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1

Indirect adaptive fuzzy control  

Microsoft Academic Search

Fuzzy controllers may be either static systems, which have a fixed rule base, or adaptive systems, which have the ability to alter their rules. A discussion of adaptive fuzzy controllers and a comparison with corresponding algebraic techniques concludes that all previous adaptive fuzzy controllers have been of the direct adaptive type. Such controllers use observations of closed loop performance to

C. G. MOORE; C. J. HARRIS

1992-01-01

2

Adaptive hierarchical fuzzy controller  

SciTech Connect

A methodology for designing adaptive hierarchical fuzzy controllers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzy controller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach

Raju, G.V.S.; Jun Zhou [Univ. of Texas, San Antonio, TX (United States). Div. of Engineering] [Univ. of Texas, San Antonio, TX (United States). Div. of Engineering

1993-07-01

3

Stable adaptive fuzzy control of nonlinear systems  

Microsoft Academic Search

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

Li-Xin Wang

1993-01-01

4

Adaptive Sugeno Fuzzy Control: A Case Study  

Microsoft Academic Search

This paper proposes an inverse fuzzy-model-based controller. The gradient-descentalgorithm can be used on-line to form adaptive fuzzy controllers. This ability allowsthe controller to be used in applications where the knowledge to control the processdoes not exist or the process is subject to changes in its dynamic characteristics. Todemonstrate the feasibility of the method simulation and experimental control testswere evaluated in

J. Abonyi; L. Nagy; F. Szeifert

5

Genetic Algorithms in Adaptive Fuzzy Control.  

National Technical Information Service (NTIS)

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...

C. L. Karr T. R. Harper

1992-01-01

6

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

7

Robust Adaptive Fuzzy Output Feedback Control System for Robot Manipulators  

Microsoft Academic Search

In this paper, we propose a novel hybrid control sys- tem for the trajectory tracking control problem of robotic systems. The design combines fuzzy system with robust adaptive control al- gorithm. The fuzzy system approximates the certainty equivalent- based optimal controller, while a robustifying adaptive control term is used to cope with uncertainties due to the presence of the exter-

Shafiqul Islam; Peter X. Liu

2011-01-01

8

Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator.  

PubMed

In this paper, a robust adaptive control strategy using a fuzzy compensator for MEMS triaxial gyroscope, which has system nonlinearities, including model uncertainties and external disturbances, is proposed. A fuzzy logic controller that could compensate for the model uncertainties and external disturbances is incorporated into the adaptive control scheme in the Lyapunov framework. The proposed adaptive fuzzy controller can guarantee the convergence and asymptotical stability of the closed-loop system. The proposed adaptive fuzzy control strategy does not depend on accurate mathematical models, which simplifies the design procedure. The innovative development of intelligent control methods incorporated with conventional control for the MEMS gyroscope is derived with the strict theoretical proof of the Lyapunov stability. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances compared with conventional adaptive control method. PMID:22575691

Fei, Juntao; Zhou, Jian

2012-12-01

9

Adaptive fuzzy multivariable controller design based on the Lyapunov scheme  

Microsoft Academic Search

In this paper a new approach to the design of multivariable adaptive-fuzzy controllers is proposed. The approach is based on fuzzy Lyapunov synthesis, that is an extension of the classical Lyapunov synthesis in the domain of computing with words. The closed-loop system is monitored and the parameters of the controller are adapted in order to minimizing the error between the

S. Pourmohammad; A. Yazdizadeh

2008-01-01

10

Indirect adaptive fuzzy control for a class of decentralized systems  

Microsoft Academic Search

A stable indirect adaptive fuzzy controller is presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of fuzzy systems, the dynamics for each subsystem are not required to be linear in a set of

Jeffrey T. Spooner; Kevin M. Passino

1997-01-01

11

H? adaptive fuzzy control for high performance brushless drives  

Microsoft Academic Search

A new design method for a high performance brushless drive system employing both H? optimal control design and fuzzy control design is described in this paper. The fuzzy control design is equipped with an adaptive learning algorithm to achieve H? tracking performance with external disturbances. It gives elevation to the selection of optimal performance weights without any trial and error

A. R. M. F. Chouika; P. Bofah; D. F. Noga

2000-01-01

12

An adaptive fuzzy approach for the incineration temperature control process  

Microsoft Academic Search

There are many factors affecting the combustion process in incinerators; one of them is how to control the temperature of the incineration and reduce the emissions. In this work, a second-order model of the adaptive fuzzy control strategy is adopted to stabilize the combustion temperature and the results demonstrate that the adaptive control strategy with the adaptive factors is a

Kai Shen; Jidong Lu; Zhenghua Li; Gang Liu

2005-01-01

13

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

14

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

15

Adaptive learning fuzzy control of a mobile robot.  

