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1

Adaptive Robust Fuzzy Control for Robot Manipulators

This paper presents an adaptive robust fuzzy control architecture for robot manipulators motion. The control objective is to adaptively compensate for the unknown nonlinearity of robot manipulators, which is represented as a fuzzy rule-base consisting of a collection of if-then rules. The algorithm embedded in the proposed architecture can automatically update fuzzy rules and, consequently, it is guaranteed to be

Feng-yih Hsu; Li-Chen Full

1994-01-01

2

Designing adaptive fuzzy controller for nonlinear systems

NASA Astrophysics Data System (ADS)

The objective of this paper is to achieve model reference adaptive fuzzy control for a nonlinear dynamical system. An adaptive fuzzy autopilot for ship course-keeping is developed. The influence of sea current and wave disturbances on course-keeping performance is also considered as random noises. Simulation results are presented.

Zhou, Bo; Shi, Aiguo; Cai, Feng; Zhang, Yongsheng; Yang, Baozhang

2003-09-01

3

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

Karr, C. Lucas; Harper, Tony R.

1992-01-01

4

Stable adaptive fuzzy controllers with application to inverted pendulum tracking

An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given-trajectory. In this paper, two adaptive fuzzy controllers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable

Li-Xin Wang

1996-01-01

5

Adaptive fuzzy sliding mode control of nonlinear system

In this paper, the fuzzy approximator and sliding mode control (SMC) scheme are considered. We propose two methods of adaptive SMC schemes that the fuzzy logic systems (approximators) are used to approximate the unknown system functions in designing the SMC of nonlinear system. In the first method, a fuzzy logic system is utilized to approximate the unknown function f of

Byungkook Yoo; Woonchul Ham

1998-01-01

6

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

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

7

Variable universe adaptive fuzzy control on the quadruple inverted pendulum

This paper focuses on the control problem of the quadruple inverted pendulum by variable universe adaptive fuzzy control.\\u000a First, the mathematical model on the quadruple inverted pendulum is described and its controllability is versified. Then,\\u000a an efficient controller on the quadruple inverted pendulum is designed by using variable universe adaptive fuzzy control theory.\\u000a Finally the simulation of the quadruple inverted

Hongxing Li; Miao Zhihong; Wang Jiayin

2002-01-01

8

This paper describes the application and simulation of an adaptive fuzzy controller for a missile model. The fuzzy control system is tested using different values of fuzzy controller correctional factor on a nonlinear missile model. It is shown that the self-tuning fuzzy controller is well suited for controlling the pitch loop of the missile control system with air turbulence and

Jianling Zhang; Jinwen An; Mina Wang

2005-01-01

9

An adaptive type-2 fuzzy logic controller for dynamic positioning

This paper presents an indirect adaptive type- 2 fuzzy logic controller (FLC) for dynamic positioning (DP) vessels with attached thrusters under various time-varying hy- drodynamic disturbances. Approximation-based adaptive control technique in combination with type-2 fuzzy logic system (FLS) is employed in the design of the control. The stability of the design is demonstrated through Lyapunov analysis where globally asymptotical convergence

Xue Tao Chen; Woei Wan Tan

2011-01-01

10

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

11

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

12

ADAPTIVE FUZZY CONTROL FOR UNDERWATER HYDRAULIC MANIPULATORS

Underwater hydraulic manipulators are usually systems hard to be modeled and present strong non-linearities in its dynamics behavior. These types of manipulators are operated, nowadays, in a master-slave configuration with simple control algorithms performing tasks in hazardous and unstructured environments. In such conditions only low accuracy simple tasks can be performed. This paper presents the application of a special fuzzy

Leonardo Bittencourt Testi; Bruno Cardozo dos Santos; Max Suell Dutra

2004-01-01

13

Nonlinear control of robot manipulators using adaptive fuzzy sliding mode control

This paper presents an adaptive robust fuzzy control architecture for robot manipulators. The control objective is to adaptively compensate for the unknown nonlinearity of robot manipulators which is represented as a fuzzy rule-base consisting of a collection of if-then rules. The algorithm embedded in the proposed architecture can automatically update fuzzy rules and, consequently it is guaranteed to be globally

Feng-Yih Hsu; Li-Chen Full

1995-01-01

14

Application of adaptive fuzzy control to ac machines

The decoupled control of torque and flux has made field-oriented control an attractive choice for high performance induction motor drives. However, changes in the speed tracking trajectory and external disturbances make it difficult to achieve an acceptable closed-loop tracking performance, especially when traditional linear controllers are used. This paper addresses this issue by applying direct and indirect adaptive fuzzy controllers

Hazem N. Nounou; Habib-ur Rehman

2007-01-01

15

NASA Astrophysics Data System (ADS)

This paper describes the application and simulation of an adaptive fuzzy controller for a missile model. The fuzzy control system is tested using different values of fuzzy controller correctional factor on a nonlinear missile model. It is shown that the self-tuning fuzzy controller is well suited for controlling the pitch loop of the missile control system with air turbulence and parameter variety. The research shows that the Popov stability criterion could successfully guarantee the stability of the fuzzy system. It provides a good method for the design of missile control system. Simulation results suggest significant benefits from fuzzy logic in control task for missile pitch loop control.

Zhang, Jianling; An, Jinwen; Wang, Mina

2005-11-01

16

Design of adaptive fuzzy controls based on natural control laws

In the design of fuzzy controllers there is a need for standardizing the selection of rule table and the scaling factors. For example, in the design of self-organizing fuzzy controllers or the rule self-regulating fuzzy controllers, the rule table or the performance table is often derived either by trial and error or from the heuristics of an expert. Recently a

M. S. Ju; D. L. Yang

1996-01-01

17

Adaptive control of robot manipulator using fuzzy compensator

This paper presents two kinds of adaptive control schemes for robot manipulator which has the parametric uncertainties. In order to compensate these uncertainties, we use the FLS (fuzzy logic system) that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation

Byung Kook Yoo; Woon Chul Ham

2000-01-01

18

Fuzzy-based adaptive bandwidth control for loss guarantees.

This paper presents the use of adaptive bandwidth control (ABC) for a quantitative packet loss rate guarantee to aggregate traffic in packet switched networks. ABC starts with some initial amount of bandwidth allocated to a queue and adjusts it over time based on online measurements of system states to ensure that the allocated bandwidth is just enough to attain the specified loss requirement. Consequently, no a priori detailed traffic information is required, making ABC more suitable for efficient aggregate quality of service (QoS) provisioning. We propose an ABC algorithm called augmented Fuzzy (A-Fuzzy) control, whereby fuzzy logic control is used to keep an average queue length at an appropriate target value, and the measured packet loss rate is used to augment the standard control to achieve better performance. An extensive simulation study based on both theoretical traffic models and real traffic traces under a wide range of system configurations demonstrates that the A-Fuzzy control itself is highly robust, yields high bandwidth utilization, and is indeed a viable alternative and improvement to static bandwidth allocation (SBA) and existing adaptive bandwidth allocation schemes. Additionally, we develop a simple and efficient measurement-based admission control procedure which limits the amount of input traffic in order to maintain the performance of the A-Fuzzy control at an acceptable level. PMID:16252823

Siripongwutikorn, Peerapon; Banerjee, Sujata; Tipper, David

2005-09-01

19

PID type fuzzy controller and parameters adaptive method

The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller

Wu Zhi Qiao; Masaharu Mizumoto

1996-01-01

20

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

NASA Technical Reports Server (NTRS)

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.

Esogbue, Augustine O.

1998-01-01

21

Neural and Fuzzy Adaptive Control of Induction Motor Drives

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. [Research Unit of Modelisation, Analyse, Command of Systems MACS (Tunisia); Sbita, L.; Abdelkrim, M. N. [6029 Universite High School of Engineering-Gabes-Tunisia (Tunisia)

2008-06-12

22

Aimed at the shortcomings that traditional fuzzy logic control method for solar LED illumination system maximum power point tracking (MPPT) control system, a novel solar LED illumination control strategy based on variable univers adaptive fuzzy PID controller is proposed.The controller can automatically adjusts the variable univers with the change of photovoltaic output power deviation, to realize the solar LED illumination

Yiwang Wang; Shuo Wu

2011-01-01

23

Fuzzy adaptive control of a certain class of SISO discrete-time processes

In this manuscript, we address the problem of the stability of a certain class of SISO discrete-time processes controlled by an adaptive fuzzy controller, by using Lyapunov stability theory. These results were recently obtained for adaptive neural controllers, and are extended here to adaptive fuzzy controllers of Sugeno's type. In order to achieve tracking of a reference signal with this

Jean-Michel Renders; Marco Saerens; Hugues Bersini

1997-01-01

24

This paper proposes an adaptive genetic algorithm (AGA) for the multi-objective optimization design of a fuzzy PID controller and applies it to the control of an active magnetic bearing (AMB) system. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules whose rule consequences employ analytical PID expressions. The PID gains are

Hung-Cheng Chen

2008-01-01

25

Discrete-time adaptive fuzzy control of continuous-time nonlinear systems

In this paper, we develop a discrete-time controller for continuous-time nonlinear plant. The exact dynamics of the plant are unknown, but some bounds of the dynamics are assumed. The controller consists of a supervisory controller and an adaptive fuzzy controller. The supervisory controller is designed to guarantee the boundedness of the state, and the adaptive fuzzy controller is designed to

Li-Xin Wang; Chen Wei

1999-01-01

26

Active vibration control of adaptive truss structure using fuzzy neural network

This paper presents design, implementation and experimental results of active vibration control of adaptive truss structure using fuzzy neural method. An adaptive truss structure with self-learning active vibration control system is developed. A fuzzy neural network (INN) controller with adaptive membership functions is presented. The experimental setup of a two-bay truss structure with active members is constructed, and the INN

Kai Zheng; Yuquan zhang; Yiyong Yang; Shaoze Yan; Lihua Dou; Jie Chen

2008-01-01

27

Adaptive freeway ramp metering and variable speed limit control: a genetic-fuzzy approach

This paper deals with the problem of ramp metering along with speed limit control of the freeway networks in order to reduce the peak hour congestion. An adaptive fuzzy control is proposed to solve the problem. To calibrate the fuzzy controller, genetic algorithm is used to tune the fuzzy sets parameters so that the total time spent in the network

A. Ghods; A. Kian; M. Tabibi

2009-01-01

28

Adaptive fuzzy sliding mode control for missile electro-hydraulic servo mechanism

The position tracking control of a missile electrohydraulic servo mechanism is studied. Since the dynamics of the system are highly nonlinear and have large extent of model uncertainties, such as big changes in load and parameters, a design method of adaptive fuzzy sliding mode control is presented. The adaptive fuzzy controller is introduced to approach the equivalent control of sliding

Juntuan Zhang; Defu Cheng; Yunfeng Liu; Guangliang Zhu

2008-01-01

29

Global Asymptotic Stabilization Using Adaptive Fuzzy PD Control.

It is well-known that standard adaptive fuzzy control (AFC) can only guarantee uniformly ultimately bounded stability due to inherent fuzzy approximation errors (FAEs). This paper proves that standard AFC with proportional-derivative (PD) control can guarantee global asymptotic stabilization even in the presence of FAEs for a class of uncertain affine nonlinear systems. Variable-gain PD control is designed to globally stabilize the plant. An optimal FAE is shown to be bounded by the norm of the plant state vector multiplied by a globally invertible and nondecreasing function, which provides a pivotal property for stability analysis. Without discontinuous control compensation, the closed-loop system achieves global and partially asymptotic stability in the sense that all plant states converge to zero. Compared with previous adaptive approximation-based global/asymptotic stabilization approaches, the major advantage of our approach is that global stability and asymptotic stabilization are achieved concurrently by a much simpler control law. Illustrative examples have further verified the theoretical results. PMID:25122847

Pan, Yongping; Yu, Haoyong; Sun, Tairen

2014-08-01

30

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller. PMID:19523623

Alavandar, Srinivasan; Nigam, M J

2009-10-01

31

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

32

propose an ABC al- gorithm called augmented Fuzzy (A-Fuzzy) control, whereby fuzzy logic control is usedIEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 5, SEPTEMBER 2005 1147 Fuzzy-Based Adaptive bandwidth control (ABC) for a quantitative packet loss rate guarantee to ag- gregate traffic in packet

Tipper, David

33

Adaptive fuzzy control of electrically stimulated muscles for arm movements.

A modified adaptive Takagi-Sugeno (TS) fuzzy logic controller (FLC) is proposed that allows a simulated elbow-like biomechanical system to accurately track sigmoidal and sinusoidal trajectories in the sagittal plane. The work is a first effort towards the implementation of a system to restore elbow movements in quadriplegics using functional neuromuscular stimulation. The single-joint musculo-skeletal system is composed of a co-contractable pair of electrically stimulated muscles; the muscle model accounts for the increase in fatigue during the tracking exercise. In the proposed controller structure, a reinforcement learning scheme is used to accomplish the parameter tuning, and the parameter projection algorithm guarantees the system stability during the adaptation process. The controller performance is evaluated using computer simulation experiments and compared with the performance achievable when a standard proportional-integrative-derivative (PID) controller is employed for the same application. The modified adaptive TSFLC outperforms the PID controller in all tested situations, with a clear-cut advantage in the case of high-frequency sinusoidal trajectories (angular frequencies spanning the interval 8-12 rad s-1). The standard controller suffers from a dramatic increase in root mean square (RMS) tracking error above the value at 8 rad s-1, e.g. ERMS > or = 0.013, whereas the correlation coefficient between the actual and desired trajectory falls almost to zero, starting from the value rho approximately equal to 0.97 at 8 rad s-1. On the other hand, the adaptive TSFLC yields ERMS < or = 0.015, with rho > or = 0.78, over the whole range of tested angular frequencies. PMID:10723872

Micera, S; Sabatini, A M; Dario, P

1999-11-01

34

Variable Universe Adaptive Fuzzy Sliding Mode Controller for a Class of Nonlinear System

\\u000a Based on integrating the property of sliding mode control (SMC) with the thought of variable universe in adaptive fuzzy control,\\u000a a design method of variable universe adaptive fuzzy sliding mode control (FSMC) is proposed. There are two sets of control\\u000a rule bases. The first set is utilized to approach the equivalent control of SMC. By adjusting the universes of input

Yunfeng Liu; Dong Miao; Yunhui Peng; Xiaogang Yang

35

Variable Universe Adaptive Fuzzy Sliding Mode Controller for a Class of Nonlinear System

\\u000a Based on integrating the property of sliding mode control (SMC) with the thought of variable universe in adaptive fuzzy control,\\u000a a design method of variable universe adaptive fuzzy sliding mode control (FSMC) is proposed. There are two sets of control\\u000a rule bases. The first set is utilized to approach the equivalent control of SMC. By adjusting the universes of input

Yunfeng Liu; Dong Miao; Yunhui Peng; Xiaogang Yang

2006-01-01

36

H?, adaptive, PID and fuzzy control: a comparison of controllers for vehicle lane keeping

We provide, in this paper an overview as well as a comparison of four controllers for the vehicle lateral control problem. H?, adaptive, fuzzy and PID controllers are compared by simulations over a test track circuit. Curvature and wind perturbations as well as variations on the speed and on the adherence coefficient are introduced in the simulations.

Salim Chaib; Mariana S. Netto; S. Mammar

2004-01-01

37

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

-fuzzy "ANFIS" control. The tracking algorithm integrated with a solar PV system has been simulated with boostMaximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS availability and vast potential, world has turned to solar photovoltaic energy to meet out its ever increasing

Paris-Sud XI, UniversitÃ© de

38

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

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

39

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

40

Adaptive fuzzy switched swing-up and sliding control for the double-pendulum-and-cart system.

In this paper, an adaptive fuzzy switched swing-up and sliding controller (AFSSSC) is proposed for the swing-up and position controls of a double-pendulum-and-cart system. The proposed AFSSSC consists of a fuzzy switching controller (FSC), an adaptive fuzzy swing-up controller (FSUC), and an adaptive hybrid fuzzy sliding controller (HFSC). To simplify the design of the adaptive HFSC, the double-pendulum-and-cart system is reformulated as a double-pendulum and a cart subsystem with matched time-varying uncertainties. In addition, an adaptive mechanism is provided to learn the parameters of the output fuzzy sets for the adaptive HFSC. The FSC is designed to smoothly switch between the adaptive FSUC and the adaptive HFSC. Moreover, the sliding mode and the stability of the fuzzy sliding control systems are guaranteed. Simulation results are included to illustrate the effectiveness of the proposed AFSSSC. PMID:19661002

Tao, Chin Wang; Taur, Jinshiuh; Chang, J H; Su, Shun-Feng

2010-02-01

41

This paper proposes an adaptive fuzzy hybrid force\\/position control scheme, which can force the end effector of robot manipulators to follow the contour of an object in lack of knowledge of the exact geometric shape. The control objective is to perform hybrid force\\/position control regardless of the existence of the manipulator dynamics. The control algorithm proposed can adaptively update the

Feng-Yih Hsu; Li-Chen Fu

1996-01-01

42

The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. 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). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers

unknown authors

2008-01-01

43

An Adaptive Filter Based Fuzzy Logic Controller Incorporating MTPA for IPMSM Drives

This paper presents an adaptive filter for fuzzy-logic controller for vector controlled based speed controller incorporating with a maximum torque per ampere (MTPA) of an interior permanent magnet synchronous motor (IPMSM) drive to minimize the torque ripple. The design and optimization of the FLC, infinite impulse response (IIR) filter and the adaptation algorithms are presented. Different operating conditions have been

Hanan M. Dawood Habbi; Muhammad Ilyas Menhas; Huang Surong

2009-01-01

44

Performance improvement of a microbial fuel cell based on adaptive fuzzy control.

Microbial fuel cells have been obtaining more and more attention with the associated abilities of continuous electrical power supply and wastewater treatment. Because of its complicated reaction mechanism and its inherent characteristics of time varying, uncertainty, strong coupling and nonlinearity, there are complex control challenges in microbial fuel cells. In this paper, an adaptive fuzzy control scheme is proposed for the microbial fuel cell system to achieve constant voltage output under different loads. A main fuzzy controller is used to track the set value, and an auxiliary fuzzy controller is applied to adjust the factors of the main controller. Simulation results show that the output voltage can track the given value well. The proposed adaptive fuzzy controller can give better steady-state behavior and faster response, and it improves the running performance of the microbial fuel cell. PMID:24816708

Fan, Liping; Li, Chong; Boshnakov, Kosta

2014-05-01

45

In this paper, an adaptive fuzzy decentralized output feedback control design is presented for a class of interconnected nonlinear pure-feedback systems. The considered nonlinear systems contain unknown nonlinear uncertainties and the states are not necessary to be measured directly. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and then a fuzzy state observer is designed and the estimations of the immeasurable state variables are obtained. Based on the adaptive backstepping dynamic surface control design technique, an adaptive fuzzy decentralized output feedback control scheme is developed. It is proved that all the variables of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and also that the observer and tracking errors are guaranteed to converge to a small neighborhood of the origin. Some simulation results and comparisons with the existing results are provided to illustrate the effectiveness and merits of the proposed approach. PMID:25051573

Li, Yongming; Tong, Shaocheng; Li, Tieshan

2015-01-01

46

This paper presents a real case study of warehouse replenishment process optimization on a selected sample of representative\\u000a materials. Optimization is performed with simulation model supported by inventory control algorithms. The adaptive fuzzy inventory\\u000a control algorithm based on fuzzy stock-outs, highest stock level and total cost is introduced. The algorithm is tested and\\u000a compared to the simulation results of the

Davorin Kofja?; Miroljub Kljaji?; Andrej Škraba; Blaž Rodi?

47

NASA Astrophysics Data System (ADS)

Because of nonlinearity and strong coupling of reaction-jet and aerodynamics compound control missile, a missile autopilot design method based on adaptive fuzzy sliding mode control (AFSMC) is proposed in this paper. The universal approximation ability of adaptive fuzzy system is used to approximate the nonlinear function in missile dynamics equation during the flight of high angle of attack. And because the sliding mode control is robustness to external disturbance strongly, the sliding mode surface of the error system is constructed to overcome the influence of approximation error and external disturbance so that the actual overload can track the maneuvering command with high precision. Simulation results show that the missile autopilot designed in this paper not only can track large overload command with higher precision than traditional method, but also is robust to model uncertainty and external disturbance strongly.

