

Shousong Hu; Ya Liu

2004-01-01

36

Development of an adaptive neuro-fuzzy method for supply air pressure control in HVAC system  

Microsoft Academic Search

An adaptive neuro-fuzzy (ANF) method is developed for the supply air pressure control loop of a heating, ventilation and air-conditioning (HVAC) system. Although a well-tuned PID controller performs well around normal working points, its tolerance to process parameter variations is severely affected due to the nature of PID controllers. The ANF controller developed overcomes this weakness. The controller design involves

Wu Jian; Cai Wenjian

2000-01-01

37

Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines  

NASA Astrophysics Data System (ADS)

In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H? tracking performance.

Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

2014-10-01

38

Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.  

PubMed

In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method. PMID:23924413

Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

2013-10-01

39

Adaptive enhanced fuzzy sliding-mode control for electrical servo drive  

Microsoft Academic Search

The design and properties of an adaptive enhanced fuzzy sliding-mode control (AEFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands are addressed in this study. A newly designed EFSMC system, in which a translation-width idea is embedded into the FSMC, is introduced initially. Moreover, to confront the uncertainties existed in practical applications, an adaptive tuner,

Rong-Jong Wai; Kuo-Ho Su

2006-01-01

40

Comparative study of artificial intelligence-based building thermal control methods – Application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network  

Microsoft Academic Search

This study’s aim is to develop diverse Artificial Intelligence-based (AI-based) thermal control logics and to compare their performances for identifying potentials as an advanced thermal control method in buildings. Towards that aim, three AI-based control logics have been developed: i) Fuzzy-based control; ii) ANFIS-based (Adaptive Neuro-Fuzzy Inference System-based) control; and iii) ANN-based (Artificial Neural Network-based) control. The last-mentioned two were

Jin Woo Moon; Sung Kwon Jung; Youngchul Kim; Seung-Hoon Han

2011-01-01

41

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

NASA Astrophysics Data System (ADS)

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.

Wade, Robert L.; Walker, Gregory W.

1996-05-01

42

Intelligent fuzzy supervisory control for distillation columns  

E-print Network

for dynamically adapting the models to achieve tight composition control. Simple control techniques do not exist for model adaptation in MIMO systems. This thesis will outline a fuzzy supervisory controller based on fuzzy logic and show that control performance...

Santhanam, Srinivasan

2012-06-07

43

Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks  

NASA Astrophysics Data System (ADS)

With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

Wu, Zhengping; Wu, Hao

44

State observer based robust adaptive fuzzy controller for nonlinear uncertain and perturbed systems.  

PubMed

A robust adaptive fuzzy controller, based on a state observer, for a nonlinear uncertain and perturbed system is presented. The state observer is introduced to resolve the problem of the unavailability of the state variables. Two control signals are added to a basic state feedback control law, deduced from a nominal model, to guarantee the tracking performance in the presence of structural uncertainties and external disturbances. The first control signal is computed from an adaptive fuzzy system and eliminates the effect of structural uncertainties and estimation errors. Updating the adjustable parameters is ensured by a PID law to obtain a fast convergence. Robustness of the closed-loop system is guaranteed by an H infinity supervisor computed from a Riccati type equation. Simulation example is presented to show the efficiency of the proposed method. PMID:15376841

Hamzaoui, Abdelaziz; Essounbouli, Najib; Benmahammed, Khier; Zaytoon, Jannan

2004-04-01

45

L?-gain adaptive fuzzy fault accommodation control design for nonlinear time-delay systems.  

PubMed

In this paper, an adaptive fuzzy fault accommodation (FA) control design with a guaranteed L(?)-gain performance is developed for a class of nonlinear time-delay systems with persistent bounded disturbances. Using the Lyapunov technique and the Razumikhin-type lemma, the existence condition of the L(?) -gain adaptive fuzzy FA controllers is provided in terms of linear matrix inequalities (LMIs). In the proposed FA scheme, a fuzzy logic system is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown fault function; a continuous-state feedback control strategy is adopted for the control design to avoid the undesirable chattering phenomenon. The resulting FA controllers can ensure that every response of the closed-loop system is uniformly ultimately bounded with a guaranteed L(?)-gain performance in the presence of a fault. Moreover, by the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(?)-gain. Finally, the achieved simulation results on the FA control of a continuous stirred tank reactor (CSTR) show the effectiveness of the proposed design procedure. PMID:21177158

Wu, Huai-Ning; Qiang, Xiao-Hong; Guo, Lei

2011-06-01

46

Adaptive Neuro-Fuzzy Control of Systems with Unknown Time Delay  

Microsoft Academic Search

We present an adaptive fuzzy logic controller, which learns a lower-order model of the system via an on-line Neural Network (NN) identification algorithm. The identification is based on the estimation of parameters of a First-Order-Plus-Dead-Time (FOPDT) model. The outputs of the NN are three parameters: gain, apparent time delay and the dominant time constant. By combining this algorithm with a

F. H. Ho; A. B. Rad; Y. K. Wong; W. L. Lo

47

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.  

PubMed

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method. PMID:25273215

Luo, Shaohua

2014-09-01

48

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain  

NASA Astrophysics Data System (ADS)

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

Luo, Shaohua

2014-09-01

49

Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods  

SciTech Connect

This paper describes the graphical simulation of a traffic environment. The environment includes streets leading to an intersection, the intersection, vehicle traffic, and signal lights in the intersection controlled by different methods. The simulation allows for the study of parameters affecting traffic environments and the study of different control strategies for traffic signal lights, including conventional, fuzzy, and adaptive control methods. Realistic traffic environments are simulated including a cross intersection, with one or more lanes of traffic in each direction, with and without turn lanes. Vehicle traffic patterns are a mixture of cars going straight and making right or left turns. The free velocities of vehicles follow a normal distribution with a mean of the ``posted`` speed limit. Actual velocities depend on such factors as the proximity and velocity of surrounding traffic, approaches to intersections, and human response time. The simulation proves the be a useful tool for evaluating controller methods. Preliminary results show that larger quantities of traffic are ``handled`` by fuzzy control methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzy control versus conventional control.

Bisset, K.R.; Kelsey, R.L.

1992-06-01

50

Simulation of traffic flow and control using conventional, fuzzy, and adaptive methods  

SciTech Connect

This paper describes the graphical simulation of a traffic environment. The environment includes streets leading to an intersection, the intersection, vehicle traffic, and signal lights in the intersection controlled by different methods. The simulation allows for the study of parameters affecting traffic environments and the study of different control strategies for traffic signal lights, including conventional, fuzzy, and adaptive control methods. Realistic traffic environments are simulated including a cross intersection, with one or more lanes of traffic in each direction, with and without turn lanes. Vehicle traffic patterns are a mixture of cars going straight and making right or left turns. The free velocities of vehicles follow a normal distribution with a mean of the posted'' speed limit. Actual velocities depend on such factors as the proximity and velocity of surrounding traffic, approaches to intersections, and human response time. The simulation proves the be a useful tool for evaluating controller methods. Preliminary results show that larger quantities of traffic are handled'' by fuzzy control methods then by conventional control methods. Also, the average time spent waiting in traffic decreases with the use of fuzzy control versus conventional control.

Bisset, K.R.; Kelsey, R.L.

1992-01-01

51

Adaptive fuzzy control with output feedback for H infinity tracking of SISO nonlinear systems.  

PubMed

Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the proposed controller are (i) to transform the nonlinear control problem into a regulation problem through suitable output feedback, (ii) to design a state observer for the estimation of the non-measurable elements of the system's state vector, (iii) to design neuro-fuzzy approximators that receive as inputs the parameters of the reconstructed state vector and give as output an estimation of the system's unknown dynamics, (iv) to use an H(infinity) control term for the compensation of external disturbances and modelling errors, (v) to use Lyapunov stability analysis in order to find the learning law for the neuro-fuzzy approximators, and a supervisory control term for disturbance and modelling error rejection. The control scheme is tested in the cart-pole balancing problem and in a DC-motor model. PMID:18763730

Rigatos, Gerasimos G

2008-08-01

52

Automation of a portable extracorporeal circulatory support system with adaptive fuzzy controllers.  

PubMed

The presented work relates to the procedure followed for the automation of a portable extracorporeal circulatory support system. Such a device may help increase the chances of survival after suffering from cardiogenic shock outside the hospital, additionally a controller can provide of optimal organ perfusion, while reducing the workload of the operator. Animal experiments were carried out for the acquisition of haemodynamic behaviour of the body under extracorporeal circulation. A mathematical model was constructed based on the experimental data, including a cardiovascular model, gas exchange and the administration of medication. As the base of the controller fuzzy logic was used allowing the easy integration of knowledge from trained perfusionists, an adaptive mechanism was included to adapt to the patient's individual response. Initial simulations show the effectiveness of the controller and the improvements of perfusion after adaptation. PMID:24894032

Mendoza García, A; Krane, M; Baumgartner, B; Sprunk, N; Schreiber, U; Eichhorn, S; Lange, R; Knoll, A

2014-08-01

53

Fractional fuzzy adaptive sliding-mode control of a 2-DOF direct-drive robot arm.  

PubMed

This paper presents a novel parameter adjustment scheme to improve the robustness of fuzzy sliding-mode control achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) architecture. The proposed scheme utilizes fractional-order integration in the parameter tuning stage. The controller parameters are tuned such that the system under control is driven toward the sliding regime in the traditional sense. After a comparison with the classical integer-order counterpart, it is seen that the control system with the proposed adaptation scheme displays better tracking performance, and a very high degree of robustness and insensitivity to disturbances are observed. The claims are justified through some simulations utilizing the dynamic model of a 2-DOF direct-drive robot arm. Overall, the contribution of this paper is to demonstrate that the response of the system under control is significantly better for the fractional-order integration exploited in the parameter adaptation stage than that for the classical integer-order integration. PMID:19022726

Efe, Mehmet Onder

2008-12-01

54

Neuro-fuzzy modeling and control  

Microsoft Academic Search

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce

JYH-SHING ROGER JANG; Chuen-Tsai Sun

1995-01-01

55

The application of fuzzy-PID and multi-neuron adaptive PID control algorithm in the control of warp tension  

Microsoft Academic Search

For the purpose of control warp tension of rapier loom and prevent negative impacts on the quality of the fabric by reasons of the excessive fluctuation. This paper took let-off system of SAURER400 rapier loom as investigated subject and laid out the application of fuzzy-PID and multi-neuron adaptive PID control method. This paper gone through with a simulation by use

Lei Li; Jiancheng Yang; Yongli Zhao; Yan Liu; Liangchao Cong

2010-01-01

56

Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System  

Microsoft Academic Search

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a

Rong-jong Wai; Jeng-dao Lee

2008-01-01

57

Adaptive Control Schemes for Discrete-Time T–S Fuzzy Systems With Unknown Parameters and Actuator Failures  

Microsoft Academic Search

This paper develops a new solution framework for adaptive output feedback fuzzy control systems aimed at effectively dealing with nonlinear systems with multiple input–multiple output (MIMO) delays and in the presence of dynamics and actuator failure uncertainties. Takagi–Sugeno (T–S) fuzzy systems are employed to represent nonlinear systems, which have desired capacity for dynamic system approximation with parametric and structural properties

Ruiyun Qi; Gang Tao; Bin Jiang; Chang Tan

2012-01-01

58

Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems  

NASA Astrophysics Data System (ADS)

This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.

Shieh, M.-Y.; Chang, K.-H.; Lia, Y.-S.

2008-02-01

59

A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes.  

PubMed

We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities. PMID:24140160

Savran, Aydogan; Kahraman, Gokalp

2014-03-01

60

494 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 9, NO. 3, MAY 2001 Stable Fault-Tolerant Adaptive Fuzzy/Neural Control  

E-print Network

-Tolerant Adaptive Fuzzy/Neural Control for a Turbine Engine Yixin Diao and Kevin M. Passino, Senior Member, IEEE Abstract--Stimulated by the growing demand for improving the reliability and performance of systems, fault learning structure in the form of Takagi­Sugeno fuzzy systems. Afterwards, the fault-tolerant con- trol

61

Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm.  

PubMed

As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047

Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter

2012-07-01

62

A neural fuzzy controller learning by fuzzy error propagation  

NASA Technical Reports Server (NTRS)

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Nauck, Detlef; Kruse, Rudolf

1992-01-01

63

Adaptive fuzzy backstepping output feedback control for a class of MIMO time-delay nonlinear systems based on high-gain observer  

Microsoft Academic Search

In this paper, an adaptive fuzzy backstepping output feedback control approach is developed for a class of multiinput and\\u000a multioutput (MIMO) nonlinear systems with time delays and immeasurable states. Fuzzy logic systems are employed to approximate\\u000a the unknown nonlinear functions, and an adaptive fuzzy high-gain observer is developed to estimate the unmeasured states.\\u000a Using the designed high-gain observer, and combining

Yongming Li; Chang’e Ren; Shaocheng Tong

64

Model reference adaptive fuzzy sliding mode control of time-delay uncertain nonlinear systems with input containing sector nonlinearities and dead-zone  

Microsoft Academic Search

In this paper, the design of a model reference adaptive fuzzy sliding mode controller (MRAFSMC) for a class of time-delay uncertain nonlinear systems with input containing sector nonlinearities and dead-zone is investigated. First, an adaptive fuzzy technique is applied to estimate the bound of the lumped uncertainties. Next, based on the Lyapunov stability theorem, a MRAFSMC is developed to solve

Chung-chun Kung; Shuo-chieh Chang

2007-01-01

65

Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network.  

PubMed

In this paper, a direct adaptive iterative learning control (DAILC) based on a new output-recurrent fuzzy neural network (ORFNN) is presented for a class of repeatable nonlinear systems with unknown nonlinearities and variable initial resetting errors. In order to overcome the design difficulty due to initial state errors at the beginning of each iteration, a concept of time-varying boundary layer is employed to construct an error equation. The learning controller is then designed by using the given ORFNN to approximate an optimal equivalent controller. Some auxiliary control components are applied to eliminate approximation error and ensure learning convergence. Since the optimal ORFNN parameters for a best approximation are generally unavailable, an adaptive algorithm with projection mechanism is derived to update all the consequent, premise, and recurrent parameters during iteration processes. Only one network is required to design the ORFNN-based DAILC and the plant nonlinearities, especially the nonlinear input gain, are allowed to be totally unknown. Based on a Lyapunov-like analysis, we show that all adjustable parameters and internal signals remain bounded for all iterations. Furthermore, the norm of state tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity. Finally, iterative learning control of two nonlinear systems, inverted pendulum system and Chua's chaotic circuit, are performed to verify the tracking performance of the proposed learning scheme. PMID:15484908

Wang, Ying-Chung; Chien, Chiang-Ju; Teng, Ching-Cheng

2004-06-01

66

Adaptive robust neural fuzzy control of uncertain systems: A Lyapunov theory approach  

Microsoft Academic Search

The objective of this research was to develop effective control strategies for uncertain nonlinear dynamical systems. In the first stage of the research, neural fuzzy controllers were proposed. Genetic algorithms were employed to design and fine-tune the proposed neural fuzzy controllers, which then were tested on an anti-lock brake system model and a ground vehicle. ^ Training or fine-tuning of

Yonggon Lee

2003-01-01

67

Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.  

PubMed

The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects. PMID:22256169

Favieiro, Gabriela W; Balbinot, Alexandre

2011-01-01

68

An adaptive fuzzy-synchronous machine stabilizer  

Microsoft Academic Search

Describes an adaptive fuzzy-synchronous machine power system stabilizer (PSS) that behaves like a proportional integral derivative (PID) controller. The implemented adaptive technique predicts tracking-error divergence and makes online adjustments to the controller gain parameters in order to obtain a faster regulation of the error signal. The proposed PSS is less sensitive to the quality of expert knowledge, and thus is

Fouad Mrad; Sami Karaki; Bassel Copti

2000-01-01

69

Adaptive neuro-fuzzy sliding mode control of multi-joint movement using intraspinal microstimulation.  

PubMed

During the last decade, intraspinal microstimulation (ISMS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoration of a desired functional limb movement through the use of ISMS is the development of a robust control strategy for determining the stimulation patterns. Accurate and stable control of limbs by functional intraspinal microstimulation is a very difficult task because neuromusculoskeletal systems have significant nonlinearity, time variability, large latency and time constant, and muscle fatigue. Furthermore, the controller must be able to compensate the effect of the dynamic interaction between motor neuron pools and electrode sites during ISMS. In this paper, we present a robust strategy for multi-joint control through ISMS in which the system parameters are adapted online and the controller requires no offline training phase. The method is based on the combination of sliding mode control with fuzzy logic and neural control. Extensive experiments on six rats are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed method. Despite the complexity of the spinal neuronal networks, our results show that the proposed strategy could provide accurate tracking control with fast convergence and could generate control signals to compensate for the effects of muscle fatigue. PMID:22711783

Asadi, Ali-Reza; Erfanian, Abbas

2012-07-01

70

Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers  

NASA Technical Reports Server (NTRS)

In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.

Yuan, Bo; Klir, George J.

1997-01-01

71

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control  

NASA Astrophysics Data System (ADS)

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

72

Adaptive fuzzy system for 3-D vision  

NASA Technical Reports Server (NTRS)

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Mitra, Sunanda

1993-01-01

73

Using Adaptive Fuzzy-Neural Control to Minimize Response Time in Cluster-Based Web Systems  

Microsoft Academic Search

\\u000a We have developed content-aware request distribution algorithm called FARD which is a client-and-server-aware, dynamic and\\u000a adaptive distribution policy in cluster-based Web systems. It assigns each incoming request to the server with the least expected\\u000a response time. To estimate the expected response times it uses the fuzzy estimation mechanism. The system is adaptive as it\\u000a uses a neural network learning ability

Leszek Borzemski; Krzysztof Zatwarnicki

2005-01-01

74

Fuzzy neural traffic control and forecasting  

Microsoft Academic Search

Fuzzy systems can be used to represent human knowledge. Traffic technology is a science where this property of fuzzy logic can be very well adapted because it is hard to make mathematical models due to human influences and complex connections between input parameters. One example of the use of fuzzy logic in traffic control is in highway speed control systems.

Hans Hellendoorn; Richard Baudrexl

1995-01-01

75

Active pneumatic vibration control by using pressure and velocity measurements and adaptive fuzzy sliding-mode controller.  

PubMed

This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control. PMID:23820746

Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei

2013-01-01

76

Active Pneumatic Vibration Control by Using Pressure and Velocity Measurements and Adaptive Fuzzy Sliding-Mode Controller  

PubMed Central

This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control. PMID:23820746

Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei

2013-01-01

77

Design of Ship Main Engine Speed Controller Based on Fuzzy Adaptive Active Disturbance Rejection Technique  

Microsoft Academic Search

Applying mathematics model of nonlinear ship main engine control and the wave disturbances to the design of electronic governor, and considering the uncertainty of model parameters and the characteristics of servo-system makes the model have the unmatched uncertainty correspondingly. In order to solve the difficulty, an active disturbance rejection nonlinear control strategy is proposed, and the fuzzy controller is used

Weigang Pan; Feng Chen

2010-01-01

78

Decentralized direct adaptive Fuzzy-Neural control of an anaerobic digestion bioprocess plant  

Microsoft Academic Search

The paper proposed to use Fuzzy-Neural Multi-Model (FNMM) identification and control system for decentralized control of distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the

Ieroham S. Baruch; Rosalba Galvan-Guerra

2008-01-01

79

An Adaptive Fuzzy Control Algorithm for Model-Independent Active Vibration Damping of Flexible Beam-Like Structures  

Microsoft Academic Search

The present study deals with an AFCA (Adaptive Fuzzy Control Algorithm) for an Euler-Bemoulli approximation of a two-dimensional version of a cantilever beam-like orthogonal tetrahedral space truss. Transient disturbances, modeled as a unit impulse, excite all the modes of the beam. The resulting transverse displacement at the free end of the beam and its corresponding rate are observed by sensors

Kelly Cohen; Tanchum Weller; Joseph Levitas; Haim Abramovich

1996-01-01

80

Adaptive genetic operators based on coevolution with fuzzy behaviors  

Microsoft Academic Search

This paper presents a technique for adapting control parameter settings associated with genetic operators. Its principal features are: 1) the adaptation takes place at the individual level by means of fuzzy logic controllers (FLCs) and 2) the fuzzy rule bases used by the FLCs come from a separate genetic algorithm (GA) that coevolves with the GA that applies the genetic

Francisco Herrera; Manuel Lozano

2001-01-01

81

Adaptive fuzzy output-feedback controller design for nonlinear systems via backstepping and small-gain approach.  

PubMed

This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples. PMID:25222716

Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip

2014-10-01

82

Robust Adaptive Controller Design for Nonlinear Time-Delay Systems via TS Fuzzy Approach  

Microsoft Academic Search

The robust control problem is investigated for a class of uncertain nonlinear time-delay systems. Via the Takagi-Sugeno (T-S) fuzzification, we obtain the T-S fuzzy systems with each local model in the form of time-delay systems with uncertain nonlinear functions. The mismatched nonlinear functions satisfy the Lipschitz condition, while the matched parts are bounded by nonlinear functions with unknown coefficients. Based

Chang-Chun Hua; Qing-Guo Wang; Xin-Ping Guan

2009-01-01

83

Based on interval type-2 adaptive fuzzy H ? tracking controller for SISO time-delay nonlinear systems  

Microsoft Academic Search

In this article, based on the adaptive interval type-2 fuzzy logic, by adjusting weights, centers and widths of proposed fuzzy neural network (FNN), the modeling errors can be eliminated for a class of SISO time-delay nonlinear systems. The proposed scheme has the advantage that can guarantee the H? tracking performance to attenuate the lumped uncertainties caused by the unmodelled dynamics,

Tsung-Chih Lin; Mehdi Roopaei

2010-01-01

84

Data-driven techniques for direct adaptive control: the lazy and the fuzzy approaches  

Microsoft Academic Search

This paper presents an approach to modeling and controlling discrete-time non-linear dynamical system on the basis of a 1nite amount of input=output observations. The controller consists of a multiple-step-ahead direct adaptive controller which, at each time step, 1rst performs a forward simulation of the closed-loop system and then makes an adaptation of the parameters of the controller. This procedure requires

Edy Bertolissi; Mauro Birattari; Gianluca Bontempi; Antoine Duchâteau; Hugues Bersini

2002-01-01

85

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS.  

PubMed

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. PMID:24975565

Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu

2014-09-01

86

Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.  

PubMed

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

2014-01-01

87

Neuro-fuzzy control of a steam boiler-turbine unit  

Microsoft Academic Search

Conceptually, fuzzy logic possesses the quality of simplicity. However, its early applications relied on trial and error in selecting either the fuzzy membership functions or the fuzzy rules. This made it depend rather too heavily on expert knowledge which may not always be available. Hence, a self-tuning or an adaptive fuzzy logic controller (FLC) such as Adaptive Neuro-Fuzzy Inference System

Fahd A. Alturki; Adel Ben Abdennour

1999-01-01

88

Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS)  

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neuro-fuzzy inference system (ANFIS) was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and the 1,000-hPa level, the temperature of the 700-hPa level, the thickness between the 500- and 1,000-hPa levels, the relative humidity at the 300-hPa level, and precipitable water content. We have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the ANFIS in the period 1970-1997 has been taught. Finally, we forecast the rainfall for the period 1998-2007. The results show that the ANFIS can predict the rainfall with reasonable accuracy.

Fallah-Ghalhary, Gholam Abbas; Habibi-Nokhandan, Majid; Mousavi-Baygi, Mohammad; Khoshhal, Javad; Shaemi Barzoki, Akbar

2010-07-01

89

An improved Adaptive Neural Fuzzy Channel Equalizer  

Microsoft Academic Search

In this paper we introduce an Adaptive Neural Fuzzy Channel Equalizer (ANFCE) based on Adaptive Neural Fuzzy Filter (ANFF). The ANFF is a five layer neural network that is able to use the expert knowledge in its structure. The structure and parameters of this network are adjusted according to the training data and the available expert knowledge. At first, ANFF

Fereshteh Gharibi; Javad RavanJamjah; Fardin Akhlaghian; Bahram ZahirAzami

2010-01-01

90

Analysis and design of fuzzy controller and fuzzy observer  

Microsoft Academic Search

This paper addresses the analysis and design of a fuzzy controller and a fuzzy observer on the basis of the Takagi-Sugeno (T-S) fuzzy model. The main contribution of the paper is the development of the separation property; that is, the fuzzy controller and the fuzzy observer can be independently designed. A numerical simulation and an experiment on an inverted pendulum

Xiao-Jun Ma; Zeng-Qi Sun; Yan-Yan He

1998-01-01

91

A "mutual update" training algorithm for fuzzy adaptive logic control/decision network (FALCON).  

PubMed

The conventional two-stage training algorithm of the fuzzy/neural architecture called FALCON may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this correspondence, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm as twice as fast and produced a lower steady-state error by an order of magnitude. PMID:18252518

Altug, S; Trussell, H J; Chow, M Y

1999-01-01

92

Decentralized indirect adaptive Fuzzy-Neural Multi-Model control of a distributed parameter bioprocess plant  

Microsoft Academic Search

The paper proposed to use recurrent fuzzy-neural multi-model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation

Ieroham S. Baruch; Rosalba Galvan-guerra; Carlos-roman Mariaca-gaspar; Patricia Melin

2008-01-01

93

Decentralized Adaptive Fuzzy-Neural Control of an Anaerobic Digestion Bioprocess Plant  

Microsoft Academic Search

The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is used as a plant data generator. It is reduced to a lumped system using the orthogonal collocation method,

Ieroham S. Baruch; Rosalba Galvan-guerra

2009-01-01

94

Designing a fuzzy model by adaptive macroevolution genetic algorithms  

Microsoft Academic Search

In this paper the adaptive macroevolution genetic algorithms are proposed to identify three different types of fuzzy models. Several newly established techniques, such as adaptive choice function and macroevolution, are adopted into the simple genetic algorithms to improve the optimization capability. The genetic algorithms used here are controlled to retain the best solution in the population until a better one

Yo-Ping Huang; Sheng-Fang Wang

2000-01-01

95

A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller  

NASA Astrophysics Data System (ADS)

This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

96

An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions  

NASA Astrophysics Data System (ADS)

In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

Ajay Kumar, M.; Srikanth, N. V.

2014-03-01

97

Indirect adaptive control of unknown multi variable nonlinear systems with parametric and dynamic uncertainties using a new neuro-fuzzy system description.  