National Technical Information Service (NTIS)

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

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

1989-01-01

16

Fuzzy Self-Adaptive PID Controller for Freeway Ramp Metering  

Microsoft Academic Search

Aiming at the nonlinear and time-varying characteristics of freeway traffic system, a fuzzy self-adaptive PID controller is designed and applied to freeway ramp metering in this paper. A traffic flow model to describe the freeway flow process is firstly built. Based on the model and in conjunction with nonlinear feedback theory, a fuzzy-PID ramp controller is then designed. The ramp

Tao Jiang; Xinrong Liang

2009-01-01

17

INDIRECT ADAPTIVE FUZZY CONTROL FOR NONLINEAR SYSTEMS WITH ONLINE MODELLING  

Microsoft Academic Search

This paper presents an indirect adaptive fuzzy control scheme for a class of single-input-single-output (SISO) nonlinear systems. A Takagi-Sugeno (T-S) fuzzy model is employed as a dynamical model of the partially known nonlinear system. Both the structure and the parameters of the T-S model are identified on-line. A T-S model based feedback linearization controller (FLC) is designed and a Lyapunov

Ruiyun Qi; Mietek A. Brdys

18

PID self-tuning control using a fuzzy adaptive mechanism  

Microsoft Academic Search

The authors present a novel proportional-integral-derivative (PID) self-tuning control using a fuzzy adaptive mechanism for regulating industrial processes. The essential idea is to parameterize a Ziegler-Nichols-like tuning formula by a single parameter ?, then to use an online fuzzy inference mechanism to self-tune the parameter. A comparative simulation study on various processes, including processes with long dead-time and non-minimum-phase processes,

S. Z. He; S. H. Tan; F. L. Xu; P. Z. Wang

1993-01-01

19

Fuzzy-based adaptive bandwidth control for loss guarantees.  

PubMed

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

20

Generating fuzzy rules for the acceleration control of an adaptive cruise control system  

Microsoft Academic Search

A procedure for the data-driven generation of fuzzy rules is described, which was used in the development of an adaptive fuzzy controller to assist a driver in vehicle speed and distance control. The driver remains in the control loop of the ACC (adaptive cruise control) system through haptical feedback via the accelerator pedal. Thus, the control of the pedal behavior

R. Holve; P. Protzel; K. Naab

1996-01-01

21

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

22

Neural and Fuzzy Adaptive Control of Induction Motor Drives  

NASA Astrophysics Data System (ADS)

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

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

2008-06-01

23

Neural and Fuzzy Adaptive Control of Induction Motor Drives  

SciTech Connect

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

24

MIMO Indirect Adaptive Fuzzy Control of Induction Motors  

Microsoft Academic Search

This paper presents a new adaptive fuzzy control technique applied to induction motors (IM). The control task of such motors is considered complicated by the fact that these motors have uncertain time-varying parameters and are subjected to unknown load disturbance. A nonlinear multi-input multi-output (MIMO) state feedback linearizing control is designed for the IM modeled in a stationary reference frame.

Manal Wahba

2007-01-01

25

Development of a Solar LED Illumination Control System Based on Variable Universe Adaptive Fuzzy PID Controller  

Microsoft Academic Search

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

26

Digital Hardware Implementation of Adaptive Fuzzy Controller for AC Motor Drive  

Microsoft Academic Search

This paper presents the digital hardware implementation of adaptive fuzzy controller for ac servo motor system based on FPGA (Field Programmable Gate Array) technology. Firstly, a mathematic modeled for typical ac motor drive is defined. Secondly, to increase the performance of the motor drive system, an AFC (Adaptive Fuzzy Controller) constructed by a fuzzy basis function and a parameter adjustable

Ying-Shieh Kung; Ming-Shyan Wang; Chung-Chun Huang

2007-01-01

27

Adaptive fuzzy control of electrically stimulated muscles for arm movements  

Microsoft Academic Search

A modified adaptive Takagi-Sugeno (TS) fuzzy logic controller (FLC) is proposed that allows a simulated elbow-like biomechanical\\u000a system to accurately track sigmoidal and sinusoidal trajectories in the sagittal plane. The work is a first effort towards\\u000a the implementation of a system to restore elbow movements in quadriplegics using functional neuromuscular stimulation. The\\u000a single-joint musculo-skeletal system is composed of a co-contractable

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

1999-01-01

28

Adaptive process control using fuzzy logic and genetic algorithms  

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, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Karr, C. L.

1993-01-01

29

Adaptive process control with fuzzy logic and genetic algorithms  

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 to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Karr, C. L.

1993-01-01

30

Composite adaptive fuzzy control for synchronizing generalized Lorenz systems.  