Wu, Zhenhui; Dong, Chaoyang

2006-11-01

48

Robust Adaptive Backstepping Motion Control of Linear Ultrasonic Motors Using Fuzzy Neural Network

A robust adaptive fuzzy neural network (RAFNN) backstepping control system is proposed to control the position of an - - motion control stage using linear ultrasonic mo- tors (LUSMs) to track various contours in this study. First, an - - motion control stage is introduced. Then, the single-axis dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which

Faa-jeng Lin; Po-huang Shieh; Po-huan Chou

2008-01-01

49

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

50

Fuzzy control of magnetic bearings

NASA Technical Reports Server (NTRS)

The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.

Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.

1991-01-01

51

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

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

52

Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system

In this paper, a new robust adaptive control architecture is proposed for operation of an inverted-pendulum mechanical system. The architecture employs a fuzzy system to adaptively compensate for the plant nonlinearities and forces the inverted pendulum to track a prescribed reference model. When matching with the model occurs, the pendulum will be stabilized at an upright position and the cart

Chaio-Shiung Chen; Wen-Liang Chen

1998-01-01

53

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

54

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

55

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

56

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems] [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering] [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control] [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)] [Electric Power Research Inst., Palo Alto, CA (United States)

1997-12-01

57

Fuzzy Logic User Adaptive Navigation Control System For Mobile Robots In Unknown Environments

This paper presents a software implementation of a user adaptive fuzzy control system for autonomous navigation in mobile robots for unknown environments. This system has been tested in a pioneer mobile robot and on a robotic wheelchair, fitted with PLS laser sensor to detect the obstacles and odometry sensors for localization of robots and the goal positions. The system is

Miguel AngelOlivares Mendez; J. A. F. Madrigal

2007-01-01

58

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

59

Injection circuit to the HV GRID using Adaptive Fuzzy Dividing Frequency-Control Method

NASA Astrophysics Data System (ADS)

This paper deals with a hybrid active power filter with injection circuit (IHAPF). It exhibits clear promise in decreasing harmonics and increasing the power factor with a comparatively low capacity active power filter. This paper concludes that the stability of the IHAPF based on spotting supply current is exceptional to that of others. To minimize the capacity of IHAPF, an adaptive fuzzy dividing frequency control method is used, which consists of two control units: a generalized integrator control unit and fuzzy adjustor unit. The generalized integrator is used for dividing the frequency integral control, while fuzzy arithmetic is used for adjusting proportional-integral coefficients timely. And the control method is generally useful and applicable to any other active filters. Compared to other IHAPF control methods, the adaptive fuzzy dividing frequency control shows the advantages of shorter response time and higher control precision. It is implemented in an IHAPF with a 100-k VA APF installed in a copper mill in Northern China. The simulation and experimental results show that the new control method is not only easy to be calculated and implemented, but also very effective in reducing harmonics.

Reddy, Bodha venugopal; Reddy, N. Narender

2012-07-01

60

An adaptive fuzzy output feedback controller is proposed for a class of uncertain MIMO nonlinear systems with unknown input nonlinearities. The input nonlinearities can be backlash-like hysteresis or dead-zone. Besides, the gains of unknown input nonlinearities are unknown nonlinear functions. Based on universal approximation theorem, the unknown nonlinear functions are approximated by fuzzy systems. The proposed method does not need the availability of the states and an observer based on strictly positive real (SPR) theory is designed to estimate the states. An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances. The semi-global asymptotic stability of the closed-loop system is guaranteed via Lyapunov approach. The applicability of the proposed method is also shown via simulations. PMID:25104646

Shahnazi, Reza

2014-08-01

61

NASA Astrophysics Data System (ADS)

This paper presents a novel approach for station-keeping control of a stratospheric airship platform in the presence of parametric uncertainty and external disturbance. First, conceptual design of the stratospheric airship platform is introduced, including the target mission, configuration, energy sources, propeller and payload. Second, the dynamics model of the airship platform is presented, and the mathematical model of its horizontal motion is derived. Third, a fuzzy adaptive backstepping control approach is proposed to develop the station-keeping control system for the simplified horizontal motion. The backstepping controller is designed assuming that the airship model is accurately known, and a fuzzy adaptive algorithm is used to approximate the uncertainty of the airship model. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control approach.

Yang, Yueneng; Wu, Jie; Zheng, Wei

2013-04-01

62

Fuzzy-based adaptive bandwidth control for loss guarantees

This paper presents the use of adaptive bandwidth control (ABC) for a quantitative packet loss rate guarantee to aggregate traffic in packet switched networks. ABC starts with some initial amount of bandwidth allocated to a queue and adjusts it over time based on online measurements of system states to ensure that the allocated bandwidth is just enough to attain the

Peerapon Siripongwutikorn; Sujata Banerjee; David Tipper

2005-01-01

63

This paper presents a new robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy PID logic control with the so-called sliding mode control, SMC, approach. The motivation for using SMC in robotics mainly relies on its appreciable features, such as design simplicity and robustness. However, the drawbacks of conventional SMC, such as chattering effects and

Ahmed Foad Amer; Elsayed Abdelhameed Sallam; Wael Mohammed Elawady

64

In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial--parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial--parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach. PMID:25438335

Li, Yongming; Tong, Shaocheng; Li, Tieshan

2014-11-25

65

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

66

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

67

Adaptive Control of TwoAxis Motion Control System Using Interval Type2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural

Faa-Jeng Lin; Po-Huan Chou

2009-01-01

68

The paper proposes a new vehicle crash-avoiding method using the fuzzy reasoning system and neural net work. The method used neural net work to calculate collision risk instead of fuzzy inference. A vehicle crash-avoiding adaptive network fuzzy interference system model is proposed. The hybrid learning algorithm is proposed to improve rapidity of convergence. For some linear parameters such as consequent

Cuimin Dong

2011-01-01

69

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

70

Towards autonomous fuzzy control

NASA Technical Reports Server (NTRS)

The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous self-organizing controllers employing system-independent control meta-knowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control actions give rise to adequate performance characteristics but could rapidly and autonomously initiate real-time adaptation whenever its performance degrades. Such an autonomous fuzzy controller would have immense practical value. It could accommodate individual variations in system characteristics and also compensate for degradations in system characteristics caused by wear and tear. It could also potentially deal with black-box systems and control scenarios. On-going research in autonomous fuzzy control is reported. The ultimate research objective is to develop robust and relatively inexpensive autonomous fuzzy control hardware suitable for use in real time environments.

Shenoi, Sujeet; Ramer, Arthur

1993-01-01

71

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

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

72

Neuro-fuzzy modeling and control

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

73

Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems.

This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm. PMID:24132033

Chen, C L Philip; Liu, Yan-Jun; Wen, Guo-Xing

2014-05-01

74

As a time-variant, nonlinear and multi-disturbance complex system, the model of the mould-level is constructed, according to analyzing of the mould-level control system of the continuous casting. Furthermore, the Kalman filter fuzzy adaptive PID control method is presented in order to realize the in-line adjustment of PID parameters and to weaken the effect of random disturbances and measure noises. The

Gengyun Yao; Fengxiang Gao; Changsong Wang; Xiao Chen

2009-01-01

75

Adaptive Fault-Tolerant Tracking Control of Near-Space Vehicle Using Takagi-Sugeno Fuzzy Models

Based on the adaptive-control technique, this paper deals with the problem of fault-tolerant tracking control for near-space-vehicle (NSV) attitude dynamics. First, Takagi-Sugeno (T-S) fuzzy models are used to describe the NSV attitude dynamics; then, an actuator-fault model is developed. Next, an adaptive fault-tolerant tracking-control scheme is proposed based on the online estimation of actuator faults, in which a compensation control

Bin Jiang; Zhifeng Gao; Peng Shi; Yufei Xu

2010-01-01

76

Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System

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

77

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

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

78

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

79

In this paper, design of an adaptive neural network based interval type-2 fuzzy logic controller (ANNIT2FL), circular and handwriting type trajectory planning are proposed to show ability of a 3-DOF( Degree of Freedom ) Scara type robot manipulator. The kinematic and the dynamic equations are used to obtain equations of motion of robot manipulator and three different rise functions are

Yusuf Sahin; Mustafa Tinkir; Arif Ankarali

2011-01-01

80

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

81

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

82

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

83

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

NASA Astrophysics Data System (ADS)

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 of the autoclave was researched. The general PID controller adopted usually in the autoclave temperature control system is hard to improve temperature control performance. Based on the advantages of fuzzy controller that does not depend on the precise mathematical model and the stabilization of PID controller, single-chip computer integrated fuzzy PID control algorithm is adopted, and the temperature system is designed, the foundational working principle was discussed. The control system includes SCM (AT89C52), temperature sensor, A/D converter circuit and corresponding circuit and interface, can make the autoclave temperature measure and control accurately. The system hardware includes main circuit, thyristor drive circuit, audible and visual alarm circuit, watchdog circuit, clock circuit, keyboard and display circuit so on, which can achieve gathering, analyzing, comparing and controlling the autoclave temperature parameter. The program of control system includes the treatment and collection of temperature data, the dynamic display program, the fuzzy PID control system, the audible and visual alarm program, et al, and the system's main software, which includes initialization, key-press processing, input processing, display, and the fuzzy PID control program was analyzed. The results showed that the fuzzy PID control system makes the adjustment time of temperature decreased and the precision of temperature control improved, the quality and the crystals size of the preparation crystals can achieve the expect experiment results.

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

2008-12-01

84

AutoTuning Fuzzy PID Control of a Pendubot System

The goal of this paper is to design a fuzzy proportional-integral-derivative (PID) controller to swing up the pendubot and maintain it in an unstable inverted equilibrium position. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules such that the PID gains are adaptive and the fuzzy PID controller has more flexibility

Chia-Ju Wu; Tsong-Li Lee; Yu-Yi Fu; Chia-Ju Li-Chun Lai

2007-01-01

85

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system 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 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level and precipitable water. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy in 90 percent of the years

Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

2009-04-01

86

NASA Technical Reports Server (NTRS)

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

Berenji, Hamid R.

1992-01-01

87

Vehicle Stability Sliding Mode Control Based on Fuzzy Switching Gain Scheduling

According to the nonlinear and parameter time-varying characteristics of vehicle stability control, an adaptive fuzzy sliding control algorithm is proposed in terms of fuzzy control principle and the approximation capability of fuzzy systems. The fuzzy sliding controller is designed based on fuzzy logic and sliding control. The switching function of sliding surface is fuzzied by the Membership function. The switching

Zhang Jin-zhu; Zhang Hong-tian

2010-01-01

88

Adaptive Neuro-Fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles

This paper proposes ANFIS logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic\\u000a modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude\\u000a of the air vehicle is controlled. The implementation framework utilizes MATLAB’s standard configuration and the Aerosim Aeronautical\\u000a Simulation Block Set which

Sefer Kurnaz; Okyay Kaynak; Ekrem Konakoglu

2007-01-01

89

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

90

Bioprocesses have been operated according to the judgment of experts who are skilled operators and have long experience. In almost all cases, these experiences are described by linguistic IF-THEN rules. Fussy inference is one of the powerful tools to incorporate linguistic rules into computational algorithms for application to process control. Fuzzy control is categorized into two types: the direct fuzzy

Hiroyuki Honda; Takeshi Kobayashi

2000-01-01

91

NAFE: Noise Adaptive Fuzzy Equalization

NASA Astrophysics Data System (ADS)

NAFE (Noise Adaptive Fuzzy Equalization) is an image processing method allowing for visualization of fine structures in SDO AIA high dynamic range images. It produces artifact-free images and gives significantly better results than methods based on convolution or Fourier transform.

Druckmüller, Miloslav

2014-11-01

92

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

93

Fuzzy control of small servo motors

NASA Technical Reports Server (NTRS)

To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.

Maor, Ron; Jani, Yashvant

1993-01-01

94

Neuro-fuzzy control of a steam boiler-turbine unit

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

95

NASA Astrophysics Data System (ADS)

This paper describes an extension of the authors' previous analysis on an application of a fuzzy logic control (FLC) to tracking and rendezvous of an adaptive space structure with a moving target in order to examine effectiveness and feasibility of the FLC. The simulation analysis was made to avoid some practical limitation and difficulty encountered in the previous experimental study in which the floating condition in the space was realized by the use of the wire-suspension system. The usefulness is shown of the FLC's using a generalized version of state evaluation rule based on an explicit function of the state variables. A rapid rendezvous is possible if the tracking is made by using the bending-deformation module. When the distance between the docking structure and the target is sufficiently short, the tracking and rendezvous must be controlled by changing the shear-deformation module, and it needs much more time to complete the rendezvous.

Matsuzaki, Yuji; Hosoda, Hiroto

96

Fuzzy model based control: stability, robustness, and performance issues

A nonlinear controller based on a fuzzy model of MIMO dynamical systems is described and analyzed. The fuzzy model is based on a set of ARX models that are combined using a fuzzy inference mechanism. The controller is a discrete-time nonlinear decoupler, which is analyzed both for the adaptive and the fixed parameter cases. A detailed stability analysis is carried

Tor A. Johansen

1994-01-01

97

Fuzzy precompensated PID controllers

While proportional integral derivative (PID) controllers are widely used in industrial applications, they exhibit poor performance when applied to systems containing unknown nonlinearities, such as deadzones, saturation, and hysteresis. In this paper, the authors propose a fuzzy logic-based precompensation approach for PID controllers. The authors demonstrate the performance of their scheme via experiments performed on a DC servomotor position control

Jong-Hwan Kim; Kwang-Choon Kim; Edwin K. P. Chong

1994-01-01

98

A fuzzy-tuned adaptive Kalman filter

In this paper, fuzzy processing is applied to the adaptive Kalman filter. The filter gain coefficients are adapted over a 50 dB range of unknown signal/noise dynamics, using fuzzy membership functions. Specific simulation results are shown for a...

Painter, John H.; Young Hwan Lho

1993-12-01

99

Analysis and design of fuzzy controller and fuzzy observer

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

100

A New Neuro-Fuzzy Adaptive Genetic Algorithm

Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and

ZHU Lili ZHANG

2003-01-01

101

In this paper a novel sensorless adaptive neurofuzzy speed controller for induction motor derives is formulated. An artificial neural network (ANN) is adopted to estimate the motor speed and thus provide a sensorless speed estimator system. The performance of the proposed adaptive neurofuzzy speed controller is evaluated for a wide range of operating conditions for induction motor. These include startup,

Farzan Rashidi

2004-01-01

102

Fuzzy PID Controller for Wind Turbines

PID and fuzzy PID controller are applied into the wind turbine with nonlinear mathematical model. 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. Fuzzy PID control is combined with PID control and fuzzy control. The advantages of fuzzy PID control

Xiao Cheng; Zhang Lei; Yan Junqiu

2009-01-01

103

An FPGA-Based Adaptive Fuzzy Coprocessor

\\u000a The architecture of a general purpose fuzzy logic coprocessor and its implementation on an FPGA based System on Chip is described.\\u000a Thanks to its ability to support a fast dynamic reconfiguration of all its parameters, it is suitable for implementing adaptive\\u000a fuzzy logic algorithms, or for the execution of different fuzzy algorithms in a time sharing fashion. The high throughput

Antonio Di Stefano; Costantino Giaconia

2005-01-01

104

Neuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1

systems such as adaptive network-based fuzzy inference system (ANFIS) [3], can be used to learn fuzzy ifNeuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1 , Lijun Jiang2 of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Ex- perimental

105

Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and\\u000a large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery\\u000a delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional\\u000a electromagnetic control valves and the delay

Ranjit Kumar Barai; Kenzo Nonami

2008-01-01

106

NASA Astrophysics Data System (ADS)

This paper presents a new implementation of a parameter adaptive PID-type fuzzy controller (PAPIDfc) for a grid-supporting inverter of battery to alleviate frequency fluctuations in a wind-diesel power system. A variable speed wind turbine that drives a permanent magnet synchronous generator is assumed for demonstrations. The PAPIDfc controller is built from a set of control rules that adopts the droop method and uses only locally measurable frequency signal. The output control signal is determined from the knowledge base and the fuzzy inference. The input-derivative gain and the output-integral gain of the PAPIDfc are tuned online. To ensure safe battery operating limits, we also propose a protection scheme called intelligent battery protection (IBP). Several simulation experiments are performed by using MATLAB®/SimPowersystems™. Next, to verify the scheme's effectiveness, the simulation results are compared with the results of conventional controllers. The results demonstrate the effectiveness of the PAPIDfc scheme to control a grid-supporting inverter of the battery in the reduction of frequency fluctuations.

Ronilaya, Ferdian; Miyauchi, Hajime

2014-10-01

107

An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any

K. S. Tang; Kim Fung Man; Guanrong Chen; S. Kwong

2001-01-01

108

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

109

Vibration Suppression of Adaptive Truss Structure Using Fuzzy Neural Network

\\u000a An adaptive truss structure with self-learning active vibration control system is developed. A fuzzy-neural network (FNN)\\u000a controller with adaptive membership functions is presented. The experimental setup of a two-bay truss structure with active\\u000a members is constructed, and the FNN controller is applied to vibration suppression of the truss. The controller first senses\\u000a the output of the accelerometer as an error

Shaoze Yan; Kai Zheng; Yangmin Li

2005-01-01

110

Fuzzy model reference learning control for cargo ship steering

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

111

Interpolation mechanism of fuzzy control

The fuzzy control algorithms used commonly at present are all regarded as some interpolation functions, which is in essence\\u000a equivalent to discrete response functions to be fitted. This means that fuzzy control method is similar to finite element\\u000a method in mathematical physics, which is a kind of direct manner or numerical method in control systems.

Hongxing Li

1998-01-01

112

Fuzzy logic in control systems: Fuzzy logic controller. I, II

NASA Technical Reports Server (NTRS)

Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.

Lee, Chuen Chien

1990-01-01

113

Dynamically focused fuzzy learning control.

A "learning system" possesses the capability to improve its performance over time by interacting with its environment. A learning control system is designed so that its "learning controller" has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. Learning controllers are often designed to mimic the manner in which a human in the control loop would learn how to control a system while it operates. Some characteristics of this human learning process may include: (i) a natural tendency for the human to focus their learning by paying particular attention to the current operating conditions of the system since these may be most relevant to determining how to enhance performance; (ii) after learning how to control the plant for some operating condition, if the operating conditions change, then the best way to control the system may have to be re-learned; and (iii) a human with a significant amount of experience at controlling the system in one operating region should not forget this experience if the operating condition changes. To mimic these types of human learning behavior, we introduce three strategies that can be used to dynamically focus a learning controller onto the current operating region of the system. We show how the subsequent "dynamically focused learning" (DFL) can be used to enhance the performance of the "fuzzy model reference learning controller" (FMRLC) and furthermore we perform comparative analysis with a conventional adaptive control technique. A magnetic ball suspension system is used throughout the paper to perform the comparative analyses, and to illustrate the concept of dynamically focused fuzzy learning control. PMID:18263006

Kwong, W A; Passino, K M

1996-01-01

114

Incremental fuzzy expert PID control

An approach to intelligent PID (proportional integral derivative) control of industrial systems which is based on the application of fuzzy logic is presented. This approach assumes that one has available nominal controller parameter settings through some classical tuning technique (Ziegler-Nichols, Kalman, etc.). By using an appropriate fuzzy matrix (similar to Macvicar-Whelan matrix), it is possible to determine small changes on

S. Tzafestas; N. P. Papanikolopoulos

1990-01-01

115

Current projects in Fuzzy Control

NASA Technical Reports Server (NTRS)

Viewgraphs on current projects in fuzzy control are presented. Three projects on helicopter flight control are discussed. The projects are (1) radio control by oral instructions; (2) automatic autorotation entry in engine failure; and (3) unmanned helicopter for sea rescue.

Sugeno, Michio

1990-01-01

116

Dynamic fuzzy control of genetic algorithm parameter coding.

An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically increase the rate of convergence and accuracy of genetic algorithms. PMID:18252316

Streifel, R J; Marks, R J; Reed, R; Choi, J J; Healy, M

1999-01-01

117

Hybrid neuro-fuzzy control approach of robot manipulators

Recently, robot manipulators are expected to perform more sophisticated tasks. Thus, these manipulators have to be intelligent enough to work in an unknown and constrained environment. In this paper, we are interested by the uses of a hybrid neuro-fuzzy control approaches for robots manipulators moving in such environment. For this purpose, two adaptive neuro-fuzzy control structures are presented, an external

Y. Touati; Y. Amirat

2003-01-01

118

A New Design of Adaptive Fuzzy Hybrid Force\\/Position Controller for Robot Manipulators

The major problems of hybrid force\\/position control arise from uncertainty of the robot manipulators and unknown parameters of the task environment. In this paper, a new design method of the hybrid force\\/position control of the robot manipulators is proposed to solve these problems. The control objective is to track the desired force and position trajectories simultaneously regardless of the unknown

Feng-Yih Hsul; Li-chen Fu

1995-01-01

119

Research on driving wheel control of cleaning robot based on fuzzy adaptive tuning PID

Aiming at the controllability of the nonholonomic system, take the differential driving wheels of the cleaning robot as a research object, with universality of the nonholonomic system considered, equation of nonholonomic constraint of the cleaning robot was also derived. In addition, by means of Chow theorem, the controllability of the cleaning robot was demonstrated. And backlash in the differential driving

Xiaobo Lai; Shiqiang Zhu; Wenxiang Wu

2009-01-01

120

Adaptive Fuzzy Systems in Computational Intelligence

NASA Technical Reports Server (NTRS)

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

Berenji, Hamid R.