PubMed

The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks. PMID:20411596

Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis

2010-04-01

98

Fuzzy logic applications to control engineering  

NASA Astrophysics Data System (ADS)

This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

Langari, Reza

1993-12-01

99

Fuzzy model reference learning control for cargo ship steering  

Microsoft Academic Search

The use of a learning control system to maintain adequate performance of a cargo ship autopilot when there are process disturbances or variations is examined. The objective is to make an initial assessment of what advantages a fuzzy learning control approach has over conventional adaptive control approaches. The simulation results indicate that the fuzzy model reference learning controller (FMRLC) has

J. R. Layne; K. M. Passino

1993-01-01

100

Fuzzy adaptive filters, with application to nonlinear channel equalization  

Microsoft Academic Search

Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come

Li-Xin Wang; Jerry M. Mendel

1993-01-01

101

Adaptive fuzzy PID temperature control system based on single-chip computer for the autoclave  

Microsoft Academic Search

The autoclave is one of main preparation equipments of crystal preparation by hydrothermal method. The preparation temperature will seriously influence crystals quality and crystals size at high temperature, how to measure and control precisely the autoclave temperature can be of real significance. The characteristic of hysteresis, nonlinearity and difficulty to acquire the precise mathematical model existing in the temperature control

F. Zhang; J. Wang; S. L. Fu; Z. T. He; X. P. Li

2008-01-01

102

Low speed control of a DC motor driving a mechanical system with fuzzy adaptive compensation  

E-print Network

and lubricated sliding junctions. For experiments, an IBM PC, a DSPACE DSP board, SE uLM and Real Time Workshop are used. All three control systems can achieve such a very low sustainable speed as 0.005 rad/sec without stick-slip oscillations, which appear when...

Hyun, Dongyoon

2012-06-07

103

Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy Inference System approach  

E-print Network

temperature is named Current Correction Temperature Control (CCTC). It manipulates the fuel cell current the fuel cell current and thereby the amount of excess hydrogen sent from the fuel cell to the burner. The system is used as a battery charger and the fuel cell current can therefore be different to the reference

Andreasen, Søren Juhl

104

Extending Fuzzy System Concepts for Control of a Vitrification Melter  

SciTech Connect

Fuzzy systems provide a mathematical framework to capture uncertainty. The complete description of real, complex systems or situations often requires far more detail and information than could ever be obtained (or understood). Fuzzy approaches are an alternative technology for both system control and information processing and management. In this paper, we present the design of a fuzzy control system for a melter used in the vitrification of hazardous waste. Design issues, especially those related to melter shutdown and obtaining smooth control surfaces, are addressed. Several extensions to commonly-applied fuzzy techniques, notably adaptive defuzzification and modified rule structures are developed.

Whitehouse, J.C. [Westinghouse Savannah River Company, AIKEN, SC (United States); Sorgel, W. [Clemson University, Clemson, SC (United States); Garrison, A. [Clemson University, Clemson, SC (United States); Schalkoff, R.J. [Clemson University, Clemson, SC (United States)

1995-08-16

105

Position control of a servopneumatic system using fuzzy compensation  

E-print Network

based on the error and the change in error. To prove the effectiveness of the proposed fuzzy compensation scheme, results obtained are compared with those from a model-based adaptive controller, supplied by Festo Corp. The results indicate...

Sathyanarayana, Sreenivas

2012-06-07

106

Fuzzy control for platoons of smart cars  

Microsoft Academic Search

An additive fuzzy system can control the throttle of cars in single lane platoons. The system used fuzzy controllers for velocity control and gap control. Fuzzy controllers create, maintain, and divide platoons on the highway. Each car's controller uses data from its car and the car in front of it. Cars drop back during platoon maneuvers to avoid the “slinky

Julie Dickerson; Hyun Mun Kim; Bart Kosko

1994-01-01

107

Fuzzy Control of Urban Drainage Systems  

Microsoft Academic Search

This paper presents a fuzzy logic approach of existing urban drainage practices. Fuzzy logic is closer by human thinking and by natural language, so is easier to use in many fields. The main concepts in fuzzy logic are summary presented. Fuzzy control system is robust, flexible and easily accepted because it included the expert knowdlege. It can be a useful

Tania HAPURNE

108

Control Engineering Practice 10 (2002) 801817 Intelligent fault-tolerant control using adaptive and learning methods  

E-print Network

December 2001 Abstract Stimulated by the growing demand for improving system performance and reliability of neural networks or fuzzy systems. On-line approximation-based stable adaptive neural/fuzzy control: Fault-tolerant control; Adaptive control; Neural/fuzzy control; Fault diagnosis; Nonlinear systems 1

109

Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach  

Microsoft Academic Search

Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse\\u000a impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can\\u000a evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate\\u000a change on wildlife species using probability-based or fuzzy

Tony Prato

2011-01-01

110

Regularized Adaptation of Fuzzy Inference Systems. Modelling the Opinion of a Medical Expert about Physical Fitness: An Application  

Microsoft Academic Search

This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using regularization, since regularization improves the robustness of standard parameter estimation algorithms leading to stable fuzzy approximation. The proposed method can be used for modelling, identification and control of physical processes. A recursive method for on-line identification of fuzzy parameters employing Tikhonov regularization is suggested. The

Mohit Kumar; Regina Stoll; Norbert Stoll

2003-01-01

111

An adaptive fuzzy logic based power system stabilizer for enhancement of power system stability  

Microsoft Academic Search

This paper presents the performance of fuzzy logic based adaptive power system stabilizer (PSS) for stability enhancement of Single Machine Infinite Bus power system. The PSS is used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. Here speed deviation and accelerated power are the two inputs to the fuzzy

K. C. Rout; P. C. Panda

2010-01-01

112

Expert system driven fuzzy control application to power reactors  

SciTech Connect

For the purpose of nonlinear control and uncertainty/imprecision handling, fuzzy controllers have recently reached acclaim and increasing commercial application. The fuzzy control algorithms often require a supervisory'' routine that provides necessary heuristics for interface, adaptation, mode selection and other implementation issues. Performance characteristics of an on-line fuzzy controller depend strictly on the ability of such supervisory routines to manipulate the fuzzy control algorithm and enhance its control capabilities. This paper describes an expert system driven fuzzy control design application to nuclear reactor control, for the automated start-up control of the Experimental Breeder Reactor-II. The methodology is verified through computer simulations using a valid nonlinear model. The necessary heuristic decisions are identified that are vitally important for the implemention of fuzzy control in the actual plant. An expert system structure incorporating the necessary supervisory routines is discussed. The discussion also includes the possibility of synthesizing the fuzzy, exact and combined reasoning to include both inexact concepts, uncertainty and fuzziness, within the same environment.

Tsoukalas, L.H.; Berkan, R.C.; Upadhyaya, B.R.; Uhrig, R.E.

1990-01-01

113

Expert system driven fuzzy control application to power reactors  

SciTech Connect

For the purpose of nonlinear control and uncertainty/imprecision handling, fuzzy controllers have recently reached acclaim and increasing commercial application. The fuzzy control algorithms often require a ``supervisory`` routine that provides necessary heuristics for interface, adaptation, mode selection and other implementation issues. Performance characteristics of an on-line fuzzy controller depend strictly on the ability of such supervisory routines to manipulate the fuzzy control algorithm and enhance its control capabilities. This paper describes an expert system driven fuzzy control design application to nuclear reactor control, for the automated start-up control of the Experimental Breeder Reactor-II. The methodology is verified through computer simulations using a valid nonlinear model. The necessary heuristic decisions are identified that are vitally important for the implemention of fuzzy control in the actual plant. An expert system structure incorporating the necessary supervisory routines is discussed. The discussion also includes the possibility of synthesizing the fuzzy, exact and combined reasoning to include both inexact concepts, uncertainty and fuzziness, within the same environment.

Tsoukalas, L.H.; Berkan, R.C.; Upadhyaya, B.R.; Uhrig, R.E.

1990-12-31

114

Attitude control of a satellite using fuzzy controllers  

Microsoft Academic Search

This paper proposes the design of fuzzy controllers for the attitude stabilization of the Republic of China Satellite (ROCSAT-1). In the first step, the original satellite controllers, two classical linearized controllers, are superseded by two fuzzy controllers. The objective is to obtain faster convergent time and lower steady-state error. The two fuzzy controllers will be consolidated to form one fuzzy

Chin-hsing Cheng; Sheng-li Shu; Po-jen Cheng

2009-01-01

115

ANFIS: adaptive-network-based fuzzy inference system  

Microsoft Academic Search

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation,

Jyh-Shing Roger Jang

1993-01-01

116

Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning for pitch control system  

NASA Astrophysics Data System (ADS)

PID and fuzzy PID controller are applied into the pitch control system. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist.

Li, Yezi; Xiao, Cheng; Sun, Jinhao

2013-03-01

117

Adaptive defuzzification for fuzzy systems modeling  

NASA Technical Reports Server (NTRS)

We propose a new parameterized method for the defuzzification process based on the simple M-SLIDE transformation. We develop a computationally efficient algorithm for learning the relevant parameter as well as providing a computationally simple scheme for doing the defuzzification step in the fuzzy logic controllers. The M-SLIDE method results in a particularly simple linear form of the algorithm for learning the parameter which can be used both off- and on-line.

Yager, Ronald R.; Filev, Dimitar P.

1992-01-01

118

Tuning of a neuro-fuzzy controller by genetic algorithm.  

PubMed

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance. PMID:18252294

Seng, T L; Bin Khalid, M; Yusof, R

1999-01-01

119

Fuzzy control system for a mobile robot  

SciTech Connect

Since the first fuzzy logic control system was proposed by Mamdani, many studies have been carried out on industrial process and real-time controls. The key problem for the application of fuzzy logic control is to find a suitable set of fuzzy control rules. Three common modes of deriving fuzzy control rules are often distinguished and mentioned: (1) expert experience and knowledge; (2) modeling operator control actions; and (3) modeling a process. In cases where an operator's skill is important, it is very useful to derive fuzzy control rules by modeling an operator's control actions. It is possible to model an operator's control behaviors in terms of fuzzy implications using the input-output data concerned with his/her control actions. The authors use the model obtained in this way as the basis for a fuzzy controller. The authors use a finite number of fuzzy or approximate control rules. To control a robot in a cluttered reactor environment, it is desirable to combine all the methods. In this paper, the authors describe a general algorithm for a mobile robot control system with fuzzy logic reasoning. They discuss the way that knowledge of fuzziness will be represented in this control system. They also describe a simulation program interface to the K2A Cybermation mobile robot to be used to demonstrate the control system.

Hai Quan Dai; Dalton, G.R.; Tulenko, J. (Univ. of Florida, Gainesville (United States))

1992-01-01

120

Position control of ionic polymer metal composite actuator based on neuro-fuzzy system  

NASA Astrophysics Data System (ADS)

This paper describes the application of Neuro-Fuzzy techniques for controlling an IPMC cantilever configuration under water to improve tracking ability for an IPMC actuator. The controller was designed using an Adaptive Neuro-Fuzzy Controller (ANFC). The measured input data based including the tip-displacements and electrical signals have been recorded for generating the training in the ANFC. These data were used for training the ANFC to adjust the membership functions in the fuzzy control algorithm. The comparison between actual and reference values obtained from the ANFC gave satisfactory results, which showed that Adaptive Neuro-Fuzzy algorithm is reliable in controlling IPMC actuator. In addition, experimental results show that the ANFC performed better than the pure fuzzy controller (PFC). Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the real-time control of the ionic polymer metal composite actuator for which the performance degrades under long-term actuation.

Nguyen, Truong-Thinh; Yang, Young-Soo; Oh, Il-Kwon

2009-07-01

121

A multiregion fuzzy logic controller for nonlinear process control  

Microsoft Academic Search

Although a fuzzy logic controller is generally nonlinear, a PI-type fuzzy controller that uses only control error and change in control error is not able to detect the process nonlinearity and make a control move accordingly. In this paper, a multiregion fuzzy logic controller is proposed for nonlinear process control. Based on prior knowledge, the process to be controlled is

S. Joe Qin; Guy Borders

1994-01-01

122

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.  

PubMed

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique. PMID:12662634

Kim, J; Kasabov, N

1999-11-01

123

Design of the Electronic Brake Pressure Modulator Using a Direct Adaptive Fuzzy Controller in Commercial Vehicles for the Safety of Braking in Fail  

NASA Astrophysics Data System (ADS)

In the brake systems, it is important to reduce the rear brake pressure in order to secure the safety of the vehicle in braking. So, there was some research that reduced and controlled the rear brake pressure exactly like a L. S. P. V and a E. L. S. P. V. However, the previous research has some weaknesses: the L. S. P. V is a mechanical system and its brake efficiency is lower than the efficiency of E. L. S. P. V. But, the cost of E. L. S. P. V is very higher so its application to the vehicle is very difficult. Additionally, when a fail appears in the circuit which controls the valves, the fail results in some wrong operation of the valves. But, the previous researchers didn't take the effect of fail into account. Hence, the efficiency of them is low and the safety of the vehicle is not confirmed. So, in this paper we develop a new economical pressure modulator that exactly controls brake pressure and confirms the safety of the vehicle in any case using a direct adaptive fuzzy controller.

Kim, Hunmo

124

Control of robotic manipulator using fuzzy logic  

Microsoft Academic Search

This paper describes the implementation of hierarchical control on a robotic manipulator using fuzzy logic. A decentralized control approach is implemented, i.e., individual controllers control the two links of the robot. The kinematic aspect of the control is treated as the supervisory mode at a higher level and the joint control is treated as the lower level. Fuzzy logic based

Kishan Kumar Kumbla; MO Jamshidi

1994-01-01

125

Dynamic positioning of drilling vessels with a fuzzy logic controller  

Microsoft Academic Search

ABSTRACT In this paper, a fuzzy logic controller for dynamic positioning of drilling vessels in deep water is presented. The core of the fuzzy controller is a set of fuzzy associative memory,(FAM) rules that correlate each group of fuzzy control input sets to a fuzzy control output set. A FAM rule is a

Yusong Cao; Tzung-hang Lee; David L Garrett; John F Chappell

2002-01-01

126

A Conditional Fuzzy Clustering with Adaptive Method  

Microsoft Academic Search

The Chiu's method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. Those rules are not explicit for the expert. This paper proposes a new method to generate Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps. The first step

A. Elmzabi; M. Bellafkih; M. Ramdani; K. Zeitouni

127

Determining limit cycles in fuzzy control systems  

Microsoft Academic Search

We consider nonlinear control systems including fuzzy logic controllers. The dynamical behavior of such systems may be much richer and more complex than that of linear systems. This paper deals with the application of classical control techniques of system analysis, including frequency domain methods, which allow one to gain a better understanding the behavior of systems controlled by fuzzy logic.

F. Gordillo; J. Aracil; T. Alamo

1997-01-01

128

Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction  

Microsoft Academic Search

The aim of this paper is to present and evaluate control strategies for adjustment and preservation of air quality, thermal and visual comfort for buildings’ occupants while, simultaneously, energy consumption reduction is achieved. Fuzzy PID, fuzzy PD and adaptive fuzzy PD control methods are applied. The inputs to any controller are: the PMV index affecting thermal comfort, the CO2 concentration

D Kolokotsa; D Tsiavos; G. S Stavrakakis; K Kalaitzakis; E Antonidakis

2001-01-01

129

Research on the controller of the digital cabin pressure regulating system based on the fuzzy gain scheduling  

Microsoft Academic Search

Based on the cabin pressure regulating system characters of the nonlinear, larger inertia and time varying parameter, the arithmetic of fuzzy gain scheduling was proposed in this paper. The control parameters can be optimized globally using the controller based on the fuzzy gain scheduling because the fuzzy inference was used to changes the parameters of the controller online to adapt

Lei Zhu; Yongling Fu; Jingquan Zhao; Dong Guo

2009-01-01

130

A fuzzy logic controller for autonomous vehicle control  

E-print Network

using current fuzzy control theory. Then, a Base Model is presented and evaluated for its utility for the vehicle following problem. Similarly, the Fuzzy Automobile Control Software (FACS) system is developed and evaluated using the same criteria...

Vinson, Yale Patrick

2012-06-07

131

Analysis of inventory difference using fuzzy controllers  

SciTech Connect

The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented.

Zardecki, A.

1994-08-01

132

Fuzzy EMG classification for prosthesis control  

Microsoft Academic Search

Proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain

Francis H. Y. Chan; Yong-Sheng Yang; F. K. Lam; Yuan-Ting Zhang; Philip A. Parker

2000-01-01

133

Fuzzy regulator design for wind turbine yaw control.  

PubMed

This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237

Theodoropoulos, Stefanos; Kandris, Dionisis; Samarakou, Maria; Koulouras, Grigorios

2014-01-01

134

Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction  

Microsoft Academic Search

In this paper we describe a neuro-fuzzy system with adaptive capability to extract fuzzy If Then rules from input and output sample data through learning. The proposed system, called radial basis function (RBF) based adaptive fuzzy system (AFS), employs the Gaussian functions to represent the membership functions of the premise part of fuzzy rules. Three architectural deviations of the RBF

Kwang Bo Cho; Bo Hyeun Wang

1996-01-01

135

Stability of fuzzy linguistic control systems  

Microsoft Academic Search

A new approach to the stability analysis of fuzzy linguistic control (FLC) systems is presented. Specifically, it is shown that the direct method of Lyapunov can be used to determine sufficient conditions for global stability of a broad class of fuzzy control schemes. Moreover, a measure of robustness is proposed that can be used to evaluate and possibly redesign a

Gholamreza Langari; M. Tomizuka

1990-01-01

136

VSS Theory Based Training of a Fuzzy Motion Control System  

Microsoft Academic Search

This paper presents a novel training algorithm for adaptive neuro-fuzzy inference systems. The algorithm combines the error backpropagation algorithm with variable structure systems approach. Expressing the parameter update rule as a dynamic system in continuous time and applying sliding mode control (SMC) method to the dynamic model of the gradient based training procedure results in the parameter stabilizing part of

M. Onder Efe; A. Murat Fiskiran; Okyay Kaynak; Imre J. Rudas

137

A fuzzy control design case: The fuzzy PLL  

NASA Technical Reports Server (NTRS)

The aim of this paper is to present a typical fuzzy control design case. The analyzed controlled systems are the phase-locked loops (PLL's)--classic systems realized in both analogic and digital technology. The crisp PLL devices are well known.

Teodorescu, H. N.; Bogdan, I.

1992-01-01

138

Improvement on fuzzy controller design techniques  

NASA Technical Reports Server (NTRS)

This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzy controllers. Careful examination of the fuzzy controllers designed by various engineers reveals distinctive classes of fuzzy controllers. Classification is believed to be helpful from different perspectives. The second issue deals with the design according to specifications, experiments related to the tuning of fuzzy controllers, according to the specification, will be discussed. General design procedure, hopefully, can be outlined in order to ease the burden of a design engineer. The third issue deals with the simplicity and limitation of the rule-based IF-THEN logical statements. The methodology of fuzzy-constraint network is proposed here as an alternative to the design practice at present. It is our belief that predicate calculus and the first order logic possess much more expressive power.

Wang, Paul P.

1993-01-01

139

Inverter air-conditioning control system using PID fuzzy controller  

Microsoft Academic Search

In this paper, temperature fuzzy controller was designed in the inverter cold and hot air-condition. The control system for inverter air conditioner was divided indoor machine and the outdoor machine. Frequency control was realized by design of hardware circuit and two-dimensional temperature controller was selected in the fuzzy control program. PID fuzzy controller suitable for real-time control was proposed. Finally,

Jiang Jing; Zhang Xuesong

2011-01-01

140

A Design Methodology for the Implementation of Fuzzy Logic Traffic Controller using Field Programmable Gate Arrays  

Microsoft Academic Search

In this thesis, an approach is proposed for the design and implementation of fuzzy traffic controllers using Field Programmable Gate Arrays (FPGAs).The focus of this study is to develop an effective traffic signaling strategy to be implemented at a typical intersection with four approaches. Adaptive traffic control using fuzzy principles has been demonstrated and reported by the authors in the

Mandar Shriram Ambre

2004-01-01

141

Development and realization of bucket wheel excavator knowledge-based neuro-fuzzy control system  

Microsoft Academic Search

Development of a new control system, which significantly increases excavating capacity, as well as availability, and reliability of the bucket wheel excavator, is presented in this paper. Reference of slewing speed and controller parameters are adapted by predicting cutting resistance of materials to be excavated. The predictive-adaptive higher-level control system is realized as a neuro-fuzzy controller. The fuzzy rules for

Branislav T. Jevtovic; Miroslav R. Matausek; Danilo J. Oklobdzija

2008-01-01

142

A simple fuzzy statistical evaluation for process control performance  

Microsoft Academic Search

This article presents a simple method for evaluating process control performance improvements by applying fuzzy conditional probability. The fuzzy conditional probability of an unwanted fuzzy event, while a known disturbance fuzzy event is active, is calculated before and after the control improvement. The probability is used as a measurement of the control error. A case study for a boiler control

Tero Joronen

2002-01-01

143

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design  

Microsoft Academic Search

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The ge- netic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, comple- ments the local mapping property of a fuzzy rule. Using this Symbi- otic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials,

Chia-feng Juang; Jiann-yow Lin; Chin-teng Lin

2000-01-01

144

Introduction to Fuzzy Control Marcelo Godoy Simoes  

E-print Network

of complex dynamic systems. Therefore, embedded fuzzy controllers automate what has traditionally been-output reaction, one can design a controller. The disadvantages are several: the process equipment may

Simões, Marcelo Godoy

145

Adaptive Fuzzy Segmentation of Magnetic Resonance Images  

Microsoft Academic Search

An algorithm is presented for the fuzzy segmentation of two and three-dimensionalmultispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities,also known as shading artifacts. The algorithm is an extension of the two-dimensionaladaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. Thisalgorithm models the intensity inhomogeneities as a gain field that causes image intensities

Dzung L. Pham; Jerry L. Prince

1999-01-01

146

Genetic-algorithm-based fuzzy control of spacecraft autonomous rendezvous  

Microsoft Academic Search

The combination of the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms is investigated. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy-logic-based expert systems have been used successfully for controlling a number of physical systems, the tasks of selecting acceptable fuzzy membership functions and

L. Michael Freeman

1997-01-01

147

Adaptively managing wildlife for climate change: a fuzzy logic approach.  

PubMed

Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods. PMID:21374089

Prato, Tony

2011-07-01

148

Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach  

NASA Astrophysics Data System (ADS)

Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods.

Prato, Tony

2011-07-01

149

Active control of blade-vortex interactions using a neuro-fuzzy controller  

NASA Astrophysics Data System (ADS)

Rotorcraft blade-vortex interactions (BVI) result in large pressure fluctuations over rotor blades leading to increased unsteady blade loads, noise, and vibration. Previous studies have indicated that an effective method for reducing BVI is through the use of active control schemes. As a workable dynamic model of the process for controller design is difficult to develop a rule-based fuzzy controller is used in this study. As the choice of the fuzzy controller parameters for acceptable performance depend on flight condition, a neural network is trained to adaptively modify the fuzzy controller parameters as a function of flight condition. The resulting neuro-fuzzy control scheme is evaluated using a numerical simulation model of BVI in order to demonstrate the effectiveness of the proposed scheme.

Swaminathan, Ramesh; Prasad, J. V. R.; Sankar, L. N.

1996-04-01

150

Waste water neutralization using a fuzzy neural network controller  

Microsoft Academic Search

In this paper, the pH neutralization process is identified and controlled using a fuzzy neural network structure. Three different approaches are compared. In the first method, a generic fuzzy controller was used in which membership functions and rule base are selected intuitively. For the second and third approaches, the fuzzy system is initialized using a fuzzy error backpropagation algorithm. While

B. Eikens; M. N. Karim; V. Saucedo; A. J. Morris

1995-01-01

151

Performance comparison of alternative fuzzy control modalities  

E-print Network

Through the medium of computer simulation, this thesis documents thorough performance caparisons of several different fuzzy control schemes, using a standard problem in the literature, the inverted pendulum. In order to have a firm basis...

House, Corey Dean

2012-06-07

152

Fuzzy controllers in nuclear material accounting  

SciTech Connect

Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored.

Zardecki, A.

1994-10-01

153

Learning and tuning fuzzy logic controllers through reinforcements  

NASA Technical Reports Server (NTRS)

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

154

Fuzzy predictive control applied to an air-conditioning system  

Microsoft Academic Search

A method of designing a nonlinear predictive controller based on a fuzzy model of the process is presented. The Takagi-Sugeno fuzzy model is used as a powerful structure for representing nonlinear dynamic systems. An identification technique which enables the acquisition of the fuzzy model from process measurements is described. The fuzzy model is incorporated as a predictor in a nonlinear

J. M. Sousa; R. Babuška; H. B. Verbruggen

1997-01-01

155

Track Seeking Hybrid Fuzzy Controller for the Compact Disc Player  

Microsoft Academic Search

A hybrid track seeking fuzzy controller for an optical compact disc player is proposed. It is shown that the proposed hybrid fuzzy controller smoothes the applied voltage to the sled motor and improves the track seeking efficiency. The proposed hybrid fuzzy controller consists of three subsystems including parking time controller, driving force controller and parameter tuner. All three of subsystems

Yao Leehter; An-min Wang; Yung-fu Cheng

2001-01-01

156

Fuzzy logic resource manager: real-time adaptation and self-organization  

NASA Astrophysics Data System (ADS)

A fuzzy logic expert system has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The resource manager (RM) is made up of five parts, the isolated platform model, the multi-platform decision tree, the fuzzy EA model, the fuzzy parameter selection tree and the fuzzy strategy tree. The platforms are each controlled by their own copy of the RM allowing them to automatically work together, i.e., they self-organize through communication without the need of a central commander. A group of platforms working together will automatically show desirable forms of emergent behavior, i.e., they will exhibit desirable behavior that was never explicitly programmed into them. This is important since it is impossible to have a rule covering every possible situation that might be encountered. An example of desirable emergent behavior is discussed as well as a method using a co-evolutionary war game for eliminating undesirable forms of behavior. The RM"s ability to adapt to changing situations is enhanced by the fuzzy parameter selection tree. Tree structure is discussed as well as various related examples.