PubMed

This paper presents a methodology of asymptotically synchronizing two uncertain generalized Lorenz systems via a single continuous composite adaptive fuzzy controller (AFC). To facilitate controller design, the synchronization problem is transformed into the stabilization problem by feedback linearization. To achieve asymptotic tracking performance, a key property of the optimal fuzzy approximation error is exploited by the Mean Value Theorem. The composite AFC, which utilizes both tracking and modeling error feedbacks, is constructed by introducing a series-parallel identification model into an indirect AFC. It is proved that the closed-loop system achieves asymptotic stability under a sufficient gain condition. Furthermore, the proposed approach cannot only synchronize two different chaotic systems but also significantly reduce computational complexity and implemented cost. Simulation studies further demonstrate the effectiveness of the proposed approach. PMID:22757551

Pan, Yongping; Er, Meng Joo; Sun, Tairen

2012-06-01

31

Fuzzy Adaptive Output-Feedback Control Design for Nonlinear Dynamic Systems with Output Delay  

Microsoft Academic Search

In this paper, a fuzzy adaptive output feedback control design method for a class of nonlinear output-delayed single-input single-output systems with guaranteed control performance is proposed. First, the Takagi and Sugeno fuzzy model is employed to approximate a nonlinear system. Next, based on the fuzzy model, a fuzzy observer-based controller is developed to achieve the control performance with a desired

Tzu-Sung Wu; Wen-Shyong Yu

2006-01-01

32

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

Microsoft Academic Search

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

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

2009-01-01

33

Adaptive fuzzy PID temperature control system based on OPC and modbus\\/TCP protocol  

Microsoft Academic Search

In this paper, an adaptive fuzzy PID controller is designed to improve the control performance of the resistance furnace, and then is applied to a remote temperature control system based on modbus\\/TCP industrial Ethernet via OPC communication. The adaptive PID controller is developed by the fuzzy toolbox in Matlab. The data acquisition and devices driving are realized by PCAuto, a

Qingbao Huang; Qianzhong She; Xiaofeng Lin

2010-01-01

34

Adaptive Fuzzy Decentralized Control for a Class of Large-Scale Nonlinear Systems with MIMO Subsystems  

Microsoft Academic Search

This paper presents a fuzzy basis function approach for adaptive decentralized control of a class of large-scale nonlinear systems with MIMO subsystems. Hybrid adaptive-robust tracking control schemes which are based on a combination of the HT tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and the HT tracking control performance is

H. Yousef; E. El-Madbouly; D. Eteim; M. Hamdy

2006-01-01

35

An indirect model reference robust fuzzy adaptive control for a class of SISO nonlinear systems  

Microsoft Academic Search

In this paper we are interested in robust adaptive fuzzy control of nonlinear SISO systems in the presence of parametric uncertainties.\\u000a The plant model structure is represented by the Takagi-Sugeno (T-S) type fuzzy system. An indirect adaptive fuzzy controller\\u000a based on model reference control scheme is proposed to provide asymptotic tracking of reference signal. The controller parameters\\u000a are computed at

Hafedh Abid; Mohamed Chtourou; Ahmed Toumi

2009-01-01

36

Adaptive control of discrete-time chaotic systems: a fuzzy control approach  

Microsoft Academic Search

This paper discusses adaptive control of a class of discrete-time chaotic systems from a fuzzy control approach. Using the T–S model of discrete-time chaotic systems, an adaptive control algorithm is developed based on some conventional adaptive control techniques. The resulting adaptively controlled chaotic system is shown to be globally stable, and its robustness is discussed. A simulation example of the

Gang Feng; Guanrong Chen

2005-01-01

37

Adaptive Fuzzy Logic Control of Permanent Magnet Synchronous Machines With Nonlinear Friction  

Microsoft Academic Search

In this paper, an adaptive fuzzy control scheme is in- troduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability- based fuzzy speed controller that capitalizes on the machine's in- verse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and param- etric uncertainties is achieved. Moreover, no

Hicham Chaoui; Pierre Sicard

2012-01-01

38

An indirect adaptive fuzzy speed controller for permanent magnet synchronous motor  

Microsoft Academic Search

This paper describes the application of indirect adaptive fuzzy control to the speed controller of permanent magnet synchronous motor (PMSM) under external load disturbances and parameter variations in the plant. The proposed indirect adaptive fuzzy speed controller is based on feedback linearization and its parameters are updated by both speed tracking error and filtered prediction error, designed by Lyapunov synthesis.