1996-01-01

121

Extending Fuzzy System Concepts for Control of a Vitrification Melter

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

122

Fuzzy-neural control of an aircraft tracking camera platform

NASA Technical Reports Server (NTRS)

A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

Mcgrath, Dennis

1994-01-01

123

The Flush Material Belt Weigh Feeder (FMBWF) has used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to

Tsung-Ying Sun; Ming-Chin Yang; Shang-Jeng Tsai; Jyun-Sian He

2009-01-01

124

be used as a range extender in an electric car, where a liquid fuel is a great advantage as opposed. The system is used as a battery charger and the fuel cell current can therefore be different to the reference implemented with the system of the electric vehicle. The presented method for controlling the reformer

Andreasen, SÃ¸ren Juhl

125

Design of a wireless controller for an automotive actuator based on PID-Fuzzy Logic

In this paper, an adaptive PID-Fuzzy Logic con- troller is proposed in order to control the angular displacement of an automobile Pierburg mechatronic actuator and achieve a wireless network control. This new PID-fuzzy logic controller topology takes advantage of the classical PID controller and the fuzzy logic technique which can improve the angular displacement precision and reduce the time delay

S. Cai; M. Becherif; M. Wack; M. Y. Ayad; A. Kebairi

2011-01-01

126

Fuzzy logic control of telerobot manipulators

NASA Technical Reports Server (NTRS)

Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.

Franke, Ernest A.; Nedungadi, Ashok

1992-01-01

127

How to combine probabilistic and fuzzy uncertainties in fuzzy control

NASA Technical Reports Server (NTRS)

Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.

Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert

1991-01-01

128

Fuzzy support vector machines for adaptive Morse code recognition.

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature. PMID:16807054

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

129

Fuzzy coordinator in control problems

NASA Technical Reports Server (NTRS)

In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant.

Rueda, A.; Pedrycz, W.

1992-01-01

130

Expert system driven fuzzy control application to power reactors

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

131

September 12, 2002 Fuzzy PI Control Design

September 12, 2002 Fuzzy PI Control Design for an Industrial Weigh Belt Feeder by Yanan Zhao demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI given. Keywords: fuzzy logic control, PI control, weigh belt feeder, gain scheduling, self

Collins, Emmanuel

132

The shape of fuzzy sets in adaptive function approximation

The shape of if-part fuzzy sets affects how well feedforward fuzzy systems approximate continuous functions. We explore a wide range of candidate if-part sets and derive supervised learning laws that tune them. Then we test how well the resulting adaptive fuzzy systems approximate a battery of test functions. No one shape emerges as the best. The sine function often does

Sanya Mitaim; Bart Kosko

2001-01-01

133

Sliding mode control of BTT missile based on fuzzy-neural approach

A novel adaptive fuzzy-neural sliding mode control scheme was proposed for missile control systems with a general set of uncertainties. Firstly, the effect of the uncertainties was synthesized one term in the design procedure. Then fuzzy-neural approximators were designed to approximate the uncertainty function. Considering the known information, adaptive fuzzy-neural control theory and sliding mode control were used to deal

Yuqiang Jin; Wenjin Gu; Jinhua Wu; Xuebao Wang

2004-01-01

134

Learning fuzzy logic control system

NASA Technical Reports Server (NTRS)

The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore, the LFLC has better performance in rise time, settling time and steady state error than to the conventional PI controller. This abstract accurately represents the content of the candidate's thesis. I recommend its publication.

Lung, Leung Kam

1994-01-01

135

Stability of interpolative fuzzy KH controllers

The classical approaches in fuzzy control (Zadeh and Mamdani) deal with dense rule bases. When this is not the case, i.e. in sparse rule bases, one has to choose another method. Fuzzy rule interpolation (proposed Þrst by Koczy and Hirota (15)) oers a possibility to construct fuzzy controllers (KH controllers) under such con- ditions. The main result of this paper

Domonkos Tikk; István Joó; László T. Kóczy; Péter Várlaki; Bernhard Moser; Tamás D. Gedeon

2002-01-01

136

Decentralized fuzzy fault tolerant control for multiple satellites attitude synchronization

This paper presents a decentralized adaptive ap- proximation design to achieve attitude tracking control for decentralized formation flying in presence of control input saturation, model uncertainties, external disturbances and re- action wheel faults. A nonsingular fast terminal sliding mode control is designed for finite time distributed cooperative attitude synchronization. In the proposed control scheme, a fuzzy logic system (FLS) is

Junquan Li; K. D. Kumar

2011-01-01

137

Tuning of a neuro-fuzzy controller by genetic algorithm.

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

138

ANFIS: adaptive-network-based fuzzy inference system

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

139

Fuzzy sliding-mode controllers with applications

This paper concerns the design of robust control systems using sliding-mode control that incorporates a fuzzy tuning technique. The control law superposes equivalent control, switching control, and fuzzy control. An equivalent control law is first designed using pole placement. Switching control is then added to guarantee that the state reaches the sliding mode in the presence of parameter and disturbance

Q. P. Ha; Q. H. Nguyen; D. C. Rye; H. F. Durrant-Whyte

2001-01-01

140

Adaptive Fuzzy Clustering Nicolas Cebron and Michael R. Berthold

propose a new adaptive active clustering scheme, based on an initial Fuzzy c-means clustering and LearningAdaptive Fuzzy Clustering Nicolas Cebron and Michael R. Berthold Department of Computer for adjustment of the classification by the user. Motivated by the concept of active learning, the learner tries

Berthold, Michael R.

141

Four wheel steering control by fuzzy approach

This study introduces a fuzzy four-wheel steering control design method for automotive vehicles. After the analysis of some stability aspects of the vehicle lateral motion, including front steering angle variations, the representation of vehicle nonlinear model by Takagi-Sugeno (T-S) fuzzy model is presented. Next, based on the fuzzy model, a fuzzy controller is developed to improve the stability of the

A. El Hajjaji; A. Ciocan; D. Hamad

2005-01-01

142

Adaptive joint fuzzy sets for function approximation

This paper presents a new method to create and tune joint fuzzy sets. Multidimensional fuzzy sets define the if-part fuzzy sets of rules in feedforward fuzzy function approximators. These joint set functions do not factor into a product of scalar fuzzy sets (such as triangles or bell curves) and so they do not ignore the correlation structure among the input

Sanya Mitaim; Bart Kosko

1997-01-01

143

Fuzzy predictive control for nitrogen removal in biological wastewater treatment

Fuzzy predictive control for nitrogen removal in biological wastewater treatment S. Marsili predictive control; wastewater treatment plant Introduction The problem of improving the nitrogen removal of the process and its variabil- ity, an adaptive controller is considered. Since incoming nitrogen and carbon

144

Realization of PID controls by fuzzy control methods

This paper shows that PID controllers can be realized by fuzzy control methods of “product-sum-gravity method” and “simplified fuzzy reasoning method”. PID controllers, however, cannot be constructed by min-max-gravity method known as Mamdani's fuzzy reasoning method. Furthermore, extrapolative reasoning can be executed by the product-sum-gravity method and simplified fuzzy reasoning method by extending membership functions of antecedent parts of fuzzy

M Mizumoto

1995-01-01

145

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

146

Fuzzy gain scheduling of PID controllers

This paper describes the development of a fuzzy gain scheduling scheme of PID controllers for process control. Fuzzy rules and reasoning are utilized online to determine the controller parameters based on the error signal and its first difference. Simulation results demonstrate that better control performance can be achieved in comparison with Ziegler-Nichols controllers and Kitamori's PID controllers

Zhen-Yu Zhao; Masayoshi Tomizuka; Satoru Isaka

1993-01-01

147

Fuzzy logic based robotic controller

NASA Technical Reports Server (NTRS)

Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.

Attia, F.; Upadhyaya, M.

1994-01-01

148

Fuzzy control of an unmanned helicopter

NASA Technical Reports Server (NTRS)

This paper discusses an application of fuzzy control to an unmanned helicopter. The authors design a fuzzy controller to achieve semi-autonomous flight of a helicopter by giving macroscopic flight commands from the ground. The fuzzy controller proposed in this study consists of two layers: the upper layer for navigation supervising the lower layer and the lower layer for ordinary rule based control. The performance of the fuzzy controller is evaluated in experiments where an industrial helicopter Yamaha R-50 is used. At present an operator can wirelessly control the helicopter through a flight computer with eight commands such as 'hover', 'fly forward', 'turn left', 'stop', etc.

Sugeno, M.; Nishino, J.; Miwa, H.

1993-01-01

149

Fuzzy logic control and optimization system

A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Lou, Xinsheng (West Hartford, CT)

2012-04-17

150

Fuzzy PID control for direct drive electro-hydraulic position servo system

In considering the saturation and dead zone nonlinearity as well as the time-variability and the time lag existed in direct drive volume control system, the traditional PID controller has the problems of parameter tuning difficulty and poor adaptability. This paper sets up a fuzzy PID control algorithm by pulling in fuzzy control theory ,which realizes self-tuning PID parameters and effectively

Yu Lin-ke; Zheng Jian-ming; Yuan Qi-long; Xiao Ji-ming; Li Yan

2011-01-01

151

Fuzzy controllers with conditionally firing rules

Mamdani (1975) controller was successfully used in many applications. One of its interpretations is that it uses a fuzzy relation as an approximation of the desirable input-output correspondence. We analyze mathematical properties of Mamdani controller and notice that it has lower computational complexity when compared to the residuum-based controller. However, we show that in standard situations, both these fuzzy controllers

Bernhard Moser; Mirko Navara

2002-01-01

152

Fuzzy regulator design for wind turbine yaw control.

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

153

An improved robust fuzzy-PID controller with optimal fuzzy reasoning

Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy

Han-xiong Li; Lei Zhang; Kai-yuan Cai; Guanrong Chen

2005-01-01

154

Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic approach for steering and speed control, a neuro-fuzzy approach for ultrasound sensing (not discussed in this paper) and an overall

Nikhal Kelkar; Tayib Samu; Ernest L. Hall

1997-01-01

155

Fuzzy logic control for camera tracking system

NASA Technical Reports Server (NTRS)

A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

1992-01-01

156

Realization of PID controls by fuzzy control methods

The author shows that proportional-integral-derivative (PID) controllers can be realized by fuzzy control methods based on the product-sum-gravity method and the simplified fuzzy reasoning method. PID controllers, however, cannot be constructed by the min-max gravity method known as the Mamdani's fuzzy reasoning method. Extrapolative reasoning can be executed by the product-sum-gravity method and the simplified fuzzy reasoning method by extending

M. Mizumoto

1992-01-01

157

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

Karr, C. L.; Phillips, J. C.

1994-01-01

158

Robust Fuzzy Controllers Using FPGAs

NASA Technical Reports Server (NTRS)

Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.

Monroe, Author Gene S., Jr.

2007-01-01

159

Refining fuzzy logic controllers with machine learning

NASA Technical Reports Server (NTRS)

In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

Berenji, Hamid R.

1994-01-01

160

Stability of fuzzy linguistic control systems

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

161

Adaptive Bandwidth Control for Efficient Aggregate QoS Provisioning

may end up overallocating the resources. In this paper, we develop an ABC algorithm based on fuzzy proposes an adaptive fuzzy bandwidth controller that efficiently provides aggregate QoS to address to attain efficient network utiliza- tion. This research proposes to use Adaptive Bandwidth Con- trol (ABC

Tipper, David

162

Analysis of direct action fuzzy PID controller structures

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the

George K. I. Mann; Bao-gang Hu; Raymond G. Gosine

1999-01-01

163

Optimizing a fuzzy logic controller for reactive navigation

NASA Astrophysics Data System (ADS)

Low-level navigation for autonomous vehicles can be accomplished efficiently by a behavioral-based approach that involves the simultaneous execution of independent sub-tasks seen as primitive behaviors. Each behavior maps sensory data into control commands in a reactive way, with no need of internal representations. A useful tool for realizing such a direct mapping is fuzzy logic, that allows the production of control rules by either manual programming or automatic learning. In prospect of implementing an articulated control system including all the low-level behaviors of navigation, this paper focuses on the problem of obtaining an efficient and robust fuzzy controller performing a single behavior and presents a method for minimizing the number of rules of a fuzzy controller developed for driving a TRC Labmate based vehicle along the wall on its right-hand side. Fuzzy rules, that map ultrasonic sensor readings onto steering velocity values, are learned automatically from training data collected during operator-driven runs of the vehicle. In addition, we address the problem of defining an appropriate performance function, that may be useful for evaluating the influence of the rule base reduction on the overall behavior of the vehicle during navigation, but also for estimating the quality of a control rule, in order to adapt rules on- line. Results of an experimental comparison between the original fuzzy wall-follower and its optimized version are reported.

Castellano, G.; Stella, Ettore; Attolico, Giovanni; Distante, Arcangelo

1997-01-01

164

Fuzzy gain scheduling for flight control

This paper presents a method for design of fuzzy gain scheduled output feedback H? controllers for affine Takagi-Sugeno (TS) systems. The method relies on recent developments in design for classical gain scheduling, based on linear matrix inequalities. It is shown how the previous results can be extended to general nonlinear systems that admits a TS fuzzy system approximation. The design

P. Bergsten; M. Persson; B. Iliev

2000-01-01

165

Fuzzy Production and Operations Budgeting and Control

\\u000a Basic information about crisp production and operations budgeting is given, as well as selected information about fuzzy numbers.\\u000a Then some ideas of how to use the fuzzy approach in production and operations budgeting and control are presented. Several\\u000a numerical examples illustrate the reasoning.

Dorota Kuchta

166

Synthesis of nonlinear control strategies from fuzzy logic control algorithms

NASA Technical Reports Server (NTRS)

Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.

Langari, Reza

1993-01-01

167

Fuzzy Cognitive Maps in modeling supervisory control systems

This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the Supervisor-FCM

Chrysostomos D. Stylios; Peter P. Groumpos

2000-01-01

168

Cascade Fuzzy Adaptive Hamming Net: A CoarsetoFine Representation Scheme for Object

Cascade Fuzzy Adaptive Hamming Net: A CoarseÂtoÂFine Representation Scheme for Object RecognitionÂ 001Â017. 1 #12; ABSTRACT In this report, we propose a cascade fuzzy adaptive Hamming net (CFAHN) which by his work, we propose a cascade fuzzy adaptive Hamming net (CFAHN), which is able to accept both binary

Chen, Sheng-Wei

169

Fuzzy control of pH using genetic algorithms

Abstruct- Establishing suitable control of pH, a requirement in a number of mineral and chemical industries, poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing such systems. In this technique, a genetic

Charles L. Karr; Edward J. Gentry

1993-01-01

170

Power system dynamic load modeling using adaptive-network-based fuzzy inference system

The representation of the dynamic characteristics of power system loads is widely used for obtaining power system operations, controls and stability limits and becomes a critical factor in power system dynamic performance. In this paper, the performance of power system dynamic load modeling using adaptive-network-base fuzzy inference system (ANFIS) is compared with traditional architectures. The ANFIS models can represent nonlinear

A. Oonsivilai; M. E. El-Hawary

1999-01-01

171

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

172

Prediction of Conductivity by Adaptive Neuro-Fuzzy Model

Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582

Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.

2014-01-01

173

ACTIVE NOISE CONTROL BASED ON FUZZY MODELS

This paper presents a new approach to acoustic noise control, by introducing a fuzzy model-based control strategy. Classical linear identification and control tools have been ap- plied to active noise control in the last two decades. In this type of control, the limitations of their applicability are well defined. Therefore, new techniques must be developed in order to increase the

C. A. Silva; J. M. Sous

2001-01-01

174

Terminology and concepts of control and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.

Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan

1990-01-01

175

Universal Fuzzy Models and Universal Fuzzy Controllers for Discrete-Time Nonlinear Systems.

This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems. PMID:25137736

Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu

2014-08-14

176

Variable-order fuzzy fractional PID controller.

In this paper, a new tuning method of variable-order fractional fuzzy PID controller (VOFFLC) is proposed for a class of fractional-order and integer-order control plants. Fuzzy logic control (FLC) could easily deal with parameter variations of control system, but the fractional-order parameters are unable to change through this way and it has confined the effectiveness of FLC. Therefore, an attempt is made in this paper to allow all the five parameters of fractional-order PID controller vary along with the transformation of system structure as the outputs of FLC, and the influence of fractional orders ? and ? on control systems has been investigated to make the fuzzy rules for VOFFLC. Four simulation results of different plants are shown to verify the availability of the proposed control strategy. PMID:25440947

Liu, Lu; Pan, Feng; Xue, Dingyu

2014-10-29

177

Fuzzy pre-compensated fuzzy self-tuning fuzzy PID controller of 3 DOF planar robot manipulators

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. Proportional-integral-derivative (PID)-type fuzzy controller is a well-known conventional motion control strategy for manipulators which ensures global asymptotic stability. To enhance the PID-type fuzzy controller performance for the control of rigid planar robot manipulators, in this paper, a fuzzy pre-compensation

A. F. Amer; E. A. Sallam; W. M. Elawady

2010-01-01

178

THE PASSIVITY CONTROL FOR TS FUZZY SYSTEMS

In this paper, we propose the notion of strict passivity to T-S fuzzy system and consider the problem of passivity control for a kind of uncertain T-S fuzzy system with time-delay. The su-cient conditions which make the closed-loop system be stable and strictly passive are obtained for the system. The conditions are expressed as linear matrix inequalities (LMIs). So checking

BAOYAN ZHU; JINBAO WANG; YANLI GU

2009-01-01

179

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

180

Parallel structure and tuning of a fuzzy PID controller

In this paper, a parallel structure of fuzzy PID control systems is proposed. It is associated with a new tuning method which, based on gain margin and phase margin specifications, determines the parameters of the fuzzy PID controller. In comparison with conventional PID controllers, the proposed fuzzy PID controller shows higher control gains when system states are away from equilibrium

Jian-Xin Xu; Chang-Chieh Hang; Chen Liu

2000-01-01

181

Fuzzy predictive control applied to an air-conditioning system

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

182

Optoelectronic fuzzy associative memory with controllable attraction basin sizes

NASA Astrophysics Data System (ADS)

We propose and demonstrate a new fuzzy associative memory model that provides an option to control the sizes of the attraction basins in neural networks. In our optoelectronic implementation we use spatial/polarization encoding to represent the fuzzy variables. Shadow casting of the encoded patterns is employed to yield the fuzzy-absolute difference between fuzzy variables.

Wen, Zhiqing; Campbell, Scott; Wu, Weishu; Yeh, Pochi

1995-10-01

183

Adaptive Silhouette Extraction In Dynamic Environments Using Fuzzy Logic

1 Adaptive Silhouette Extraction In Dynamic Environments Using Fuzzy Logic Xi Chen, Zhihai He University of Missouri, Columbia, MO 65203, USA Abstract - Extracting a human silhouette from an image. Although there are a number of silhouette extraction algorithms proposed in the literature, most approaches

He, Zhihai "Henry"

184

Fuzzy supervisory PI control of a binary distillation column via distributed control systems

In this paper, the fuzzy supervisory PI control is implemented via distributed control systems (DCS) to control a binary distillation column. The fuzzy c-mean clustering is used to identify the membership functions and fuzzy rules are determined using fuzzy gain scheduling technique. Thus, the need of heuristic method for designing fuzzy membership functions and rules from expert knowledge is omitted.