Smith, James F., III

2004-08-01

157

Design of Main Steam Temperature Cascade Control System Based on Fuzzy Self-Tuning PID Controller  

Microsoft Academic Search

To obtain perfect performances of the main steam temperature control system, a new scheme is proposed. The scheme substitutes fuzzy self-tuning PID controller for the main regulator of cascade control system. The controller is composed of fuzzy controller and PID controller. According to the error and error rate of the control system and fuzzy control rules, the fuzzy controller can

Jing Zeng; Youcheng Xie; Lei Chen

2008-01-01

158

Adaptive Fuzzy Synchronization of Two Different Chaotic Systems with Stochastic Unknown Parameters  

NASA Astrophysics Data System (ADS)

In this paper, we present a method for synchronizing two different chaotic systems that have unknown parameters that are affected by stochastic variations generated by the Wiener process. The parameters are expressed by the sum of their mean values and the white Gaussian noise multiplied by the diffusion matrices. To describe the unknown nonlinear function yielded by Itô's lemma due to the unknown diffusion matrices, a fuzzy logic system is employed. Using adaptive fuzzy control, the response system is synchronized with the drive system within an arbitrarily small error bound. Numerical simulations show the effectiveness of the proposed method.

Yoo, W. J.; Ji, D. H.; Won, S. C.

159

Autonomous navigation system using a fuzzy adaptive nonlinear h? filter.  

PubMed

Although nonlinear H? (NH?) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH? filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H? (FANH?) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH? filter continually adjusts the higher order of the Taylor development thorough adaptive bounds  and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH? navigation filter are compared to the NH? navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH? filter. PMID:25244587

Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim

2014-01-01

160

Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H? Filter  

PubMed Central

Although nonlinear H? (NH?) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH? filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H? (FANH?) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH? filter continually adjusts the higher order of the Taylor development thorough adaptive bounds (?i) and adaptive disturbance attenuation (?), which significantly increases the UAV localization performance. The results obtained using the FANH? navigation filter are compared to the NH? navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH? filter. PMID:25244587

Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim

2014-01-01

161

Intelligent control based on fuzzy logic and neural net theory  

NASA Technical Reports Server (NTRS)

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

Lee, Chuen-Chien

1991-01-01

162

Fuzzy control of a universal battery charger  

Microsoft Academic Search

Fuzzy control of a Universal Battery Charger is investigated. Effective control of the charging process is complex, due to the exponential relationship between the charging current and the charging time. Classical control algorithms such as PID, PI etc. are difficult to implement in this event, due to nonlinear current-time relationships of rechargeable batteries. Initial peak charging current is estimated depending

R. M. Ivanov; Stoyan Gishin

1999-01-01

163

Uncovering transcriptional interactions via an adaptive fuzzy logic approach  

PubMed Central

Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast. PMID:19961622

2009-01-01

164

Fuzzy models in control systems of boiler aggregate technological processes  

Microsoft Academic Search

Application of fuzzy models in thermal power control systems is considered. Structure of fuzzy system for thermal object (boiler) is described. Results of the design and scheme-technical realization of boiler aggregate control systems are reported. Use of the suggested fuzzy models for control of fuel–air relationship and boiler water level are described as examples.

A. M. Prokhorenkov; A. S. Sovlukov

2002-01-01

165

Fuzzy predictive control of a solar power plant  

Microsoft Academic Search

This work presents the application of fuzzy predictive control to a solar power plant. The proposed predictive controller uses fuzzy characterization of goals and constraints, based on the fuzzy optimization framework for multi-objective satisfaction problems. This approach enhances model based predictive control (MBPC) allowing the specification of more complex requirements. A brief description of the solar power plant and its

Andrés Flores; Doris Saez; Juan Araya; Manuel Berenguel; Aldo Cipriano

2005-01-01

166

INTELLIGENT TRAFFIC LIGHTS CONTROL BY FUZZY LOGIC  

Microsoft Academic Search

One of the ways to overcome traffic problems in large cities is through the development of an intelligent monitoring and control of traffic lights system. This paper addresses the design and implementation of a n intelligent traffic lights controller based on fuzzy logic technology. A software has been developed to simulate the situation of an isolated traffic junction based on

Tan Kok Khiang; Marzuki Khalid; Rubiyah Yusof

1996-01-01

167

Fuzzy Controller for a Gas Turbine Plant  

Microsoft Academic Search

A novel control for a gas turbine plant using fuzzy scheme is proposed. At the inlet, the compressor in the gas turbine plant is equipped with an adjustable inlet guide vane (IGV), which is controlling the airflow to the entire turbine at all load conditions. At any load change the IGV staggering angle is adjusted such, that the change in

T. R. Rangaswamy; J. Shanmugam; T. Thyagarajan

2006-01-01

168

Fuzzy Decision in Airplane Speed Control  

Microsoft Academic Search

A fuzzy decision system for helping air-traffic experts in controlling airplane velocities and in keeping an airplane flight within several constraints established to air lane sections is proposed in this paper. Automatic systems for air-traffic control are essential due to the ever increasing number of airplanes flying all over the world, the amount of environmental and airplane constraints and the

Agnaldo V. Lovato; Ernesto Araujo; José D. S. da Silva

2006-01-01

169

Intelligent fuzzy controller of a quadrotor  

Microsoft Academic Search

The aim of this work is to describe an intelligent system based on fuzzy logic that is developed to control a quadrotor. A quadrotor is a helicopter with four rotors, that make the vehicle more stable but more complex to model and to control. The quadrotor has been used as a testing platform in the last years for various universities

Matilde Santos; Victoria López; Franciso Morata

2010-01-01

170

Structure identification of generalized adaptive neuro-fuzzy inference systems  

Microsoft Academic Search

This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using

Mohammad Fazle Azeem; Madasu Hanmandlu; Nesar Ahmad

2003-01-01

171

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous  

NASA Technical Reports Server (NTRS)

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

172

A Neuro-fuzzy Adaptive Power System Stabilizer Using Genetic Algorithms  

Microsoft Academic Search

This article presents the design technique of an adaptive power system stabilizer using adaptive neuro-fuzzy inference systems trained via data obtained from genetic algorithms. The parameters of a standard power system stabilizer are tuned using adaptive neuro-fuzzy inference systems to achieve a certain damping ratio and settling time at all load points within a wide region of operation. The overall

M. A. Awadallah; H. M. Soliman

2009-01-01

173

Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system  

Microsoft Academic Search

A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns

Gail A. Carpenter; Stephen Grossberg; David B. Rosen

1991-01-01

174

Introduction to n-adaptive fuzzy models to analyze public opinion on AIDS  

E-print Network

There are many fuzzy models like Fuzzy matrices, Fuzzy Cognitive Maps, Fuzzy relational Maps, Fuzzy Associative Memories, Bidirectional Associative memories and so on. But almost all these models can give only one sided solution like hidden pattern or a resultant output vector dependent on the input vector depending in the problem at hand. So for the first time we have defined a n-adaptive fuzzy model which can view or analyze the problem in n ways (n >=2) Though we have defined these n- adaptive fuzzy models theorectically we are not in a position to get a n-adaptive fuzzy model for n > 2 for practical real world problems. The highlight of this model is its capacity to analyze the same problem in different ways thereby arriving at various solutions that mirror multiple perspectives. We have used the 2-adaptive fuzzy model having the two fuzzy models, fuzzy matrices model and BAMs viz. model to analyze the views of public about HIV/ AIDS disease, patient and the awareness program. This book has five chapters and 6 appendices. The first chapter just recalls the definition of four fuzzy models used in this book and gives illustration of some of them. Chapter two introduces the new n-adaptive fuzzy models. Chapter three uses for the first time 2 adaptive fuzzy models to study psychological and sociological problems about HIV/AIDS. Chapter four gives an outline of the interviews. Chapter five gives the suggestions and conclusion based on our study. Of the 6 appendices four of them are C-program made to make the working of the fuzzy model simple.

Dr. W. B. Vasantha Kandasamy; Dr. Florentin Smarandache

2006-02-18

175

Intelligent fuzzy controller for a lead-acid battery charger  

Microsoft Academic Search

An intelligent fuzzy controller for a lead-acid battery charger has been investigated in this research. The fuzzy controller implies an intelligent two step charging method. At the first stage a high current fast charge fuzzy algorithm charges the battery to almost 70% of it's full capacity. At the second stage the battery voltage is maintained at a set value and

R. Ivanov; S. Gishin

1999-01-01

176

An application of fuzzy set theory to inventory control models  

Microsoft Academic Search

A method for solving an inventory control problem, of which input data are described by triangular fuzzy numbers will be presented here. The continuous review model of the inventory control problem with fuzzy input data will be focused in, and a new solution method will be presented. For the reason that the result should be a fuzzy number because of

Mitsuo Gen; Yasuhiro Tsujimura; Dazhong Zheng

1997-01-01

177

Fuzzy Modeling with Adaptive Simulated Annealing  

E-print Network

approach uses Takagi-Sugeno models and Adaptive Simulated Annealing (ASA) to .... At first , and to investigate the efficiency of the ASA method in this kind of .... Kohonen SOM ( Self Organizing Map ) to realize the clustering ( or vector.

178

New fuzzy wavelet network for modeling and control: The modeling approach  

NASA Astrophysics Data System (ADS)

In this paper, a fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts. The presented network combines TSK fuzzy models with wavelet transform and ROLS learning algorithm while still preserve the property of linearity in parameters. In order to reduce the number of fuzzy rules, fuzzy clustering is invoked. In the clustering algorithm, those wavelets that are closer to each other in the sense of the Euclidean norm are placed in a group and are used in the consequent part of a fuzzy rule. Antecedent parts of the rules are Gaussian membership functions. Determination of the deviation parameter is performed with the help of gold partition method. Here, mean of each function is derived by averaging center of all wavelets that are related to that particular rule. The overall developed fuzzy wavelet network is called fuzzy wave-net and simulation results show superior performance over previous networks. The present work is complemented by a second part which focuses on the control aspects and to be published in this journal( [17]). This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems.

Ebadat, Afrooz; Noroozi, Navid; Safavi, Ali Akbar; Mousavi, Seyyed Hossein

2011-08-01

179

Simulating Human Lifting Motions Using Fuzzy-Logic Control  

Microsoft Academic Search

Human motion simulation is an ill-posed problem. In order to predict unique lifting motion trajectories, a motion simulation model based on fuzzy-logic control is presented. The human body was represented by a 2-D five-segment model, and the neural controller was specified by fuzzy logic. Fuzzy rules were defined with their antecedent part describing the fuzzy variables of scaled positional error

Xingda Qu; Maury A. Nussbaum

2009-01-01

180

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.  

PubMed

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot. PMID:15828650

Lin, Chuan-Kai

2005-04-01

181

A Laboratory Testbed for Embedded Fuzzy Control  

ERIC Educational Resources Information Center

This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses students'…

Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

2011-01-01

182

FUZZY LOGIC CONTROL OF AC INDUCTION MOTORS  

EPA Science Inventory

The paper discusses the fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads. lectric motors use 60% of the electrical energy generated in t...

183

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm  

NASA Technical Reports Server (NTRS)

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

184

Intelligent Coordinator of Fuzzy Controller-Agents for Indoor Environment Control in Buildings Using 3-D Fuzzy Comfort Set  

Microsoft Academic Search

In this paper, we develop an intelligent coordinator (IC) of fuzzy controller-agents (FCAs) for indoor environmental conditions control in buildings using a 3-D fuzzy comfort concept as an information granule. The proposed intelligent coordination model has hierarchical structure. This centralized coordinator consists of two subsystems the master and slave agents. These subsystems are implemented by fuzzy logic rules. The master

Anastasios I. Dounis; Christos Caraiscos

2007-01-01

185

Generalizations of fuzzy linguistic control points in geometric design  

NASA Astrophysics Data System (ADS)

Control points are geometric primitives that play an important role in designing the geometry curve and surface. When these control points are blended with some basis functions, there are several geometric models such as Bezier, B-spline and NURBS(Non-Uniform Rational B-Spline) will be produced. If the control points are defined by the theory of fuzzy sets, then fuzzy geometric models are produced. But the fuzzy geometric models can only solve the problem of uncertainty complex. This paper proposes a new definition of fuzzy control points with linguistic terms. When the fuzzy control points with linguistic terms are blended with basis functions, then a fuzzy linguistic geometric model is produced. This paper ends with some numerical examples illustrating linguistic control attributes of fuzzy geometric models.

Sallehuddin, M. H.; Wahab, A. F.; Gobithaasan, R. U.

2014-07-01

186

A Fuzzy Heater Control System Stimulating Thermal Cycling of Flight Hardware for a Thermal Environmental Test  

NASA Astrophysics Data System (ADS)

The flight hardware suffers thermal cycling in space environment. The temperature range of the hardware is controlled between -45 C and 85 C for the space-flight test environment in a thermal vacuum chamber on ground. A Heater Control System (HCS) provides thirty heating points to simulate the thermal status of flight hardware. The control is configured in traditional PD algorithm and implemented in a workstation of a control room. Since the thermal mass is different for the different articles, the pre-determined parameters of PD control cannot fit all articles. The fuzzy logics are then proposed to be adaptive to the different articles. The fuzzy control is implemented with LabVIEW in a PXI industrial computer. The remote GPIB instruments of hibay are interfaced to PXI computer via Ethernet communication. In summary, the overall system takes advantages of GPIB standardized component, increasing capabilities, adaptive control with a fuzzy algorithm, and distributed control architecture.

Chang, Chih-Li; Chen, Yow-Hwa; Pan, Hsu-Pin; Cheng, Robert; Hsiao, Chiuder

2004-08-01

187

Design of fuzzy control systems with guaranteed stability  

Microsoft Academic Search

This paper addresses the analysis and design of fuzzy control systems. The fuzzy systems are represented by a family of local state space models with aggregation. The controller is designed by considering each local state feedback controller and a compensating controller. The compensating controller is based on the well-known variable structure control theory. It is shown that this controller guarantees

G. Feng; S. G. Cao; N. W. Rees; C. K. Chak

1997-01-01

188

Reliable Fuzzy Control for a Class of Discrete-Time Nonlinear Systems Using Multiple Fuzzy Lyapunov Functions  

Microsoft Academic Search

This brief deals with the problem of reliable Hinfin fuzzy control for a class of discrete-time nonlinear systems with actuator faults by using multiple fuzzy Lyapunov functions. The Takagi and Sugeno fuzzy model is employed to represent a nonlinear system. A sufficient condition for the existence of reliable Hinfin fuzzy controllers is given in terms of linear matrix inequalities (LMIs),

Huai-Ning Wu; Hong-Yue Zhang

2007-01-01

189

Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault  

Microsoft Academic Search

This paper is focused on reliable fuzzy $H_{\\\\infty }$ controller design for active suspension systems with actuator delay and fault. The Takagi–Sugeno (T–S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a

Hongyi Li; Honghai Liu; Huijun Gao; Peng Shi

2012-01-01

190

Designing fuzzy controllers from a variable structures standpoint  

Microsoft Academic Search

A procedure is presented for designing fuzzy controllers based upon variable structures techniques. Three such controllers are presented: the fuzzy equivalents of sliding-mode controllers, saturating controllers, and tanh controllers. By using an approach based upon variables structures (VSS) techniques, the stability of each of these controllers is assured. By using a sliding surface, the order of the rule base is

Jacob S. Glower; Jeffrey Munighan

1997-01-01

191

SoPC-Based Adaptive PID Control System Design for Magnetic Levitation System  

Microsoft Academic Search

This paper develops an adaptive proportional-inte- gral-derivative (APID) control system to deal with the metallic sphere position control of a magnetic levitation system (MLS), which is an intricate and highly nonlinear system. The proposed control system consists of an adaptive PID controller and a fuzzy compensation controller. The adaptive PID controller is a main tracking controller, and the parameters of

Chih-Min Lin; Ming-Hung Lin; Chun-Wen Chen

2011-01-01

192

Hybrid PID-fuzzy control scheme for managing energy resources in buildings  

Microsoft Academic Search

Both indoor temperature regulation and energy resources management in buildings require the design and the implementation of efficient and readily adaptable control schemes. One can use standard schemes, such as “on\\/off” and PID, or “advanced” schemes, such as MPC (Model Predictive Control). Another approach would be considering artificial intelligence tools. In this sense, fuzzy logic allows controlling temperature and managing

Benjamin Paris; Julien Eynard; Stéphane Grieu; Monique Polit

2011-01-01

193

Maximum entropy approach to fuzzy control  

NASA Technical Reports Server (NTRS)

For the same expert knowledge, if one uses different &- and V-operations in a fuzzy control methodology, one ends up with different control strategies. Each choice of these operations restricts the set of possible control strategies. Since a wrong choice can lead to a low quality control, it is reasonable to try to loose as few possibilities as possible. This idea is formalized and it is shown that it leads to the choice of min(a + b,1) for V and min(a,b) for &. This choice was tried on NASA Shuttle simulator; it leads to a maximally stable control.

Ramer, Arthur; Kreinovich, Vladik YA.

1992-01-01

194

Fuzzy Current-Mode Control and Stability Analysis  

NASA Technical Reports Server (NTRS)

In this paper a current-mode control (CMC) methodology is developed for a buck converter by using a fuzzy logic controller. Conventional CMC methodologies are based on lead-lag compensation with voltage and inductor current feedback. In this paper the converter lead-lag compensation will be substituted with a fuzzy controller. A small-signal model of the fuzzy controller will also be developed in order to examine the stability properties of this buck converter control system. The paper develops an analytical approach, introducing fuzzy control into the area of CMC.

Kopasakis, George

2000-01-01

195

Study of Fuzzy Control in Direct Torque Control system  

Microsoft Academic Search

Due to the multi-variable, strong coupling, nonlinear and time-varying characteristics of induction motor, it is not easy for direct torque control (DTC) system with traditional PID controller to get ideal performance. In order to improve the performance of Direct Torque Control system, a fuzzy PID regulator of motor speed is proposed to adjust the nonlinear control variable. According to the

Mei Baishan; Liu Haihua; Zhang Jinping

2009-01-01

196

Observer-based robust fuzzy controller design for uncertain stochastic TS fuzzy model with passivity performance  

Microsoft Academic Search

The issue of observer-based robust passive fuzzy controller design is discussed and investigated in this paper for uncertain stochastic Takagi-Sugeno (T-S) fuzzy model with external disturbance. For describing the stochastic behaviors of system, the stochastic differential equation is used to structure the stochastic T-S fuzzy model for representing the nonlinear stochastic systems. Using the Lyapunov function and passivity theory, the

Cheung-Chieh Ku; Pei-Hwa Huang; Wen-Jer Chang

2009-01-01

197

Robust H? reliable control for uncertain singular stochastic fuzzy systems  

Microsoft Academic Search

This paper is concerned with the design problem of robust Hinfin reliable controller for nonlinear singular stochastic systems with actuator failures via a fuzzy-control approach. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear singular stochastic system with norm-bounded parameter uncertainties. The objective is to design a state feedback fuzzy controller such that, for all admissible uncertainties as

Aiqing Zhang; Huajing Fang

2008-01-01

198

Robust Reliable Fuzzy Control for Uncertain Markovian Jump Singular Systems  

Microsoft Academic Search

This paper deals with the problem of robust reliable control for Markovian jump nonlinear singular systems with Wiener process via a fuzzy-control approach. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear singular system with norm-bounded parameter uncertainties and Markovian jump parameters. The objective is to design a state feedback fuzzy controller such that, for all admissible uncertainties

Aiqing Zhang

2008-01-01

199

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models  

PubMed Central

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

200

Adaptive vector quantization with fuzzy distortion measure for image coding  

NASA Astrophysics Data System (ADS)

Despite the proven superiority of vector quantization (VQ) over scalar quantization (SQ) in terms of rate distortion theory, currently existing vector quantization algorithms, still, suffer from several practical drawbacks, such as codebook initialization, long search-process, and optimization of the distortion measure. We present a new adaptive vector quantization algorithm that uses a fuzzy distortion measure to find a globally optimum codebook. The generation of codebooks is facilitated by a self-organizing neural network-based clustering that eliminates adhoc assignment of the codebook size as required by standard statistical clustering. In addition, a multiresolution wavelet decomposition of the original image enhances the process of codebook generation. Preliminary results using standard monochrome images demonstrate excellent convergence of the algorithm, significant bit rate reduction, and yield reconstructed images with high visual quality and good PSNR and MSE. Extension of this adaptive VQ to color image compression is currently under investigation.

Pemmaraju, Suryalakshmi; Mitra, Sunanda; Long, L. Rodney; Thoma, George R.; Shieh, Yao-Yang; Roberson, Glenn H.

1996-04-01

201

Neuro-fuzzy synthesis of flight control electrohydraulic servo  

Microsoft Academic Search

Presents a switching type neuro-fuzzy control synthesis. The control algorithm supposes as a component part a neurocontrol designed to optimize a performance index. Whenever the neurocontrol saturates or a certain performance parameter of the system decreases, the scheme of control switches to a feasible and reliable fuzzy logic control. Describes the procedure of return to the optimizing neurocontrol which is

Ioan Ursu; Felicia Ursu; Lucian Iorga

2001-01-01

202

Computer control system based on fuzzy control for boilers  

NASA Astrophysics Data System (ADS)

According tp the features of the combustion process of boiler the optimization of combustion is implemented by using fuzzy control principle. The paper states a control strategy implementing different control regulation in different phases (coarse, fine and precision tuning) for enhancing the thermal efficiency of combustion of boiler. The practice shows that the thermal efficiency increased 2.8%.

Zheng, Dezhong; Shang, Liping; Shi, Jinghao

2000-10-01

203

Eliminating current sensors of Indirect Matrix Converter using neuro-fuzzy controller  

Microsoft Academic Search

This paper describes non-linear average model of Indirect Matrix Converter (IMC) with an output LC filter in stationary and rotating reference frames. The defects of pervious control strategies based on derived average model are discussed and a novel adaptive neuro-fuzzy controller is proposed. Eliminating output current sensors, good dynamic performance in any operating point without overshoot and balanced output voltages

Alireza Jahangiri; Ahmad Radan

2011-01-01

204

Intelligent fuzzy controller for event-driven real time systems  

NASA Technical Reports Server (NTRS)

Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

1992-01-01

205

Fuzzy Swing-Up and Fuzzy Sliding-Mode Balance Control for a Planetary-Gear-Type Inverted Pendulum  

Microsoft Academic Search

An energy-compensated fuzzy swing-up and balance control is investigated for the planetary-gear-type inverted pendulum (PIP) in this paper. The proposed control scheme consists of a fuzzy swing-up controller (FSC), a fuzzy sliding balance controller (FSBC), and a fuzzy compensation mechanism. The PIP with the designed FSC can upswing the pendulum quickly and have the controlled system be stable in the

Yeong-Hwa Chang; Chia-Wen Chang; Jin-Shiuh Taur; Chin-Wang Tao

2009-01-01

206

Active structural control by fuzzy logic rules: An introduction  

SciTech Connect

A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

Tang, Yu [Argonne National Lab., IL (United States). Reactor Engineering Div.; Wu, Kung C. [Texas Univ., El Paso, TX (United States). Dept. of Mechanical and Industrial Engineering

1996-12-31

207

Active structural control by fuzzy logic rules: An introduction  

SciTech Connect

An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

Tang, Y.

1995-07-01

208

A Type2 Fuzzy Switching Control System for Biped Robots  

Microsoft Academic Search

In this paper, a type-2 fuzzy switching control system is proposed for a biped robot, which includes switched nonlinear system modeling, type-2 fuzzy control system design, and a type-2 fuzzy modeling algorithm. A new switched system model is proposed to represent the continuous-time dynamic and discrete-event dynamic of a walking biped as a whole, which is helpful to analyze the

Zhi Liu; Yun Zhang; Yaonan Wang

2007-01-01

209

Stable and robust fuzzy control for uncertain nonlinear systems  

Microsoft Academic Search

Presents the stability and robustness analysis for multivariable fuzzy control systems subject to parameter uncertainties based on a single-grid-point (SGP) approach. To perform the analysis, we represent a multivariable nonlinear system using a TS-fuzzy plant model. Three design approaches of fuzzy controllers are introduced to close the feedback loop. By estimating the matrix measures of the system parameters and parameter

H. K. Lam; F. H. Frank Leung; Peter Kwong-shun Tam

2000-01-01

210

Reliable Control for Fuzzy Stochastic Hyperbolic Systems with Time Delay  

Microsoft Academic Search

This paper investigates the robust reliable control problems for the fuzzy stochastic hyperbolic systems with time delay and actuator faults. Based on linear matrix inequality (LMI) and Lyapunov-Krasovskii approach, two classes of designing methods of reliable controllers for the fuzzy stochastic hyperbolic systems are presented. The corresponding closed-loop systems are asymptotically stable in mean square not only when all control

Yizhong Wang; Jinye Wang; Huaguang Zhang

2006-01-01

211

Fuzzy logic control implementation in sensorless PM drive systems  

E-print Network

Fuzzy logic control implementation in sensorless PM drive systems Kasim M. Al-Aubidy1 and Ghada M. In this paper, a fuzzy logic controller is proposed for the real-time control of a Sensorless PM drive system in applications where simplicity, reliability and stability are more important issues. Furthermore, the proposed

212

Control of Magnetic Levitation System Using Fuzzy Logic Control  

Microsoft Academic Search

This paper presents the investigation on a system model for the stabilisation of a Magnetic Levitation System (Maglev's). Furthermore, the investigation on Proportional Integrated Derivative Controller (PID) also reported here. In this paper shows to design both PID and Fuzzy Logic Control (FLC) based on the system model. Maglev's give the contribution in industry and this system has reduce the

A. K. Ahmad; Z. Saad; M. K. Osman; I. S. Isa; S. Sadimin; S. S. Abdullah

2010-01-01

213

What procedure to choose while designing a fuzzy control? Towards mathematical foundations of fuzzy control  

NASA Technical Reports Server (NTRS)

Fuzzy control has been successfully applied in industrial systems. However, there is some caution in using it. The reason is that it is based on quite reasonable ideas, but each of these ideas can be implemented in several different ways, and depending on which of the implementations chosen different results are achieved. Some implementations lead to a high quality control, some of them not. And since there are no theoretical methods for choosing the implementation, the basic way to choose it now is experimental. But if one chooses a method that is good for several examples, there is no guarantee that it will work fine in all of them. Hence the caution. A theoretical basis for choosing the fuzzy control procedures is provided. In order to choose a procedure that transforms a fuzzy knowledge into a control, one needs, first, to choose a membership function for each of the fuzzy terms that the experts use, second, to choose operations of uncertainty values that corresponds to 'and' and 'or', and third, when a membership function for control is obtained, one must defuzzy it, that is, somehow generate a value of the control u that will be actually used. A general approach that will help to make all these choices is described: namely, it is proved that under reasonable assumptions membership functions should be linear or fractionally linear, defuzzification must be described by a centroid rule and describe all possible 'and' and 'or' operations. Thus, a theoretical explanation of the existing semi-heuristic choices is given and the basis for the further research on optimal fuzzy control is formulated.