Lu Yongkun; Xia Chaoying

2008-01-01

39

Design of a Hybrid Stable Adaptive Fuzzy Controller Employing Lyapunov Theory and Harmony Search Algorithm  

Microsoft Academic Search

This brief proposes hybrid stable adaptive fuzzy controller design procedures utilizing the conventional Lyapunov theory and, the relatively newly devised harmony search (HS) algorithm-based stochastic approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory performance with a

Kaushik Das Sharma; Amitava Chatterjee; Anjan Rakshit

2010-01-01

40

On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems  

Microsoft Academic Search

In this paper, the adaptive fuzzy-neural controllers tuned on- line for a class of unknown nonlinear dynamical systems are proposed. To approximate the unknown nonlinear dynamical systems, the fuzzy-neural approximator is established. Furthermore, the control law and update law to tune on-line both the B-spline membership functions and the weighting factors of the adaptive fuzzy-neural controller are derived. Therefore, the

Yih-guang Leu; Tsu-tian Lee; Wei-yen Wang

1997-01-01

41

Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&Go  

Microsoft Academic Search

This paper presents a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km\\/h, thereby achieving\\u000a the main functions of both adaptive cruise control (ACC) and Stop&Go control. A fuzzy longitudinal controller is synthesized\\u000a by inputting the difference of the actual relative distance and the safe distance obtained from the preceding vehicle, and\\u000a the relative speed, and then

Ching-Chih Tsai; Shih-Min Hsieh; Chien-Tzu Chen

2010-01-01

42

Adaptive fuzzy decentralized control fora class of large-scale nonlinear systems  

Microsoft Academic Search

In this paper, direct and indirect adaptive output-feedback fuzzy decentralized controllers for a class of uncertain large-scale nonlinear systems are developed. The proposed controllers do not need the availability of the state variables. By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations, and a new

Shaocheng Tong; Han-Xiong Li; Guanrong Chen

2004-01-01

43

Stable indirect adaptive switching control for fuzzy dynamical systems based on T–S multiple models  

Microsoft Academic Search

A new indirect adaptive switching fuzzy control method for fuzzy dynamical systems, based on Takagi–Sugeno (T–S) multiple models is proposed in this article. Motivated by the fact that indirect adaptive control techniques suffer from poor transient response, especially when the initialisation of the estimation model is highly inaccurate and the region of uncertainty for the plant parameters is very large,

Nikolaos A. Sofianos; Yiannis S. Boutalis

2012-01-01

44

A robust H? control design for swarm formation control of multi-agent systems: A decentralized adaptive fuzzy approach  

Microsoft Academic Search

In this paper, a decentralized adaptive control scheme for multi-agent formation control is proposed. This control method is based on artificial potential functions integrated with adaptive fuzzy H? technique. We consider fully actuated mobile agents with partially unknown models, where an adaptive fuzzy logic system is used to approximate the unknown system dynamics. The H? control theory is used to

Bijan Ranjbar Sahraei; Faridoon Shabaninia

2010-01-01

45

Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors  

Microsoft Academic Search

This paper describes the design of the robust adaptive fuzzy controller for uncertain single-input single-output nonlinear dynamical systems with unknown nonlinearities. These unknown nonlinearities are approximated by the fuzzy system with~a set of fuzzy IF–THEN rules whose parameters are adjusted on-line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track~a given

Jang-hyun Park; Sam-jun Seo; Gwi-tae Park

2003-01-01

46

A fuzzy adaptive variable structure controller with applications to robot manipulators  

Microsoft Academic Search

A new adaptive fuzzy control algorithm is developed in this paper, which has a regular fuzzy controller and a supervisory control term. This control algorithm does not require the system model, but has stability assurance for the closed-loop controlled system. The design is simple, in the sense that both the membership functions and the rule base are simple, yet generic.

Ya-chen Hsu; Guanrong Chen; Han-xiong Li

2001-01-01

47

Indirect adaptive fuzzy control for a class of nonaffine nonlinear systems with unknown control directions  

Microsoft Academic Search

This article presents an indirect adaptive fuzzy control scheme for a class of nonlinear uncertain nonaffine systems with\\u000a unknown control directions. The nonlinear nonaffine system is first transformed into an affine form by using a Taylor series\\u000a expansion, and then fuzzy systems are employed to approximate the equivalent affine system’s unknown nonlinearities. By modifying\\u000a the estimated input control gain and

Salim Labiod; Thierry Marie Guerra

2010-01-01

48

Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation  

Microsoft Academic Search

The present paper describes the development of an indirect adaptive fuzzy control scheme employing feedback linearizing technique. The scheme proposes the development of a fuzzy certainty equivalence controller for controlling non-linear plants. This controller is designed on the basis of plant parameters estimated online at each sampling instant using bacterial foraging optimization (BFO) technique, a stochastic optimization technique, popularly employed