Poramade Cheingjong; Suvalai Pratishthananda

2008-01-01

185

Predicting Toxicity against the fathead Minnow by Adaptive Fuzzy Marco Pintorea

these methods, Fuzzy Logic concepts, based on the possibility to handle the Âªconcept of partial truthÂº), structural-activity models were built by Adaptive Fuzzy Partition (AFP). This method consists in modelingPredicting Toxicity against the fathead Minnow by Adaptive Fuzzy Partition Marco Pintorea , Nade

Gini, Giuseppina

186

TS Control — The Link between Fuzzy Control and Classical Control Theory

Fuzzy controller can be approximated or generalized respectively by replacing the fuzzy sets in the rule conclusions by real numbers or functions. Such a controller is called a TS controller and can be seen as a classical gain scheduling controller. Therefore, TS control can be interpreted as fuzzy and classical control as well. Besides this, for this type of control

Kai Michels

187

Comparison between the performance of two classes of fuzzy controllers

NASA Technical Reports Server (NTRS)

This paper presents an application comparison between two classes of fuzzy controllers: the Clearness Transformation Fuzzy Controller (CTFC) and the CRI-based Fuzzy Controller. The comparison is performed by studying the application of the controllers to simulation examples of nonlinear systems. The CTFC is a new approach for the organization of fuzzy controllers based on a cognitive model of parameter driven control, the notion of fuzzy patterns to represent fuzzy knowledge and the Clearness Transformation Rule of Inference (CTRI) for approximate reasoning. The approach facilitates the implementation of the basic modules of the controller: the fuzzifier, defuzzifier, and the control protocol in a rule-based architecture. The CTRI scheme for approximate reasoning does not require the formation of fuzzy relation matrices yielding improved performance in comparison with the traditional organization of fuzzy controllers.

Janabi, T. H.; Sultan, L. H.

1992-01-01

188

Coordination of Excitation and Governing Control Based on Fuzzy Logic

Coordination of Excitation and Governing Control Based on Fuzzy Logic Taiyou Yong, Robert H. In this paper, we present a fuzzy logic based method for the excitation control and governing control. Fuzzy logic is applied to generate two compensating signals to modify the controls during system disturbances

189

Fuzzy Control for the Swing-Up of the Inverted Pendulum System

NASA Astrophysics Data System (ADS)

The nonlinear inverted-pendulum system is an unstable and non-minimum phase system. It is often used to be the controlled target to test the qualities of the controllers like PID, optimal LQR, Neural network, adaptive, and fuzzy logic controller, etc. This paper will describe a new fuzzy controller for an inverted pendulum system. In this case, a fuzzy controller followed with a state space controller was implemented for control. It is achieved to design a control condition for the pendulum to swing up in one direction only because that the movement of throwing a bowling ball can only from one side to the unstable equilibrium point. Simulation and experimental results show that the fuzzy control can swing up the single inverted pendulum in short time with well stability and strong robustness.

Wu, Yu; Zhu, Peiyi

190

Fuzzy control of a double-inverted pendulum

A high-accuracy and high-resolution fuzzy controller is designed to stabilize a double-inverted pendulum at an upright position successfully. A new idea of dealing with multivariate systems is described. The composition coefficient is gained by combining the fuzzy control theory with the optimal control theory. The fuzzy control rules of a double-inverted pendulum are given and a powerful fuzzy decision way

Fuyan Cheng; Guomin Zhong; Youshan Li; Zhengming Xu

1996-01-01

191

Fuzzy sliding-mode control for a Mini-UAV

This work addresses a fuzzy sliding-mode controller, which is mainly composed of the sliding mode controller and the fuzzy inference mechanism, for a mini unmanned air vehicle (UAV) with propellers to follow the predetermined trajectory. In this paper, a sliding-mode controller with a sliding surface is designed. And a fuzzy sliding-mode controller is proposed, such that a simple fuzzy inference

Fu-Kuang Yeh; Ching-Mu Chen; Jian-Ji Huang

2010-01-01

192

Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H? Filter

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

193

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

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

194

A hybrid of fuzzy and fuzzy self-tuning PID controller for servo electro-hydraulic system

Because of the existing hybrid fuzzy PID controller does not perform well when applied to servo electro-hydraulic system (SEHS). Forasmuch, when the system parameters changes will require a new adjustment variable of PID controller. Therefore, a hybrid of fuzzy and fuzzy self-tuning PID controller is proposed in this paper. The proposed control scheme is separated into two parts, fuzzy controller

Kwanchai Sinthipsomboon; Issaree Hunsacharoonroj; Joseph Khedari; Watcharin Pongaen; Pornjit Pratumsuwan

2011-01-01

195

Simplified fuzzy logic based MTPA speed control of IPMSM drive

This paper presents a simplified fuzzy logic based speed controller of an interior permanent synchronous motor (IPMSM) drive for maximum torque per ampere (MTPA) of stator current with inherent nonlinearities of the motor. The fundamentals of fuzzy logic algorithms as related to motor control applications are illustrated. A simplified fuzzy speed controller for the IPMSM drive has been found to

Casey Butt; M. A. Hoque; M. A. Rahman

2003-01-01

196

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

197

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

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

198

Autonomous Robot Motion Control Using Fuzzy PID Controller

\\u000a Autonomous robots roles are increasing in different aspects of engineering and everyday life. This paper describes an autonomous\\u000a robot motion control system based on fuzzy logic Proportional Integral Derivative (PID) controller. Fuzzy rules are embedded\\u000a in the controller to tune the gain parameters of PID and to make them helpful in real time applications. This paper discusses\\u000a the design aspects

Vaishali Sood

199

Limitations of simplified fuzzy logic controller for IPM motor drive

This paper explores the limitations of a simplified fuzzy logic based speed controller incorporating approximated maximum torque per ampere (MTPA) mode of operation of an interior permanent magnet synchronous motor (IPMSM) as compared to a nonsimplified fuzzy logic based system without MTPA mode operation. The fundamentals of fuzzy logic algorithms as related to motor control, applications, are illustrated. Nonlinear expressions

Casey Butt; M. A. Rahman

2004-01-01

200

An application of fuzzy set theory to inventory control models

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

201

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

202

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

203

Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control

NASA Astrophysics Data System (ADS)

Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.

Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel

2014-12-01

204

Fuzzy Control/Space Station automation

NASA Technical Reports Server (NTRS)

Viewgraphs on fuzzy control/space station automation are presented. Topics covered include: Space Station Freedom (SSF); SSF evolution; factors pointing to automation & robotics (A&R); astronaut office inputs concerning A&R; flight system automation and ground operations applications; transition definition program; and advanced automation software tools.

Gersh, Mark

1990-01-01

205

FUZZY LOGIC CONTROL OF AC INDUCTION MOTORS

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...

206

NASA Technical Reports Server (NTRS)

This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.

Sultan, Labib; Janabi, Talib

1992-01-01

207

PI and fuzzy logic controllers for shunt Active Power Filter--a report.

This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads. PMID:21982358

P, Karuppanan; Mahapatra, Kamala Kanta

2012-01-01

208

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

209

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

210

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

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

211

Qualitative robust fuzzy control with applications to 1992 ACC benchmark

Robust control has long been the purview of quantitative linear control techniques, while qualitative symbolic control has been deemed more suitable to obtaining complex control objectives that require only low-output precision. The intelligent techniques of fuzzy control have, however, shown promise in obtaining results comparable to those obtained from H? and H2 robust control techniques. Often though, these fuzzy control

Stephen Paul Linder; Bahram Shafai

1999-01-01

212

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

213

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

2015-01-01

214

The simplification of fuzzy control algorithm and hardware implementation

NASA Technical Reports Server (NTRS)

The conventional interface composition algorithm of a fuzzy controller is very time and memory consuming. As a result, it is difficult to do real time fuzzy inference, and most fuzzy controllers are realized by look-up tables. Here, researchers derive a simplified algorithm using the defuzzification mean of maximum. This algorithm takes shorter computation time and needs less memory usage, thus making it possible to compute the fuzzy inference on real time and easy to tune the control rules on line. A hardware implementation based on a simplified fuzzy inference algorithm is described.

Wu, Z. Q.; Wang, P. Z.; Teh, H. H.

1991-01-01

215

Complex-Fuzzy Adaptive Image Restoration - An Artificial-Bee-Colony-Based Learning Approach

\\u000a A complex-fuzzy approach using complex fuzzy sets is proposed in the paper to deal with the problem of adaptive image noise\\u000a cancelling. A image may be corrupted by noise, resulting in the degradation of valuable image information. Complex fuzzy set\\u000a (CFS) is in contrast with traditional fuzzy set in membership description. A CFS has the membership state within the complex-valued

Chunshien Li; Fengtse Chan

2011-01-01

216

Application of fuzzy logic controller to active power filters

In this paper, the way to use one of the most powerful problem-solving methodologies, fuzzy logic, to enhance the quality of the power system is described. To prove the power of fuzzy logic for the modeling of nonlinear systems, the modeling of active power filters with a fuzzy logic based control strategy is presented as a case study and its

Merih Palandöken; Murat Aksoy; Mehmet Tümay

2004-01-01

217

Supervisory control of fuzzy discrete event systems: a formal approach

Fuzzy {\\\\it discrete event systems} (DESs) were proposed recently by Lin and\\u000aYing [19], which may better cope with the real-world problems with fuzziness,\\u000aimpreciseness, and subjectivity such as those in biomedicine. As a continuation\\u000aof [19], in this paper we further develop fuzzy DESs by dealing with\\u000asupervisory control of fuzzy DESs. More specifically, (i) we reformulate the\\u000aparallel

Daowen Qiu; D. Qiu

2005-01-01

218

Adaptive nonlinear flight control

Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is

Rolf Theoduor Rysdyk

1998-01-01

219

NASA Astrophysics Data System (ADS)

The ultrasonic motor has a heavy nonlinearity, which varies with driving conditions and possesses variable dead-zone in the control input associated with applied load torque. The dead-zone is a problem as an accurate positioning actuator for industrial applications and it is important to eliminate the dead-zone in order to improve the control performance. This paper proposes a new position control scheme of ultrasonic motors to overcome dead-zone employing model reference adaptive control (MRAC) with fuzzy inference. The dead-zone is compensated by fuzzy inference, and backstepping control performs accurate position control. As compared with MRAC which uses an augmented error, backstepping control can analyze a transient response. Mathematical models are formulated and experimental results are given to validate the proposed position control scheme.

Senjyu, Tomonobu; Yoshida, Tomohiro; Uezato, Katsumi; Funabashi, Toshihisa

220

Robust H infinity fuzzy control for a class of uncertain discrete fuzzy bilinear systems.

The main theme of this paper is to present robust fuzzy controllers for a class of discrete fuzzy bilinear systems. First, the parallel distributed compensation method is utilized to design a fuzzy controller, which ensures the robust asymptotic stability of the closed-loop system and guarantees an H(infinity) norm-bound constraint on disturbance attenuation for all admissible uncertainties. Second, based on the Schur complement and some variable transformations, the stability conditions of the overall fuzzy control system are formulated by linear matrix inequalities. Finally, the validity and applicability of the proposed schemes are demonstrated by a numerical simulation and the Van de Vusse example. PMID:18348932

Li, Tzuu-Hseng; Tsai, Shun-Hung; Lee, Jia-Zhen; Hsiao, Ming-Ying; Chao, Chan-Hong

2008-04-01

221

Development of a Fuzzy Expert system based on PCS7 and FuzzyControl++ Cement Mill control

The basic idea of this work was to study the application of expert systems and fuzzy logic in the field of diagnostic and industrial maintenance. For this, a fuzzy expert system designed, developed and simulated in Ain Touta cement society in Batna in the East of Algeria. Dedicated to control cement mill. The application of fuzzy logic and expert systems

L. Hayet Mouss; Sonia Benaicha

222

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

223

Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

NASA Astrophysics Data System (ADS)

The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals of sustainable development on a regional scale.

Chang, N.

2006-12-01

224

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

225

Bacterial Foraging Based Optimization Design of Fuzzy PID Controllers

In this paper, a bacterial foraging optimization scheme (BFOS) is proposed for the multi-objective optimization design of\\u000a a fuzzy PID controller and applies it to the control of an active magnetic bearing (AMB) system. Different from PID controllers\\u000a with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules whose rule consequences employ analytical\\u000a PID expressions. The

Hung-cheng Chen

2008-01-01

226

Nonlinear Fuzzy Hybrid Control of Spacecraft

NASA Technical Reports Server (NTRS)

This paper describes a new approach for intelligent control of a spacecraft with large angle maneuvers. This new approach, based on fuzzy logic, determines the required torque to achieve a robust, high performance attitude response. This scheme extends the robustness, performance and portability of the existing linear or nonlinear attitude controllers. Formulations are presented for attitude-control but can be extended to other applications. A simulation study, which uses the new control strategy to stabilize the motion of the Microwave Anisotropy Probe spacecraft in the presence of disturbances and saturations, demonstrates the merits of the proposed scheme.

Mason, Paul A. C.; Crassidis, John L.; Markley, F. Landis

1999-01-01

227

Harmonic Control Based on Fuzzy Logic

NASA Astrophysics Data System (ADS)

Proliferation of nonlinear loads in power systems has increased harmonic pollution and deteriorated power quality. Passive filtering has typically been the standard technology for harmonic and reactive power compensation .With the advancements in power electronics, active filtering is being more widely considered given its flexibility and precise control. However, cost, complexity, and reliability are considered the major drawbacks of active filters. In this paper a new fuzzy logic is introduced to control the harmonic in the power system, which has more advantages such as simplicity, ease of application, flexibility, speed and ability to deal with imprecision and uncertainties .The introduction of fuzzy logic can not only reduce harmonic,but also correct the power factor.

Wu, Shihong; Dang, Gang; Wang, Jun; Li, Xiaohui; Zhang, Zhixia; Jiang, Fengli

228

Fuzzy control algorithm for universal active filter

In this paper, a fuzzy algorithm is employed to control a three-phase unified power quality conditioner (UPQC). The UPQC is an active filter (AF) and it compensates the reactive power and harmonics in both the voltage and current caused by nonlinear loads. The UPQC makes use of two back-to-back connected IGBT-based voltage source inverters (VSIs) with a common DC bus.

B. N. Singh; H. Chandra; K. Al-Haddad

1998-01-01

229

A fuzzy logic controller for aircraft flight control

This paper describes a model of an autopilot controller based on fuzzy algorithms. The controller maneuvers an aircraft from level flight into a final-approach flight path and maintains the aircraft along the glide path until just before touchdown. To evaluate the performance and effectiveness of the model, the aircraft response to controller actions is simulated using flight simulation techniques. The

Lawrence I. Larkin

1984-01-01

230

Control of a fluidized bed combustor using fuzzy logic

Fuzzy logic--an artificial intelligence technique--can be employed to exploit the wealth of information human experts have learned about complex systems while attempting to control them. This information is usually of a qualitative nature that is unusable by rigid conventional control techniques. Fuzzy logic, uses as a control method, manipulates linguistically expressed, heuristic knowledge from a human expert to derive control

S. J. Koffman; R. C. Brown; R. R. Fullmer

1996-01-01

231

FUZZY MODELLING AND CONTROL OF MARINE DIESEL ENGINE PROCESS

The paper gives an introduction of knowledge modelling techniques i.e. fuzzy models suitable for diesel engine diagnosis and control. Two examples are illustrated for engine faulty condition diagnosis and two simulated examples are given for fuzzy control of diesel engine process: 1. diesel oil viscosity control (Mamdani model used) and 2. shaft speed control (T-S model used). É incomplete and

Radovan Antonic

232

Fuzzy goal-driven intelligent control for satellite environmental qualification

This paper reports on the application of Fuzzy Reference Gain-Scheduling Control (FRGS) to control a thermal-vacuum unit that emulates space environmental conditions for satellite and space device qualification. FRGS is a variation of fuzzy control that changes the controller gain surface in accordance to distinct operational conditions established by the reference (goal). This system allows to incorporate the experience of

Ernesto Araujo; Karl Kienitz; Sandra Sandri

2011-01-01

233

An experimental fuzzy controller for DC-DC converters

A fuzzy control algorithm for switching power converters is proposed. Being free of complex equations and heavy computation, the control algorithm can be implemented by a digital controller using a fixed point signal processor. This paper presents experimental results which demonstrate the capability of the fuzzy controller in regulating high speed switching DC\\/DC power converters

W. C. So; C. K. Tse; Y. S. Lee

1995-01-01

234

Fuzzy control of ionic polymer-metal composites.

Ionic polymer-metal composites (IPMCs) have advantages of softness and flexibility to be used in biomedical applications. In this paper a fuzzy logic controller (FLC) has been designed for achieving the goal of tracking. Also co-evolutionary-based genetic algorithms technique has been employed to optimize membership functions and fuzzy rules. The simulation results show that fuzzy controller has higher performance in comparison with other controllers. PMID:18002928

Khadivi, H; Aghazadeh, B S; Lucas, C

2007-01-01

235

An Interval Fuzzy Controller for Vehicle Active Suspension Systems

A novel interval type-2 fuzzy controller architecture is proposed to resolve nonlinear control problems of vehicle active suspension systems. It integrates the Takagi-Sugeno (T-S) fuzzy model, interval type-2 fuzzy reasoning, the Wu-Mendel uncertainty bound method, and selected optimization algorithms together to construct the switching routes between generated linear model control surfaces. The stability analysis of the proposed approach is presented.

Jiangtao Cao; Ping Li; Honghai Liu

2010-01-01

236

Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology

NASA Astrophysics Data System (ADS)

We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.

Bonissone, Piero P.

1995-06-01

237

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

238

Active structural control by fuzzy logic rules: An introduction

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

239

Active structural control by fuzzy logic rules: An introduction

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

240

A design methodology of constraint-based fuzzy logic controller

Constraint-based fuzzy logic controllers (CFLCs) have been recognized as a unified framework of control system design. Arbitrary types of constraints are allowed in a CFLC to specify the desired states of a plant. The purpose of this paper is to present a design methodology of CFLCs, transforming the intention of a control system into a network of fuzzy constraints. The

David Chiang; Robert Lai

2001-01-01

241

Application of fuzzy sliding-mode control in robot

NASA Astrophysics Data System (ADS)

The system of Pb-211 robot waist is a nonlinear hydraulic servo control system. It is very difficult to achieve speedy response without overshoot by the PID control algorithm for the system control. To improve the performance of the system, a new controller is designed with a fuzzy sliding-mode control algorithm, which makes use of the merits both the fuzzy control and the sliding-mode control algorithm. The simulation results show that the new controller is effective, which can achieve high speediness and steady accuracy without overshoot. The fuzzy sliding-mode control has obvious advantage compared the traditional PID algorithm, and it has strong robust too.

Fu, Yongling; Wang, Yan

2006-11-01

242

A fuzzy-logic-based controller for active rectifier

This paper presents a current-sensorless active rectifier specifically designed for low-cost applications. Both the control and the modulation use fuzzy logic. A simple model of the system is given in order to explain the current feedforward control using fuzzy logic and the fuzzy-logic-based modulation, then, the new algorithm is analyzed looking for high performance even using small passive elements and

Carlo Cecati; Antonio Dell' Aquila; Marco Liserre; Antonio Ometto

2003-01-01

243

Control of a fluidized bed combustor using fuzzy logic

Fuzzy logic--an artificial intelligence technique--can be employed to exploit the wealth of information human experts have learned about complex systems while attempting to control them. This information is usually of a qualitative nature that is unusable by rigid conventional control techniques. Fuzzy logic, uses as a control method, manipulates linguistically expressed, heuristic knowledge from a human expert to derive control actions for a described system. As an alternative approach to classical controls, fuzzy logic is examined for start-up control and normal regulation of a bubbling fluidized bed combustor. To validate the fuzzy logic approach, the fuzzy controller is compared to a classical proportional and integral (PI) controller, commonly used in industrial applications, designed by Ziegler-Nichols tuning.

Koffman, S.J. [Purdue Univ., West Lafayette, IN (United States). School of Mechanical Engineering; Brown, R.C. [Iowa State Univ., Ames, IA (United States). Dept. of Mechanical Engineering; Fullmer, R.R. [Utah State Univ., Logan, UT (United States). Dept. of Mechanical and Aerospace Engineering

1996-01-01

244

Speed control of induction motor using genetic algorithm based fuzzy controller

The fuzzy logic controller has been focused in the field of vector control of induction motors. However, a systematic method for designing and tuning the fuzzy logic controller is not developed yet. In this paper, an auto-tuning method for fuzzy logic controller based on the genetic algorithm is presented. In the proposed method, normalization parameters and membership function parameters of

Won-Seok Oh; Young-Tae Kim; Chang-Sun Kim; Tae-Seok Kwon; Hee-Jun Kim

1999-01-01

245

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

246

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

AN APPROACH TO SUPERVISORY CONTROL OF AN ENERGY MANAGEMENT CONTROL SYSTEM USING FUZZY LOGIC Reza Langari Center for Fuzzy Logic, Robotics and Intelligent Systems Research and Department of Mechanical Engineering Texas A&M University...

Langari, R.