Kreinovich, Vladik YA.; Quintana, Chris; Lea, Robert

1991-01-01

214

Fuzzy logic control of AC induction motors  

NASA Astrophysics Data System (ADS)

The fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads is discussed. Electric motors use 60 percent of the electrical energy generated in the U.S. An improvement of 1 percent in operating efficiency of all electric motors could result in savings of 17 billion kWh per year in the U.S. New techniques are required to extract maximum performance from modern motors. One possibility, FLC, has recently demonstrated success in solving control problems of nonlinear, multivariable systems such as ac induction motors and adjustable motor-speed drives. Simulated results of a microprocessor-based fuzzy logic motor controller (FLMC) are described. The investigation includes a motor stator voltage control scheme to minimize motor input power at specified speed/torque conditions; simulation of ac motor performance; and development of a FLMC for optimized motor efficiency. Simulated FLMC results compare favorably with other motor control approaches. Potential energy savings are quantitated based on the preliminary predictions of FLMC performance.

Cleland, J.; Turner, W.; Wang, P.; Espy, T.; Chappell, P. J.

215

A fast pruning algorithm for an Efficient Adaptive Fuzzy Neural Network  

Microsoft Academic Search

A fast pruning algorithm for an Efficient Adaptive Fuzzy Neural Network (EAFNN) is presented in this paper. An EAFNN is a Takagi-Sugeno-Kang (TSK) type fuzzy model which is functionally equivalent to the Ellipsoidal Basis Function (EBF) neural network. An EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology

Du Juan; Er Meng Joo

2010-01-01

216

Adaptive tuning of a Kalman filter using the fuzzy integral for an intelligent navigation system  

Microsoft Academic Search

This paper describes the development of an intelligent, adaptive tuning system for a Kalman filter to optimally integrate data from an inertial navigation system (INS) and the Global Positioning System (GPS). This system is particularly useful for accurate navigation of an aircraft during maneuvering periods. The tuning algorithm is based on fuzzy logic. Specifically, the inference method in the fuzzy

Roya Rahbari; Barrie W. Leach; Jeremy Dillon; C. W. de Silva

2002-01-01

217

Adaptive Control Strategies for Flexible Robotic Arm  

NASA Technical Reports Server (NTRS)

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.

Bialasiewicz, Jan T.

1996-01-01

218

Stabilization ball and beam by fuzzy logic control strategy  

NASA Astrophysics Data System (ADS)

Fuzzy logic controller is a controller for designing the challenging nonlinear control systems by If-Then laws that is like human intelligence and it increase the accuracy of the control action .This paper present a success control function using a Fuzzy System approach which is to control the Ball-Beam balance system, throughout modeling, simulation, and implementation. First we applied fuzzy logic for system which means for the outer loop a fuzzy logic controller is designed and for the inner loop of a ball and beam system a PD controller is implemented. We design a traditional PID controller and pole placement controller for the beam position in order to compare the performance of these three types of controllers; thus FLC found to give better transient and steady state results and there is less overshoot in compare with classical PID and pole placement controller. Simulation results are presented to show the better performance of the ball and beam using these controllers.

Asadi, Houshyar; Mohammadi, Arash; Oladazimi, Maysam

2012-01-01

219

Stabilization ball and beam by fuzzy logic control strategy  

NASA Astrophysics Data System (ADS)

Fuzzy logic controller is a controller for designing the challenging nonlinear control systems by If-Then laws that is like human intelligence and it increase the accuracy of the control action .This paper present a success control function using a Fuzzy System approach which is to control the Ball-Beam balance system, throughout modeling, simulation, and implementation. First we applied fuzzy logic for system which means for the outer loop a fuzzy logic controller is designed and for the inner loop of a ball and beam system a PD controller is implemented. We design a traditional PID controller and pole placement controller for the beam position in order to compare the performance of these three types of controllers; thus FLC found to give better transient and steady state results and there is less overshoot in compare with classical PID and pole placement controller. Simulation results are presented to show the better performance of the ball and beam using these controllers.

Asadi, Houshyar; Mohammadi, Arash; Oladazimi, Maysam

2011-12-01

220

Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning  

NASA Astrophysics Data System (ADS)

The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.

Lin, J.; Zheng, Y. B.

2012-07-01

221

Self-learning fuzzy controllers based on temporal back propagation  

NASA Technical Reports Server (NTRS)

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

Jang, Jyh-Shing R.

1992-01-01

222

Reliable control design of fuzzy dynamic systems with time-varying delay  

Microsoft Academic Search

This paper focuses on the reliable fuzzy control design problem for fuzzy control systems with time delay. Based on linear matrix inequality (LMI) approach, a method for designing a reliable fuzzy controller is presented. The resulting fuzzy control systems are reliable in the sense that asymptotic stability is achieved not only when all control components are operating well, but also

Bing Chen; Xiaoping Liu

223

Reliable control design of fuzzy dynamic systems with time-varying delay  

Microsoft Academic Search

Abstract This paper focuses on the reliable fuzzy control design problem for fuzzy control systems with time delay. Based on linear matrix inequality (LMI) approach, a method for designing a reliable fuzzy controller is presented. The resulting fuzzy control systems are reliable in the sense that asymptotic stability is achieved not only when all control components are operating well, but

Bing Chen; Xiaoping Liu

2004-01-01

224

Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization  

Microsoft Academic Search

In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that

Boumediene ALLAOUA; Abdellah LAOUFI; Brahim GASBAOUI; Abdelfatah NASRI; Abdessalam ABDERRAHMANI

2008-01-01

225

Stability analysis for a class of Takagi–Sugeno fuzzy control systems with PID controllers  

Microsoft Academic Search

A new stability analysis method for a class of fuzzy control systems based on Takagi–Sugeno models and PID controllers is proposed in this paper. It has shown that the traditional Takagi–Sugeno fuzzy state models with certain matrix structures are equivalent to fuzzy transfer function models, which have two consequents in each rule. Then, when the PID controllers utilising the overall

Le Hung Lan

2007-01-01

226

Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters  

Microsoft Academic Search

Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method

Qilian Liang; Jerry M. Mendel

2000-01-01

227

Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems  

Microsoft Academic Search

The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of

S. Jassar; Z. Liao; L. Zhao

2009-01-01

228

Fuzzy Economizer control using a Prolog-C inference engine  

E-print Network

This research is in two parts: I. Develop a generic tool to perform fuzzy inference on a wide class of systems.Thisis done using Prolog and C. 2.Develop a hierarchical control scheme using this fuzzy inference mechanism tool for a constant volume...

Belur, Raghuveer R.

2012-06-07

229

Coordination of Distributed Fuzzy Behaviors in Mobile Robot Control  

NASA Technical Reports Server (NTRS)

This presentation describes an approach to behavior coordination and conflict resolution within the context of a hierarchical architecture of fuzzy behaviors. Coordination is achieved using weighted decision-making based on behavioral degrees of applicability. This strategy is appropriate for fuzzy control of systems that can be represented by hierarchical or decentralized structures.

Tunstel, E.

1995-01-01

230

A new design method for reliable fuzzy control  

Microsoft Academic Search

This paper concerns about the reliable control design problem for the fuzzy hyperbolic model (FHM) with time-varying delay and actuator faults. FHM is a universal approximate as well as a global model and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model, and so the model parameters can

Xijun Zhu; Jingjing Wang; Yueming Dai

2008-01-01

231

Fuzzy logic control of a solar power plant  

Microsoft Academic Search

This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy

Francisco R. Rubio; Manuel Berenguel; Eduardo F. Camacho

1995-01-01

232

Fuzzy attitude control for a nanosatellite in leo orbit  

NASA Astrophysics Data System (ADS)

Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In this work, a tailored fuzzy controller is designed for a nanosatellite and is compared with a traditional Proportional Integrative Derivative (PID) controller. Both control methodologies are compared within the same specific mission. The orbit height varies along the mission from injection at around 380 km down to a 200 km height orbit, and the mission requires pointing accuracy over the whole time. Due to both the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, a robust and efficient ADCS is required. For these reasons a fuzzy logic controller is implemented as the brain of the ADCS and its performance and efficiency are compared to a traditional PID. The fuzzy controller is designed in three separated controllers, each one acting on one of the Euler angles of the satellite in an orbital frame. The fuzzy memberships are constructed taking into account the mission requirements, the physical properties of the satellite and the expected performances. Both methodologies, fuzzy and PID, are fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. Finally both methods are probed in different environments to test their characteristics. The simulations show that the fuzzy controller is much more efficient (up to 65% less power required) in single maneuvers, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. A brief mission description is depicted as well as the design process of both ADCS controllers. Finally the validation process and the results obtained during the simulations are described. Those results show that the fuzzy logic methodology is valid for small satellites' missions benefiting from a well-developed artificial intelligence theory.

Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria; Aviles, Taisir

233

Optimal fuzzy control of the spindle motor in a CD-ROM drive using genetic algorithms  

Microsoft Academic Search

An optimal controller of the spindle motor in a CD-ROM drive is designed using fuzzy logic with genetic algorithms. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are searched by means of genetic algorithms. Computer simulations demonstrate that the fuzzy controller

Gwo-Ruey Yu; Rey-Chue Hwang; Chi-Pei Lin

2004-01-01

234

Advance of Systematic Design Methods on Fuzzy Control  

E-print Network

The heating, ventilation and air-conditioning (HVAC) system possesses some characteristics such as multi-parameters, nonlinear, and coupled parameters. Aimed at control problems, the author targets real-time fuzzy control and research systematically...

Zhang, J.; Chen, Y.

2006-01-01

235

Distributed traffic signal control using fuzzy logic  

NASA Technical Reports Server (NTRS)

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

Chiu, Stephen

1992-01-01

236

Grey prediction and fuzzy control method for road tunnel ventilation  

Microsoft Academic Search

To control road tunnel ventilation is very difficult because of its high no-linearity and random property. In this paper the grey prediction and fuzzy control method is developed for road tunnel ventilation control. The method includes two prominent blocks, i.e., grey predictor and fuzzy controller. The grey predictor is realized based on discrete GM(1,1) and can rollingly predict the system

Jiantao Chen; Yunhua Li; Xingjun Tian

2011-01-01

237

Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback  

E-print Network

Parameter specification of a fuzzy inference system (HS) with the aid of artificial neural networks allows the creation of complex, multi-dimensional models that are computationally efficient and numerically robust. An adaptive neuro-fuzzy inference...

Schurter, Kyle Christopher

2012-06-07

238

Fuzzy throttle and brake control for platoons of smart cars  

Microsoft Academic Search

Additive fuzzy systems can control the velocity and the gap between cars in single-lane platoons. The overall system consists of throttle and brake controllers. We first designed and tested a throttle-only fuzzy system on a validated car model and then with a real car on highway 1–15 in California. We used this controller to drive the “smart” car on the

Hyun Mun Kim; Julie Dickerson; Bart Kosko

1996-01-01

239

Advances in Adaptive Control Methods  

NASA Technical Reports Server (NTRS)

This poster presentation describes recent advances in adaptive control technology developed by NASA. Optimal Control Modification is a novel adaptive law that can improve performance and robustness of adaptive control systems. A new technique has been developed to provide an analytical method for computing time delay stability margin for adaptive control systems.

Nguyen, Nhan

2009-01-01

240

Enhancing Transparent Fuzzy Controllers Through Temporal Concepts: An Application to Computer Games  

Microsoft Academic Search

In the last years, FML (Fuzzy Markup Language) is emerging as one of the most efficient and useful language to define a fuzzy control thanks to its capability of modeling Fuzzy Logic Controllers in a human-readable and hardware independent way, i.e. the so-called Transparent Fuzzy Controllers (TFCs). However, although a FML fuzzy control is suitable to be employed in a

Giovanni Acampora; Vincenzo Loia; Autilia Vitiello

2010-01-01

241

The application of variable universe fuzzy PID controller in computer-aided alignment of lithography projector  

NASA Astrophysics Data System (ADS)

A variable universe fuzzy PID algorithm is designed to control the misalignment of the lithography projection optics to meet the requirement of high image quality. This paper first simulates the alignment of Schwarzschild objective designed by us. Secondly, the variable universe fuzzy PID control is introduced to feed back the misalignment of Schwarzschild objective to the control system to drive the stage which holds the objective. So the position can be adjusted automatically. This feedback scheme can adjust the variables' universe self-adaptively by using fuzzy rules so that the concrete function and parameters of the contraction-expansion factor are not necessary. Finally, the proposed approach is demonstrated by simulations. The results show that, variable universe fuzzy PID method exhibits better performance in both improving response speed and decreasing overshoot compared to conventional PID and fuzzy PID control methods. In addition, the interference signal can be effectively restrained. It is concluded that this method can improve the dynamic and static properties of system and meet the requirement of fast response.

Zhang, Mei; Zheng, Meng; Li, Yanqiu

2013-12-01

242

Adaptive neuro-fuzzy estimation of optimal lens system parameters  

NASA Astrophysics Data System (ADS)

Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

Petkovi?, Dalibor; Pavlovi?, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani

2014-04-01

243

Power control of SAFE reactor using fuzzy logic  

NASA Astrophysics Data System (ADS)

Controlling the 100 kW SAFE (Safe Affordable Fission Engine) reactor consists of design and implementation of a fuzzy logic process control system to regulate dynamic variables related to nuclear system power. The first phase of development concentrates primarily on system power startup and regulation, maintaining core temperature equilibrium, and power profile matching. This paper discusses the experimental work performed in those areas. Nuclear core power from the fuel elements is simulated using resistive heating elements while heat rejection is processed by a series of heat pipes. Both axial and radial nuclear power distributions are determined from neuronic modeling codes. The axial temperature profile of the simulated core is matched to the nuclear power profile by varying the resistance of the heating elements. The SAFE model establishes radial temperature profile equivalence by establishing 32 control zones as the nodal coordinates. Control features also allow for slow warm up, since complete shutoff can occur in the heat pipes if heat-source temperatures drop/rise below a certain minimum value, depending on the specific fluid and gas combination in the heat pipe. The entire system is expected to be self-adaptive, i.e., capable of responding to long-range changes in the space environment. Particular attention in the development of the fuzzy logic algorithm shall ensure that the system process remains at set point, virtually eliminating overshoot on start-up and during in-process disturbances. The controller design will withstand harsh environments and applications where it might come in contact with water, corrosive chemicals, radiation fields, etc. .

Irvine, Claude

2002-01-01

244

Time-Delay Dependent State Feedback Fuzzy-Predictive Control of Time-Delay TS Fuzzy Model  

Microsoft Academic Search

A new predictive control approach based on time-delay T-S fuzzy model is developed by combining time-delay T-S fuzzy model and time-delay state feedback predictive control. Multi-time delayed states and multi-time delayed inputs are considered in the proposed predictive control algorithm. The predictive control is designed by using time-delay T-S fuzzy model as predictive model of the Time-delay state feedback predictive

Shubin Wang; Yanyun Wang; Lingkao Zhang

2008-01-01

245

Internal model control with a fuzzy model: application to an air-conditioning system  

Microsoft Academic Search

Fuzzy models can represent highly nonlinear processes and can smoothly integrate a priori knowledge with information obtained from process data. A nonlinear controller can be designed by incorporating an inverted fuzzy model of the process in an internal model control (IMC) scheme. This paper presents an identification procedure for a Takagi-Sugeno fuzzy model, which is based on product-space fuzzy clustering.

J. M. Sousa; R. Babuska; H. B. Verbruggen

1997-01-01

246

Reliable LQ fuzzy control for nonlinear discrete-time systems via LMIs  

Microsoft Academic Search

This paper studies the reliable linear quadratic (LQ) fuzzy regulator problem for nonlinear discrete-time systems with actuator faults. The Takagi-Sugeno fuzzy model is employed to represent a nonlinear system. A sufficient condition expressed in linear matrix inequality (LMI) terms for the existence of reliable guaranteed cost (GC) fuzzy controllers is obtained. The fuzzy controller directly obtained from the LMI solutions

Huai-ning Wu

2004-01-01

247

Optimal fuzzy controller for backing up a truck using simulated annealing algorithm  

Microsoft Academic Search

Design of a fuzzy controller requires specification of both membership functions and decision rules. Specification of membership function for a fuzzy logic controller has been an important issue. The traditional way of selecting membership functions has been, in most cases, an adhoc procedure. In this paper, an optimization algorithm based on simulated annealing for designing fuzzy membership functions for fuzzy

Syed A. Akbar; Wiley E. Thompson

1996-01-01

248

Fuzzy Control of Stochastic Global Optimization Algorithms and Very ...  

E-print Network

methods seem to be a good (sometimes , the only) way to go . ... speed it up significantly and to reduce enormously (perhaps eliminate) the ... controller) that does nothing more than emulate human reasoning ... quenching factor . The fuzzy ...

249

Adaptive sequential controller  

DOEpatents

An adaptive sequential controller (50/50') for controlling a circuit breaker (52) or other switching device to substantially eliminate transients on a distribution line caused by closing and opening the circuit breaker. The device adaptively compensates for changes in the response time of the circuit breaker due to aging and environmental effects. A potential transformer (70) provides a reference signal corresponding to the zero crossing of the voltage waveform, and a phase shift comparator circuit (96) compares the reference signal to the time at which any transient was produced when the circuit breaker closed, producing a signal indicative of the adaptive adjustment that should be made. Similarly, in controlling the opening of the circuit breaker, a current transformer (88) provides a reference signal that is compared against the time at which any transient is detected when the circuit breaker last opened. An adaptive adjustment circuit (102) produces a compensation time that is appropriately modified to account for changes in the circuit breaker response, including the effect of ambient conditions and aging. When next opened or closed, the circuit breaker is activated at an appropriately compensated time, so that it closes when the voltage crosses zero and opens when the current crosses zero, minimizing any transients on the distribution line. Phase angle can be used to control the opening of the circuit breaker relative to the reference signal provided by the potential transformer.

El-Sharkawi, Mohamed A. (Renton, WA); Xing, Jian (Seattle, WA); Butler, Nicholas G. (Newberg, OR); Rodriguez, Alonso (Pasadena, CA)

1994-01-01

250

A hybrid adaptive control strategy for a smart prosthetic hand.  

PubMed

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is in progress to extend this methodology to a five-fingered, three-dimensional prosthetic hand. PMID:19964853

Chen, Cheng-Hung; Naidu, D Subbaram; Perez-Gracia, Alba; Schoen, Marco P

2009-01-01

251

An algorithm of the adaptive grid and fuzzy interacting multiple model  

NASA Astrophysics Data System (ADS)

This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model (AGFIMM) for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model (FSMM) algorithm's cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model (IMM) algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for engineering applications.

Zhang, Yuan; Guo, Chen; Hu, Hai; Liu, Shubo; Chu, Junbo

2014-09-01

252

Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants  

Microsoft Academic Search

A data-driven adaptive neurofuzzy controller is presented for the water-level control of U-tube steam generators in nuclear power plants. This neurofuzzy controller is capable of learning the control action principles from the data obtained using other methods of automatic or manual control. There are four inputs in the neurofuzzy system, yet only eighty fuzzy rules involved. Therefore, the fuzzy system

Sudath R. Munasinghe; Min-Soeng Kim; Ju-Jang Lee

2005-01-01

253

An Efficient Adaptive Fuzzy Neural Network (EAFNN) Approach for Short Term Load Forecasting  

Microsoft Academic Search

\\u000a In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed\\u000a approach is based on an ellipsoidal basis function (EBF) neural network, which is functionally equivalent to the TSK model-based\\u000a fuzzy system. EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal\\u000a Brain Surgeon (OBS)

Juan Du; Meng Joo Er; Leszek Rutkowski

2010-01-01

254

Adaptive neuro-fuzzy approach for predicting hardness of deposited TiN/ZrN multilayer coatings.  

PubMed

This paper presents an adaptive neuro-fuzzy approach based on first order function of fuzzy model for establishing the relationship between control factors and thin films properties of TiN/ZrN coatings on Si(100) wafer substrates. A statistical model was designed to explore the space of the processes by an orthogonal array scheme. Eight control factors of closed unbalance magnetron sputtering system were selected for modeling the process, such as interlayer material, argon and nitrogen flow rate, titanium and zirconium target current, rotation speed, work distance, and bias voltage. Analysis of variance (ANOVA) was carried out for determining the influence of control factors. In this study, with the application of ANOVA, the smallest effect of control factors was eliminated. The adaptive neuro-fuzzy inference system (ANFIS) was applied as a tool to model the deposited process with five significant control factors. The experimental results show that ANFIS demonstrates better accuracy than additive model for the film hardness. The root mean square error between prediction values and experimental values were archived to 0.04. PMID:21128476

Yang, Yu-Sen; Huang, Wesley; Huang, Guo-Ping; Chou, Jyh-Horng

2010-07-01

255

Application of self-adjustment PID fuzzy controller in inverter air-conditioning control system  

NASA Astrophysics Data System (ADS)

Two-dimensional fuzzy temperature controller was used in the system of inverter air-conditioner to control the room temperature. Self-adjustment PID fuzzy controller was proposed to realize real-time control function. V / F control mode was used to compensate with low voltage, the average sampling algorithm was proposed to implement SPWM waveform modulation. Finally, the feasibility of self-adjustment PID fuzzy controller was verified by simulation, the actual operation results had proved that self-adjustment PID fuzzy controller had reliable, good output waveform and small harmonic wave, which could meet operational requirements of inverter air-conditioner.

Jiang, Jing; Zhang, Xuesong

2013-03-01

256

Neuro-fuzzy controller to navigate an unmanned vehicle.  

PubMed

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105

Selma, Boumediene; Chouraqui, Samira

2013-12-01

257

PID plus fuzzy controller structures as a design base for industrial applications  

E-print Network

of the scaling factors of a fuzzy PID-type controller with other fuzzy systems, used in the excitation control; PID controllers; Control-system design 1. Fuzzy control vs. PID control: fight or collaboration controllers are robust and simple to design. b) There exists a clear relationship between PID and system

Reznik, Leon

258

Evolving fuzzy decision tree structure that adapts in real-time  

Microsoft Academic Search

A fuzzy logic algorithm has been developed that automatically allocates electronic attack (EA) resources distributed over different platforms in real-time. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the

James F. III smith

2005-01-01

259

Adaptive Inflow Control System  

E-print Network

This article presents the idea and realization for the unique Adaptive Inflow Control System being a part of well completion, able to adjust to the changing in time production conditions. This system allows to limit the flow rate from each interval at a certain level, which solves the problem of water and gas breakthroughs. We present the results of laboratory tests and numerical calculations obtaining the characteristics of the experimental setup with dual-in-position valves as parts of adaptive inflow control system, depending on the operating conditions. The flow distribution in the system was also studied with the help of three-dimensional computer model. The control ranges dependences are determined, an influence of the individual elements on the entire system is revealed.

Volkov, Vasily Y; Zhuravlev, Oleg N; Nukhaev, Marat T; Shchelushkin, Roman V

2014-01-01

260

Sampled-Data Fuzzy Controller for Time-Delay Nonlinear Systems: Fuzzy-Model-Based LMI Approach  

Microsoft Academic Search

This paper presents the stability analysis and performance design for a sampled-data fuzzy control system with time delay, which is formed by a nonlinear plant with time delay and a sampled-data fuzzy controller connected in a closed loop. As the sampled-data fuzzy controller can be implemented by a microcontroller or a digital computer, the implementation time and cost can be

H. K. Lam; F. H. Frank Leung

2007-01-01

261

Fuzzy semi-active control of MR damper for structural base isolation  

Microsoft Academic Search

This paper presents four types of semi-active control on Magnetorheological (MR) Damper in an experimental base isolation structure model with three degree-of-freedom. The semi-active control methods include proportional-derivative (PD) control, and three fuzzy control methods: rule-based fuzzy logic control, auto-tuning fuzzy PD control, and discrete fuzzy PD control. The main purpose is to compare the response effect between passive control

Han Wang; Heidar A. Malki; Gangbing Song

2009-01-01

262

ADAPTIVE FUZZY SLIDING-MODE CONTROL OF SPEED SENSORLESS UNIVERSAL FIELD ORIENTED INDUCTION MOTOR DRIVE WITH ON-LINE STATOR RESISTANCE TUNING  

Microsoft Academic Search

In this paper, a speed sensorless induction motor drive is introduced which is direct vector controlled in a universal field-oriented (UFO) reference frame. This chosen reference frame is easily linked with direct and indirect rotor, stator and air gap field orientation control schemes of the induction machine (IM) drives using a stator to rotor virtual turn ratio. Based on partial

J. SOLTANI; Y. ABDOLMALEKI; M. HAJIAN

263

Controlling the speed of Coding Line Conveyor using fuzzy logic  

Microsoft Academic Search

This paper presents performance improvement of a Coding Line Conveyer system (CLC), which is a key component of the automated parcels sorting complexes PP2000 used in logistics centers of Swiss Post. Normally, CLC operates with constant speed. The rules based on a fuzzy logic are applied to adapt the speed of CLC transportation belts. The rules take into account how

Atanas Atanassov

2009-01-01

264

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks  

NASA Astrophysics Data System (ADS)

Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

2010-09-01

265

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks  

NASA Astrophysics Data System (ADS)

Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

2011-01-01

266

Skew-Tree Based Multistage Fuzzy Controller for Nonlinear Systems  

Microsoft Academic Search

In this paper a multistage fuzzy controller is designed and implemented on the ball and beam system which is a well-known benchmark in the control area because of its nonlinear, unstable non-minimum phase behavior. The most important property of the proposed controller is its simplicity whiles it fully mimics human's actions for controlling this system. In comparison to classical controllers

Abbas Khosravi; Jie Lu; Xianyi Zeng; R. Barzamini

2007-01-01

267

Electric control system of inverter air-conditioning based on fuzzy control  

Microsoft Academic Search

Fuzzy controller of temperature was designed in the inverter air-condition with dual purpose both cold and hot. The control system of inverter air conditioner was divided two parts, that is, indoor machine and the outdoor machine. The frequency control of indoor and outside machine was realized by control software in the foundation of hardware circuit design. Fuzzy control program was

Jing Jiang; Xuesong Zhang

2010-01-01

268

Fuzzy discrete event system modeling and temporal fuzzy reasoning in urban traffic control  

Microsoft Academic Search

In this paper, P DEVS-based fuzzy supervisory conirdlcr is applicd to an urban traffic control problem also modeled from a discrete event perspective via state flow formalism. Time varying nature of intersections, involvement of human behavior in the systcm and distributed naturc of the problcin at large makes the urban trafic control a challenging problem. Specifically, bucause of this distributed

A. Akramizadeh; M.-R. Akbarzadeh-T; M. Khademi

2004-01-01

269

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions  

Microsoft Academic Search

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature conver- gence problem and improve the final results. One of

Francisco Herrera; Manuel Lozano

2003-01-01

270

Adaptive control with nonconvex parameterization  

Microsoft Academic Search

Deals with the problem of nonconvex parameterization in adaptive control for a class of nonlinear dynamic systems. New algorithms for stable adaptive control are introduced. Sufficient conditions for achieving control objectives of adaptive control with new algorithms are investigated. Simulation examples are presented for illustration.