Suvadeep Banerjee; Ankush Chakrabarty; Sayan Maity; Amitava Chatterjee

2011-01-01

49

Modeling and simulation of Adaptive Neuro-Fuzzy controller for Chopper-Fed DC Motor Drive  

Microsoft Academic Search

The classical controllers algorithm is both simple and reliable, and has been applied to thousands of control loops in various industrial applications over the past 60 years (89%-90% of applications). This paper presents the neuro- fuzzy controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS)

Yousif I. Al-Mashhadany

2011-01-01

50

Self adaptive neuro-fuzzy control of FES-assisted paraplegics indoor rowing exercise  

Microsoft Academic Search

This paper describes the development of a self adaptive neuro-fuzzy control mechanism for FES-assisted indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of function of lower body through the application of functional electrical stimulation (FES). The neuro-fuzzy control technique is a control technique that combines fuzzy logic controller and a neural network, which

Z. Hussain; S. Z. Yahaya; R. Boudville; K. A. Ahmad; M. H. Mohd Noor

2011-01-01

51

Fuzzy Indirect Adaptive Control Scheme for Nonlinear Systems Based on Lyapunov Approach and Sliding Mode  

Microsoft Academic Search

In this work, we are interested to fuzzy indirect adaptive control for SISO nonlinear systems in presence of para- metric uncertainties. The plant model structure is represented by a fuzzy system. The essential idea of the on-line parametric estimation of the plant model is based on a comparison of measured state with the estimated one. The design of adaptive law

Hafedh Abid; Mohamed Chtourou; Ahmed Toumi

2007-01-01

52

TS model based indirect adaptive fuzzy control using online parameter estimation  

Microsoft Academic Search

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively

Chang-woo Park; Young-wan Cho

2004-01-01

53

T-S model based indirect adaptive fuzzy control using online parameter estimation.  

PubMed

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively to the parameter perturbation. In order to show the applicability of the proposed estimator, an existing fuzzy state feedback controller is adopted and indirect adaptive fuzzy control design with the proposed estimator is shown. From the numerical simulations and experiments, it is shown that the derived adaptive law works for the estimation model to follows the parameterized plant model and the overall control system has robustness to the parameter perturbation. PMID:15619930

Park, Chang-Woo; Cho, Young-Wan

2004-12-01

54

Adaptive fuzzy approach for H ? temperature tracking control of continuous stirred tank reactors  

Microsoft Academic Search

In this paper, an adaptive fuzzy temperature controller is proposed for a class of continuous stirred tank reactors (CSTRs) based on input–output feedback linearization. Since for control implementation concentrations of all species are needed, based on the observability concept, a fuzzy logic system is used to estimate the concentration dependent terms and other unknown system parameters in the control law,

Shahin Salehi; Mohammad Shahrokhi

2008-01-01

55

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system  

Microsoft Academic Search

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,

M. B. Djukanovic; M. S. Calovic; B. V. Vesovic; D. J. Sobajic

1997-01-01

56

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

NASA Technical Reports Server (NTRS)

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

Kopasakis, George

1997-01-01

57

Adaptive fuzzy sliding mode controller for two cascaded tanks level control  

Microsoft Academic Search

This paper proposes an indirect adaptive fuzzy sliding mode controller for a nonlinear plant. Fast convergence of the controlled system is first obtained from the indirect approach by roughly estimating the nonlinear functions of the plant. Then the sliding mode control approach with boundary layer is used to compensate the deteriorated system performance due to the model mismatch and unmodeled

Nawaporn Waurajitti; Jongkol Ngamwiwit; Yotin Prempraneerach

2000-01-01

58

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

PubMed

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

Wai, Rong-Jong; Lee, Jeng-Dao

2008-01-01

59

Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems.  

PubMed

In this paper, direct and indirect adaptive output-feedback fuzzy decentralized controllers for a class of uncertain large-scale nonlinear systems are developed. The proposed controllers do not need the availability of the state variables. By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations, and a new hybrid adaptive fuzzy control methodology is proposed by combining the adaptive fuzzy systems with H infinity control and the sliding mode control techniques. Based on Lyapunov stability theorem, the stability of the closed-loop systems can be verified. Moreover, the proposed overall control schemes guarantee that all the signals involved are bounded and achieve the H infinity-tracking performance. To demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper. PMID:15369121

Tong, Shaocheng; Li, Han-Xiong; Chen, Guanrong

2004-02-01

60

Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control  

Microsoft Academic Search

In the operation of wastewater treatment plants a key variable is dissolved oxygen (DO) content in the bioreactors. The paper describes the development of an adaptive fuzzy control strategy for tracking the DO reference trajectory applied to the Benchmark Simulation Model n.1. The design methodology of this data-driven controller uses the Lyapunov synthesis approach with a parameter projection algorithm to