247

Control of a pneumatic gantry robot for grinding: a neuro-fuzzy approach to PID tuning

This paper addresses an application that involves the grinding of the edges of steel blanks with a pneumatic gantry robot. It presents a PID tuning method that uses an adaptive neuro-fuzzy inference system (ANFIS) to model the relationship between the controller gains and the target output response, with the response specification set by desired percent overshoot and settling time. The

Murad Samhouri; Asghar Raoufi; Brian Surgenor

2005-01-01

248

Coordinated signal control for arterial intersections using fuzzy logic

NASA Astrophysics Data System (ADS)

Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.

Kermanian, Davood; Zare, Assef; Balochian, Saeed

2013-09-01

249

Parameterized linear matrix inequality techniques in fuzzy control system design

This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations

H. D. Tuan; P. Apkarian; T. Narikiyo; Y. Yamamoto

2001-01-01

250

Neuro-fuzzy Control of an Intelligent Mobile Robot

This paper describes the reactive controlling of a mobile robotic system using a hybrid approach by adopting both neural network and fuzzy logic, so that, an autonomous robot should move in a crowded unknown environment to reach at a decided goal. A fuzzy logic controller with a set of certain rules is used to obtain a goal reaching task. While

Dinesh Kumar; Kapil Dhama

2012-01-01

251

An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

ERIC Educational Resources Information Center

This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)

Mamdani, E. H.; Assilian, S.

1975-01-01

252

Design and Stability Analysis of Fuzzy Switching PID Controller

Fuzzy switching PID (FS-PID) controller proposed in this paper consists of a TS-PID controller and a conventional PID controller which switching according to the premise variable in operating regime rules. This technique integrates the advantages of both the fuzzy controller and PID controller but eliminates their disadvantages. FS-PID controller provides good performance during the transient phase and alleviates oscillatory when

Baozhu Jia; Guang Ren; Gang Long

2006-01-01

253

Computation of basic fuzzy controllers by approximation of time-optimal controllers

This paper presents a method to compute robust fast nonlinear controllers for real plants for which a-not necessarily precise-mathematical model is available. The development is based on ideas from fuzzy control, but in contrast to usual fuzzy controller designs, most of the rules are not derived from heuristics but rather are mathematical formulae which, together with the standard fuzzy quantization

Thomas Heckenthaler; Sebastian Engell

1994-01-01

254

Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization

prediction errors appears to be essential. For that purpose a neuro-fuzzy predictor based on the ANFIS model Introduction Maintenance activity combines dierent methods, tools and techniques to reduce costs while justied and an illustra- tion based on the adaptive neuro-fuzzy inference system is given. The proposed

Paris-Sud XI, UniversitÃ© de

255

A Self-tuning Fuzzy Robotic Force Controller

Most industrial robots are controlled as position servo-based manipulators. This has made most advanced force control algorithms unpractical and difficult to implement. In this paper a position based fuzzy PID force controller is proposed to regulate contact force of a six degree of freedom industrial robot where the environment contact stiffness varies considerably. Based on a relationship between fuzzy PID and conventional PID control laws and the application of a simple fuzzy self-tuning method, the controller is tuned and satisfying experimental results have been obtained to validate its efficiency.

unknown authors

2002-01-01

256

Backpropagation through time training of a neuro-fuzzy controller.

The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too. PMID:20945520

Koprinkova-Hristova, Petia

2010-10-01

257

Modal-space reference-model-tracking fuzzy control of earthquake excited structures

NASA Astrophysics Data System (ADS)

This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.

Park, Kwan-Soon; Ok, Seung-Yong

2015-01-01

258

Design of fuzzy neural network based control system for cement rotary kiln

This paper presents a fuzzy neural network control system for the process of cement production with rotary cement kiln. Since the dynamic characteristics and reaction process parameters are with large inertia, pure hysteresis, nonlinearity and strong coupling, a fuzzy neural network controller combining both the advantages of neural network and fuzzy control is applied. This fuzzy neural network controller adjusts

Zheng Li

2010-01-01

259

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

260

Parallelization of a fuzzy control algorithm using quantum computation

Quantum computation is proposed for the parallelization of a fuzzy logic control (FLC) algorithm. Quantum computation speeds up the fuzzy inference since serial operations between matrices of large dimensionality are now replaced by a one-step quantum addition or a quantum subtraction. The unitarity properties of the algorithm prove that the FLC stands for a simulator of a quantum computing machine.

Gerasimos G. Rigatos; Spyros G. Tzafestas

2002-01-01

261

Optimized Fuzzy Controller Architecture for Field Programmable Gate Arrays

This paper describes an optimized fuzzy Controller (FC) ar- chitecture and its realization with field programmable gate arrays (FP- GAs). In consideration of data dependencies and minor user restrictions within the definition of fuzzy rules (FRs), it is possible to develop a high speed FPGA architecture. A prototype of the FC operates at 5MHz

Hartmut Surmann; Ansgar Ungering; Karl Goser

1992-01-01

262

Fuzzy modeling of inventory control system in uncertain environment

In this paper a fuzzy model is developed to represent a single item continuous inventory control system. The continuous review is implemented through a reorder point and order quantity. The demand and lead time uncertainties are considered simultaneously. The uncertainties are treated with the fuzzy logic theory. The lead-time and the reorder point are described by the linguistic terms. A

L. Kamal; J.-L. Sculfort

2007-01-01

263

Fuzzy support vector machines for adaptive Morse code recognition

Morse code is now being harnessed for use in rehabilitation applications of augmentative–alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code

Cheng-Hong Yang; Li-Cheng Jin; Li-Yeh Chuang

2006-01-01

264

Fuzzy sliding mode mould level control in continuous casting process

We present a method of fuzzy sliding mode control for mould level, which has been a major control task in continuous casting plants. The mould level control system involves nonlinearities such as stick-slip friction in the sliding gate, time-delay, friction force variations between molten steel and the inner wall of mould, and nozzle logging\\/unclogging. We propose fuzzy sliding mode control

Jatinder S. Bedi; Yeongseob Kueon; Yigon Kim; Chang-Gyoon Lim

1999-01-01

265

Advance of Systematic Design Methods on Fuzzy Control

ICEBO2006, Shenzhen, China Co ntrol Systems for Energy Efficiency and Comfort, Vol. V-2-5 Advance of Systematic Design Methods on Fuzzy Control1 Jili Zhang Yongpan Chen Ph.D. Professor Doctoral Candidate School of Municipal... control rules by using reference trace in phase plane. Simulation and test prove that this method has better control performances. Besides, Qingwei Chen puts forward the laminar hierarchical fuzzy control method in workshop air conditioning system...

Zhang, J.; Chen, Y.

2006-01-01

266

Fuzzy logic controllers: A knowledge-based system perspective

NASA Technical Reports Server (NTRS)

Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.

Bonissone, Piero P.

1993-01-01

267

A fuzzy controller for DC-DC converters

A controller for DC-DC converters based on fuzzy logic is proposed. Being free of complex equations and heavy computation, the controller is expected to control converters that operate at high frequencies. This paper presents the derivation of fuzzy control rules for the basic converter circuits and simulations of the performance of the closed-loop converters in respect of start-up transient, load

W. C. So; C. K. Tse; Y. S. Lee

1994-01-01

268

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

269

Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.

Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665

Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir

2015-01-01

270

A Fuzzy System for Adaptive Network Routing A. Pasupuleti*

capacity. Based on a set of fuzzy rules, link cost is dynamically assigned depending upon the present algorithms. The proposed fuzzy based algorithm always gave optimal performance under varying load conditions and topologies. Keywords: Network routing, link cost, shortest path routing, Fuzzy logic 1. Introduction

Shenoy, Nirmala

271

Adaptive fuzzy k-NN classifier for EMG signal decomposition.

An adaptive fuzzy k-nearest neighbour classifier (AFNNC) for EMG signal decomposition is presented and evaluated. The developed classifier uses an adaptive assertion-based classification approach for setting a minimum classification threshold. The similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit firing pattern information: passive and active. The performance of the developed classifier was evaluated using synthetic signals with specific properties and experimental signals and compared with the performance of an adaptive template matching classifier, the adaptive certainty classifier (ACC). Across the sets of simulated and experimental EMG signals used for comparison, the AFNNC had better average classification performance overall, but due to the assignment of higher numbers of MUPs it made relatively more errors. Nonetheless, these increased error rates would still be acceptable for most clinical uses of decomposed EMG data. An independent and a related set of simulated signals were used for testing. For the independent simulated signals of varying intensity, the AFNNC had on average an improved correct classification rate (CCr) (8.1%) but an increased error rate (Er) (1.5%) compared to ACC. For the related simulated signals with varying amounts of shape and/or firing pattern variability, the AFNNC on average had an improved CCr (5%) but a slightly increased Er (0.3%) compared to ACC. For experimental signals, the AFNNC on average had improved CCr (6%) but an increased Er (2.1%) compared to ACC. The greatest gains in AFNNC performance relative to that of the ACC occurred when the variability of MUP shapes within motor unit potential trains was high suggesting that compared to a template matching assignment strategy the NN assignment paradigm is better able to ameliorate the classification problems caused by MUP instability. PMID:16406673

Rasheed, Sarbast; Stashuk, Daniel; Kamel, Mohamed

2006-09-01

272

Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic

NASA Technical Reports Server (NTRS)

This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.

Yang, Li-Farn

1996-01-01

273

Fuzzy logic applications to expert systems and control

NASA Technical Reports Server (NTRS)

A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.

Lea, Robert N.; Jani, Yashvant

1991-01-01

274

Fuzzy PI control of an industrial weigh belt feeder

This paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of

Yanan Zhao

2002-01-01

275

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

276

Fuzzy neural-based control for nonlinear time-varying delay systems.

In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by N fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control. PMID:18179067

Hwang, Chih-Lyang; Chang, Li-Jui

2007-12-01

277

Tuning fuzzy PD and PI controllers using reinforcement learning.

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples. PMID:20605021

Boubertakh, Hamid; Tadjine, Mohamed; Glorennec, Pierre-Yves; Labiod, Salim

2010-10-01

278

Sampled-data fuzzy controller for time-delay nonlinear systems: fuzzy-model-based LMI approach.

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 reduced. However, the sampling activity and time delay, which are potential causes of system instability, will complicate the system dynamics and make the stability analysis much more difficult than that for a pure continuous-time fuzzy control system. In this paper, a sampled-data fuzzy controller with enhanced nonlinearity compensation ability is proposed. Based on the fuzzy-model-based control approach, linear matrix inequality (LMI)-based stability conditions are derived to guarantee the system stability. By using a descriptor representation, the complexity of the sampled-data fuzzy control system with time delay can be reduced to ease the stability analysis, which effectively leads to a smaller number of LMI-stability conditions. Information of the membership functions of both the fuzzy plant model and fuzzy controller are considered, which allows arbitrary matrices to be introduced, to ease the satisfaction of the stability conditions. An application example will be given to show the merits and design procedure of the proposed approach. Furthermore, LMI-based performance conditions are derived to aid the design of a well-performed sampled-data fuzzy controller. PMID:17550116

Lam, H K; Leung, Frank H F

2007-06-01

279

Fuzzy-polar control of wind-turbine generator

This paper presents a wind-turbine blade pitch angle controller based on fuzzy polar technique. the technique takes advantage of fuzzy-linguistic modeling in expressing the natural non-linearity or imprecision of the wind-turbine system in determining pitch angles for speed and power regulation. The fuzzy-polar method presents wind-turbine state in the phase-plane in terms of its rotational speed deviation and acceleration. The state vectors thus derived serve as an indicator of the magnitude of departure from the nominal operating point. In order to shift operating state back to the phase plane origin, an acceleration or deceleration control is applied through the pitch-angle adjustment mechanism as defined by the fuzzy-linguistic control law. The performance of the pitch control design method is demonstrated on a simulated wind-turbine-driven synchronous generator.

Idowu, P. [Penn State Univ., Middletown, PA (United States)

1995-12-31

280

Simulation and design of fuzzy sliding-mode controller for ship heading-tracking

NASA Astrophysics Data System (ADS)

In considering the characteristic of a rudder, the maneuvers of a ship were described by an unmatched uncertain nonlinear mathematic model with unknown virtual control coefficient and parameter uncertainties. In order to solve the uncertainties in the ship heading control, specifically the controller singular and paramount re-estimation problem, a new multiple sliding-mode adaptive fuzzy control algorithm was proposed by combining Nussbaum gain technology, the approximation property of fuzzy logic systems, and a multiple sliding-mode control algorithm. Based on the Lyapunov function, it was proven in theory that the controller made all signals in the nonlinear system of unmatched uncertain ship motion uniformly bounded, with tracking errors converging to zero. Simulation results show that the demonstrated controller design can track a desired course fast and accurately. It also exhibits strong robustness peculiarity in relation to system uncertainties and disturbances.

Yuan, Lei; Wu, Hansong

2011-03-01

281

Control allocation of ASV based on linear programming and fuzzy logic

NASA Astrophysics Data System (ADS)

Future Aero Space Vehicle flies through both the atmospheric and extra atmospheric fields, which implies the autonomy and adaptability to the uncertainties from the system faults and changing environments. Algorithms based on fuzzy logic and linear programming are presented, which can implement the autonomous control reconfigurations under uncertainties via the redundant actuators. The compensation branch minimizes the difference between the desired control objectives and the actual achievable control if the control power is deficient. Otherwise the optimization branch optimizes some sub-objectives by utilizing the excess control power. The fuzzy logic-based regulator tunes the weight vector of the objective functions by the expert rules to obtain the optimized allocation results under various environments with considerations of the control effectiveness. It is illustrated that the algorithms can satisfy the control performance, save the fuel and smooth the allocation output.

Chi, Pei; Chen, Zongji; Zhou, Rui

2006-11-01

282

Intelligent control algorithms are introduced into the control system of temperature and humidity. A multi-mode control algorithm of PI-Single Neuron is proposed for single loop control of temperature and humidity. In order to remove the coupling between temperature and humidity, a new decoupling method is presented, which is called fuzzy decoupling. The decoupling is achieved by using a fuzzy controller

Xianxia Zhang; Jian Wang; Tinggao Qin

2003-01-01

283

Altitude control system of autonomous airship based on fuzzy logic

A kind of design method of compound control system is proposed based on fuzzy logic control, according to the problem of altitude control for unmanned autonomous airship. By considering the scheme of buoyancy control system in the airship, the flight kinematics model is established based on forces acted on the airship. During the longitudinal movements, the altitude control system is

Guo Jian-guo; Zhou Jun

2008-01-01

284

A fuzzy control system design based on double module structure

NASA Astrophysics Data System (ADS)

In the high-precision servo system, not only request quickness but also response characteristics without overshoot, usually request strong robot for position loop. The traditional control system design usually is finished under the linear condition which neglects some factors. It makes the more differences between designed model and actual system, so the general PID adjuster and the single basic fuzzy control are different to meet the need. The paper designs a complex fuzzy controller based on double structure and variable coefficient, at the same time adopts Cooperative Processing module with fuzzy reasoning function, which make the system fuzzy reasoning speed improved and realize the online reasoning. So the system capability is improved, the experiment proved the system has the good capability.

Zhang, Zhiyuan; Zhang, Jie; Cheng, Yan

2007-12-01

285

Intelligent fuzzy supervisory control for distillation columns

to be controlled. In many difficult process situations such as steelmaking furnaces [7], cement kilns [13], presses in the glass industry [4] and distillation columns [12], such models do not exist. While there are a variety of adaptive techniques which can...

Santhanam, Srinivasan

2012-06-07

286

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

287

On methods for improving performance of PI-type fuzzy logic controllers

To improve limitations of fuzzy PI controller especially when applied to high order systems, we propose two types of fuzzy logic controllers that take out appropriate amounts of accumulated control input according to fuzzily described situations in addition to the incremental control input calculated by conventional fuzzy PI controllers. The structures of the proposed controller were motivated by the problems

Jihong Lee

1993-01-01

288

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

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

289

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an advantage that it can be implemented using a microcontroller or a digital computer to lower the implementation cost and time. However, discontinuity introduced by the sampling activity

H. K. Lam

2009-01-01

290

NASA Astrophysics Data System (ADS)

A terminal sliding mode fuzzy control based on multiple sliding surfaces was proposed for ship course tracking steering, which takes account of rudder characteristics and parameter uncertainty. In order to solve the problem, the controller was designed by employing the universal approximation property of fuzzy logic system, the advantage of Nussbaum function, and using multiple sliding mode control algorithm based on the recursive technique. In the last step of designing, a nonsingular terminal sliding mode was utilized to drive the last state of the system to converge in a finite period of time, and high-order sliding mode control law was designed to eliminate the chattering and make the system robust. The simulation results showed that the controller designed here could track a desired course fast and accurately. It also exhibited strong robustness peculiarly to system, and had better adaptive ability than traditional PID control algorithms.

Yuan, Lei; Wu, Han-Song

2010-12-01

291

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

292

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

293

Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference online XX XX XXXX Keywords: Proton Exchange Membrane fuel cell degradation, Prognostic and Health nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro

Paris-Sud XI, UniversitÃ© de

294

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

295

Adaptive control for accelerators

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

296

Implementation of a new fuzzy vector control of induction motor.

The aim of this paper is to present a new approach to control an induction motor using type-1 fuzzy logic. The induction motor has a nonlinear model, uncertain and strongly coupled. The vector control technique, which is based on the inverse model of the induction motors, solves the coupling problem. Unfortunately, in practice this is not checked because of model uncertainties. Indeed, the presence of the uncertainties led us to use human expertise such as the fuzzy logic techniques. In order to maintain the decoupling and to overcome the problem of the sensitivity to the parametric variations, the field-oriented control is replaced by a new block control. The simulation results show that the both control schemes provide in their basic configuration, comparable performances regarding the decoupling. However, the fuzzy vector control provides the insensitivity to the parametric variations compared to the classical one. The fuzzy vector control scheme is successfully implemented in real-time using a digital signal processor board dSPACE 1104. The efficiency of this technique is verified as well as experimentally at different dynamic operating conditions such as sudden loads change, parameter variations, speed changes, etc. The fuzzy vector control is found to be a best control for application in an induction motor. PMID:24629620

Rafa, Souad; Larabi, Abdelkader; Barazane, Linda; Manceur, Malik; Essounbouli, Najib; Hamzaoui, Abdelaziz

2014-05-01

297

Adaptive nonlinear flight control

NASA Astrophysics Data System (ADS)

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

Rysdyk, Rolf Theoduor

1998-08-01

298

Identification of uncertain nonlinear systems for robust fuzzy control.

In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by Skrjanc et al. (2005). With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control. PMID:19683234

Senthilkumar, D; Mahanta, Chitralekha

2010-01-01

299

Intelligent fuzzy immune PID controller design for multivariable process control system

Based on biological immune principle and fuzzy theory, this paper presents an intelligent fuzzy immune PID control scheme to solve the control difficulties of industry process with multi-variables. The least square algorithm was used for offline optimization to form immune feedback control system. The application on cement rotary kiln control was discussed in detail as an example. The rotary kiln

Zheng Li

2010-01-01

300

A genetic-fuzzy control application to ramp metering and variable speed limit control

This paper proposes a fuzzy control approach for the traffic-responsive ramp metering and variable speed limits control, in order to reduce the peak-hour congestion on freeways. The objective of control is to minimize the total time spent in the traffic network. To ease the calibration process of fuzzy controller and improve the overall performance of ramp metering and variable speed

Amir Hosein Ghods; Ashkan Rahimi Kian; Masoud Tabibi

2007-01-01

301

High-Precision Fuzzy Control Strategy in Continuous Casting Mold Servo Control System

Caster vibration waveform with variable, non-sinusoidal vibration characteristics, the device uses electro-hydraulic servo drive control algorithm using variable universe fuzzy control, robustness, high control precision, fast response. The paper focuses on variable universe fuzzy controller of rigor the flex-factors that ensure its applicability, and integration testing to determine model parameters, and through computer simulation validity and accuracy.