Ivan Yu. Tyukin; Danil V. Prokhorov; Valery A. Terekhov

2003-01-01

271

Adaptive neuro fuzzy inference system for profiling of the atmosphere  

NASA Astrophysics Data System (ADS)

Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June-September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2014-03-01

272

Fuzzy logic control of the building structure with CLEMR dampers  

NASA Astrophysics Data System (ADS)

The semi-active control technology has been paid more attention in the field of structural vibration control due to its high controllability, excellent control effect and low power requirement. When semi-active control device are used for vibration control, some challenges must be taken into account, such as the reliability and the control strategy of the device. This study presents a new large tonnage compound lead extrusion magnetorheological (CLEMR) damper, whose mathematical model is introduced to describe the variation of damping force with current and velocity. Then a current controller based on the fuzzy logic control strategy is designed to determine control currents of the CLEMR dampers rapidly. A ten-floor frame structure with CLEMR dampers using the fuzzy logic control strategy is built and calculated by using MATLAB. Calculation results show that CLEMR dampers can reduce the seismic responses of structures effectively. Calculation results of the fuzzy logic control strategy are compared with those of the semi-active limit Hrovat control structure, the passive-off control structure, and the uncontrolled structure. Comparison results show that the fuzzy logic control strategy can determine control currents of CLEMR dampers quickly and can reduce seismic responses of the structures more effectively than the passive-off control strategy and the uncontrolled structure.

Zhang, Xiang-Cheng; Xu, Zhao-Dong; Huang, Xing-Huai; Zhu, Jun-Tao

2013-04-01

273

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING Int. J. Adapt. Control Signal Process. 2011; 25:813830  

E-print Network

; unscented Kalman predictor; fuzzy logic; error covariance 1. INTRODUCTION Process safety and reliability.com). DOI: 10.1002/acs.1243 Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor Xuemin prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter

Chen, Sheng

274

Optimal fuzzy controller for backing up a truck using simulated annealing algorithm  

NASA Astrophysics Data System (ADS)

Design of a fuzzy controller requires specification of both membership functions and decision rules. Specification of membership function for a fuzzy logic controller has been an important issue. The traditional way of selecting membership functions has been, in most cases, an adhoc procedure. In this paper, an optimization algorithm based on simulated annealing for designing fuzzy membership functions for fuzzy controllers is introduced. An optimization algorithm for designing the membership functions and fuzzy rule base for a fuzzy controller is presented. An optimal fuzzy controller using proposed technique for backing up a truck problem is designed and implemented, in which optimal fuzzy membership functions and fuzzy rules are designed. Also, a neural network controller for the same truck problem is designed to compare our results.

Akbar, Syed A.; Thompson, Wiley E.

1996-06-01

275

Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes  

NASA Technical Reports Server (NTRS)

It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

Duerksen, Noel

1997-01-01

276

Fuzzy logic control of ball and beam system  

Microsoft Academic Search

Fuzzy logic controller (FLC) is an attractive alternative to existing classical or modern controllers for designing the challenging Non-linear control systems as it provides a heuristically method by If-Then Rules which resembles human intelligence. This paper presents the design and analysis of a FLC for the outer loop and a PD controller for the inner loop of a ball and

M. Amjad; M. I. Kashif; S. S. Abdullah; Z. Shareef

2010-01-01

277

A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers  

NASA Technical Reports Server (NTRS)

This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.

Woodard, Stanley E.; Garg, Devendra P.

1998-01-01

278

Adaptive control for accelerators  

DOEpatents

An adaptive feedforward control loop is provided to stabilize accelerator beam loading of the radio frequency field in an accelerator cavity during successive pulses of the beam into the cavity. A digital signal processor enables an adaptive algorithm to generate a feedforward error correcting signal functionally determined by the feedback error obtained by a beam pulse loading the cavity after the previous correcting signal was applied to the cavity. Each cavity feedforward correcting signal is successively stored in the digital processor and modified by the feedback error resulting from its application to generate the next feedforward error correcting signal. A feedforward error correcting signal is generated by the digital processor in advance of the beam pulse to enable a composite correcting signal and the beam pulse to arrive concurrently at the cavity.

Eaton, Lawrie E. (Los Alamos, NM); Jachim, Stephen P. (Los Alamos, NM); Natter, Eckard F. (Santa Fe, NM)

1991-01-01

279

Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.  

PubMed

This paper deals with the robust asymptotic stabilization for a class of nonlinear singularly perturbed systems using the fuzzy sliding mode control technique. In the proposed approach the original system is decomposed into two subsystems as slow and fast models by the singularly perturbed method. The composite fuzzy sliding mode controller is designed for stabilizing the full order system by combining separately designed slow and fast fuzzy sliding mode controllers. The two-time scale design approach minimizes the effect of boundary layer system on the full order system. A stability analysis allows us to provide sufficient conditions for the asymptotic stability of the full order closed-loop system. The simulation results show improved system performance of the proposed controller as compared to existing methods. The experimentation results validate the effectiveness of the proposed controller. PMID:24636524

Nagarale, Ravindrakumar M; Patre, B M

2014-05-01

280

Fuzzy sampled-data control for uncertain vehicle suspension systems.  

PubMed

This paper investigates the problem of sampled-data H? control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy H? disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method. PMID:24043419

Li, Hongyi; Jing, Xingjian; Lam, Hak-Keung; Shi, Peng

2014-07-01

281

Sinusoidal rotatory chair system by an auto-tuning fuzzy PID controller  

SciTech Connect

This paper presents DC servo motor speed control characteristics by fuzzy logic controller and considers position following control response with controller. A sinusoidal rotatory chair system using an auto tuning fuzzy PID control was designed to evaluate the vestibular function. Then the system is investigated for the effects of change by the fuzziness of fuzzy variable. If this system is supported by a channel, it is considered for application in industry of multi joint robot and precision parallel driving.

Park, H.A. [Kwangju Health Inst. of Science (Korea, Republic of). Dept. of Computer and Information Processing; Cha, I.S. [Dong Shin Univ., Chonnam (Korea, Republic of). Dept. of Electrical and Electronic Engineering; Baek, H.L. [Cho Sun Univ., Kwangju (Korea, Republic of). Dept. of Electrical Engineering

1995-12-31

282

A two-layered fuzzy logic controller for proportional hydraulic system  

Microsoft Academic Search

Existing fuzzy control methods do not perform well when applied to proportional hydraulic systems (PHS) containing nonlinearities arising from unknown deadzones. In this paper, a two-layered fuzzy logic controller is proposed for controlling a PHS with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a usual fuzzy PD controller. This system was implemented for PHS

Pornjit Pratumsuwan; Siripun Thongchai

2009-01-01

283

Fuzzy model based control for a mineral flotation plant  

Microsoft Academic Search

This paper describes a generalized predictive control (GPC) algorithm based on fuzzy models and its application to the tailing grade control in a mineral flotation plant. The control strategy is evaluated using a dynamic process simulator and their results are compared with those obtained with a conventional GPC

A. Cipriano; M. Ramos

1994-01-01

284

Fuzzy Logic Control for Non Linear Car Air Conditioning  

Microsoft Academic Search

A practical application of a fuzzy control system for a non-linear air conditioning system in the automobile climate control system was carried out and the simulation results are presented. Temperature control in an automobile passenger environment is more complex than that of a static room in a building. With regards to both driver and passenger comfort and safety, a lot

Mohd Fauzi Othman; Siti Marhainis Othman

2006-01-01

285

A Fuzzy-Wavelet-Network-Based Position Control for PMSM  

Microsoft Academic Search

A robust position controller with variable structure control (VSC) and dynamic recurrent fuzzy wavelet network (DRFWN) technique for permanent magnet synchronous motor (PMSM) is presented in this paper. Based on the VSC method, the closed-loop system can system dynamics with an invariance property to uncertainties. However, the sliding controller based the assumption of known uncertainty bounds often cause the chattering

Wang Jun; Peng Hong; Xia Ling

2006-01-01

286

FUZZY LOGIC CONTROLLER DEPLOYED FOR INDOOR AIR QUALITY CONTROL IN NATURALLY VENTILATED ENVIRONMENTS  

Microsoft Academic Search

This paper provides new indoor air quality control (IAQ) based on fuzzy logic control in natural ventilated indoor environments where no other ventilation approaches are installed or can be installed due to space limitation or economical reasons. For the presented fuzzy logic controller, four distributed sensors inside the indoor environment are used to provide the basic measurable inputs to the

Mohammad Abdel; Kareem Jaradat; A. Al-Nimr

2009-01-01

287

Fuzzy decision and control, the Bayes context  

E-print Network

. Downloaded on February 18,2010 at 14:19:43 EST from IEEE Xplore. Restrictions apply. I 'I boundaries are fuzzy. As designers, we will choose the events to be unique and then do our best to model the Ai as vector-space subsets. Equation-(1....E. and Zadeh, L.A.(1970). Decision- making in a Fuzzy Environment. Management Science. no. 4. pp. 141-164. 1768 Authorized licensed use limited to: Texas A M University. Downloaded on February 18,2010 at 14:19:43 EST from IEEE Xplore. Restrictions apply. ...

Painter, John H.

1993-12-15

288

Fuzzy logic control for active bus suspension system  

NASA Astrophysics Data System (ADS)

In this study an active controller is presented for vibration suppression of a full-bus suspension model that use air spring. Since the air spring on the full-bus model may face different working conditions, auxiliary chambers have been designed. The vibrations, caused by the irregularities of the road surfaces, are tried to be suppressed via a multi input-single output fuzzy logic controller. The effect of changes in the number of auxiliary chambers on the vehicle vibrations is also investigated. The numerical results demonstrate that the presented fuzzy logic controller improves both ride comfort and road holding.

Turkkan, Mujde; Yagiz, Nurkan

2013-02-01

289

Optimization of Fuzzy Controller of Permanent Magnet Synchronous Motor  

NASA Astrophysics Data System (ADS)

Present study aims at discussing how to optimize the fuzzy controller of Permanent Magnet Synchronous Motor (PMSM). By reducing the influence of parameter changes of plant using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) of Taguchi Method and Multi-Criteria Decision Making (MCDM), it shall be possible to improve robust characteristics of control system, thus promoting the output quality and performance of PMSM plant. Meanwhile, an analytical model for the parameters and output quality of fuzzy controllers was set up and optimal parameters were designed using Genetic Algorithm (GA). Generally speaking, PMSM controller has a long-lasting infrastructure without complex computation, of which the Small-The-Better (STB) output features of PMSM include: Overshoot, rise time and settling time. In previous design of controllers, only individual quality characteristics were considered without overall output design of multiple quality characteristics. By using a controller based on fuzzy logic method in cooperation with parameterization method of TOPSIS, this study intended to discuss how to ensure optimum output quality and performance (overshoot, rise time and settling time) under different noise factors (speeds and loads, etc.). With a PC-based infrastructure that combines PC-based motor controller system and Matlab/Simulink software for simulation process, it seeks to obtain optimum parameters of controllers and implement a PMSM fuzzy control system with vector control function. The computer simulation results have proved the validity and feasibility of entire infrastructure with possible desirable effects.

Yu, Kuang-Cheng; Hsu, Shou-Ping; Hung, Yung-Hsiang

290

Texture segmentation based on an adaptively fuzzy clustering neural network  

Microsoft Academic Search

This work presents a novel approach to the segmentation of a textured image. We give a new validity function to check the validity of cluster number, it ensures the clustering results being fit for the real data structure by the aid of training of neural network. Then we synthesize traditional fuzzy clustering approaches and neural network to research the texture

Cheng-Bo Wang; Hong-Bin Wang; Qi-Bin Mei

2004-01-01

291

Fuzzy logic control of mechanical ventilation during anaesthesia.  

PubMed

We have examined a new approach, using fuzzy logic, to the closed-loop feedback control of mechanical ventilation during general anaesthesia. This control system automatically adjusts ventilatory frequency (f) and tidal volume (VT) in order to achieve and maintain the end-tidal carbon dioxide fraction (FE'CO2) at a desired level (set-point). The controller attempts to minimize the deviation of both f and VT per kg body weight from 10 bpm and 10 ml kg-1, respectively, and to maintain the plateau airway pressure within suitable limits. In 30 patients, undergoing various surgical procedures, the fuzzy control mode was compared with human ventilation control. For a set-point of FE'CO2 = 4.5 vol% and during measurement periods of 20 min, accuracy, stability and breathing pattern did not differ significantly between fuzzy logic and manual ventilation control. After step-changes in the set-point of FE'CO2 from 4.5 to 5.5 vol% and vice versa, overshoot and rise time did not differ significantly between the two control modes. We conclude that to achieve and maintain a desired FE'CO2 during routine anaesthesia, fuzzy logic feedback control of mechanical ventilation is a reliable and safe mode of control. PMID:8957981

Schäublin, J; Derighetti, M; Feigenwinter, P; Petersen-Felix, S; Zbinden, A M

1996-11-01

292

Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival  

E-print Network

Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival Hazlina Hamdan for breast cancer. I. INTRODUCTION Breast cancer is one of the most common cancers to afflict the female population. It is estimated that one in nine women in the UK will develop breast cancer at some point

Aickelin, Uwe

293

Adaptive network fuzzy inference system used in interference cancellation of radar seeker  

Microsoft Academic Search

The method of adaptive network fuzzy inference system (ANFIS) applied to the interference cancellation system of radar seeker was described in this paper. When the antiaircraft missile, which adopts the pulse Doppler radar seeker, attacks the low altitude target in the down-looking mode, the seeker of missile will receive strong ground clutter. As we all know the ground clutter will

Xiang Li

2010-01-01

294

Post-construction settlement of rockfill dams analyzed via adaptive network-based fuzzy inference systems  

Microsoft Academic Search

An attempt has been made to investigate the possibility of using adaptive network-based fuzzy inference systems to predict the post-construction settlement of rockfill dams. Four types of dams, namely, central core, sloping core, compacted membrane faced, and dumped membrane faced rockfill dams are considered in this study. An index is defined to indicate the combined compressibility of the dam embankment

Ghassem Habibagahi

2002-01-01

295

Application of fuzzy models in control systems of boiler aggregates technological processes  

Microsoft Academic Search

The application of fuzzy models in thermal power control systems is considered. The structure of a fuzzy system for a thermal object (boiler) is described. Results of the design and a schematic realization of boiler aggregate control systems are reported. Use of the suggested fuzzy models for control of the fuel-air relationship and boiler water level are described as examples

A. M. Prokhorenkov; I. V. Saburov; A. S. Sovlukov

2001-01-01

296

Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm  

Microsoft Academic Search

Scientists at the U.S. Bureau of Mines are currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic affords a mechanism for incorporating the uncertainty inherent in most control problems into conventional expert systems. Although fuzzy logic-based expert systems have been used successfully for controlling a number of physical systems,

Charles L. Karr

1991-01-01

297

Fuzzy Adaptive Interacting Multiple Model Nonlinear Filter for Integrated Navigation Sensor Fusion  

PubMed Central

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF. PMID:22319400

Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

2011-01-01

298

Wind energy conversion systems using fuzzy sliding mode control  

Microsoft Academic Search

The paper describes a manner in which the energy-reliability optimization of wind energy conversion system's operation can be achieved by means of the fuzzy sliding mode control. An appropriate sliding surface has been found in the speed-power plane, which allows the operation more or less close to the optimal regimes characteristic. What is more, by torque controlling the generator, an

Qi Chen; LiangHai Chen; LinGao Wang

2011-01-01

299

A Framework for Fuzzy Logic based UAV Navigation and Control  

Microsoft Academic Search

A two module fuzzy logic controller that also includes a separate error calculating box is derived for autonomous navigation and control of small manned - unmanned aerial vehicles demonstrating ability to fly through specified waypoints in a 3-D environment repeatedly, perform trajectory tracking, and, duplicate \\/ follow another vehicle's trajectory. A MATLAB standard configuration environment and the Aerosim Aeronautical Simulation

Lefteris Doitsidis; Kimon P. Valavanis; Nikos Tsourveloudis; Michael Kontitsis

2004-01-01

300

Fuzzy predictive control for nitrogen removal in biological wastewater treatment  

E-print Network

Fuzzy predictive control for nitrogen removal in biological wastewater treatment S. Marsili wastewater is too low, full denitrification is difficult to obtain and an additional source of organic carbon predictive control; wastewater treatment plant Introduction The problem of improving the nitrogen removal

301

Fuzzy control of spindle power in end milling processes  

Microsoft Academic Search

This paper reports a fuzzy control system for power regulation in end milling processes. This control system is capable of adjusting both feedrate and spindle speed simultaneously. Experiments have been carried out using both steel and aluminum workpieces of various cutting geometries. Different tools (HSS and carbide tools of different diameters and different number of teeth) have been used for

Ming Liang; Tet Yeap; Saeed Rahmati; Zhixin Han

2002-01-01

302

A Haptic and Fuzzy Logic Controller for Biometric User Verification  

Microsoft Academic Search

In any business, security in the form of access control is of great importance. This paper outlines an algorithm which can be used for user verification. The system consists of an application which uses a haptic device to capture behavioral biometric features. A fuzzy logic controller then acts as the decision-making or user verification module. The algorithm can initially be

Andrea Kanneh; Ziad Sakr

2008-01-01

303

Fuzzy Petri net-based programmable logic controller  

Microsoft Academic Search

Programmable logic controllers (PLCs) are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In the case of simple regulation problems between two steps it could be of great interest to introduce a notion of “fuzzy events” in order to denote a continuous evolution from one state

David Andreu; Jean-claude Pascal; Robert Valette

1997-01-01

304

Fuzzy Behavioral Control for Multi-Robot Border Patrol  

E-print Network

Fuzzy Behavioral Control for Multi-Robot Border Patrol Alessandro Marino and Fabrizio Caccavale--This paper deals with the problem of multi-robot border patrolling. The patrolling algorithm is designed by three Pioneer robots. Index Terms--Behavioral control; Border Patrol; Platoon of vehicles; Multi

Parker, Lynne E.

305

Fuzzy self-learning control for magnetic servo system  

NASA Technical Reports Server (NTRS)

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

306

A Fuzzy Adaptive Request Distribution Algorithm for Cluster-based Web Systems  

Microsoft Academic Search

This paper presents a novel algorithm for distribution of user requests sent to a Web-server cluster driven by a Web switch. Our algorithm called FARD (fuzzy adaptive request distribution) is a client-and-server-aware, dynamic and adaptive dispatching policy. It assigns each incoming request to the server with the least expected response time, estimated for that individual request. To estimate the expected

Leszek Borzemski; Krzysztof Zatwarnicki

2003-01-01

307

Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes  

NASA Technical Reports Server (NTRS)

It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

Duerksen, Noel

1996-01-01

308

Fuzzy cellular model of signal controlled traffic stream  

E-print Network

Microscopic traffic models have recently gained considerable importance as a mean of optimising traffic control strategies. Computationally efficient and sufficiently accurate microscopic traffic models have been developed based on the cellular automata theory. However, the real-time application of the available cellular automata models in traffic control systems is a difficult task due to their discrete and stochastic nature. This paper introduces a novel method of traffic streams modelling, which combines cellular automata and fuzzy calculus. The introduced fuzzy cellular traffic model eliminates main drawbacks of the cellular automata approach i.e. necessity of multiple Monte Carlo simulations and calibration issues. Experimental results show that the evolution of a simulated traffic stream in the proposed fuzzy cellular model is consistent with that observed for stochastic cellular automata. The comparison of both methods confirms that the computational cost of traffic simulation is considerably lower for...

P?aczek, Bart?omiej

2011-01-01

309

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques  

NASA Astrophysics Data System (ADS)

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

310

Fuzzy Group Models for Adaptation in Cooperative Information Retrieval Contexts  

Microsoft Academic Search

\\u000a Cooperation in information retrieval contexts can be used to share query results inside groups of individuals with common\\u000a objectives, provided that all of them are aware of each other. The strength of the social relationships between group members\\u000a is in most cases a matter of comparative degree, and thus relationships can be modelled through fuzzy conceptual associations.\\u000a These associations can

Miguel-Ángel Sicilia; Elena García

311

Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions  

Microsoft Academic Search

Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,

Alex. B. McBratney; Inakwu O. A. Odeh

1997-01-01

312

Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions  

Microsoft Academic Search

Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic,

B. McBratney; Inakwu O. A. Odeh

313

New approaches to relaxed quadratic stability condition of fuzzy control systems  

Microsoft Academic Search

This paper deals with the quadratic stability conditions of fuzzy control systems that relax the existing conditions reported in the previous literatures. Two new conditions are proposed and shown to be useful in analyzing and designing fuzzy control systems. The first one employs the S-procedure to utilize information regarding the premise parts of the fuzzy systems. The next one enlarges

Euntai Kim; Heejin Lee

2000-01-01

314

Reliable Fuzzy Control for Continuous-Time Nonlinear Systems With Actuator Failures  

Microsoft Academic Search

This paper is concerned with the design of reliable Hinfin fuzzy controllers for continuous-time nonlinear systems with actuator failures. The Takagi and Sugeno fuzzy model is employed to represent a nonlinear system. The objective is to find a stabilizing state-feedback fuzzy controller such that the nominal Hinfin performance is optimized while satisfying a prescribed Hinfin performance constraint in the actuator

Huai-Ning Wu; Hong-Yue Zhang

2006-01-01

315

Fuzzy Comfort and its Use in the Design of an Intelligent Coordinator of Fuzzy Controller-Agents for Environmental Conditions Control in Buildings  

Microsoft Academic Search

The theme of this paper is the design of an intelligent coordinator (IC) of fuzzy controller-agents (FCAs) to control indoor environmental conditions in buildings by using a 3-D fuzzy comfort set. The basic factors that participate in the control of indoor environmental conditions are the controllers and users' comfort requirements. Harmonization of these factors results in energy saving, and occupants'

Anastasios I. Dounis; Christos Caraiscos

316

Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration  

NASA Technical Reports Server (NTRS)

Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

2003-01-01

317

Adaptive fuzzy leader clustering of complex data sets in pattern recognition  

NASA Technical Reports Server (NTRS)

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

318

Predictive fuzzy control for a mobile welding robot seam tracking  

Microsoft Academic Search

For the curved weld seam tracking problem of wheeled welding mobile robot used in shipbuilding and large spherical tank welding, predictive fuzzy control method was proposed in this paper. To overcome the disadvantage of rotational arc sensor can not detect the next seam position, the algorithm employed weighted least square fitting method to predict the weld seam next position and

Gao Yanfeng; Zhang Hua; Mao Zhiwei; Peng Junfei

2008-01-01

319

Aspects of Genetic Algorithm-Designed Fuzzy Logic Controllers.  

National Technical Information Service (NTIS)

The research described in the report is twofold. First, the basic approach to developing a fuzzy logic controller (FLC) using genetic algorithms (GA's) is presented. The GA-designed FLC is developed for a specific physical system, a pH titration system. S...

C. L. Karr, J. W. Fleming, P. A. Vann

1994-01-01

320

Workshop on Fuzzy Control Systems and Space Station Applications  

NASA Technical Reports Server (NTRS)

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented.

Aisawa, E. K. (compiler); Faltisco, R. M. (compiler)

1990-01-01

321

Development of fuzzy algorithms for servo systems  

Microsoft Academic Search

Consideration is given to the possibility of applying fuzzy algorithms in a microprocessor-based servomotor controller, which requires faster and more accurate response compared with other industrial processes. The performance of proportional-integral-derivative control, model reference adaptive control, and fuzzy controllers is compared in terms of steady-state error, settling time, and response time. Limitations of fuzzy control algorithms are described

Y. F. Li; C. C. Lau

1989-01-01

322

Visual servoing system based on ANFIS (adaptive neuro fuzzy inference system)  

NASA Astrophysics Data System (ADS)

Research in this visual servoing field in the past few decades has produced remarkable results, leading to many exciting expectations as well as new challenges. However, because of the complicated calculation of the inverse Jacobian, it is difficult to implement in real time. Therefore, instead of using the inverse Jacobian, this paper employs the ANFIS (Adaptive Neuro Fuzzy Inference System) approach for visual servo control of a robot manipulator. It is based on visual feedback and no prior information about the kinematics of robot and the camera calibration are unnecessary. Firstly, to efficiently control a manipulator, 3D space is divided into two 2D spaces. And then, we acquire training data from each 2D space and ANFIS is learned by the training data. We categorize the robot movement into two kinds of actions. That is, TOWARD action is performed, in the xy plane, by joint 1 and APPROACH action is performed, in the plane orthogonal to the xy plane, by joint 2 and joint 3. The time varying object can be tracked by controlling both actions in each plane and the simulation results show the validation of our approach.

Choi, Gyu-Jong; Lee, Kyoung-Soo; Ahn, Doo-Sung

2001-10-01

323

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control  

NASA Technical Reports Server (NTRS)

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

Yen, John; Wang, Haojin; Daugherity, Walter C.

1992-01-01

324

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.  

PubMed

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller. PMID:22938339

Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

2012-08-01

325

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model  

NASA Astrophysics Data System (ADS)

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.

Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

2012-08-01

326

Fuzzy Adaptive Swarm Optimization Algorithm for Discrete Environments  

NASA Astrophysics Data System (ADS)

The heuristic methods have been widely developed for solution of complicated optimization methods. Recently hybrid methods that are based on combination of different approaches have shown more potential in this regard. Fuzzy simulation and Particle Swarm Optimization algorithm are integrated to design a hybrid intelligent algorithm to solve the np-hard problem such as travelling salesman problem in efficient and faster way of solutions. The results obtained with the proposed method show its potential in achieving both accuracy and speed in small and medium size problems, compared to many advanced methods.

Zahedi, M. Hadi; S. Haghighi, M. Mehdi

327

Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.  