Carlos Alberto Coelho Belchior; Rui Alexandre Matos Araújo; Jorge Afonso Cardoso Landeck

61

Stable indirect adaptive control based on discrete-time TS fuzzy model  

Microsoft Academic Search

This paper presents an indirect adaptive fuzzy control scheme for uncertain nonlinear asymptotically stable plants. A discrete-time T–S fuzzy input–output model is employed to approximate the unknown plant dynamics. The T–S fuzzy model is fed with its own states, which are indeed its past outputs, rather than the measurements from the plants. Entirely based on this model, a feedback linearization

Ruiyun Qi; Mietek A. Brdys

2008-01-01

62

Adaptive Fuzzy Urban Traffic Flow Control Using a Cooperative Multi-Agent System based on Two Stage Fuzzy Clustering  

Microsoft Academic Search

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This study devises an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this we have worked on a model

Fatemeh Daneshfar; Javad RavanJamJah; Fathollah Mansoori; Hassan Bevrani; Bahram Zahir Azami

2009-01-01

63

Adaptive fuzzy process control of integrated circuit wire bonding  

Microsoft Academic Search

One step in the assembly of integrated circuits is wire bonding, requiring expert knowledge to optimize critical process characteristics. This paper describes a fuzzy logic controller which sets parameters for the wire bonding process for gold ball wire bonds, specifically controlling bonded ball diameter and shear strength density. While the focus is on control of ball bonds, the method is

Clark D. Kinnaird; Alireza Khotanzad

1999-01-01

64

Stable adaptive fuzzy controller with time-varying dead-zone  

Microsoft Academic Search

This paper proposes an adaptive fuzzy control scheme for a class of continuous-time nonlinear dynamic systems for which explicit linear parameterizations of the uncertainties are either unknown or impossible. To improve robustness under the approximation errors and disturbances, the proposed scheme includes a dead-zone in adaptation laws which varies its size adaptively. The assumption of known bounds on the approximation

Keun-mo Koo

2001-01-01

65

Adaptive fuzzy frequency hopper  

Microsoft Academic Search

An adaptive fuzzy system generates the frequency hopping sequence for a spread spectrum communications system. The system learns rules from data and acts as a pseudorandom number generator. The IMSL uniform random number generator gives training samples. An adaptive scheme learns associations between previous samples and the current sample and encodes these as fuzzy rules. The output fuzzy set for

Peter J. Pacini; Bart Kosko

1995-01-01

66

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

PubMed

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

67

Indirect Adaptive Fuzzy Coordinated Excitation and SVC Control for Multi-Machine Power System  

Microsoft Academic Search

Coordinated control scheme for generator excitation and static VAR compensator (SVC) using an indirect adaptive fuzzy logic control (IAFLC) of multi-machine power systems based on multi-input-multi-output feedback linearization technique is developed in this paper. The power system considered in this paper consists of two generators and infinite bus connected through a network of transformers and transmission lines. The fuzzy controller

H. A. Yousef; M. A. Wahba; B. Bouchiba

2006-01-01

68

Combining Genetic Algorithms and Lyapunov-Based Adaptation for Online Design of Fuzzy Controllers  

Microsoft Academic Search

This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law

Vincenzo Giordano; David Naso; Biagio Turchiano

2006-01-01

69

Interval type-2 fuzzy model based indirect adaptive tracking control design for nonlinear systems with dead-zones  

Microsoft Academic Search

In this paper, an H? control performance via interval type-2 fuzzy adaptive tracking control scheme for a class of nonlinear systems with dead-zones is proposed. The Takagi-Sugeno (T-S) fuzzy model is used for representing a nonlinear system, where the parameters of the fuzzy model are obtained from both the fuzzy rules and online updates. An inverse function is cascaded with

Ho-Sheng Chen; Wen-Shyong Yu

2010-01-01

70

One-step-ahead adaptive control for a general class of nonlinear dynamic systems based on fuzzy models  

Microsoft Academic Search

A one-step-ahead adaptive control scheme based on fuzzy models is presented for a class of nonlinear dynamic systems which have no restriction on the structure. The specific features of the fuzzy systems are taken into consideration. The proposed method has the following properties: 1) by properly designing the fuzzy system, the modeling error converges to an error band which can

Feng Wan; Li-xin Wang; Youxian Sun

2001-01-01

71

Robust adaptive fuzzy control and its application to ship roll stabilization  

Microsoft Academic Search

A robust adaptive fuzzy control (RAFC) scheme, which can be used to control a class of uncertain nonlinear systems in which the robust relative degree is equal to the system's degree, is proposed for the problem of ship roll stabilization. The uncertain system can be transformed into the controllability canonical form by the global diffeomorphism. Using the universal approximation capability