Yong Tian; Ling-hua Wang

2010-01-01

302

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

303

Fuzzy controller synthesis for an inverted pendulum system

A frequently discussed issue in the use of fuzzy systems for control design is related to the ad hoc nature by which controller synthesis is performed, where incorporation of the designer's knowledge into the synthesis procedure is often not straightforward. This paper describes a controller synthesis procedure based on the idea of expanding the usable region of a linear control

S. Yurkovich; M. Widjaja

1996-01-01

304

Fuzzy Logic PID Control of Automatic Voltage Regulator System

The application of a simple microcontroller to deal with a three variable input and a single output fuzzy logic controller, with Proportional - Integral - Derivative (PID) response control built-in has been tested for an automatic voltage regulator. The fuzzifiers are based on fixed range of the variables of output voltage. The control output is used to control the wiper

Aye Aye Mon

2009-01-01

305

In order to design a fuzzy controller for complex nonlinear systems, the work of this paper deals with developing the relaxed stability conditions for continuous-time affine Takagi-Sugeno (T-S) fuzzy models. By applying the passivity theory and Lyapunov theory, the relaxed stability conditions are derived to guarantee the stability and passivity property of closed-loop systems. Based on these relaxed stability conditions, the synthesis of fuzzy controller design problem for passive continuous-time affine T-S fuzzy models can be easily solved via the Optimal Convex Programming Algorithm (OCPA) and Linear Matrix Inequality (LMI) technique. At last, a simulation example for the fuzzy control of a nonlinear synchronous generator system is presented to manifest the applications and effectiveness of proposed fuzzy controller design approach. PMID:19389667

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

2009-07-01

306

? ? Fuzzy Control for Systems with Repeated Scalar Nonlinearities

This paper is concerned with the H\\u000a ? control problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with repeated scalar nonlinearities. A modified\\u000a T-S fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing\\u000a a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as

Hongli Dong; Zidong Wang; Huijun Gao

307

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

308

Neuro-Fuzzy Control of a Robotic Manipulator

NASA Astrophysics Data System (ADS)

In this paper, to solve the problem of control of a robotic manipulator's movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator's dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator

Gierlak, P.; Muszy?ska, M.; ?ylski, W.

2014-08-01

309

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control

Gang Feng

2006-01-01

310

A PID type fuzzy controller with self-tuning scaling factors

By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller. In order to improve further the performance of the transient state and the steady state of the PID type controller, we develop a method to tune the scaling factors of the PID type fuzzy controller on line. Simulation of the

Zhi-Wei Woo; Hung-Yuan Chung; Jin-Jye Lin

2000-01-01

311

Control-Oriented Fuzzy Multi-Model Identification of a Highly Nonlinear Missile

A novel fuzzy identification algorithm is introduced that is based on fuzzy interpolation of locally linear models obtained from a nonlinear system. Fuzzy clustering is employed to automatically decompose operating envelope of a nonlinear system into optimal number of operating partitions by means of optimal number of rules in a Tagaki-Sugeno fuzzy set direct realistic extension to flight control is

S. V. Hashemi; A. R. Mehrabian; J. Roshanian

2006-01-01

312

A fuzzy chip-based real-time fault classifier in a power controller

A fuzzy chip-based electrical power faults classifier is presented in this paper. The system, which utilizes a fuzzy chip designed for the fuzzy rule base inference, detects the faults in the electrical power system in real time and activates the circuit control unit to take the appropriate actions. A set of features are extracted, and two sets of fuzzy inference

Xing Wu; Chwan-Hwa Wu

1994-01-01

313

Stability analysis of sampled-data output-feedback polynomial fuzzy-model-based control systems

This paper presents the stability analysis of sampled-data output-feedback polynomial fuzzy-model-based control systems. A sampled-data output-feedback polynomial fuzzy controller is proposed to control the nonlinear plant represented by the polynomial fuzzy model. The proposed sampled-data output-feedback polynomial fuzzy controller makes use of the system output for control. Furthermore, due to the sampling activity, the control signal will be kept constant

H. K. Lam; Mohammad Narimani

2010-01-01

314

New design and stability analysis of fuzzy proportional-derivative control systems

This paper describes the design principle, tracking performance, and stability analysis of a fuzzy proportional-derivative (PD) controller. First, the fuzzy PD controller is derived from the conventional continuous-time linear PD controller. Then, the fuzzification, control-rule base, and defuzzification in the design of the fuzzy PD controller are discussed in detail. The resulting controller is a discrete-time fuzzy version of the

Heidar A. Malki; Huaidong Li; Guanrong Chen

1994-01-01

315

Adaptive neural-based fuzzy modeling for biological systems.

The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems. PMID:23376801

Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong

2013-04-01

316

An architecture for designing fuzzy logic controllers using neural networks

NASA Technical Reports Server (NTRS)

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

Berenji, Hamid R.

1991-01-01

317

Fuzzy sampled-data control for uncertain vehicle suspension systems.

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

318

Autonomous vehicle motion control, approximate maps, and fuzzy logic

NASA Technical Reports Server (NTRS)

Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.

Ruspini, Enrique H.

1993-01-01

319

Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.

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

320

Application of genetic algorithms to tuning fuzzy control systems

NASA Technical Reports Server (NTRS)

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

Espy, Todd; Vombrack, Endre; Aldridge, Jack

1993-01-01

321

A Novel Nonlinear PID Controller Designed By Takagi-Sugeno Fuzzy Model

TS-PID fuzzy controller is proposed as a novel framework for nonlinear PID controller design in this paper. The so-called TS-PID fuzzy controllers are a class of Takagi-Sugeno (TS) fuzzy controllers whose rule consequences employ PID expressions. Based on Lyapunov theory, a design and stability analysis method of TS-PID fuzzy controllers is presented. This approach utilizes some analytical techniques of linear

Zhihong Xiu; Wei Wang

2006-01-01

322

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

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

323

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

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

Chuan-kai Lin

2005-01-01

324

Fuzzy controller: design, evaluation, parallel and hierarchial combination with a PID controller

Fuzzy techniques still remain ill-accepted in the control community. As a matter of fact, they rely on a new relation between the real world and the scientists. Whereas some theoretical studies are carried out on this subject, experiments on processes show what fuzzy techniques can bring to the control theory. This paper deals with some implementations of control structures using

R. Ketata; D. De Geest; A. Titli

1995-01-01

325

Petri net based programmable fuzzy controller targeted for distributed control environments

The main purpose of this paper is to present the programmable fuzzy controller (PFC), obtained with a synchronized colored Petri net model. The net model is used as the common formalism to support the integration of different ways of modelling discrete event system control targeted for real time operation. The goal is to integrate, in a common specification, fuzzy control

L. Gomes; A. Steiger-Garcao

1995-01-01

326

A fuzzy logic controller for a dry rotary cement kiln

The dry rotary cement kiln is the most important part of the cement plant. Cement kilns exhibit time-varying nonlinear behavior and relatively few measurements are available, consequently, automatic control is usually restricted to a few simple control loops on secondary variables, leaving the responsibility for the control of primary variables to the kiln operators. In this paper a fuzzy logic

Mazhar Tayel; M. R. M. Rizk; H. A. Hagras

1997-01-01

327

A fuzzy PID controller being like parameter varying PID

A fuzzy-PID controller using the minimum inference engine and center average defuzzification is analyzed and shown that it behaves approximately like a parameter varying PID controller. We then try to analyze the effect of this kind of controller when using different rule bases. Simulation results are used to demonstrate the feasibility of this method

Tsung-Tai Huang; Hung-Yuan Chung; Jin-Jye Lin

1999-01-01

328

Cement roller press control by fuzzy logic reasoning

An application of fuzzy logic inference technique on cement grinding roller control is proposed. The control of cement grinding roller is that the oil-pressure is commanded to follow a desired setting pressure. The original method of determining the setting pressure in terms of driving current is a PI-like method. But by this method, the grinding roller control will result in

Chih-Min Lin; Po-Nam Chin

1996-01-01

329

Improved fuzzy control of dual clutch transmission during launch process

Based on a certain manual transmission of a heavy duty truck, a dual clutch transmission (DCT) structure was proposed and the simulation model was built by means of Matlab\\/Simulink. The control strategy, that the engine speed was controlled to follow the target speed, was put forward. Exact mathematical model of DCT is difficult to gain, thus fuzzy controller was adopted

Jinle Zhang; Biao Ma; Changsong Zheng; Hailing Zhang; Yingfeng Zhang

2010-01-01

330

Design and performance comparison of fuzzy logic based tracking controllers

NASA Technical Reports Server (NTRS)

Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.

Lea, Robert N.; Jani, Yashvant

1992-01-01

331

Adaptive hybrid control of manipulators

NASA Technical Reports Server (NTRS)

Simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecuture is presented. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal and a force feedforward term, and it achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers and an auxiliary signal, and it accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in on-line control with high sampling rates.

Seraji, H.

1987-01-01

332

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

333

Full design of fuzzy controllers using genetic algorithms

NASA Technical Reports Server (NTRS)

This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.

Homaifar, Abdollah; Mccormick, ED

1992-01-01

334

Fuzzy logic control system to provide autonomous collision avoidance for Mars rover vehicle

NASA Technical Reports Server (NTRS)

NASA is currently involved with planning unmanned missions to Mars to investigate the terrain and process soil samples in advance of a manned mission. A key issue involved in unmanned surface exploration on Mars is that of supporting autonomous maneuvering since radio communication involves lengthy delays. It is anticipated that specific target locations will be designated for sample gathering. In maneuvering autonomously from a starting position to a target position, the rover will need to avoid a variety of obstacles such as boulders or troughs that may block the shortest path to the target. The physical integrity of the rover needs to be maintained while minimizing the time and distance required to attain the target position. Fuzzy logic lends itself well to building reliable control systems that function in the presence of uncertainty or ambiguity. The following major issues are discussed: (1) the nature of fuzzy logic control systems and software tools to implement them; (2) collision avoidance in the presence of fuzzy parameters; and (3) techniques for adaptation in fuzzy logic control systems.

Murphy, Michael G.

1990-01-01

335

Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks.

Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays. Index Terms-Fuzzy delay trend analysis, intertalkspurt, intratalkspurt, multilayer perceptrons (MLPs), network delay estimation, playout buffering, playout delay adaptation, time delay neural networks (TDNNs), voice over Internet protocol (VoIP). PMID:16252825

Ranganathan, Mohan Krishna; Kilmartin, Liam

2005-09-01

336

Fuzzy decision and control, the Bayes context

. 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..., Texas 77843-3128 painter@zadok.tamu.edu Abstract This is a highly condensed version of a much longer paper. The long version may be obtained directly from the author. This present short paper gives only partial results, showing how it is that Fuzzy...

Painter, John H.

1993-12-15

337

Fuzzy logic control of a nitrogen laser Siu-Chung Tam, MEMBER SPIE

Fuzzy logic control of a nitrogen laser Siu-Chung Tam, MEMBER SPIE Siong-Chai Tan Wah-Peng Neo Sze report on the use of a fuzzy logic control scheme to improve the stability of a pulsed nitrogen laser on the results of implementing a fuzzy logic controller on an experimental nitrogen laser. The performance

Doran, Simon J.

338

Research on motion control of mobile robot with fuzzy PID arithmetic

Through analysis the kinematics model of Dongfeng Ilsoccer robot, considering time change, nonlinear and other characteristics of this system, a fuzzy PID. Parameter self-tuning PID control method combining fuzzy control with traditional PID control is present. To contrapose the problems of robot soccer motion system, the methods of dynamically regulate the three PID parameters (kp, ki, kd) based on fuzzy

Tian Qi; Cheng Li; Wang Kai; Liu Yan

2009-01-01

339

FUZZY LOGIC-BASED SOLAR CHARGE CONTROLLER FOR MICROBATTERIES Pritpal Singh and Jagadeesan of a micro- charge/discharge controller has not. In this paper we present a novel, fuzzy logic-based solar is adjusted by modulating the duty cycle of the buck converter's switching MOSFET using a fuzzy logic control

Singh, Pritpal

340

In this work, a model predictive control method combined with fuzzy identification, is applied to the design of the thermoelectric (TE) power control in the SP-100 space reactor. The future TE power is predicted by using the fuzzy model identified by a subtractive clustering method of a fast and robust algorithm. The objectives of the proposed fuzzy model predictive controller

Man Gyun Na; Belle R. Upadhyaya

2006-01-01

341

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

342

NASA Technical Reports Server (NTRS)

Fuzzy control techniques are analyzed to explain why the fuzzy control that is based on the expert's knowledge is often smoother and more stable than the control performed manually by the same experts. A precise mathematical explanation of this phenomenon is presented. Results obtained make it possible to predict the quality of the fuzzy control.

Kreinovich, V.; Lea, R.; Fuentes, O.; Lokshin, A.

1992-01-01

343

NASA Astrophysics Data System (ADS)

This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.

Abdurrahim, Mahabuba; Abdullah Khan, M.; Edriss, Ali Ahmed

2012-01-01

344

NASA Astrophysics Data System (ADS)

This paper presents a design procedure for a Robust and Adaptive Fuzzy Logic based Power System Stabilizer (RAFLPSS) to improve the small signal stability of Power System. The parameters of RAFLPSS are tuned by adaptive neural network. This RAFLPSS uses ANFIS network (Adaptive Network based Fuzzy Inference System) which provides a natural framework of multi-layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid-learning algorithm tunes the fuzzy rules and the membership functions of the RAFLPSS. The dynamic performance of SMIB system with the proposed RAFLPSS under different operating conditions and change in system parameters has been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic based PSS (FPSS) are compared with the proposed RAFLPSS. The simulation results demonstrate that the proposed RAFLPSS performs well in damping and quicker response when compared with the other two PSSs.

Abdurrahim, Mahabuba; Abdullah Khan, M.; Edriss, Ali Ahmed

2011-12-01

345

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

346

Fuzzy logic control implementation in sensorless PM drive systems

. In this paper, a fuzzy logic controller is proposed for the real-time control of a Sensorless PM drive system hardware and software design is simple and can be implemented by a single-chip microcontroller for real-time, Real-time systems. 1. Introduction With recent developments in magnet materials, it appears

347

A Framework for Fuzzy Logic based UAV Navigation and Control

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

348

Tuning a fuzzy controller using quadratic response surfaces

NASA Technical Reports Server (NTRS)

Response surface methodology, an alternative method to traditional tuning of a fuzzy controller, is described. An example based on a simulated inverted pendulum 'plant' shows that with (only) 15 trial runs, the controller can be calibrated using a quadratic form to approximate the response surface.

Schott, Brian; Whalen, Thomas

1992-01-01

349

Fuzzy PID controller: Design, performance evaluation, and stability analysis

This paper presents a design for a new fuzzy logic proportional-integral-derivative (PID) controller. The main motivation for this design was to control some known nonlinear systems, such as robotic manipulators, which violate the conventional assumption of the linear PID controller. This controller is developed by first describing the discrete-time linear PID control law and then progressively deriving the steps necessary

James Carvajal; Guanrong Chen; Haluk Ögmen

2000-01-01

350

NASA Technical Reports Server (NTRS)

The fuzzy controllers studied in this paper are the ones that employ N trapezoidal-shaped members for input fuzzy sets, Zadeh fuzzy logic and a centroid defuzzification algorithm for output fuzzy set. The author analytically proves that the structure of the fuzzy controllers is the sum of a global nonlinear controller and a local nonlinear proportional-integral-like controller. If N approaches infinity, the global controller becomes a nonlinear controller while the local controller disappears. If linear control rules are used, the global controller becomes a global two-dimensional multilevel relay which approaches a global linear proportional-integral (PI) controller as N approaches infinity.

Ying, Hao

1993-01-01

351

Control of a flexible beam using fuzzy logic

NASA Technical Reports Server (NTRS)

The goal of this project, funded under the NASA Summer Faculty Fellowship program, was to evaluate control methods utilizing fuzzy logic for applicability to control of flexible structures. This was done by applying these methods to control of the Control Structures Interaction Suitcase Demonstrator developed at Marshall Space Flight Center. The CSI Suitcase Demonstrator is a flexible beam, mounted at one end with springs and bearing, and with a single actuator capable of rotating the beam about a pin at the fixed end. The control objective is to return the tip of the free end to a zero error position (from a nonzero initial condition). It is neither completely controllable nor completely observable. Fuzzy logic control was demonstrated to successfully control the system and to exhibit desirable robustness properties compared to conventional control.

Mccullough, Claire L.

1991-01-01

352

This paper presents methodologies and technologies for ultrasonic localization and pose tracking of an autonomous mobile robot (AMR) by using a fuzzy adaptive extended information filtering (FAEIF) scheme. A novel ultrasonic localization system, which consists of two ultrasonic transmitters and three receivers, is proposed to estimate both the static and the dynamic position and orientation of the AMR. FAEIF is

Hung-Hsing Lin; Ching-Chih Tsai; Jui-Cheng Hsu

2008-01-01

353

Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

ERIC Educational Resources Information Center

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

2012-01-01

354

Neuro-fuzzy in six DOF tele-robotic control

A force-force bilateral scheme based on neuro-fuzzy control was designed for a six DOF tele-robotic system. An open architecture controller for the six DOF tele-robotic system has been successfully implemented. Increased system bandwidth system can be achieved with the new embedded PUMA 760 and PUMA 260 controllers. Both the slave and master robot controllers comprise a PC running a real-time

W. Po-ngaen; R. Choomuang; J. Bhuripanyo

2008-01-01

355

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

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

356

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

357

Congestion Control in Computer Networks using Fuzzy Logic C. CHRYSOSTOMOU, A. PITSILLIDES

currently in use, before we motivate the utility of Fuzzy Logic based control. Then, through a number of examples, we illustrate the power of the methodology by the successful application of fuzzy basedCongestion Control in Computer Networks using Fuzzy Logic C. CHRYSOSTOMOU, A. PITSILLIDES

Pitsillides, Andreas

358

FUZZY LOGIC MOTOR CONTROL FOR POLLUTION PREVENTION AND IMPROVED ENERGY EFFICIENCY

The paper discusses an EPA program investigating fuzzy logic motor control for improved pollution prevention and energy efficiency. nitial computer simulation and laboratory results have demonstrated that fuzzy logic energy optimizers can consistently improve motor operational ef...

359

#12; #12; !"""# Sonar Behavior-Based Fuzzy Control for a Mobile Robot S. Thongchai, S describes how fuzzy control can be ap- plied to a sonar-based mobile robot. Behavior-based fuzzy control for HelpMate behaviors was designed us- ing sonar sensors. The fuzzy controller provides a mechanism

360

Hybrid Takagi-Sugeno Fuzzy FED PID Control of Nonlinear Systems

NASA Astrophysics Data System (ADS)

The new method of proportional-integral-derivative (PID) controller is proposed in this paper for a hybrid fuzzy PID controller for nonlinear system. The important feature of the proposed approach is that it combines the fuzzy gain scheduling method and a fuzzy fed PID controller to solve the nonlinear control problem. The resultant fuzzy rule base of the proposed controller contains one part. This single part of the rules uses the Takagi-Sugeno method for solving the nonlinear problem. The simulation results of a nonlinear system show that the performance of a fed PID Hybrid Takagi-Sugeno fuzzy controller is better than that of the conventional fuzzy PID controller or Hybrid Mamdani fuzzy FED PID controller.

Hamed, Basil; El Khateb, Ahmad

2008-06-01

361

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

362

Learning Fuzzy Rule-Based Neural Networks for Control

Learning Fuzzy Rule-Based Neural Networks for Control Charles M. Higgins and Rodney M. Goodman functions and an initial rule representation are learned; second, the rules are compressed as much learned is expressed in terms of linguistic rules. In this paper, we propose a method for learning

363

Fuzzy moving sliding mode control with application to robotic manipulators

This paper presents a fuzzy tuning approach to sliding mode control for tracking-performance enhancement in a class of nonlinear systems. The sliding surface can rotate or shift in the phase space in such a direction that the tracking behaviour can be improved. It is shown that with arbitrary initial conditions, the reaching time and tracking error in the approaching phase

Quang P. Ha; David C. Rye; Hugh F. Durrant-Whyte

1999-01-01

364

Fuzzy Logic Controlled Miniature LEGO Robot for Undergraduate Training System

Fuzzy logic enables designers to control complex systems more effectively than traditional approaches as it provides a simple way to arrive at a definite conclusion upon ambiguous, imprecise or noisy information. In this paper, we describe the development of two miniature LEGO robots, which are the line following and the light searching mobile robots to provide a better understanding of

N. Z. Azlan; F. Zainudin; H. M. Yusuf; S. F. Toha; S. Z. S. Yusoff; N. H. Osman

2007-01-01

365

An inverse dynamics based robot control method using fuzzy identifiers

Summary form only given. In the trajectory control of robotic manipulators, the main difficulty is that the dynamics involved is coupled and nonlinear. A method for obtaining a nonlinear model is presented. To match the gravity, centripetal, Coriolis and inertial effects in the robot dynamics model, fuzzy logic systems which are represented as 3-layer feedforward neural networks are used. One

K. Erbatur; O. Kaynak; I. Rudas

1997-01-01

366

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

367

A fuzzy controlled pneumatic gripper for asparagus harvesting

The paper presents a grasping unit for asparagus harvesting based on a commercially available pneumatic gripper. Highly reliable low-cost tactile sensors were developed to determine contact pressure during grasping. Experimental tests were conducted on all system components in order to achieve a thorough knowledge of the physical ptsenomena which determine system behaviour. A fuzzy control system was developed on the

G. Mattiazzo; S. Mauro; T. Raparelli; M. Velardocchia

1995-01-01

368

Fuzzy PI control design for an industrial weigh belt feeder

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensors, which makes standard autotuning methods difficult to implement. The paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder.