PubMed

It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The results indicate that the modified neuro-fuzzy model applied to the flood prediction seems to have reached encouraging results for the river basin under examination. The comparison of the modified neuro-fuzzy predictions with the observed data was satisfactory, where the error resulted from the testing period was varied between 2.632% and 5.560%. Thus, this program may also serve as a tool for real-time flood monitoring and process control. PMID:17302300

Aqil, M; Kita, I; Yano, A; Nishiyama, S

2006-01-01

328

Neuro-fuzzy control of structures using acceleration feedback  

NASA Astrophysics Data System (ADS)

This paper described a new approach for the reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on the development of a correlation between the accelerations of the building (controller input) and the voltage applied to the MR damper (controller output). This correlation forms the basis for the development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

Schurter, Kyle C.; Roschke, Paul N.

2001-08-01

329

Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling  

NASA Technical Reports Server (NTRS)

Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.

Chiu, Stephen

1996-01-01

330

Adaptive neuro-fuzzy fusion of sensor data  

NASA Astrophysics Data System (ADS)

A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.

Petkovi?, Dalibor

2014-11-01

331

Fuzzy Knowledge-Based Approach to Treating Uncertainty in Inventory Control  

Microsoft Academic Search

Inventory control in complex manufacturing environments encounters various sources of uncertainity and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modeled by fuzzy if-then rules. The

Dobrilla Petrovic; Edward Sweeney

1994-01-01

332

Direct Torque Control System for Permanent Magnet Synchronous Machine with Fuzzy Speed Pi Regulator  

NASA Astrophysics Data System (ADS)

The Permanent Magnet Synchronous Machine (PMSM) speed regulation with a conventional PI regulator reduces the speed control precision, increase the torque fluctuation, and consequentially low performances of the whole system. With utilisation of fuzzy logic method, this paper presents the self adaptation of conventional PI regulator parameters Kp and Ki (proportional and integral coefficients respectively), using to regulate the speed in Direct Torque Control strategy (DTC). The ripples of both torque and flux are reduced remarkable, small overshooting and good dynamic of the speed and torque. Simulation results verify the proposed method validity.

Nabti, K.; Abed, K.; Benalla, H.

2008-06-01

333

Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach  

Microsoft Academic Search

Takagi-Sugeno (TS) fuzzy models (1985, 1992) can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input\\/output (I\\/O) submodels. In this paper, the TS fuzzy model approach is extended to the stability analysis and control design for both continuous and discrete-time nonlinear systems with time delay. The

Yong-Yan Cao; P. M. Frank

2000-01-01

334

Evolving fuzzy rules in a learning classifier system  

NASA Technical Reports Server (NTRS)

The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

Valenzuela-Rendon, Manuel

1993-01-01

335

Clustering of noisy image data using an adaptive neuro-fuzzy system  

NASA Technical Reports Server (NTRS)

Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.

Pemmaraju, Surya; Mitra, Sunanda

1992-01-01

336

Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system  

Microsoft Academic Search

This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation

Hikmet Esen; Mustafa Inalli; Abdulkadir Sengur; Mehmet Esen

2008-01-01

337

Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems  

Microsoft Academic Search

The aim of this study is to demonstrate the usefulness of an adaptive neuro-fuzzy inference system (ANFIS) for the modelling of ground-coupled heat pump (GCHP) system. The GCHP system connected to a test room with 16.24m2 floor area in F?rat University, Elaz?? (38.41°N, 39.14°E), Turkey, was designed and constructed. The heating and cooling loads of the test room were 2.5

Hikmet Esen; Mustafa Inalli; Abdulkadir Sengur; Mehmet Esen

2008-01-01

338

Control of a benchmark structure using GA-optimized fuzzy logic control  

E-print Network

simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction...

Shook, David Adam

2009-05-15

339

Reliable Control of Fuzzy Descriptor Systems with Time-Varying Delay  

Microsoft Academic Search

The reliable fuzzy controller design problem of T-S fuzzy descriptor systems with time-varying delay is introduced. Based\\u000a on linear matrix inequality approach, a less conservative reliable controller design method is presented. The resulting fuzzy\\u000a control systems are reliable in the sense that asymptotic stability is achieved not only when all control components are operating\\u000a well, but also in the presence

Yuhao Yuan; Zhonghu Yuan; Qingling Zhang; Daqing Zhang; Bing Chen

2006-01-01

340

Intelligent MBWIMA\\/UMTS protocol using cascade fuzzy logic control for UTRA TDD mode  

Microsoft Academic Search

An intelligent medium-access-control (MAC) protocol based on cascade fuzzy-logic-control (CFLC), consisting of a fuzzy Vmax (maximum number of voice\\/video slots) control and a fuzzy data-rate control for universal mobile telecommunications system (UMTS) terrestrial radio access time-division duplex (UTRA TDD) mode is presented. Voice, data, and video are integrated for transmission using CFLC-based movable-boundary wireless multiple access (MBWIMA) in the UMTS

Chih-Yang Kao; Jeich Mar

2003-01-01

341

Comparison of several types of speed controllers with the kind based on fuzzy logic  

Microsoft Academic Search

The paper deals with comparison of PI sliding mode, state space and fuzzy controller for speed control of synchronous motor drive. A short description of controllers is given. The goals of the paper are to give a short review of those speed controllers and their comparison according to often discussed questions. Is a fuzzy controller better then other ones? When

P. Kucer; I. Chrabaszez; J. Altus; P. Spanik; B. Dobrucky

1996-01-01

342

Aircraft adaptive learning control  

NASA Technical Reports Server (NTRS)

The optimal control theory of stochastic linear systems is discussed in terms of the advantages of distributed-control systems, and the control of randomly-sampled systems. An optimal solution to longitudinal control is derived and applied to the F-8 DFBW aircraft. A randomly-sampled linear process model with additive process and noise is developed.

Lee, P. S. T.; Vanlandingham, H. F.

1979-01-01

343

Quadcopter see and avoid using a fuzzy controller  

Microsoft Academic Search

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish

Miguel A. Olivares-Mendez; Luis Mejias; Pascual Campoy; Ignacio Mellado-Bataller

2012-01-01

344

Fuzzy Incremental Hierarchical Sliding Mode Control for Underactuated Systems  

Microsoft Academic Search

This paper designs a fuzzy incremental hierarchical sliding mode controller for a class of under actuated systems. By capturing the physical structure of the class, the hierarchical sliding surfaces with incremental structure are designed as follows. The first-layer sliding surface is firstly defined. Then, the first-layer sliding surface and one of the left states are employed to construct the second-layer

Dianwei Qian; Jianqiang Yi; Yufen Ma

2010-01-01

345

Adaptive control of periodic systems  

NASA Astrophysics Data System (ADS)

Adaptive control is needed to cope with parametric uncertainty in dynamical systems. The adaptive control of LTI systems in both discrete and continuous time has been studied for four decades and the results are currently used widely in many different fields. In recent years, interest has shifted to the adaptive control of time-varying systems. It is known that the adaptive control of arbitrarily rapidly time-varying systems is in general intractable, but systems with periodically time-varying parameters (LTP systems) which have much more structure, are amenable to mathematical analysis. Further, there is also a need for such control in practical problems which have arisen in industry during the past twenty years. This thesis is the first attempt to deal with the adaptive control of LTP systems. Adaptive Control involves estimation of unknown parameters, adjusting the control parameters based on the estimates, and demonstrating that the overall system is stable. System theoretic properties such as stability, controllability, and observability play an important role both in formulating of the problems, as well as in generating solutions for them. For LTI systems, these properties have been studied since 1960s, and algebraic conditions that have to be satisfied to assure these properties are now well established. In the case of LTP systems, these properties can be expressed only in terms of transition matrices that are much more involved than those for LTI systems. Since adaptive control problems can be formulated only when these properties are well understood, it is not surprising that systematic efforts have not been made thus far for formulating and solving adaptive control problems that arise in LTP systems. Even in the case of LTI systems, it is well recognized that problems related to adaptive discrete-time system are not as difficult as those that arise in the continuous-time systems. This is amply evident in the solutions that were derived in the 1980s and 1990s for all the important problems. These differences are even more amplified in the LTP case; some problems in continuous time cannot even be formulated precisely. This thesis consequently focuses primarily on the adaptive identification and control of discrete-time systems, and derives most of the results that currently exist in the literature for LTI systems. Based on these investigations of discrete-time adaptive systems, attempts are made in the thesis to examine their continuous-time counterparts, and discuss the principal difficulties encountered. The dissertation examines critically the system theoretic properties of LTP systems in Chapter 2, and the mathematical framework provided for their analysis by Floquet theory in Chapter 3. Assuming that adaptive identification and control problems can be formulated precisely, a unified method of developing stable adaptive laws using error models is treated in Chapter 4. Chapter 5 presents a detailed study of the adaptation in SISO discrete-time LTP systems, and represents the core of the thesis. The important problems of identification, stabilization, regulation, and tracking of arbitrary signals are investigated, and practically implementable stable adaptive laws are derived. The dissertation concludes with a discussion of continuous-time adaptive control in Chapter 6 and discrete multivariable systems in Chapter 7. Directions for future research are indicated towards the end of the dissertation.

Tian, Zhiling

346

Predictive functional control based on fuzzy model for heat-exchanger pilot plant  

Microsoft Academic Search

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong

I. Skrjanc; D. Matko

2000-01-01

347

A New Fuzzy Increment Inverse Control for Unknown Nonlinear Discrete Dynamical System  

Microsoft Academic Search

A new fuzzy increment inverse control based on input\\/output fuzzy hyperbolic model is proposed for general unknown nonlinear discrete-time dynamical system in this paper. The increment inverse controller is derived from the input\\/output fuzzy hyperbolic model, and it is based on property of hyperbolic tangent function and reduces the complexity of system. The stability of control system is derived. The

Zhenwei Liu; Huaguang Zhang

2007-01-01

348

Robust H infinity Reliable Fuzzy Control for Markovian Jump Nonlinear Singular Systems  

Microsoft Academic Search

This paper deals with the problem of robust H? reliable control for Markovian jump nonlinear singular systems with Wiener process via a fuzzy-control approach. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear singular system with norm-bounded parameter uncertainties and Markovian jump parameters. The objective is to design a state feedback fuzzy controller such that, for all admissible

Aiqing Zhang

2009-01-01

349

Robust anti-windup controller design of time-delay fuzzy systems with actuator saturations  

Microsoft Academic Search

This study presents a robust anti-windup fuzzy control approach for uncertain nonlinear time-delay systems with actuator saturations. The discussed system dynamics is presented by the Takagi–Sugeno (T–S) fuzzy model. To facilitate the design process, the nonlinearity of input saturation is characterized by a specific sector condition. Given a stabilizing dynamic output feedback fuzzy controller, an anti-windup control approach is then

Chen-Sheng Ting; Yong-Nong Chang

2011-01-01

350

Sustaining ecological integrity with respect to climate change: a fuzzy adaptive management approach.  

PubMed

A fuzzy adaptive management framework is proposed for evaluating the vulnerability of an ecosystem to losing ecological integrity as a result of climate change in an historical period (ex post evaluation) and selecting the best compensatory management action for reducing potential adverse impacts of future climate change on ecological integrity in a future period (ex ante evaluation). The ex post evaluation uses fuzzy logic to test hypotheses about the extent of past ecosystem vulnerability to losing ecological integrity and the ex ante evaluation uses the fuzzy minimax regret criterion to determine the best compensatory management action for alleviating potential adverse impacts of climate change on ecosystem vulnerability to losing ecological integrity in a future period. The framework accounts for uncertainty regarding: (1) the relationship between ecosystem vulnerability to losing ecological integrity and ecosystem resilience; (2) the relationship between ecosystem resilience and the extent to which observed indicators of ecological integrity depart from their thresholds; (3) the extent of future climate change; and (4) the potential impacts of future climate change on ecological integrity and ecosystem resilience. The adaptive management element of the framework involves using the ex post and ex ante evaluations iteratively in consecutive time segments of the future time period to determine if and when it is beneficial to adjust compensatory management actions to climate change. A constructed example is used to demonstrate the framework. PMID:20424839

Prato, Tony

2010-06-01

351

Sustaining Ecological Integrity with Respect to Climate Change: A Fuzzy Adaptive Management Approach  

NASA Astrophysics Data System (ADS)

A fuzzy adaptive management framework is proposed for evaluating the vulnerability of an ecosystem to losing ecological integrity as a result of climate change in an historical period ( ex post evaluation) and selecting the best compensatory management action for reducing potential adverse impacts of future climate change on ecological integrity in a future period ( ex ante evaluation). The ex post evaluation uses fuzzy logic to test hypotheses about the extent of past ecosystem vulnerability to losing ecological integrity and the ex ante evaluation uses the fuzzy minimax regret criterion to determine the best compensatory management action for alleviating potential adverse impacts of climate change on ecosystem vulnerability to losing ecological integrity in a future period. The framework accounts for uncertainty regarding: (1) the relationship between ecosystem vulnerability to losing ecological integrity and ecosystem resilience; (2) the relationship between ecosystem resilience and the extent to which observed indicators of ecological integrity depart from their thresholds; (3) the extent of future climate change; and (4) the potential impacts of future climate change on ecological integrity and ecosystem resilience. The adaptive management element of the framework involves using the ex post and ex ante evaluations iteratively in consecutive time segments of the future time period to determine if and when it is beneficial to adjust compensatory management actions to climate change. A constructed example is used to demonstrate the framework.

Prato, Tony

2010-06-01

352

Adaptive control in adaptive optics for directed-energy systems  

Microsoft Academic Search

This paper presents an adaptive control scheme for adaptive optics. Adaptive compensation is needed in many adaptive optics applications because wind velocities and the strength of atmospheric turbulence can change rapidly, rendering classical fixed-gain reconstruction algorithms far from optimal. The paper also presents a method for generating frequency- weighted deformable-mirror modes, which are important for optimal performance of the adaptive

Yu-Tai Liu; J. Steve Gibson

2007-01-01

353

[The control method design of thermal treatment system via fuzzy logic].  

PubMed

A novel system is proposed to control the liquid nitrogen cooling and radio frequency heating of tissue to achieve effective thermal ablation in the treatment using fuzzy logic controller and fuzzy logic PID type controller separately. Results of ex-vivo pig liver experiments demonstrate that this system is useful and could p control the desired treatment procedure. PMID:22916471

Song, Mingyang; Cai, Zhanghao; Bai, Jingfeng; Sun, Jianqi

2012-05-01

354

Research on the temperature control system based on fuzzy self-tuning PID  

Microsoft Academic Search

The temperature control system is increasingly playing an important role in industrial production. Recently, lots of researches have been investigated for the temperature control system based on various control strategies. The temperature control system based on fuzzy self-tuning PID is proposed in this paper. The new algorithm based on fuzzy self-tuning PID can improve the performance of the system. Also,

Jiang Wei

2010-01-01

355

An optimization of EGR control system for gasoline using fuzzy PID strategy  

Microsoft Academic Search

In order to get a robust control for a gasoline engine EGR (exhaust gas recirculation) system, a fuzzy PID controller is developed and its performance is studied by comparing to the normal PID controller. First a GT-power model for the gasoline engine (with EGR) is constructed and verified. Based on this model, a PID control system and a fuzzy PID

Liu Biao; Huang Ming; Yang Xiaolong; Xia Xiaolang

2010-01-01

356

Between fuzzy-PID and PID-conventional controllers: a good choice  

Microsoft Academic Search

The main problem in the synthesis of a control system is its parameter adjustment. Fuzzy logic controllers (FLC) have been proved to be very efficient in controlling complex and difficult-to-model processes. One of the main problems of these fuzzy controllers is that there is no systematic procedure for tuning. So, the necessity of methods for guiding the initial selection of

M. Santos; J. M. de la Cruz; S. Dormido; A. P. de Madrid

1996-01-01

357

Genetic-Fuzzy Sliding Mode Controller for a DC Servomotor system  

Microsoft Academic Search

A Genetic-Fuzzy Sliding Mode Controller is presented for DC Servomotor system control. The fuzzy logic controller was optimized by Genetic Algorithm method to reduce and eliminate the chattering phenomenon. To demonstrate the effectiveness of the presented approach, a comparison between the proposed system, and standard Sliding Mode controller were conducted. Simulation results have shown the advantages of choosing the proposed

Mohammad A. Jaradat; Mohammad I. Awad; Bashar Sami El-Khasawneh

2012-01-01

358

Fuzzy-timing Petri net model of temperature control for car air conditioning system  

Microsoft Academic Search

This paper proposes a fuzzy-timing Petri net method for distributed temperature control to achieve optimum air temperatures inside a car, considering the comfort of each passenger. The optimum HVAC control has been applied to a car air conditioning system, and a fuzzy controller is used to independently control temperatures at various locations inside the car, considering each passenger's comfort temperature

Keiichi Watanuki; Tadao Murata

1999-01-01

359

The application of coal intelligent fuzzy control system in the coordinated control system  

Microsoft Academic Search

Supercritical unit in a power plant, boiler system has a large delay, large inertia, etc., a control system using classical control strategy can not be a good conditioning effect, this use of fuzzy control with intelligent PID control system and changes in coal phase combination of methods to improve control and reduce the system's transition time, and effectively restrain the

Xin-hang Xu; Qiu-hong Sun; Hong-tao Zhang; Ming-fa Zhang

2010-01-01

360

ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS  

E-print Network

ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS MOODY T. CHU \\Lambda Abstract imaging system. Adaptive optics refers to the process of removing unwanted wave front distortions with the use of a phase corrector before the image is formed. The basic idea in adaptive optics is to control

361

ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS  

E-print Network

ON AN ADAPTIVE CONTROL ALGORITHM FOR ADAPTIVE OPTICS APPLICATIONS MOODY T. CHU Abstract imaging system. Adaptive optics refers to the process of removing unwanted wave front distortions with the use of a phase corrector before the image is formed. The basic idea in adaptive optics is to control

362

Nonlinear and adaptive control  

NASA Technical Reports Server (NTRS)

The primary thrust of the research was to conduct fundamental research in the theories and methodologies for designing complex high-performance multivariable feedback control systems; and to conduct feasibiltiy studies in application areas of interest to NASA sponsors that point out advantages and shortcomings of available control system design methodologies.

Athans, Michael

1989-01-01

363

Fuzzy control for a nonlinear mimo-liquid level problem  

SciTech Connect

Nonlinear systems are very common in the chemical process industries. Control of these systems, particularly multivariable systems, is extremely difficult. In many chemical plants, because of this difficulty, control is seldom optimal. Quite often, the best control is obtained in the manual mode using experienced operators. Liquid level control is probably one of the most common control problems in a chemical plant. Liquid level is important in heat exchanger control where heat and mass transfer rates can be controlled by the amount of liquid covering the tubes. Distillation columns, mixing tanks, and surge tanks are other examples where liquid level control is very important. The problem discussed in this paper is based on the simultaneous level control of three tanks connected in series. Each tank holds slightly less than 0.01 m{sup 3} of liquid. All three tanks are connected, Liquid is pumped into the first and the third tanks to maintain their levels. The third tank in the series drains to the system exit. The levels in the first and third tank control the level in the middle tank. The level in the middle tank affects the levels in the two end tanks. Many other chemical plant systems can be controlled in a manner similar to this three-tank system. For example, in any distillation column liquid level control problems can be represented as a total condenser with liquid level control, a reboiler with liquid level control, with the interactive column in between. The solution to the three-tank-problem can provide insight into many of the nonlinear control problems in the chemical process industries. The system was tested using the fuzzy logic controller and a proportional-integral (PI) controller, in both the setpoint tracking mode and disturbance rejection mode. The experimental results are discussed and comparisons between fuzzy controller and the standard PI controller are made.

Smith, R. E. (Ronald E.); Mortensen, F. N. (Fred N.); Wantuck, P. J. (Paul J.); Parkinson, W. J. (William Jerry),

2001-01-01

364

Robust controller design for fuzzy parametric uncertain systems: an optimal control approach.  

PubMed

A new approach of designing a robust controller for fuzzy parametric uncertain systems is proposed. A linear time invariant (LTI) system with fuzzy coefficients is called as fuzzy parametric uncertain system (FPUS). The proposed method envisages conversion of the FPUS into an uncertain (interval) state space controllable canonical form system in terms of its alpha cut. Further, the problem of designing a robust controller is translated into an optimal control problem minimizing a cost function. For matched uncertainty, it is shown that the optimal control problem is a linear quadratic regulator (LQR) problem, which can be solved to obtain a robust controller for FPUS. The numerical examples and simulation results show the effectiveness of the proposed method in terms of robustness of the controller. PMID:23148996

Patre, Balasaheb M; Bhiwani, R J

2013-03-01

365

Fuzzy control with genetic algorithm in a batch bioreactor.  

PubMed

In this study, the growth medium temperature in a batch bioreactor was controlled at the set point by using fuzzy model-based control method. Fuzzy control parameters which are membership functions and relation matrix were found using genetic algorithm. Heat input given from the immersed heater and the cooling water flow rate were selected as the manipulated variables in order to control the growth medium temperature in the bioreactor. Controller performance was tested in the face of different types of input variables. To eliminate the noise on the temperature measurements, first-order filter was used in the control algorithm. The achievement of the temperature control was analyzed in terms of both microorganism concentration which was reached at the end of the stationary phase and the performance criteria of Integral of the Absolute Error. It was concluded that the cooling flow rate was suitable as manipulated variable with regard to microorganism concentration. On the other hand, performance of the controller was satisfactory when the heat input given from the immersed heater was manipulated variable. PMID:24037514

Ahio?lu, Suna; Altinten, Ayla; Ertunç, Suna; Erdo?an, Sebahat; Hapo?lu, Hale

2013-12-01

366

Reliable mixed I fuzzy static output feedback control for nonlinear systems with sensor faults  

Microsoft Academic Search

This paper is concerned with the design of reliable mixed L2\\/H? static output feedback (SOF) fuzzy controllers for nonlinear continuous-time systems with complete sensor faults. The Takagi and Sugeno (T–S) fuzzy model is employed to represent a nonlinear system. A sufficient condition for the existence of reliable mixed L2\\/H? SOF fuzzy controllers is presented in terms of a set of

Huai-ning Wu; Hong-yue Zhang

2005-01-01

367

Reliable LQ fuzzy control for continuous-time nonlinear systems with actuator faults  

Microsoft Academic Search

This paper deals with the reliable linear quadratic (LQ) fuzzy control problem for continuous-time nonlinear systems with actuator faults. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear system. By using multiple Lyapunov functions, an improved linear matrix inequality (LMI) method for the design of reliable LQ fuzzy controllers is investigated, which reduces the conservatism of using a

Huai-ning Wu

2004-01-01

368

Reliable H221E; Control for TS fuzzy systems with time varying delay and actuator faults  

Microsoft Academic Search

This paper is concerned with the design of reliable H221E; fuzzy controller for continuous-time nonlinear systems with time delay and actuator faults. Based on a parameter-dependent Lyapunov Function and general actuator failure (which covers the cases of normal operation, partial degradation and outage), a novel design method of reliable fuzzy controller for T-S Fuzzy system with time varying delay is

Hamdi Gassara; Ahmed El Hajjaji; Maher Chaabane

2010-01-01

369

Evolutionary parameter optimization of a fuzzy controller which is used to control a sewage treatment plant  

E-print Network

inexpensive equipment, which controls parts of the plant in a new way. Fuzzy controllers are often used controllers (Craig 1989) are usually not used for this task. The expert knowledge how to operate such a plant). Such controllers have been used very successfully over the last 10 years in a number of sewage treatment plants

Ebner, Marc

370

Fuzzy RBF neural network control for networked control systems based on modified Smith predictor  

Microsoft Academic Search

Network delay highly degrades the control performance of networked control systems (NCS). Aiming to random and uncertain network delay, time-variant or nonlinear controlled plant and imprecise Smith predictor models, a novel approach is proposed that modified Smith predictor combined with fuzzy radial basis function neural network (FRBFNN). This approach can identify the controlled plant on-line, timely adjusts the weights of

Du Feng; Qian Qingquan

2008-01-01

371

Real-Time Fuzzy Control of Sensorless PM Drive Systems Dr. Kasim M. Al-Aubidy  

E-print Network

Real-Time Fuzzy Control of Sensorless PM Drive Systems Dr. Kasim M. Al-Aubidy Philadelphia drive systems. In this paper, a fuzzy logic controller is proposed for the real-time control the capability of such a drive system in applications where simplicity, reliability and stability are more

372

Fuzzy logic and genetic algorithms for intelligent control of structures using MR dampers  

Microsoft Academic Search

Fuzzy logic control (FLC) and genetic algorithms (GA) are integrated into a new approach for the semi-active control of structures installed with MR dampers against severe dynamic loadings such as earthquakes. The interactive relationship between the structural response and the input voltage of MR dampers is established by using a fuzzy controller rather than the traditional way by introducing an

Gang Yan; Lily L. Zhou

2004-01-01

373

Optimal control for fuzzy linear partial differential algebraic equations using Simulink  

Microsoft Academic Search

In this paper, optimal control for fuzzy linear partial differential algebraic equations (FPDAE) with quadratic performance is obtained using Simulink. By using the method of lines, the FPDAE is transformed into a fuzzy differential algebraic equations (FDAE). Hence, the optimal control of FPDAE can be found out by finding the optimal control of the corresponding FDAE. The goal is to

N. Kumaresan; Kuru Ratnavelu; Bernardine R. Wong

2011-01-01

374

Image-Based Flame Control of a Premixed Gas Burner Using Fuzzy Logics  

Microsoft Academic Search

This paper presents an application of fuzzy logics for controlling the combustion flame of a premixed gas burner. The control objective is to achieve a good characteristic of combustion flame by visualizing the colors of the flame while maintaining its size as desired. The fuzzy logic controller adjusts the flow rates of gas and air independently through two stepper-driven needle

Apichart Tuntrakoon; Suwat Kuntanapreeda

2003-01-01

375

Neuro-fuzzy controller for gas turbine in biomass-based electric power plant  

Microsoft Academic Search

Biomass gasification is a technology that transforms solid biomass into syngas. The gas turbine controller regulates both the gas turbine and the gas turbine generator. Two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the operating conditions of

Francisco Jurado; Manuel Ortega; Antonio Cano; José Carpio

2002-01-01

376

Adaptive fuzzy systems for backing up a truck-and-trailer  

Microsoft Academic Search

Fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. The robustness of the neural systems was tested by removing random subsets of training data in learning sequences. The neural systems performed well but required

Seong-Gon Kong; Bart Kosko

1992-01-01

377

Active control of flexible structures using a fuzzy logic algorithm  

NASA Astrophysics Data System (ADS)

This study deals with the development and application of an active control law for the vibration suppression of beam-like flexible structures experiencing transient disturbances. Collocated pairs of sensors/actuators provide active control of the structure. A design methodology for the closed-loop control algorithm based on fuzzy logic is proposed. First, the behavior of the open-loop system is observed. Then, the number and locations of collocated actuator/sensor pairs are selected. The proposed control law, which is based on the principles of passivity, commands the actuator to emulate the behavior of a dynamic vibration absorber. The absorber is tuned to a targeted frequency, whereas the damping coefficient of the dashpot is varied in a closed loop using a fuzzy logic based algorithm. This approach not only ensures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The developed controller is applied to the AFWAL/FIB 10 bar truss. Simulated results using MATLAB© show that the closed-loop system exhibits fairly quick settling times and desirable performance, as well as robustness characteristics. To demonstrate the robustness of the control system to changes in the temporal dynamics of the flexible structure, the transient response to a considerably perturbed plant is simulated. The modal frequencies of the 10 bar truss were raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. For these cases, too, the developed control law provides adequate settling times and rates of vibrational energy dissipation.