Yansheng Yang; Changjiu Zhou; Xinle Jia

2002-01-01

72

Quaternion-based indirect adaptive fuzzy predictive control for attitude tracking of satellites  

Microsoft Academic Search

An indirect adaptive fuzzy predictive control method is presented for attitude tracking of satellites with model uncertainty and external disturbances in this paper. Firstly, the satellite attitude tracking error equation is formulated with quaternion, and a nonlinear predictive control law for attitude error quaternion model of satellites is derived. Then, the uncertain section in predictive control law from system model

Yerong Zhou; Wei Huo

2010-01-01

73

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

74

Adaptive Fuzzy Neural Network Control Design via a TS Fuzzy Model for a Robot Manipulator Including Actuator Dynamics  

Microsoft Academic Search

This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In

Rong-jong Wai; Zhi-wei Yang

2008-01-01

75

A New Approach to Adaptive Damping Control for Statistic VAR Compensators Based on Fuzzy Logic  

Microsoft Academic Search

This paper presents an approach for designing a fuzzy logic-based adaptive SVC damping In controller for damping low frequency power oscillations. Power systems are often subject to low Frequency electro-mechanical oscillations resulting from electrical disturbances. Generally, power system stabilizers are designed to provide damping against this kind of oscillations. Another means to achieve damping is to design supplementary damping controllers

Alireza Sedaghati

2005-01-01

76

Adaptive impedance control based on dynamic recurrent fuzzy neural network for upper-limb rehabilitation robot  

Microsoft Academic Search

The controller design is one of the major difficulties in realizing robot-aided rehabilitation program. The purpose of our study is to develop an adaptive impedance force control strategy based on dynamic recurrent fuzzy neural network to maintain the stability of the rehabilitation robot system in the case when the patient's physical condition makes a change. An on-line identification method was

Guozheng Xu; Aiguo Song

2009-01-01

77

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

NASA Astrophysics Data System (ADS)

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

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

2014-06-01

78

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system  

SciTech Connect

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

79

Fuzzy dynamic output feedback control with adaptive rotor imbalance compensation for magnetic bearing systems.  

PubMed

This paper presents a dynamic output feedback control with adaptive rotor-imbalance compensation based on an analytical Takagi-Sugeno fuzzy model for complex nonlinear magnetic bearing systems with rotor eccentricity. The rotor mass-imbalance effect is considered with a linear in the parameter approximator. Through the robust analysis for disturbance rejection, the control law can be synthesized in terms of linear matrix inequalities. Based on the suggested fuzzy output feedback design, the controller may be much easier to implement than conventional nonlinear controllers. Simulation validations show that the proposed robust fuzzy control law can suppress the rotor imbalance-induced vibration and has excellent capability for high-speed tracking and levitation control. PMID:15462450

Huang, Shi-Jing; Lin, Lih-Chang

2004-08-01

80

Design of a model identification fuzzy adaptive controller and stability analysis of nonlinear processes  

Microsoft Academic Search

This paper deals with the design of a model identification fuzzy adaptive controller with real-time scaling factors adjustment and the stability analysis of nonlinear distributed parameter systems. The solution branch of such systems frequently contains limit points (or turning points) which represent the boundary between stability and instability of the system. Hence, stability analysis is required for the determination of

D. I. Sagias; E. N. Sarafis; C. I. Siettos; G. V. Bafas

2001-01-01

81

Observer-Based Adaptive Fuzzy Backstepping Control for a Class of Stochastic Nonlinear Strict-Feedback Systems.  

PubMed

In this paper, two adaptive fuzzy output feedback control approaches are proposed for a class of uncertain stochastic nonlinear strict-feedback systems without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the fuzzy state observer, and by combining the adaptive backstepping technique with fuzzy adaptive control design, an adaptive fuzzy output feedback control approach is developed. To overcome the problem of "explosion of complexity" inherent in the proposed control method, the dynamic surface control (DSC) technique is incorporated into the first adaptive fuzzy control scheme, and a simplified adaptive fuzzy output feedback DSC approach is developed. It is proved that these two control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in mean square, and the observer errors and the output of the system converge to a small neighborhood of the origin. A simulation example is provided to show the effectiveness of the proposed approaches. PMID:21788195

Tong, Shaocheng; Li, Yue; Li, Yongming; Liu, Yanjun

2011-07-22

82

Adaptive fuzzy control of a single-phase sinusoidal rectifier with step-up\\/down characteristics  

Microsoft Academic Search

This paper proposes an adaptive fuzzy logic controller for feedback output voltage control of a single-phase sinusoidal rectifier with step-up\\/down characteristics. These rectifiers, with a sliding mode current controller, ensure a near unity power factor operation and an input current with low harmonic content. The reference of the current controller is a sinusoidal waveform whose amplitude is modulated by the

Tito G. Amaral; V. F. Pires; M. Crisostomo; J. F. Silva

2000-01-01

83

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

PubMed

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

Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

2013-10-01

84

Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems.  