Yanan Zhao

2003-01-01

369

Expert neural network with fuzzy coding for process control

This paper introduces a new intelligent method using neural networks and fuzzy logic to control a hot coil strip process having a look-up table. An expert neural network with a gating network made by multilayer neural networks is used for model a rule based look-up table that has a quantization error caused by an offset of the input variables. Also,

Minho Lee; Hoon-Suk Byun; Cheol Hoon Park; Soo-Young Lee; Joodong Lee; Byunghwa Ham; Hyungseok Cho; Heebaek Ro

1997-01-01

370

Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm

NASA Astrophysics Data System (ADS)

In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.

Mittal, Ruchi; Kaur, Magandeep

2010-11-01

371

This paper presents an adaptive-network-based fuzzy inference system (ANFIS)-fuzzy data envelopment analysis (FDEA)) for long-term natural gas (NG) consumption forecasting and analysis. Six models are proposed to forecast annual NG demand. 104 ANFIS have been constructed and tested in order to find the best ANFIS for natural gas (NG) consumption. Two parameters have been considered in construction and examination of

A. Behrouznia; M. Saberi; A. Azadeh; S. M. Asadzadeh; P. Pazhoheshfar

2010-01-01

372

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

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

373

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

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

374

Comparative study of a learning fuzzy PID controller and a self-tuning controller.

The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller. PMID:11515942

Kazemian, H B

2001-01-01

375

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Berenji, Hamid R.

1992-01-01

376

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research. PMID:25608292

Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

2015-02-01

377

Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

NASA Astrophysics Data System (ADS)

Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.

Gering, Stefan; Adamy, Jürgen

2014-12-01

378

Adaptive Delay Compensation in Multi-Dithering Adaptive Control

demonstrating sub-microsecond response time in closed-loop adaptive control. I. INTRODUCTION SynchronousAdaptive Delay Compensation in Multi-Dithering Adaptive Control Dimitrios N. Loizos 1,2 , Paul P, a delay-insensitive architecture for gra- dient descent adaptive control, based on parallel synchronous

Cauwenberghs, Gert

379

Coordinated adaptive distribution volt\\/VAr controls

This paper presents the philosophy and field results of beta tests of an adaptive system for controlling distribution voltages and VAr flows. This system includes adaptive pole-top capacitor bank controls (ACCs), an adaptive transformer LTC voltage control (ATC) and an adaptive line regulator control (ARC). This adaptive system consists of these distributed intelligent devices, independent of outside communication, to accomplish

E. T. Jauch; M. V. V. S. Yalla; A. P. Craig

1999-01-01

380

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

381

Fuzzy Knowledge-Based Approach to Treating Uncertainty in Inventory Control

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

382

A fuzzy logic based spacecraft controller for six degree of freedom control and performance results

NASA Technical Reports Server (NTRS)

The development philosophy of the fuzzy logic controller is explained, details of the rules and membership functions used are given, and the early results of testing of the control system for a representative range of scenarios are reported. The fuzzy attitude controller was found capable of performing all rotational maneuvers, including rate hold and rate maneuvers. It handles all orbital perturbations very efficiently and is very responsive in correcting errors.

Lea, Robert N.; Hoblit, Jeffrey; Jani, Yashvant

1991-01-01

383

Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller

A fuzzy-controlled active state-of-charge controller (FC-ASCC) for improving the charging behavior of a lithium-ion (Li-ion) battery is proposed. The proposed FC-ASCC is designed to replace the general constant-voltage charging mode by two kinds of modes: sense and charge. A fuzzy-controlled algorithm is built with the predicted charger performance to program the charging trajectory faster and to keep the charge operation

Guan-Chyun Hsieh; Liang-Rui Chen; Kuo-Shun Huang

2001-01-01

384

How to control if even experts are not sure: Robust fuzzy control

NASA Technical Reports Server (NTRS)

In real life, the degrees of certainty that correspond to one of the same expert can differ drastically, and fuzzy control algorithms translate these different degrees of uncertainty into different control strategies. In such situations, it is reasonable to choose a fuzzy control methodology that is the least vulnerable to this kind of uncertainty. It is shown that this 'robustness' demand leads to min and max for &- and V-operations, to 1-x for negation, and to centroid as a defuzzification procedure.

Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert; Tolbert, Dana

1992-01-01

385

Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input\\/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived.

Wei Li

1998-01-01

386

Intelligent digitally re-designed PAM fuzzy controller for nonlinear systems

We develop an intelligent digitally re-designed PAM fuzzy controller for nonlinear systems. Takagi-Sugeno (TS) fuzzy model is used to model the nonlinear systems and a continuous-time fuzzy-model-based controller is designed based on an extended parallel-distributed-compensation method. The digital controllers are determined from existing analogue controllers. The proposed method provides an accurate and effective method for digital control of continuous-time nonlinear

Ho Jae Lee; Young Hoon Joo; Jin Bae Park; Leang-San Shieh

1999-01-01

387

Neuro-fuzzy systems for intelligent robot navigation and control under uncertainty

This paper describes neuro-fuzzy systems for intelligent robot navigation and control under uncertainty. First, we present a new neuro-fuzzy system architecture for behavior navigation of a mobile robot in unknown environments. In this neuro-fuzzy system, a neural network is used to process range information for understanding distribution of obstacles in local regions; while fuzzy sets and a rule base are

Wei Li

1995-01-01

388

Semiactive car suspension controller design using fuzzy-logic technique

In this work, the development of fuzzy-logic controller for a semi-active car suspension system using (1\\/4) quarter car model is presented. The main aim of the controller is to minimize the body's displacement velocity. A passive suspension system responds only to the deflection of the strut. While the semi-active system setup can dissipate energy from the system at an appropriate

M. M. Rashid; M. A. HussainZ; N. Abd. Rahim

2002-01-01

389

A fuzzy logic based approach to direct load control

Demand side management programs are strategies designed to alter the shape of the load curve. In order to successfully implement such a strategy, customer acceptance of the program is vital. It is thus desirable to design a model for direct load control which may accommodate customer preferences. This paper presents a methodology for optimizing both customer satisfaction and utility unit commitment savings, based on a fuzzy load model for the direct load control of appliances.

Bhattacharyya, K.; Crow, M.L. [Univ. of Missouri, Rolla, MO (United States). Dept. of Electrical Engineering] [Univ. of Missouri, Rolla, MO (United States). Dept. of Electrical Engineering

1996-05-01

390

This paper presents an integrated environment for the rapid prototyping of a robust fuzzy proportional-integral-derivative (PID) controller that allows rapid realization of novel designs. Both the design of the fuzzy PID controller and its integration with the classical PID in a global control system are developed. The architecture of the fuzzy PID controller is basically composed of three parallel fuzzy

Ahmed Rubaai; Marcel J. Castro-Sitiriche; Abdul R. Ofoli

2008-01-01

391

This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained. PMID:25216493

Yu, Jinpeng; Shi, Peng; Yu, Haisheng; Chen, Bing; Lin, Chong

2014-09-01

392

NASA Astrophysics Data System (ADS)

Intelligent control algorithms are introduced into the control system of temperature and humidity. A multi-mode control algorithm of PI-Single Neuron is proposed for single loop control of temperature and humidity. In order to remove the coupling between temperature and humidity, a new decoupling method is presented, which is called fuzzy decoupling. The decoupling is achieved by using a fuzzy controller that dynamically modifies the static decoupling coefficient. Taking the control algorithm of PI-Single Neuron as the single loop control of temperature and humidity, the paper provides the simulated output response curves with no decoupling control, static decoupling control and fuzzy decoupling control. Those control algorithms are easily implemented in singlechip-based hardware systems.

Zhang, Xianxia; Wang, Jian; Qin, Tinggao

2003-09-01

393

Adaptive control in adaptive optics for directed-energy systems

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

394

At present fuzzy logic control is receiving increasing emphasis in process control applications. The paper describes the application of fuzzy logic control in a power system that uses a 12-pulse bridge converter associated with superconductive magnetic energy storage (SMES) unit. The fuzzy control is used in both the frequency and voltage control loops, replacing the conventional control method. The control

M. G. Rabbani; J. B. X. Devotta; S. Elangovan

1997-01-01

395

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

396

Fuzzy controller training using particle swarm optimization for nonlinear system control.

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance. PMID:17976603

Karakuzu, Cihan

2008-04-01

397

The PLC System of Egg Powder Treatment Based on Fuzzy Control Algorithm

In this paper, we take the electric control system of egg powder treatment as an example. By means of fuzzy control technology and transducer technology, the system overcomes the instability of the system, the difficulty in parameter tuning and the problem of grading speed regulation in the traditional control field. Fuzzy controller based on PLC (programmable logic controller) direct lookup

Yanmin Song; Zhongwei Bi; Kun Liu

2007-01-01

398

Fuzzy logic for control of roll and moment for a flexible wing aircraft

A fuzzy-logic based multi-input\\/multi-output roll controller designed for the Advanced Technology Wing (ATW) aircraft model is presented. The ATW integrates active controls with a flexible wing structure to provide optimal wing shapes to meet particular flight performance criteria. The use of a fuzzy controller for roll rate and load alleviation control was investigated. Fuzzy rules were developed to determine the

Stephen Chiu; Sujeet Chand; Doug Moore; Ashwani Chaudhary

1991-01-01

399

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

400

This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811

Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C

2013-03-01

401

On-line fuzzy logic control of tube bending

NASA Astrophysics Data System (ADS)

This paper describes the simulation and on-line fuzzy logic control of tube bending. By combining elasticity and plasticity theories, a conventional model was developed. The results from simulation were compared with those obtained from testing. The experimental data reveal that there exists certain level of uncertainty and nonlinearity in tube bending, and its variation could be significant. To overcome this, a on-line fuzzy logic controller with self-tuning capabilities was designed. The advantages of this on-line system are (1) its computational requirement is simple in comparison with more algorithmic-based controllers, and (2) the system does not need prior knowledge of material characteristics. The device includes an AC motor, a servo controller, a forming mechanism, a 3D optical sensor, and a microprocessor. This automated bending machine adopts primary and secondary errors between the actual response and desired output to conduct on-line rule reasoning. Results from testing show that the spring back angle can be effectively compensated by the self- tuning fuzzy system in a real-time fashion.

Lieh, Junghsen; Li, Wei Jie

2005-11-01

402

A NOISE ADAPTIVE FUZZY EQUALIZATION METHOD FOR PROCESSING SOLAR EXTREME ULTRAVIOLET IMAGES

A new image enhancement tool ideally suited for the visualization of fine structures in extreme ultraviolet images of the corona is presented in this paper. The Noise Adaptive Fuzzy Equalization method is particularly suited for the exceptionally high dynamic range images from the Atmospheric Imaging Assembly instrument on the Solar Dynamics Observatory. This method produces artifact-free images and gives significantly better results than methods based on convolution or Fourier transform which are often used for that purpose.

Druckmueller, M., E-mail: druckmuller@fme.vutbr.cz [Institute of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology, Technicka 2, 616 69 Brno (Czech Republic)

2013-08-15

403

Dynamic Neuro-modelling Using Bacterial Foraging Optimisation with Fuzzy Adaptation

This paper presents current work on fuzzy adaptation of chemotactic step size of bacterial foraging algorithm and its application to optimisation of parameters of a neural network, i.e. weights, biases and slope parameters of activation function, in modelling of a single-link flexible manipulator. Experimental input-output data pairs gathered from a laboratory-scale single-link flexible manipulator rig are used both in the

H. Supriyono; M. O. Tokhi

2012-01-01

404

Quadcopter see and avoid using a fuzzy controller

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

405

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

Nguyen, Nhan T.

2009-01-01

406

Reducing travel energy costs for a subway train via fuzzy logic controls

Train controls at the Montreal Subway are revisited. A simulation environment is developed and used to analyze the desirability of replacing present (PID based) controls by fuzzy logic controllers which consume less energy. Two fuzzy logic controllers are proposed: the first one turned out to produce practically no improvement, but the second one achieved an average of 6% energy savings

M. Khanbaghi; R. P. Malhame

1994-01-01

407

Study on missile rudder servo system based on Mixed Fuzzy-PID control algorithm

Considering about the characteristics of high speed, short response time and complex external environment of winged missile, a control method, which combined with Mixed Fuzzy- PID control algorithm and traditional PID algorithm, for digital missile rudder servo system is proposed. The control strategy includes two closed loops, position loop and speed loop. Mixed Fuzzy-PID control algorithm is used in the

Yingzhe Wu; Hui Li; Ye Bi; Bo Guo

2011-01-01

408

A composite self tuning strategy for fuzzy control of dynamic systems

NASA Technical Reports Server (NTRS)

The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

Shieh, C.-Y.; Nair, Satish S.

1992-01-01

409

This paper presents a comparative evaluation of the proportional-integral, sliding mode and fuzzy logic controllers for applications to power converters. The mismatch between the characteristics which lead to varying performance is outlined. This paper also demonstrates certain similarities of both the fuzzy logic controller and sliding mode controller. Sensitivity of these controllers to supply voltage disturbances and load disturbances is

V. S. C. Raviraj; P. C. Sen

1997-01-01

410

A new self-tuned PID-type fuzzy controller as a combination of two-term controllers.

The present paper is a venture into the domain of proportional-integral-derivative (PID) -type adaptive fuzzy logic controllers (FLC's) and proposes a new algorithm which is realized by a self-tuned PI-type FLC (in velocity form) in parallel with a self-tuned PD-type FLC (in position form). Each of these PI/PD controllers implements a supervisory static FLC for adaptive online modification of the output scaling factor (SF) of a static PI/PD FLC. The proposed scheme is developed with a view to having a PID-type FLC with an architecture, simple enough for practical implementation, which at the same time has substantially satisfactory performance for a wide class of processes. Simulation studies on a range of processes reveal that the proposed controller has better performance compared to many of its existing counterparts. PMID:15272796

Bhattacharya, S; Chatterjee, A; Munshi, S

2004-07-01

411

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

412

Research on the fuzzy predictive control for calcining temperature of the rotary cement kiln

According to the analysis of the characteristics of time-varying and nonlinear, long delays in industry processes, a fuzzy predictive controller based on the T-S fuzzy model predictive control algorithm is designed for calcining temperature of the rotary cement kiln in this paper. First, a T-S fuzzy model for temperature control system is constructed. The input variables are divided according to

Guo Feng; Liu Bin; Hao Xiaochen; Gao Peng

2010-01-01

413

Simplified fuzzy-logic-based MTPA speed control of IPMSM drive

This paper presents a simplified fuzzy-logic-based speed controller of an interior permanent-magnet synchronous motor (IPMSM) drive for maximum torque per ampere (MTPA) of stator current with inherent nonlinearities of the motor. The fundamentals of fuzzy logic algorithms as related to motor control applications are illustrated. A simplified fuzzy speed controller for the IPMSM drive has been found to maintain high

Casey B. Butt; M. Ashraful Hoque; M. Azizur Rahman

2004-01-01

414

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

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

415

New methodology for analytical and optimal design of fuzzy PID controllers

Describes a methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and, genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning

Baogang Hu; George K. I. Mann; Raymond G. Gosine

1999-01-01

416

Fuzzy control for a nonlinear mimo-liquid level problem

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

417

Adaptive control with aerospace applications

NASA Astrophysics Data System (ADS)

Robust and adaptive control techniques have a rich history of theoretical development with successful application. Despite the accomplishments made, attempts to combine the best elements of each approach into robust adaptive systems has proven challenging, particularly in the area of application to real world aerospace systems. In this research, we investigate design methods for general classes of systems that may be applied to representative aerospace dynamics. By combining robust baseline control design with augmentation designs, our work aims to leverage the advantages of each approach. This research contributes the development of robust model-based control design for two classes of dynamics: 2nd order cascaded systems, and a more general MIMO framework. We present a theoretically justified method for state limiting via augmentation of a robust baseline control design. Through the development of adaptive augmentation designs, we are able to retain system performance in the presence of uncertainties. We include an extension that combines robust baseline design with both state limiting and adaptive augmentations. In addition we develop an adaptive augmentation design approach for a class of dynamic input uncertainties. We present formal stability proofs and analyses for all proposed designs in the research. Throughout the work, we present real world aerospace applications using relevant flight dynamics and flight test results. We derive robust baseline control designs with application to both piloted and unpiloted aerospace system. Using our developed methods, we add a flight envelope protecting state limiting augmentation for piloted aircraft applications and demonstrate the efficacy of our approach via both simulation and flight test. We illustrate our adaptive augmentation designs via application to relevant fixed-wing aircraft dynamics. Both a piloted example combining the state limiting and adaptive augmentation approaches, and an unpiloted example with adaptive augmentation show the ability of our approach to retain desired performance in the presence of relevant system uncertainty. Finally, we present alternative adaptive augmentation design developed to mitigate time delays at the system input and which demonstrates significant improvement over an existing widely used adaptive augmentation approach when applied to fixed wing aircraft dynamics.

Gadient, Ross

418

A hybrid fuzzy logic and PID controller for control of nonlinear HVAC systems

Heating, Ventilating and Air Conditioning (HVAC) plant is a multivariable, nonlinear and non minimum phase system, which control of this plant, is very difficult. This paper presents a new approach to control of HVAC system. The proposed method is a hybrid of fuzzy logic and PID controller. Simulation results show that this control strategy is very robust, flexible and alternative

Abdolreza Rahmati; Farzan Rashidi; Mehran Rashidi

2003-01-01

419

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots. PMID:25398185

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2014-11-12

420

Voice-controlled modular fuzzy neural controller with enhanced user autonomy

In this article, a fuzzy neural network (FNN)-based approach is presented to interpret imprecise natural language (NL) commands\\u000a for controlling a machine. This system, (1) interprets fuzzy linguistic information in NL commands for machines, (2) introduces\\u000a a methodology to implement the contextual meaning of NL commands, and (3) recognizes machine-sensitive words from the running\\u000a utterances which consist of both in-vocabulary

Koliya Pulasinghe; Keigo Watanabe; K. Kiguchi; K. Izumi

2003-01-01

421

Fuzzy Logic Controller for Low Temperature Application

NASA Technical Reports Server (NTRS)

The most common temperature controller used in low temperature experiments is the proportional-integral-derivative (PID) controller due to its simplicity and robustness. However, the performance of temperature regulation using the PID controller depends on initial parameter setup, which often requires operator's expert knowledge on the system. In this paper, we present a computer-assisted temperature controller based on the well known.

Hahn, Inseob; Gonzalez, A.; Barmatz, M.

1996-01-01

422

Fuzzy neuron model-free control for continuous steel casting processes

Based on a neuron model and a neuron control method, the fuzzy neuron model-free control method for the mold level control of continuous steel casting processes is proposed in this paper. The fuzzy algorithm is used to tune the neuron gain, and the nonlinear transfer is adopted to produce the neuron inputs. The simulation experiments with an example are made.

Wang Ning

2000-01-01

423

Robust, Non-Fragile Controller Synthesis Using Model-Based Fuzzy Systems

Robust, Non-Fragile Controller Synthesis Using Model-Based Fuzzy Systems: A Linear Matrix, for their support and motivation. v #12;Robust, Non-Fragile Controller Synthesis Using Model-Based Fuzzy Systems Albuquerque, New Mexico November, 1997 #12;vii #12;Robust, Non-Fragile Controller Synthesis Using Model

Jadbabaie, Ali

424

An approach for stability analysis of polynomial fuzzy model-based control systems

Stability analysis of polynomial fuzzy model-based (PFMB) control systems under the parallel distributed compen- sation (PDC) design technique is investigated. A new polynomial fuzzy controller (PFC) is introduced to release conservativeness in the existing approaches. Compared to the conventional (PFC), the controller under consideration in this paper has a favorable property which introduces some more variables in the stability conditions

Mohammand Narimani; H. K. Lam; K. Althoefer; R. Dilmaghani; Charles Wolfe; C. Deters

2011-01-01

425

An Optimal Fuzzy Self-Tuning PID Controller for Robot Manipulators via Genetic Algorithm

This paper deals with the problem of optimizing a fuzzy self-tuning PID controller for robot manipulators. Fuzzy PID controllers have been developed and applied in many fields in the last fifteen years. However, there is no systematic method to design Membership Functions (MFs) for these controllers. We propose a simple method based on Genetic Algorithms (GA) to find optimal input

J. L. Meza; R. Soto; J. Arriaga

2009-01-01

426

A robust self-tuning scheme for PI and PD-type fuzzy controllers

Proposes a simple but robust model independent self-tuning scheme for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF) is adjusted online by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error (e) and change of error (?e) of the controlled variable using the most natural

Rajani K. Mudi; Nikhil R. Pal

1999-01-01

427

FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY

The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...