Cohen, Kelly; Weller, Tanchum; Ben-Asher, Joseph Z.

2002-08-01

378

PC based speed control of dc motor using fuzzy logic controller  

SciTech Connect

The dc motor is extensively used as constant speed drive in textile mills, paper mills, printing press, etc.. If the load and supply voltage are time varying, the speed will be changed. Since last few decades the conventional PID controllers are used to maintain the constant speed by controlling the duty ratio of Chopper. Generally, four quadrant chopper is used for regenerative braking and reverse motoring operation. Fuzzy Logic is newly introduced in control system. Fuzzy Control is based on Fuzzy Logic, a logical system which is too much closer in spirit to human thinking and natural language. The Fuzzy Logic Controller (FLC) provides a linguistic control strategy based on knowledge base of the system. Firstly, the machine is started very smoothly from zero to reference speed in the proposed scheme by increasing the duty ratio. Then change and rate of change of speed (dN, dN/dt), change and rate of change input voltage (dV, dV/dt) and load current are input to FLC. The new value of duty ratio is determined from the Fuzzy rule base and defuzzification method. The chopper will be 'ON' according to new duty ratio to maintain the constant speed. The dynamic and steady state performance of the proposed system is better than conventional control system. In this paper mathematical simulation and experimental implementation are carried out to investigate the drive performance.

Mandal, S.K.; Kanphade, R.D.; Lavekar, K.P.

1998-07-01

379

H? control for switched fuzzy systems via dynamic output feedback: Hybrid and switched approaches  

NASA Astrophysics Data System (ADS)

Fuzzy T-S model has been proven to be a practical and effective way to deal with the analysis and synthesis problems for complex nonlinear systems. As for switched nonlinear system, describing its subsystems as fuzzy T-S models, namely switched fuzzy system, naturally is an alternative method to conventional control approaches. In this paper, the H? control problem for a class of switched fuzzy systems is addressed. Hybrid and switched design approaches are proposed with different availability of switching signal information at switching instant. The hybrid control strategy includes two parts: fuzzy controllers for subsystems and state updating controller at switching instant, and the switched control strategy contains the controllers for subsystems. It is demonstrated that the conservativeness is reduced by introducing the state updating behavior but its cost is an online prediction of switching signal. Numerical examples are given to illustrate the effectiveness of proposed approaches and compare the conservativeness of two approaches.

Xiang, Weiming; Xiao, Jian; Iqbal, Muhammad Naveed

2013-06-01

380

Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey  

NASA Astrophysics Data System (ADS)

SummaryIn this study, an adaptive neuro fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors including average monthly water bill, population, number of households, gross national product, monthly average temperature observed, monthly total rainfall, monthly average humidity observed and inflation rate. Water consumption modeling in this way will be more consistent than doing it using a single variable as more effective parameter could be incorporated. The ANFIS system is applied to modeling monthly water consumptions of Izmir, Turkey. The results indicated that ANFIS can be successfully applied for monthly water consumption modeling.

Yurdusev, Mehmet Ali; Firat, Mahmut

2009-02-01

381

Comparative experiments of robust and adaptive control with new robust adaptive controllers for robot manipulators  

Microsoft Academic Search

Several conceptually different robust adaptive schemes, namely, a smooth robust adaptive sliding mode controller with guaranteed transient performance, a robust desired compensation adaptive controller with guaranteed transient performance, a simple robust performance based adaptive control scheme which has a nonlinear PID feedback structure with adaptation gains, and a robust desired compensation adaptive controller which is synthesized by combining the design

Bin Yao; Masayoshi Tomizuka

1994-01-01

382

Fuzzy control iterative algorithm for beam uniformity enhancement of gas laser through boundary layer flow  

NASA Astrophysics Data System (ADS)

Fuzzy control iterative algorithm for beam uniformity enhancement of gas laser through boundary layer flow is presented. The iterative process of the proposed algorithm is controlled by the fuzzy control theory. In each loop, the MSE and the TUI value are evaluated to fuzzily determine which one is relatively greater, leading to different approaches in the iteration. Computer simulation results proved the effectiveness of the proposed method. Gerchberg- Saxton algorithm, profile smoothing algorithm and fuzzy control iterative algorithm are applied to the diffractive optical element design for the correction of laser beam distorted by a Blausius boundary layer. Fuzzy control iterative algorithm leads to better result than Gerchberg-Saxton algorithm and profile smoothing algorithm. Both mean square error and top ununiformity index of the result obtained by fuzzy control iterative algorithm are rather low.

Wu, Xu; Wu, Kenan; Jiang, Wenting

2014-07-01

383

Error Correction, Control Systems and Fuzzy Logic  

NASA Technical Reports Server (NTRS)

This paper will be a discussion on dealing with errors. While error correction and communication is important when dealing with spacecraft vehicles, the issue of control system design is also important. There will be certain commands that one wants a motion device to execute. An adequate control system will be necessary to make sure that the instruments and devices will receive the necessary commands. As it will be discussed later, the actual value will not always be equal to the intended or desired value. Hence, an adequate controller will be necessary so that the gap between the two values will be closed.

Smith, Earl B.

2004-01-01

384

Autonomous Speedsprayer using Differential Global Positioning System, Genetic Algorithm and Fuzzy Control  

Microsoft Academic Search

A fuzzy controller was developed for the autonomous operation of a speedsprayer in an orchard. The autonomous operation with a fuzzy controller was graphically simulated under the real orchard conditions. A differential global positioning system (DGPS) receiver was used to determine the direction of travel and four ultrasonic sensors were used to detect obstacles during operation. The results of the

Seong In Cho; Jae Hoon Lee

2000-01-01

385

Genetic-based robust optimal design for one-input fuzzy PID controllers  

Microsoft Academic Search

This paper explores an optimal robust design problem for a feedback system with one-input fuzzy PID controllers and simplification of the design problem. We tend to achieve robust stability and performance of one-input fuzzy PID control systems. Robustness is addressed in terms of stability margin and multiplicative perturbations. The design method is proposed in the framework of an optimal solver

Y.-D. Cao; B.-G. Hu; D.-J. Gao

2001-01-01

386

Behavior Coordination of Mobile Robotics Using Supervisory Control of Fuzzy Discrete Event Systems  

Microsoft Academic Search

In order to incorporate the uncertainty and impre- ciseness present in real-world event-driven asynchronous systems, fuzzy discrete event systems (DESs) (FDESs) have been proposed as an extension to crisp DESs. In this paper, first, we propose an extension to the supervisory control theory of FDES by redefin- ing fuzzy controllable and uncontrollable events. The proposed supervisor is capable of enabling

Awantha Jayasiri; George K. I. Mann; Raymond G. Gosine

2011-01-01

387

Fuzzy logic based intelligent control of a variable speed cage machine wind generation system  

Microsoft Academic Search

The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with

M. G. Simoes; B. K. Bose; R. J. Spiegel

1997-01-01

388

Design of an innovative Hybrid Fuzzy Controller for laser surface treatments  

Microsoft Academic Search

This article introduces an innovative fuzzy logic control system for laser surface treatments, which allows to increase significantly the uniformity and final quality of the process, reducing the rejection rate and increasing the productivity and efficiency of the treatment. The proposed hybrid structure combines a PD fuzzy logic controller, with a pure integral action, both fully decoupled, improving considerably the

José Antonio Pérez; Alberto Luaces; Roland Pastorino

2009-01-01

389

A fuzzy logic controller applied to power system stabilizer for a synchronous machine power system  

Microsoft Academic Search

A fuzzy logic controller has been applied to a power system stabilizer for a synchronous machine infinite bus power system. Speed deviation (??) and acceleration (d?\\/dt ) of the synchronous machine are chosen as the input signals to the fuzzy controller in order to achieve good dynamic performance over a wide range of operating conditions. The use of two look-up

J. Shi; L. H. Herron; A. Kalam

1992-01-01

390

Abstract--This paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based  

E-print Network

approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems, Fuzzy logic, Fuzzy Controlled Planning and control. I. INTRODUCTION he economic dispatch problem (EDP and non-convex economic dispatch problem. In [4] authors present a novel string structure for solving

Boyer, Edmond

391

Regenerative braking for electric vehicle based on fuzzy logic control strategy  

Microsoft Academic Search

In order to recycle more energy in the process of regenerative braking, we design a regenerative braking force calculation controller based on fuzzy logic. The sugeno's interface fuzzy logic controller has three-inputs including the driver's brake requirements, vehicle speed and batteries' SOC and one-output which is the regenerative braking force. To ensure charging safety, the influence of batteries' temperature is

Zijian Zhang; Guoqing Xu; Weimin Li; Liang Zheng

2010-01-01

392

An intelligent control system for resistance spot welding using a neural network and fuzzy logic  

Microsoft Academic Search

An intelligent control system based on fuzzy logic able to compensate for variations and errors during automatic resistance spot welding (RSW) and produce consistent sound welds was developed. A fuzzy logic control (FLC) scheme was employed to overcome the lack of a precise mathematical model of the process. Electrode displacement, indicative of weld nugget growth, was used as the feedback

Min Jou; C. J. Li

1995-01-01

393

A survey of fuzzy logic monitoring and control utilisation in medicine  

Microsoft Academic Search

Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical models in terms of linguistic rules. Since the 1980s new techniques have appeared from which fuzzy logic has been applied extensively in medical systems. The justification for

Mahdi Mahfouf; Maysam F. Abbod; Derek A. Linkens

2001-01-01

394

Counterpropagation fuzzy-neural network for city flood control system  

NASA Astrophysics Data System (ADS)

SummaryThe counterpropagation fuzzy-neural network (CFNN) can effectively solve highly non-linear control problems and robustly tune the complicated conversion of human intelligence to logical operating system. We propose the CFNN for extracting flood control knowledge in the form of fuzzy if-then rules to simulate a human-like operating strategy in a city flood control system through storm events. The Yu-Cheng pumping station, Taipei City, is used as a case study, where storm and operating records are used to train and verify the model's performance. Historical records contain information of rainfall amounts, inner water levels, and pump and gate operating records in torrential rain events. Input information can be classified according to its similarity and mapped into the hidden layer to form precedent if-then rules, while the output layer gradually adjusts the linked weights to obtain the optimal operating result. A model with increasing historical data can automatically increase rules and thus enhance its predicting ability. The results indicate the network has a simple basic structure with efficient learning ability to construct a human-like operating strategy and has the potential ability to automatically operating the flood control system.

Chang, Fi-John; Chang, Kai-Yao; Chang, Li-Chiu

2008-08-01

395

Adaptive Compliant Motion Control for Dexterous Manipulators  

Microsoft Academic Search

This article presents two adaptive schemes for compliant mo tion control of dexterous manipulators. The first scheme is developed using an adaptive impedance control approach for torque-controlled manipulators, whereas the second strategy is an adaptive admittance controller for position-controlled manipulators. The proposed controllers are very general and computationally efficient, as they do not require knowledge of the manipulator dynamic model

Richard Colbaugh; Homayoun Seraji; Kristin Glass

1995-01-01

396

A scheme of adaptive control  

Microsoft Academic Search

A new scheme of adaptive control is proposed. This scheme does not require a priori knowledge of the structure of the plant\\u000a to be controlled. The principal part of the scheme is a procedure which decides the order of the model of the plant. A criterion\\u000a for the order determination is developed. Using this criterion, we can decide whether to

Makio Ishiguro

1978-01-01

397

A fuzzy controlled three-phase centrifuge for waste separation  

SciTech Connect

The three-phase centrifuge technology discussed in this paper was developed by Neal Miller, president of Centech, Inc. The three-phase centrifuge is an excellent device for cleaning up oil field and refinery wastes which are typically composed of hydrocarbons, water, and solids. The technology is unique. It turns the waste into salable oil, reusable water, and landfill-able solids. No secondary waste is produced. The problem is that only the inventor can set up and run the equipment well enough to provide an optimal cleanup. Demand for this device has far exceeded a one man operation. There is now a need for several centrifuges to be operated at different locations at the same time. This has produced a demand for an intelligent control system, one that could replace a highly skilled operator, or at least supplement the skills of a less experienced operator. The control problem is ideally suited to fuzzy logic, since the centrifuge is a highly complicated machine operated entirely by the skill and experience of the operator. A fuzzy control system was designed for and used with the centrifuge.

Parkinson, W.J.; Smith, R.E. [Los Alamos National Lab., NM (United States); Miller, N. [Centech, Inc., Casper, WY (United States)

1998-02-01

398

A Methodology for Investigating Adaptive Postural Control  

NASA Technical Reports Server (NTRS)

Our research on postural control and human-environment interactions provides an appropriate scientific foundation for understanding the skill of mass handling by astronauts in weightless conditions (e.g., extravehicular activity or EVA). We conducted an investigation of such skills in NASA's principal mass-handling simulator, the Precision Air-Bearing Floor, at the Johnson Space Center. We have studied skilled movement-body within a multidisciplinary context that draws on concepts and methods from biological and behavioral sciences (e.g., psychology, kinesiology and neurophysiology) as well as bioengineering. Our multidisciplinary research has led to the development of measures, for manual interactions between individuals and the substantial environment, that plausibly are observable by human sensory systems. We consider these methods to be the most important general contribution of our EVA investigation. We describe our perspective as control theoretic because it draws more on fundamental concepts about control systems in engineering than it does on working constructs from the subdisciplines of biomechanics and motor control in the bio-behavioral sciences. At the same time, we have attempted to identify the theoretical underpinnings of control-systems engineering that are most relevant to control by human beings. We believe that these underpinnings are implicit in the assumptions that cut across diverse methods in control-systems engineering, especially the various methods associated with "nonlinear control", "fuzzy control," and "adaptive control" in engineering. Our methods are based on these theoretical foundations rather than on the mathematical formalisms that are associated with particular methods in control-systems engineering. The most important aspects of the human-environment interaction in our investigation of mass handling are the functional consequences that body configuration and stability have for the pick up of information or the achievement of overt goals. It follows that an essential characteristic of postural behavior is the effective maintenance of the orientation and stability of the sensory and motor "platforms" (e.g., head or shoulders) over variations in the human, the environment and the task. This general skill suggests that individuals should be sensitive to the functional consequences of body configuration and stability. In other words, individuals should perceive the relation between configuration, stability, and performance so that they can adaptively control their interaction with the surroundings. Human-environment interactions constitute robust systems in that individuals can maintain the stability of such interactions over uncertainty about and variations in the dynamics of the interaction. Robust interactions allow individuals to adopt orientations and configurations that are not optimal with respect to purely energetic criteria. Individuals can tolerate variation in postural states, and such variation can serve an important function in adaptive systems. Postural variability generates stimulation which is "textured" by the dynamics of the human-environment system. The texture or structure in stimulation provides information about variation in dynamics, and such information can be sufficient to guide adaption in control strategies. Our method were designed to measure informative patterns of movement variability.

McDonald, P. V.; Riccio, G. E.

1999-01-01

399

The Research of Sewage Treatment Control System Based on Fuzzy PID  

Microsoft Academic Search

Adoption the PLC (Programmable Logic Controller) of high reliability, strong anti-interference ability designs a sewage treatment control system for a certain chemical industry, which realizes the automatic control of the sewage treatment. Fuzzy PID (Piping and Instrument Diagram) controller is designed for the bache of non-linear control in the system, through fuzzy control algorithm on-line adjustment PID parameters, replaces the

Gao Shuangxi; Cao Shufu

2009-01-01

400

AI approach to optimal var control with fuzzy reactive loads  

SciTech Connect

This paper presents an artificial intelligence (AI) approach to the optimal reactive power (var) control problem. The method incorporates the reactive load uncertainty in optimizing the overall system performance. The artificial neural network (ANN) enhanced by fuzzy sets is used to determine the memberships of control variables corresponding to the given load values. A power flow solution will determine the corresponding state of the system. Since the resulting system state may not be feasible in real-time, a heuristic method based on the application of sensitivities in expert system is employed to refine the solution with minimum adjustments of control variables. Test cases and numerical results demonstrate the applicability of the proposed approach. Simplicity, processing speed and ability to model load uncertainties make this approach a viable option for on-line var control.

Abdul-Rahman, K.H.; Shahidehpour, S.M. [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Electrical and Computer Engineering] [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Electrical and Computer Engineering; Daneshdoost, M. [Southern Illinois Univ., Carbondale, IL (United States). Dept. of Electrical and Computer Engineering] [Southern Illinois Univ., Carbondale, IL (United States). Dept. of Electrical and Computer Engineering

1995-02-01

401

Introduction to Fuzzy Set Theory  

NASA Technical Reports Server (NTRS)

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Kosko, Bart

1990-01-01

402

An Approach to Supervisory Control of an Energy Management Control System Using Fuzzy Logic  

E-print Network

In this paper an approach to supervisory control of multi-stage industrial control systems is presented. This approach is based on the notion of an internal reference model, and further makes use of a fuzzy multi-objective optimization strategy...

Langari, R.

403

Modeling gunshot bruises in soft body armor with an adaptive fuzzy system.  

PubMed

Gunshots produce bruise patterns on persons who wear soft body armor when shot even though the armor stops the bullets. An adaptive fuzzy system modeled these bruise patterns based on the depth and width of the deformed armor given a projectile's mass and momentum. The fuzzy system used rules with sinc-shaped if-part fuzzy sets and was robust against random rule pruning: Median and mean test errors remained low even after removing up to one fifth of the rules. Handguns shot different caliber bullets at armor that had a 10%-ordnance gelatin backing. The gelatin blocks were tissue simulants. The gunshot data tuned the additive fuzzy function approximator. The fuzzy system's conditional variance V[Y/X = x] described the second-order uncertainty of the function approximation. Handguns with different barrel lengths shot bullets over a fixed distance at armor-clad gelatin blocks that we made with Type 250 A Ordnance Gelatin. The bullet-armor experiments found that a bullet's weight and momentum correlated with the depth of its impact on armor-clad gelatin (R2 = 0.881 and p-value < 0.001 for the null hypothesis that the regression line had zero slope). Related experiments on plumber's putty showed that highspeed baseball impacts compared well to bullet-armor impacts for large-caliber handguns. A baseball's momentum correlated with its impact depth in putty (R2 = 0.93 and p-value < 0.001). A bullet's momentum similarly correlated with its armor-impact in putty (R2 = 0.97 and p-value < 0.001). A Gujarati-Chow test showed that the two putty-impact regression lines had statistically indistinguishable slopes for p-value = 0.396. Baseball impact depths were comparable to bullet-armor impact depths: Getting shot with a .22 caliber bullet when wearing soft body armor resembles getting hit in the chest with a 40-mph baseball. Getting shot with a .45 caliber bullet resembles getting hit with a 90-mph baseball. PMID:16366262

Lee, Ian; Kosko, Bart; Anderson, W French

2005-12-01

404

The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller  

NASA Astrophysics Data System (ADS)

The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.

Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin

405

Nonmonotonic Observer-Based Fuzzy Controller Designs for Discrete Time T-S Fuzzy Systems Via LMI.  

PubMed

In this paper, based on the nonmonotonic Lyapunov functions, a new less conservative state feedback controller synthesis method is proposed for a class of discrete time nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy systems. Parallel distributed compensation (PDC) state feedback is employed as the controller structure. Also, a T-S fuzzy observer is designed in a manner similar to state feedback controller design. The observer and the controller can be obtained separately and then combined together to form an output feedback controller by means of the Separation theorem. Both observer and controller are obtained via solving a sequence of linear matrix inequalities. Nonmonotonic Lyapunov method allows the design of controllers for the aforementioned systems where other methods fail. Illustrative examples are presented which show how the proposed method outperforms other methods such as common quadratic, piecewise or non quadratic Lyapunov functions. PMID:24733035

Derakhshan, Siavash Fakhimi; Fatehi, Alireza; Sharabiany, Mehrad Ghasem

2014-12-01

406

Signal frequency based self-tuning fuzzy controller for semi-active suspension system.  

PubMed

A new kind of fuzzy control scheme, based on the identification of the signal's main frequency and the behavior of the ER damper, is proposed to control the semi-active suspension system. This method adjusts the fuzzy controller to achieve the best isolation effect by analyzing the main frequency's characters and inspecting the change of system parameters. The input of the fuzzy controller is the main frequency and the optimal damping ratio is the output. Simulation results indicated that the proposed control method is very effective in isolating the vibration. PMID:12861618

Sun, Tao; Huang, Zhen-Yu; Chen, Da-Yue; Tang, Lei

2003-01-01

407

Type-1 and Type-2 Fuzzy Logic and Sliding-Mode Based Speed Control of Direct Torque and Flux Control Induction Motor Drives - A Comparative Study  

NASA Astrophysics Data System (ADS)

In this research study, the performance of direct torque and flux control induction motor drive (IMD) is presented using five different speed control techniques. The performance of IMD mainly depends on the design of speed controller. The PI speed controller requires precise mathematical model, continuous and appropriate gain values. Therefore, adaptive control based speed controller is desirable to achieve high-performance drive. The sliding-mode speed controller (SMSC) is developed to achieve continuous control of motor speed and torque. Furthermore, the type-1 fuzzy logic speed controller (T1FLSC), type-1 fuzzy SMSC and a new type-2 fuzzy logic speed controller are designed to obtain high performance, dynamic tracking behaviour, speed accuracy and also robustness to parameter variations. The performance of each control technique has been tested for its robustness to parameter uncertainties and load disturbances. The detailed comparison of different control schemes are carried out in a MATALB/Simulink environment at different speed operating conditions, such as, forward and reversal motoring under no-load, load and sudden change in speed.

Ramesh, Tejavathu; Panda, A. K.; Kumar, S. Shiva

2013-08-01

408

Fuzzy Control Hardware for Segmented Mirror Phasing Algorithm  

NASA Technical Reports Server (NTRS)

This paper presents a possible implementation of a control model developed to phase a system of segmented mirrors, with a PAMELA configuration, using analog fuzzy hardware. Presently, the model is designed for piston control only, but with the foresight that the parameters of tip and tilt will be integrated eventually. The proposed controller uses analog circuits to exhibit a voltage-mode singleton fuzzifier, a mixed-mode inference engine, and a current-mode defuzzifier. The inference engine exhibits multiplication circuits that perform the algebraic product composition through the use of operational transconductance amplifiers rather than the typical min-max circuits. Additionally, the knowledge base, containing exemplar data gained a priori through simulation, interacts via a digital interface.

Roth, Elizabeth

1999-01-01

409

Adaptable state based control system  

NASA Technical Reports Server (NTRS)

An autonomous controller, comprised of a state knowledge manager, a control executor, hardware proxies and a statistical estimator collaborates with a goal elaborator, with which it shares common models of the behavior of the system and the controller. The elaborator uses the common models to generate from temporally indeterminate sets of goals, executable goals to be executed by the controller. The controller may be updated to operate in a different system or environment than that for which it was originally designed by the replacement of shared statistical models and by the instantiation of a new set of state variable objects derived from a state variable class. The adaptation of the controller does not require substantial modification of the goal elaborator for its application to the new system or environment.

Rasmussen, Robert D. (Inventor); Dvorak, Daniel L. (Inventor); Gostelow, Kim P. (Inventor); Starbird, Thomas W. (Inventor); Gat, Erann (Inventor); Chien, Steve Ankuo (Inventor); Keller, Robert M. (Inventor)

2004-01-01

410

Adaptation of TS fuzzy models without complexity expansion: HOSVD-based approach  

Microsoft Academic Search

One direction of measured data-set based modeling applies fuzzy logic identification tools and results in a fuzzy rule-base model. A typical problem of fuzzy identification methods is that the complexity of the resulting fuzzy rule-base, namely the number of rules in the rule-base, explodes with the modeling accuracy. As a result, the topic of fuzzy rule-base complexity reduction techniques emerged

Péter Baranyi; Annamária R. Várkonyi-Kóczy; Yeung Yam; Ron J. Patton

2005-01-01

411

Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent  

NASA Technical Reports Server (NTRS)

Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

Kopasakis, George

1997-01-01

412

Neuro-fuzzy approaches for identification and control of nonlinear systems  

Microsoft Academic Search

Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier\\/controller capable of perceiving the operating environment and imitating human operator with high performance. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or time varying nature of the system under investigation. In this

M. Onder Efe; Okyay Kaynak

1999-01-01

413

Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines  

E-print Network

Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines Kasim M. Al which consists of groups of search coils are inserted into the motor stator. A simple neuro-fuzzy in washing machine applications where simplicity, reliability and stability are more important issues

414

Norepinephrine weaning in septic shock patients by closed loop control based on fuzzy logic  

PubMed Central

Introduction The rate of weaning of vasopressors drugs is usually an empirical choice made by the treating in critically ill patients. We applied fuzzy logic principles to modify intravenous norepinephrine (noradrenaline) infusion rates during norepinephrine infusion in septic patients in order to reduce the duration of shock. Methods Septic patients were randomly assigned to norepinephrine infused either at the clinician's discretion (control group) or under closed-loop control based on fuzzy logic (fuzzy group). The infusion rate changed automatically after analysis of mean arterial pressure in the fuzzy group. The primary end-point was time to cessation of norepinephrine. The secondary end-points were 28-day survival, total amount of norepinephine infused and duration of mechanical ventilation. Results Nineteen patients were randomly assigned to fuzzy group and 20 to control group. Weaning of norepinephrine was achieved in 18 of the 20 control patients and in all 19 fuzzy group patients. Median (interquartile range) duration of shock was significantly shorter in the fuzzy group than in the control group (28.5 [20.5 to 42] hours versus 57.5 [43.7 to 117.5] hours; P < 0.0001). There was no significant difference in duration of mechanical ventilation or survival at 28 days between the two groups. The median (interquartile range) total amount of norepinephrine infused during shock was significantly lower in the fuzzy group than in the control group (0.6 [0.2 to 1.0] ?g/kg versus 1.4 [0.6 to 2.7] ?g/kg; P < 0.01). Conclusions Our study has shown a reduction in norepinephrine weaning duration in septic patients enrolled in the fuzzy group. We attribute this reduction to fuzzy control of norepinephrine infusion. Trial registration Trial registration: Clinicaltrials.gov NCT00763906. PMID:19068113

Merouani, Mehdi; Guignard, Bruno; Vincent, Francois; Borron, Stephen W; Karoubi, Philippe; Fosse, Jean-Philippe; Cohen, Yves; Clec'h, Christophe; Vicaut, Eric; Marbeuf-Gueye, Carole; Lapostolle, Frederic; Adnet, Frederic

2008-01-01

415

Chapter 7. Evolving Connectionist and Fuzzy - Connectionist Systems: Theory and Applications for Adaptive, On-line Intelligent Systems  

Microsoft Academic Search

The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on- line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created during the operation of the

Nikola Kasabov

416

Adaptive Force Control in Compliant Motion  

NASA Technical Reports Server (NTRS)

This paper addresses the problem of controlling a manipulator in compliant motion while in contact with an environment having an unknown stiffness. Two classes of solutions are discussed: adaptive admittance control and adaptive compliance control. In both admittance and compliance control schemes, compensator adaptation is used to ensure a stable and uniform system performance.

Seraji, H.

1994-01-01

417

Direct comparison of Neural Network, Fuzzy Logic and Model Prediction Variable Structure vortex flow controllers  

E-print Network

Predictive Variable Structure and Fuzzy Logic based controllers for the same benchmark problem. Evaluation criteria consist of closed-loop system performance, activity level of the VFC nozzles, ease of controller synthesis, time required to synthesize...

Joshi, Praveen Sudhakar

2012-06-07

418

Controlling of grid connected photovoltaic lighting system with fuzzy logic  

SciTech Connect

In this study, DC electrical energy produced by photovoltaic panels is converted to AC electrical energy and an indoor area is illuminated using this energy. System is controlled by fuzzy logic algorithm controller designed with 16 rules. Energy is supplied from accumulator which is charged by photovoltaic panels if its energy would be sufficient otherwise it is supplied from grid. During the 1-week usage period at the semester time, 1.968 kWh energy is used from grid but designed system used 0.542 kWh energy from photovoltaic panels at the experiments. Energy saving is determined by calculations and measurements for one education year period (9 months) 70.848 kWh. (author)

Saglam, Safak; Ekren, Nazmi; Erdal, Hasan [Technical Education Faculty, Marmara University, Istanbul 34722 (Turkey)

2010-02-15

419

Development of a GA-Fuzzy-Immune PID Controller with Incomplete Derivation for Robot Dexterous Hand  

PubMed Central

In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal. PMID:25097881

Liu, Xin-hua; Chen, Xiao-hu; Zheng, Xian-hua; Li, Sheng-peng; Wang, Zhong-bin

2014-01-01

420

Development of a GA-fuzzy-immune PID controller with incomplete derivation for robot dexterous hand.  

PubMed

In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal. PMID:25097881

Liu, Xin-hua; Chen, Xiao-hu; Zheng, Xian-hua; Li, Sheng-peng; Wang, Zhong-bin

2014-01-01

421

Power system stability using fuzzy logic based Unified Power Flow Controller in SMIB power system  

Microsoft Academic Search

The paper presents a control method of damping low frequency power system oscillations using fuzzy logic based Unified Power Flow Controller (UPFC) installed in a single-machine infinite-bus (SMIB) power system. The objective of the Fuzzy Logic based UPFC controller is to damp power system oscillations. UPFC controller based upon amplitude modulation index of shunt converter (exciter) mE has been designed.

Rajan Manrai; Rintu Khanna; Balwinder Singh; Pooja Manrai

2012-01-01

422

Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic  

DOEpatents

A decentralized fuzzy logic control system for one vehicle or for multiple robotic vehicles provides a way to control each vehicle to converge on a goal without collisions between vehicles or collisions with other obstacles, in the presence of noisy input measurements and a limited amount of compute-power and memory on board each robotic vehicle. The fuzzy controller demonstrates improved robustness to noise relative to an exact controller.

Feddema, John T. (Albuquerque, NM); Driessen, Brian J. (Albuquerque, NM); Kwok, Kwan S. (Albuquerque, NM)

2002-01-01

423

An Intelligent Monitoring System of Medium-Frequency Induction Furnace Based on Fuzzy Control  

Microsoft Academic Search

Aiming at the problem such as slowly-varying, pure-lag and low accuracy in the temperature control process of medium-frequency induction furnace, designed an intelligent monitoring system based on fuzzy control. The major hardware is composed of Program Logic Controller and its special expansion modules, the algorithm of fuzzy controller is completely realized via software programming. In order to improve the resolution

Zhang Zheng; Wan Guocheng; Liu Xiaowei

2010-01-01

424

Adaptive control of Unmanned Aerial Systems  

E-print Network

Adaptive control is considered to be one of the key enabling technologies for future high-performance, safety-critical systems such as air-breathing hypersonic vehicles. Adaptive flight control systems offer improved ...

Dydek, Zachary Thompson

2010-01-01

425

Participatory Evolving Fuzzy Modeling  

Microsoft Academic Search

This paper introduces an approach to develop evolving fuzzy rule-based models based on the idea of participatory learning. Participatory learning is a means to learn and revise beliefs based on what is already known or believed. Participatory learning naturally induces unsupervised dynamic fuzzy clustering algorithms and provides an effective alternative construct evolving functional fuzzy models and adaptive fuzzy systems. Evolving

E. Lima; F. Gomide; R. Ballini

2006-01-01

426

Adaptation of the fuzzy k-nearest neighbor classifier for manufacturing automation  

SciTech Connect

The use of supervised pattern recognition technologies for automation in the manufacturing environment require the development of systems that are easy to train and use. In general, these systems attempt to emulate an inspection or measurement function typically performed by a manufacturing engineer or technician. This paper describes a self-optimizing classification system for automatic decision making in the manufacturing environment. This classification system identifies and labels unique distributions of product defects denoted as signatures. The technique relies on encapsulating human experience through a teaching method to emulate the human response to various manufacturing situations. This has been successfully accomplished through the adaptation and extension of a feature-based, fuzzy k-nearest neighbor (k-NN) classifier that has been implemented in a pair-wise fashion. The classifier works with pair-wise combinations of the user-defined classes so that a significant reduction in feature space and problem complexity can be achieved. This k-NN implementation makes extensive use of hold-one-out results and fuzzy ambiguity information to optimize its performance. A semiconductor manufacturing case study will be presented. The technique uses data collected from in-line optical inspection tools to interpret and rapidly identify characteristic signatures that are uniquely associated with the manufacturing process. The system then alerts engineers to probable yield-limiting conditions that require attention.

Tobin, K.W.; Gleason, S.S.; Karnowski, T.P.

1998-01-01

427

Design of a genetic fuzzy controller for the nuclear steam generator water level control  

Microsoft Academic Search

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for

Man Gyun Na

1998-01-01

428

Adaptive gain improves reactor control  

SciTech Connect

An interesting application of the modern control theory technique called adaptive control is presently in use on a process located at a plant in the Standard Oil Co. (Ind.) system. The results of this application are: first, the transformation of an uncontrollable process to a controllable one; and, second, a significant economic savings to the corporation. It also shows that a detailed analysis of the mechanical, chemical and control systems can provide both the basis for revising an existing control system and some of the reasons why that system is inadequate. The contrast between classical and modern control theories is seen in four major areas. In classical systems, the controller manipulates the error to calculate its output. The error is subjected to a limited number of analog computing functions such as multiplication by a constant and integration. Each controller has one specific strategy--to hold the measured variable at the setpoint. And each controller controls only one variable, independent of all others. In a modern system, the controller can manipulate many different variables in addition to the error to compute its output. Its computing power is unlimited in a practical sense. It can do all classical computations plus many others, including a table look-up. The controller can independently change its strategy as a function of time or the condition of the process. It can also control many variables at one time to accomplish a complex objective. The implementation of a modern control theory project requires a good understanding of the dynamic, as well as the steady-state character of the process. As you can see we are limited only by our understanding of the process, our imagination, and the economics of the situation.

Whatley, M.J.; Pott, D.C.

1984-05-01

429

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2  

NASA Technical Reports Server (NTRS)

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

Lea, Robert N. (editor); Villarreal, James A. (editor)

1991-01-01

430

Predictor-Based Model Reference Adaptive Control  

NASA Technical Reports Server (NTRS)

This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now classical model reference adaptive control. A simulation example is presented in which numerical evidence indicates that the proposed controller yields improved transient characteristics.

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

2010-01-01

431

ADAPTIVE OPTICS CONTROL FOR LASER MATERIAL PROCESSING  

E-print Network

ADAPTIVE OPTICS CONTROL FOR LASER MATERIAL PROCESSING S. Mauch , J. Reger , E. Beckert Control-mail: erik.beckert@iof.fraunhofer.de) Abstract: An adaptive optics system is used for correcting tip: adaptive optics, tip-tilt control, Kalman-filtering, material processing 1. INTRODUCTION In laser material

Knobloch,Jürgen

432

On the appropriate uses of fuzzy systems: fuzzy sliding mode position control of a switched reluctance motor  

Microsoft Academic Search

The use of switched reluctance motors in position servo applications is complicated by their non-linear torque production characteristics. In this paper, the use of fuzzy logic to overcome this problem is described. The approach taken is that of exact input-output linearisation of the non-linear motor characteristic using a fast inner control loop in conjunction with a slower outer position control

D. S. Reay; M. Mirkazemi-Moud; B. W. Williams

1995-01-01

433

Fuzzy signal detection theory: analysis of human and machine performance in air traffic control, and analytic considerations  

Microsoft Academic Search

This paper applies fuzzy SDT (signal detection theory) techniques, which combine fuzzy logic and conventional SDT, to empirical data. Two studies involving detection of aircraft conflicts in air traffic control (ATC) were analysed using both conventional and fuzzy SDT. Study 1 used data from a preliminary field evaluation of an automated conflict probe system, the User Request Evaluation Tool (URET).

ANTHONY MASALONIS; RAJA PARASURAMAN

2003-01-01

434

Strapdown fiber optic gyrocompass using adaptive network-based fuzzy inference system  

NASA Astrophysics Data System (ADS)

This paper aims to propose a new strapdown fiber optic gyrocompass (strapdown FOGC) using adaptive network-based fuzzy inference system (ANFIS). The strapdown FOGC is based on the principle of strapdown inertial navigation system and utilizes electromagnetic log (EM log) and damping equalizer to bound oscillatory errors existing in attitude and heading. As the introduction of damping technique, extra errors are aroused by EM log errors. To decrease the extra errors, ANFIS is utilized to adjust the damping ratio automatically in terms of the ship maneuver conditions. The simulation and trial results indicate that, compared with the conventional gyrocompass scheme, the proposed one can reduce the attitude and heading errors and improve the system performance effectively.

Li, Qian; Ben, Yueyang; Sun, Feng

2014-01-01

435

A boiler-turbine system control using a fuzzy auto-regressive moving average (FARMA) model  

Microsoft Academic Search

This paper presents an application of an online self-organizing fuzzy logic controller to a boiler-turbine system of a fossil power plant. The control rules and the membership functions of the proposed fuzzy logic controller are generated automatically without using a plant model. A boiler-turbine system is described as a multi-input multioutput (MIMO) nonlinear system in this paper. Then, three single-loop

Un-Chul Moon; Kwang Y. Lee

2003-01-01

436

Finite-time reliable guaranteed cost fuzzy control for discrete-time nonlinear systems  

Microsoft Academic Search

The problem of finite-time reliable guaranteed cost fuzzy control for discrete-time nonlinear systems with actuator faults is investigated in this article. We first provide a novel and explicit interpretation for finite-time reliable guaranteed cost fuzzy control. Second, a suitable reliable controller is designed such that the provided performance criterion is satisfied for the normal and possible actuator fault cases. Moreover,

Dedong Yang; Kai-Yuan Cai

2011-01-01

437

Fuzzy control of optical PPM CDMA with M-ary orthogonal signaling  

NASA Astrophysics Data System (ADS)

This paper introduces an incorporated spectral-amplitude coding (SAC) optical code-division multiple-access (OCDMA) scheme. One novel class of optical signature codes based on combinatorial designs is employed with M-ary pulse-position modulation (PPM) signaling to improve the system performance beyond the interference limit. A union upper bound on the bit error rate (BER) is derived and the performance characteristics are then discussed with a variety of system parameters. Furthermore, fuzzy logic (FL) control is proposed to provide tolerance of different degrees of reliability in multirate transmission and to achieve distinct service differentiation for multimedia applications. It is shown that the proposed system can effectively suppress noise effects and offer improved adaptation capabilities for multi-quality network requirements in comparison with systems without optimization.

Cui, K.; Leeson, M. S.; Hines, E. L.

2008-06-01

438

Numerical Simulation of Fuzzy-PID Tension Control System Based on Rotary MRF Damper  

Microsoft Academic Search

A rotary MRF (magnetorheological fluids) damper is introduced in tension control systems. An experimental tension control system is designed by using the MRF damper as tension control actuator. Control strategies of the tension control system are studied. A fuzzy-PID feedback controller for the tension control system is constructed, modeled, and numerically simulated by means of MATLAB\\/ SIMULINK, where the variable

Hu Li-yong; Zhu Xi-lin; Zheng Di; Zhan Jian-ming

2009-01-01

439

Enhancing dissolved oxygen control using an on-line hybrid fuzzy-neural soft-sensing model-based control system in an anaerobic/anoxic/oxic process.  

PubMed

An on-line hybrid fuzzy-neural soft-sensing model-based control system was developed to optimize dissolved oxygen concentration in a bench-scale anaerobic/anoxic/oxic (A(2)/O) process. In order to improve the performance of the control system, a self-adapted fuzzy c-means clustering algorithm and adaptive network-based fuzzy inference system (ANFIS) models were employed. The proposed control system permits the on-line implementation of every operating strategy of the experimental system. A set of experiments involving variable hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO), and mixed-liquid return ratio (r) was carried out. Using the proposed system, the amount of COD in the effluent stabilized at the set-point and below. The improvement was achieved with optimum dissolved oxygen concentration because the performance of the treatment process was optimized using operating rules implemented in real time. The system allows various expert operational approaches to be deployed with the goal of minimizing organic substances in the outlet while using the minimum amount of energy. PMID:24052227

Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Ma, Yongwen; Wang, Yan

2013-12-01

440

A fuzzy logic based motion controller for a multi-degree-of-freedom robot arm  

E-print Network

This thesis addresses the task of producing a generic control algorithm for Cartesian motion of manipulators. The algorithm employs a self organizing fuzzy logic estimator to produce joint space changes given Cartesian error. The output from...

Pate, Billy Blakley

2012-06-07

441

Takagi-Sugeno fuzzy modeling and chaos control of partial differential systems  

NASA Astrophysics Data System (ADS)

In this paper a unified approach is presented for controlling chaos in nonlinear partial differential systems by a fuzzy control design. First almost all known chaotic partial differential equation systems are represented by Takagi-Sugeno fuzzy model. For investigating design procedure, Kuramoto-Sivashinsky (K-S) equation is selected. Then, all linear subsystems of K-S equation are transformed to ordinary differential equation (ODE) systems by truncated Fourier series of sine-cosine functions. By solving Riccati equation for each ODE systems, parallel stabilizing feedback controllers are determined. Finally, a distributed fuzzy feedback for K-S equation is designed. Numerical simulations are given to show that the distributed fuzzy controller is very easy to design, efficient, and capable to extend.

Vasegh, Nastaran; Khellat, Farhad

2013-12-01

442

Fuzzy-based Navigation and Control of a Non-Holonomic Mobile Robot  

E-print Network

In recent years, the use of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, a theoretical model of a fuzzy based controller for an autonomous mobile robot is developed. The paper begins with the mathematical model of the robot that involves the kinematic model. Then, the fuzzy logic controller is developed and discussed in detail. The proposed method is successfully tested in simulations, and it compares the effectiveness of three different set of membership of functions. It is shown that fuzzy logic controller with input membership of three provides better performance compared with five and seven membership functions.

Rashid, Razif; Begam, Mumtaj; Arrofiq, M

2010-01-01

443

Self-tuning Fuzzy Control Method Based on the Trajectory Performance of the Phase Plane  

E-print Network

The phase plane is already an important method to design fuzzy control systems and analyze their stability. The concept of the real-time response trajectory characteristic vectors and angles between the real-time characteristic vectors on the phase...

Zhang, J.; Chen, Y.; Xiong, J.

2006-01-01

444

Fuzzy Decision Trees for Planning and Autonomous Control of a Coordinated Team of UAVs.  

National Technical Information Service (NTIS)

A fuzzy logic resource manager that enables a collection of unmanned aerial vehicles (UAVs) to automatically cooperate to make meteorological measurements will be discussed. Once in flight no human intervention is required. Planning and real-time control ...

I. J. Smith, T. H. Nguyen

2007-01-01

445

Temperature control of glass melting furnace with fuzzy logic and conventional PI control  

Microsoft Academic Search

This paper presents a practical application of fuzzy logic control of the temperature of glass-melting furnace. Because of the complexity and nonlinearity, temperature control of glass-melting furnace is still delegated to human operator. Though the overall characteristics of glass-melting furnace are complex and nonlinear, one portion of the furnace characteristics can be modeled as a linear system. The linear portion

Un-Chul Moon; Kwang Y. Lee

2000-01-01

446

Chaos Optimization Strategy on Fuzzy-immune-PID Control of the Turbine Governing System  

Microsoft Academic Search

Aiming at the non-linear links such as time lag, inertia, dead time and saturation within the steam turbine governing system, we designed a fuzzy-immune-PID control system based on a mutative scale chaos optimization method, the principium of immune feedback system and the theory of fuzzy control. The proposed algorithm was used in tuning-parameter design of the steam turbine governing system

Shuangxin Wang; Yan Jiang; Hui Yang

2006-01-01

447

Robust Reliable Control for a Class of Fuzzy Dynamic Systems with Time-Varying Delay  

Microsoft Academic Search

\\u000a This paper deals with the problem of robust reliable control design for a class of fuzzy uncertain systems with time-varying\\u000a delay. The system under consideration is more general than those in other existent works. A reliable fuzzy control design\\u000a scheme via state feedback is proposed in terms of linear matrix inequality (LMI). The asymptotic stability of the closed-loop\\u000a system is

Youqing Wang; Donghua Zhou

2005-01-01

448

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg  

Microsoft Academic Search

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the genetic algorithm, which\\u000a is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part and the on-line part. In\\u000a the off-line part, by taking the overshoot, rise time, and settling time of system unit step response as the performance indexes\\u000a and

Guan-zheng Tan; An-ping Li

2003-01-01

449

Integration of a fuzzy guidance-control system for a command interceptor against hypersonic target  

Microsoft Academic Search

Extensive research efforts have been made to design or improve flight guidance and control systems. With the phenomenal growth in soft computing techniques, there is a growing interest to apply these techniques to missile flight guidance and control systems. This paper investigates the application of fuzzy logic for the development of fuzzy guidance laws for command-to-line-of-sight skid-to-turn (CLOS-STT) missile. The

Y. Z. Elhalwagy; M. Tarbouchi

2001-01-01

450

A Fuzzy PID Cascade Control System of a Hydro-viscous Drive Speed Regulating Start  

Microsoft Academic Search

The effect of hydro-viscous drive speed regulating start depends both on reasonable structural design and appropriate control strategy. But the control system used at present cannot completely satisfy practical requirements. In order to resolve the problem, a fuzzy PID cascade control system is presented based on cascade control theory in this paper. In auxiliary control loop a PID controller is

Meng Qing-rui; Hou You-fu

2011-01-01

451

Adaptive Power System Stabilizer using Intelligent Control Techniques  

Microsoft Academic Search

This paper presents an adaptive Power System Stabilizer (PSS) using an Adaptive Network Based Fuzzy Inference System (ANFIS) and Genetic Algorithms (GAs). Firstly, genetic algorithms are used to tune a conventional PSS on a wide range of operating conditions and then, the relationship between these operating points and the PSS parameters is learned by the ANFIS. The ANFIS optimally selects

Jesús Fraile-Ardanuy; Pedro J. Zufiria

452

Dual-arm manipulators with adaptive control  

NASA Technical Reports Server (NTRS)

The described and improved multi-arm invention of this application presents three strategies for adaptive control of cooperative multi-arm robots which coordinate control over a common load. In the position-position control strategy, the adaptive controllers ensure that the end-effector positions of both arms track desired trajectories in Cartesian space despite unknown time-varying interaction forces exerted through a load. In the position-hybrid control strategy, the adaptive controller of one arm controls end-effector motions in the free directions and applied forces in the constraint directions; while the adaptive controller of the other arm ensures that the end-effector tracks desired position trajectories. In the hybrid-hybrid control strategy, the adaptive controllers ensure that both end-effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the cross-coupling effects between the arms are treated as disturbances which are compensated for by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. Circuits in the adaptive feedback and feedforward controllers are varied by novel adaptation laws.

Seraji, Homayoun (Inventor)

1991-01-01

453

Fuzzy Rule Suram for Control System of a Solar Energy Wood Drying Chamber  

NASA Astrophysics Data System (ADS)

The paper reports used the fuzzy rule Suram for control system of a wood drying chamber with solar as source of energy. Rule suram based of fuzzy logic with variables of weather is temperature ambient and conditions of air is humidity ambient, it implemented for wood drying process. The membership function of variable of state represented in error value and change error with typical of triangle and trapezium map. Result of Analysis to reach 8 fuzzy rule to control the output system can be constructed in a number of way of weather and conditions of air. It used to minimum of the consumption of electric energy by heater. The rule suram used to stability and equilibrium of schedule of drying in chamber by control of temperature and humidity. The result of implemented of fuzzy rule suram with the modification of membership function in range [0.5, 1] represented approximate to he conditions riel.

Situmorang, Zakarias; Wardoyo, Retantyo; Hartati, Sri; Eko Istiyanto, Jazi

2009-08-01

454

Flight Test Approach to Adaptive Control Research  

NASA Technical Reports Server (NTRS)

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.

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

2011-01-01

455

Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller  

NASA Astrophysics Data System (ADS)

This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi-Sugeno-Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.

Zori?, Nemanja D.; Simonovi?, Aleksandar M.; Mitrovi?, Zoran S.; Stupar, Slobodan N.; Obradovi?, Aleksandar M.; Luki?, Nebojša S.

2014-10-01

456

Summary report: A preliminary investigation into the use of fuzzy logic for the control of redundant manipulators  

NASA Technical Reports Server (NTRS)

The Rice University Department of Mechanical Engineering and Materials Sciences' Robotics Group designed and built an eight degree of freedom redundant manipulator. Fuzzy logic was proposed as a control scheme for tasks not directly controlled by a human operator. In preliminary work, fuzzy logic control was implemented for a camera tracking system and a six degree of freedom manipulator. Both preliminary systems use real time vision data as input to fuzzy controllers. Related projects include integration of tactile sensing and fuzzy control of a redundant snake-like arm that is under construction.

Cheatham, John B., Jr.; Magee, Kevin N.

1991-01-01

457

Precompensation for a hybrid fuzzy PID control of a proportional hydraulic system  

Microsoft Academic Search

While classical PID controllers are sensitive to variations in the system parameters, fuzzy controllers do not need precise information about the system variables in order to be effective. However, PID controllers are better able to control and minimize the steady state error of the system. Hybridization of these two controller structures comes to one mind immediately to exploit the beneficial

P. Pratumsuwan; S. Thongchai

2009-01-01

458

Combined Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System for Improving a Short-Term Electric Load Forecasting  

Microsoft Academic Search

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period\\u000a of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load\\u000a forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total

Ronaldo R. B. De Aquino; Geane B. Silva; Milde M. S. Lira; Aida A. Ferreira; Manoel A. Carvalho Jr; Otoni Nóbrega Neto; Josinaldo B. De Oliveira

2007-01-01

459

Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networks  

Microsoft Academic Search

This study presents a functional-link-based neuro-fuzzy network (FLNFN) structure for nonlinear system control. The proposed\\u000a FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal\\u000a polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed\\u000a FLNFN model is a nonlinear

Chin-Teng Lin; Cheng-Hung Chen; Cheng-Jian Lin

460

Implementation of Adaptive Digital Controllers on Programmable Logic Devices  

NASA Technical Reports Server (NTRS)

Much has been made of the capabilities of Field Programmable Gate Arrays (FPGA's) in the hardware implementation of fast digital signal processing functions. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used Proportional-Integral-Derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM- based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a Digital Signal Processor (DSP) device or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using DSP devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, Pulse Width Modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacemap. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive-control algorithm approaches. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Montenegro, Justino (Technical Monitor)

2002-01-01

461

Implementation of Adaptive Digital Controllers on Programmable Logic Devices  

NASA Technical Reports Server (NTRS)

Much has been made of the capabilities of FPGA's (Field Programmable Gate Arrays) in the hardware implementation of fast digital signal processing. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used proportional-integral-derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM-based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a DSP (Digital Signal Processor) or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using digital signal processor (DSP) devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, pulse width modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive control algorithm approaches. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacecraft. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Monenegro, Justino (Technical Monitor)

2002-01-01

462

28 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 2, NO. 1, JANUARY 2011 Adaptive Control of a Wind Turbine With Data  

E-print Network

swarm fuzzy algorithm is used to solve it. Index Terms--Adaptive control, blade pitch angle, data mining research has expanded in scope to cover domains such as, for example, wind energy conversion [1], [2 to opti- mize power at low frequency and high frequency scenarios [14]. However, in the previous research

Kusiak, Andrew

463

ADAPTIVE INVERSE CONTROL FOR NONLINEAR SYSTEM  

Microsoft Academic Search

The research on the application of adaptive inverse control method has received much attention in recent years. But its main problem, when ap- plied to controlling nonlinear systems, is how to adapt the inverse controller. The BPTM (backprop through (plant) model) algorithm and the \\

QIAOGE LIU; MENGYIN FU; ZHIHONG DENG

2005-01-01

464

A Decentralized Adaptive Control of Flexible Satellite  

Microsoft Academic Search

The minimal controller synthesis (MCS) is an extension of hyperstable model reference adaptive control (MRAC) algorithm. The aim of MCS is to achieve excellent closed-loop control despite the presence of system parameter variations, external disturbances, dynamic coupling within the system and system nonlinearities. The MCS was successfully applied to the problem of decentralized adaptive schemes. A modification on the decentralized

Thawar T. Arif

2007-01-01