428

Feedback error learning and nonlinear adaptive control

In this paper, we present our theoretical investigations of the technique of feedback error learning (FEL) from the viewpoint of adaptive control. We first discuss the relationship between FEL and nonlinear adaptive control with adaptive feedback linearization, and show that FEL can be interpreted as a form of nonlinear adaptive control. Second, we present a Lyapunov analysis suggesting that the

Jun Nakanishi; Stefan Schaal

429

Feedback error learning and nonlinear adaptive control

Abstract In this paper, we present our theoretical investigations of the technique of feedback error learning (FEL) from the viewpoint of adaptive control. We first discuss the relationship between FEL and nonlinear adaptive control with adaptive feedback linearization, and show that FEL can be interpreted as a form of nonlinear adaptive control. Second, we present a Lyapunov analysis suggesting that

Jun Nakanishi; Stefan Schaal

2004-01-01

430

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

431

Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

NASA Astrophysics Data System (ADS)

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

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

2014-07-01

432

Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system

NASA Astrophysics Data System (ADS)

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to predict MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using MATLAB/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Petkovi?, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlovi?, Nenad T.; Akib, Shatirah

2014-07-01

433

PC based speed control of dc motor using fuzzy logic controller

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

434

Fuzzy Linear Parameter Varying Modeling and Control of an Anti-Air Missile

An analytical framework for fuzzy modeling and control of nonlinear systems using a set of linear models is presented. Fuzzy clustering is applied on the aerodynamic coefficients of a missile to obtain an optimal number of rules in a Tagaki-Sugeno fuzzy rule-set. Next, the obtained membership functions and rule-sets are applied to a set of linear optimal controllers towards extraction

Ali Reza Mehrabian; Seyed Vahid Hashemi

2007-01-01

435

Fuzzy chaos control for vehicle lateral dynamics based on active suspension system

NASA Astrophysics Data System (ADS)

The existing research of the active suspension system (ASS) mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.

Huang, Chen; Chen, Long; Jiang, Haobin; Yuan, Chaochun; Xia, Tian

2014-07-01

436

Fuzzy control of bioprocess in Japan

Process control of bioprocess has been carried out by the judgment of the experts, who are the skilled operators and have lots of experiences for the control of the process. In almost all cases, those experiences are described linguistic IF-THEN rules. Fussy inference is one of the powerful tools to incorporate the linguistic rules to the computer for process control.

Hiroyuki Honda; Takeshi Kobayashi

2000-01-01

437

Design, modelling, implementation, and intelligent fuzzy control of a hovercraft

NASA Astrophysics Data System (ADS)

A Hovercraft is an amphibious vehicle that hovers just above the ground or water by air cushion. The concept of air cushion vehicle can be traced back to 1719. However, the practical form of hovercraft nowadays is traced back to 1955. The objective of the paper is to design, simulate and implement an autonomous model of a small hovercraft equipped with a mine detector that can travel over any terrains. A real time layered fuzzy navigator for a hovercraft in a dynamic environment is proposed. The system consists of a Takagi-Sugenotype fuzzy motion planner and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including the right and left views from which he makes his next step towards the goal in the free space. It intelligently combines two behaviours to cope with obstacle avoidance as well as approaching a goal using a proportional navigation path accounting for hovercraft kinematics. MATLAB/Simulink software tool is used to design and verify the proposed algorithm.

El-khatib, M. M.; Hussein, W. M.

2011-05-01

438

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

This paper studies reliable linear quadratic (LQ) fuzzy regulator problem for nonlinear discrete-time systems with actuator faults. The Takagi and 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 can guarantee the stability of the closed-loop overall fuzzy system, while provide a guaranteed cost on the quadratic cost function of the system in the normal and actuator fault cases. Furthermore, an optimal reliable GC fuzzy controller in the sense of minimizing a bound on the worst or nominal case guaranteed cost is also given by means of an LMI optimization procedure. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method. PMID:15376870

Wu, Huai-Ning

2004-04-01

439

Supervisory control of an energy management control system via fuzzy logic

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 to compute the control action via an iterative approach. This framework has been shown to be effective in the operation of an energy

Raghu Belur; Reza Langari

1993-01-01

440

, sewage treatment plants may need to be redesigned or extended. Instead of reconstructing large parts inexpensive equipment, which controls parts of the plant in a new way. Fuzzy controllers are often used controls parts of the plant in a new way and thereby leads to an improved water quality. Until

Ebner, Marc

441

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

442

Adaptive optics: Wavefront reconstruction by adaptive filtering and control

This dissertation concerns a class of adaptive optics problems in which phase distortions due to atmospheric turbulence are corrected by adaptive wavefront reconstruction with a deformable mirror-i.e., the control loop that drives the mirror adapts in real time to time-varying atmospheric conditions, rather than the linear time-invariant control loops used in conventional ``adaptive optics''. The basic problem is posed here

Chi-Chao Chang

2000-01-01

443

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

444

This article introduces an adaptive controller for a class of nonlinear discrete-time systems, based on self adjustable networks called Multi-Input Fuzzy Rules Emulated Networks (MIFRENs), and its reinforcement learning algorithm. Because of the universal function approximation of MIFREN, the first MIFREN called MIFREN(c) is used to estimate a long-term cost function, which demonstrates as a performance index for the tuning procedure. Another network or MIFREN(a) is designed as a direct controller via the human knowledge through defined If-Then rules. The selection procedure for any system parameters, such as learning rates and some constant parameters, is represented by the proof of proposed theorems. The system's performance is demonstrated by computer simulations via selected nonlinear discrete-time systems, and comparison results with other controllers to validate theoretical development. PMID:18675416

Treesatayapun, Chidentree

2008-10-01

445

This article presents a fuzzy control implemented in a movil platform to pursuit the complicated trajectories with the purpose to make all the route without losing the trajectory. The fuzzy control system determines the movement of two actuators from the reading of three sensors on line. The tests are made and it is compared with a classic control for trajectories

ALFONSO ALZATE GÓMEZ; ANDRÉS LÓPEZ LÓPEZ; CARLOS RESTREPO PATIÑO

2007-01-01

446

Design and implementation of a fuzzy logic yaw controller

NASA Astrophysics Data System (ADS)

This paper describes a fuzzy logic controller (FLC) designed and implemented to control the yaw angle of a 10 kW fixed speed teetered-rotor wind turbine presently being commissioned at the University of Texas at El Paso. The technical challenge of this project is that the wind turbine represents a highly stochastic nonlinear system. The problems associated with the wind turbine yaw control are of a similar nature as those experienced with position control of high inertia equipment like tracking antenna, gun turrets, and overhead cranes. Furthermore, the wind turbine yaw controller must be extremely cost-effective and highly reliable in order to be economically viable compared to the fossil fueled power generators.

Wu, Kung C.; Swift, Andrew H.; Craver, W. Lionel, Jr.; Chang, Yi-Chieh

1993-08-01

447

NASA Astrophysics Data System (ADS)

This paper proposes a method for designing high performance fuzzy controllers with a compact rule system. The method is mainly derived from flexible parameterized membership functions (FPMFs) and an intelligent genetic algorithm (IGA) which is superior to the traditional GAs in solving large parameter optimization problems. An FPMF consists of flexible trapezoidal fuzzy sets that the fuzzy set is encoded using five parameters. Furthermore, the membership functions and fuzzy rules are simultaneously determined by effectively encoding all the system parameters into chromosomes. Therefore, the optimal design of fuzzy controllers is formulated as a large parameter optimization problem, which can be effectively solved by IGA. The proposed method is demonstrated by two well-known problems, truck backing and cart centering problems. It is shown empirically that the performance of the proposed method is superior to those of existing methods in terms of the numbers of time steps and fuzzy rules.

Ho, Shinn-Ying; Ho, Shinn-Jang; Chen, Tai-Kang

448

Energy management system based on fuzzy control approach for hybrid electric vehicle

In order to realize optimal distribution between two types of energy in hybrid electric vehicle (HEV) and assure the reasonable operation of motor and battery, an optimal method based on energy fuzzy control strategy is presented. The proposed energy fuzzy control strategy is modeled in SIMULINK and incorporated into the vehicle simulation software ADVISOR. Then, to the lack of the

Wang Yifeng; Zhang Yun; Wu Jian; Chen Ning

2009-01-01

449

A Study on Fuzzy Control of Energy Management System in Hybrid Electric Vehicle

In order to realize optimal distribution between two types of energy in HEV and assure the reasonable operation of motor and battery, an optimal method based on energy fuzzy control strategy is presented. The proposed energy fuzzy control strategy is modeled in Simulink and incorporated into the vehicle simulation software ADVISOR. Then, to the lack of the traditional method of

Zhang Danhong; Zhou Yan; Liu Kai-Pei; Chen Qing-Quan

2009-01-01

450

Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle

In a parallel-type hybrid electric vehicle (HEV), torque assisting and battery recharging control using the electric machine is the key point for efficient driving. In this paper, by adopting the decision-making property of fuzzy logic, the driving map for an HEV is made according to driving conditions. In this fuzzy logic controller, the induction machine torque command is generated from

Hyeoun-Dong Lee; Seung-Ki Sul

1998-01-01

451

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

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

452

A fuzzy neural dynamics based tracking controller for a nonholonomic mobile robot

In this paper, a fuzzy neural dynamics based tracking controller for nonholonomic wheeled mobile robots is proposed. The nonholonomic kinematic constraints are considered in the development of the controller. The proposed model is suitable for both continuous and discrete paths. Fuzzy rules are formulated to deal with the discontinuity in path directions. This model is capable of generating smooth velocity

Yanrong Hu; S. X. Yang

2003-01-01

453

Multivariable fuzzy supervisory control for the laminar cooling process of hot rolled slab

A multivariable fuzzy system is presented for higher level supervision of industrial process. With the help of a statistical process controller, this fuzzy supervisory control system can replace the human operator in the laminar cooling process. Since a feedforward compensator is designed to suppress the external disturbance, the proposed system can automatically find the proper operating points for the cooling

Shouping Guan; Han-Xiong Li; S. K. Tso

2001-01-01

454

Application of fuzzy logic control to the design of semi-active suspension systems

This paper describes the practical application of fuzzy logic to the design of semi-active suspension systems control strategies. The intelligent suspension systems are based on a continuously adjustable shock absorber and an electronic control unit. Two different fuzzy approaches are described: the first strategy is based on the actions of the driver and the second is based on the vehicle

C. F. Nicolas; J. Landaluze; E. Castrillo; M. Gaston; R. Reyero

1997-01-01

455

The implementation of a FPGA based fuzzy controller for DC\\/DC converters is described in this paper. The fuzzy control developed is evaluated with experimental measurements of the closed loop performance of a buck DC\\/DC converter in respect of the line and load regulation

Rafael Ramos; Xavier Roset; A. Manuel

2000-01-01

456

Adaptive Controller Effects on Pilot Behavior

NASA Technical Reports Server (NTRS)

Adaptive control provides robustness and resilience for highly uncertain, and potentially unpredictable, flight dynamics characteristic. Some of the recent flight experiences of pilot-in-the-loop with an adaptive controller have exhibited unpredicted interactions. In retrospect, this is not surprising once it is realized that there are now two adaptive controllers interacting, the software adaptive control system and the pilot. An experiment was conducted to categorize these interactions on the pilot with an adaptive controller during control surface failures. One of the objectives of this experiment was to determine how the adaptation time of the controller affects pilots. The pitch and roll errors, and stick input increased for increasing adaptation time and during the segment when the adaptive controller was adapting. Not surprisingly, altitude, cross track and angle deviations, and vertical velocity also increase during the failure and then slowly return to pre-failure levels. Subjects may change their behavior even as an adaptive controller is adapting with additional stick inputs. Therefore, the adaptive controller should adapt as fast as possible to minimize flight track errors. This will minimize undesirable interactions between the pilot and the adaptive controller and maintain maneuvering precision.

Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.

2014-01-01

457

A new fuzzy controller is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model for upswing and stabilization control of inverted pendulum system. The fuzzy controller takes the angle and angular velocity of the pendulum and the position and velocity of the cart as its input items, and the driving force as its output item.

Jianqiang Yi; Naoyoshi Yubazaki; Kaoru Hirota

2001-01-01

458

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 48, NO. 4, AUGUST 2001 757 An Optimal Fuzzy PIDÂintegralÂderivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID

Coello, Carlos A. Coello

459

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

460

A neuro-fuzzy controller for axial power distribution an nuclear reactors

A neuro-fuzzy control algorithm is applied for the core power distribution in a pressurized water reactor. The inputs of the neural fuzzy system are composed of data from each region of the reactor core. Rule outputs consist of linear combinations of their inputs (first-order Sugeno-Takagi type). The consequent and antecedent parameters of the fuzzy rules are updated by the backpropagation

Man Gyun Na; B. R. Upadhyaya

1998-01-01

461

Introduction to Fuzzy Control Marcelo Godoy Simoes

human beings perform control tasks, recognize patterns, or make decisions. There exists a mismatch in 1965, emerged as a tool to deal with uncertain, imprecise, or qualitative decision-making problems of artificial intelligence techniques have been used to convert human experience into a form understandable

SimÃµes, Marcelo Godoy

462

An active passive absorber by using hierarchical fuzzy methodology for vibration control

NASA Astrophysics Data System (ADS)

It has been shown that piezoelectric materials are highly promising as passive electromechanical vibration absorbers when shunted with electrical networks. However, these passive devices have limitations that restrict their practical applications. The main goal of this study is to develop an innovative approach for achieving a high performance adaptive piezoelectric absorber—an active-passive hybrid configuration. This investigation addresses the first application of the concept of hierarchy for controlling fuzzy systems in such an active-passive absorber. It attempts to demonstrate the general methodology by decomposing a large-scale system into smaller subsystems in a parallel structure so that the method developed here can be applied for studying complex systems. The design of the lower-level controllers takes into account each subsystem ignoring the interactions among them, while a higher-level controller handles subsystem interactions. One of the main advantages of using a hierarchical fuzzy system is to minimize the size of the rule base by eliminating "the curse of dimensionality". Therefore, the computational complexity in the process can be reduced as a consequence of the rule-base size reduction. Although the performance of the optimal passive absorber is already much better than the original system (no absorber), the intelligent active-passive absorber can still significantly outperform the passive system.

Lin, J.

2007-07-01

463

Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support.

Xia, Feng; Zhao, Wenhong; Sun, Youxian; Tian, Yu-Chu

2007-01-01

464

The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of Brain-Computer Interface (BCI). We propose here to use an Adaptive Neuron-Fuzzy Inference System (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. The continuous wavelet transform will be used to extract highly representative features from selected scales. The performance of ANFIS will be compared with the well-known support vector machine classifier. PMID:18002681

Darvishi, Sam; Al-Ani, Ahmed

2007-01-01

465

A fuzzy controlled three-phase centrifuge for waste separation

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

466

Adaptive control of Unmanned Aerial Systems

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

467

Adaptive control of artificial pancreas systems - a review.

Artificial pancreas (AP) systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes. PMID:24691384

Turksoy, Kamuran; Cinar, Ali

2014-01-01

468

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

Shehadeh, Hana; Lea, Robert N.

1992-01-01

469

Fuzzy bang-bang relay controller for satellite attitude control system

Two level bang-bang controllers are generally used in conjunction with the thrust reaction actuator for spacecraft\\/satellite attitude control. These controllers are fast acting and dispense time dependent; full or no thrust-power to control the satellite attitude in minimum time. A minimum time-fuel attitude control system extends the life of a satellite and is the main focus of this paper. Fuzzy

Farrukh Nagi; S. K. Ahmed; A. A. Zainul Abidin; F. H. Nordin

2010-01-01

470

\\u000a This paper deals with the direct torque control of PMBLDC motor using hybrid (Genetic algorithm based fuzzy logic) controller\\u000a to improve the performance of the control scheme. Though the conventional controllers are commonly used in practice, they\\u000a have failed to perform satisfactorily under non linear conditions and parameter variations. In the proposed work, a hybrid\\u000a controller (using genetic algorithm based

E. Kaliappan; C. Sharmeela; A. V. Sayee Krishna

471

Adaptive controller for hyperthermia robot

This paper describes the development of an adaptive computer control routine for a robotically, deployed focused, ultrasonic hyperthermia cancer treatment system. The control algorithm developed herein uses physiological models of a tumor and the surrounding healthy tissue regions and transient temperature data to estimate the treatment region`s blood perfusion. This estimate is used to vary the specific power profile of a scanned, focused ultrasonic transducer to achieve a temperature distribution as close as possible to an optimal temperature distribution. The controller is evaluated using simulations of diseased tissue and using limited experiments on a scanned, focused ultrasonic treatment system that employs a 5-Degree-of-Freedom (D.O.F.) robot to scan the treatment transducers over a simulated patient. Results of the simulations and experiments indicate that the adaptive control routine improves the temperature distribution over standard classical control algorithms if good (although not exact) knowledge of the treated region is available. Although developed with a scanned, focused ultrasonic robotic treatment system in mind, the control algorithm is applicable to any system with the capability to vary specific power as a function of volume and having an unknown distributed energy sink proportional to temperature elevation (e.g., other robotically deployed hyperthermia treatment methods using different heating modalities).

Kress, R.L.

1997-03-01

472

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

473

NASA Astrophysics Data System (ADS)

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

474

The main goal of this study was to study the performance of fuzzy logic controllers combined with simplified hybrid amplitude/pulse-width (AM/PW) modulation to regulate muscle force via nerve electrical stimulation. The recruitment curves with AM/PW and AM modulations were constructed for the calf muscles of rabbits. Integrated with the modulation methods, a proportional-integral-derivative (PID) and three fuzzy logic controllers were designed and applied for the electrical stimulation of tibial nerves to control the ankle torque under isometric conditions. The performance of the two modulation methods combined with the four controllers was compared when the ankle was fixed at three positions for both in vivo experiments and model simulations using a nonlinear muscle model. For the animal experiments, AM/PW modulation performed better than AM modulation alone. The fuzzy PI controller performed marginally better and was resistant to external noises, though it tended to have a larger overshoot. The performance of the controllers had a similar trend in the three different joint positions, and the simulation results with the nonlinear model matched the experimental results well. In conclusion, AM/PW modulation improved controller performance, while the contribution of fuzzy logic was only marginal. PMID:22422279

Lin, C-C K; Liu, W-C; Chan, C-C; Ju, M-S

2012-04-01

475

NASA Astrophysics Data System (ADS)

The main goal of this study was to study the performance of fuzzy logic controllers combined with simplified hybrid amplitude/pulse-width (AM/PW) modulation to regulate muscle force via nerve electrical stimulation. The recruitment curves with AM/PW and AM modulations were constructed for the calf muscles of rabbits. Integrated with the modulation methods, a proportional-integral-derivative (PID) and three fuzzy logic controllers were designed and applied for the electrical stimulation of tibial nerves to control the ankle torque under isometric conditions. The performance of the two modulation methods combined with the four controllers was compared when the ankle was fixed at three positions for both in vivo experiments and model simulations using a nonlinear muscle model. For the animal experiments, AM/PW modulation performed better than AM modulation alone. The fuzzy PI controller performed marginally better and was resistant to external noises, though it tended to have a larger overshoot. The performance of the controllers had a similar trend in the three different joint positions, and the simulation results with the nonlinear model matched the experimental results well. In conclusion, AM/PW modulation improved controller performance, while the contribution of fuzzy logic was only marginal.

Lin, C.-C. K.; Liu, W.-C.; Chan, C.-C.; Ju, M.-S.

2012-04-01

476

Application of fuzzy GA for optimal vibration control of smart cylindrical shells

NASA Astrophysics Data System (ADS)

In this paper, a fuzzy-controlled genetic-based optimization technique for optimal vibration control of cylindrical shell structures incorporating piezoelectric sensor/actuators (S/As) is proposed. The geometric design variables of the piezoelectric patches, including the placement and sizing of the piezoelectric S/As, are processed using fuzzy set theory. The criterion based on the maximization of energy dissipation is adopted for the geometric optimization. A fuzzy-rule-based system (FRBS) representing expert knowledge and experience is incorporated in a modified genetic algorithm (GA) to control its search process. A fuzzy logic integrated GA is then developed and implemented. The results of three numerical examples, which include a simply supported plate, a simply supported cylindrical shell, and a clamped simply supported plate, provide some meaningful and heuristic conclusions for practical design. The results also show that the proposed fuzzy-controlled GA approach is more effective and efficient than the pure GA method.

Jin, Zhan