PubMed

This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed. PMID:23453235

Shahriari kahkeshi, Maryam; Sheikholeslam, Farid; Zekri, Maryam

2013-05-01

85

Station-keeping control for a stratospheric airship platform via fuzzy adaptive backstepping approach  

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

86

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

87

A fuzzy adaptive learning control network with on-line structure and parameter learning.  

PubMed

This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of input/output variables increase, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the proposed learning algorithm partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the proposed learning algorithm can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. The users thus need not give it any a priori knowledge or even any initial information on these. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning Control Network (RFALCON) is further proposed. The proposed RFALCON is constructed by integrating two FALCONs, one FALCON as a critic network, and the other as an action network. By combining temporal difference techniques, stochastic exploration, and a proposed on-line supervised structure/parameter learning algorithm, a reinforcement structure/parameter learning algorithm is proposed, which can construct a RFALCON dynamically through a reward/penalty signal. The ball and beam balancing system is presented to illustrate the performance and applicability of the proposed models and learning algorithms. PMID:9040059

Lin, C J

1996-11-01

88

FAFC: fast adaptive fuzzy AQM controller for TCP\\/IP networks  

Microsoft Academic Search

Recently, many active queue management (AQM) algorithms have been proposed to address performance degradations of end-to-end congestion control. However, these AQM algorithms present weaknesses for stabilizing delays in heavily loaded networks. In this paper, we describe a novel adaptive fuzzy control algorithm to improve best effort TCP\\/IP networks performance. Compared to traditional AQM algorithms (RED, PID and others), our proposal

Yassine HADJADJ AOUL; Abdelhamid NAFAA; Daniel NEGRU; Ahmed MEHAOUA

2004-01-01

89

Adaptive Learning Approach of Fuzzy Logic Controller with Evolution for Pursuit-Evasion Games  

Microsoft Academic Search

\\u000a This paper studies a simplified pursuit-evasion problem. We assume that the evader moves with constant speed along a trajectory\\u000a that is well-defined and known a priori. The objective of steering control of the pursuer modeled as a nonholonomic unicycle-type\\u000a mobile robot is to intercept the moving evader. An adaptive learning approach of fuzzy logic controller is developed as an\\u000a inverse

Hung-Chien Chung; Jing-Sin Liu

2010-01-01

90

Direct adaptive fuzzy-neural-network control for robot manipulator by using only position measurements  

Microsoft Academic Search

This study focuses on the development of a direct adaptive fuzzy-neural-network control (DAFNNC) for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this

Rong-Jong Wai; Zhi-Wei Yang; Chih-Yi Shih

2010-01-01

91

Design of new adaptive fuzzy logic controller for nonlinear plants with unknown or time-varying dead zones  

Microsoft Academic Search

An adaptive fuzzy logic controller (FLC) is designed for plants with unknown and\\/or time-varying dead zones. The steady-state control resolutions with perturbing action, which are different from the ones in the transient states, are used to cancel out the unknown and\\/or time-varying dead-zone effects. Automatically adjusted control resolutions play a key role as a fuzzy dead-zone inverse. The control resolutions

Seok-Yong Oh; Dong-Jo Park

1998-01-01

92

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

SciTech Connect

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

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

1992-01-01

93

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

SciTech Connect

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

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

1992-06-01

94

Fuzzy adaptive particle swarm optimization  

Microsoft Academic Search

A fuzzy system is implemented to dynamically adapt the inertia weight of the particle swarm optimization algorithm (PSO). Three benchmark functions with asymmetric initial range settings are selected as the test functions. The same fuzzy system has been applied to all three test functions with different dimensions. The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization

Yuhui Shi; R. C. Eberhart

2001-01-01

95

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

PubMed

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

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

2014-08-01

96

Fuzzy controller for rapid nickel-cadmium batteries charger through adaptive neuro-fuzzy inference system (ANFIS) architecture  

Microsoft Academic Search

ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates

Arun Khosla; Shakti Kumar; K. K. Aggarwal

2003-01-01

97

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

PubMed

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

98

Towards Autonomous Fuzzy Control.  

National Technical Information Service (NTIS)

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 adju...

S. Shenoi A. Ramer

1993-01-01

99

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

100

Adaptive dynamic surface control for uncertain nonlinear systems with interval type-2 fuzzy neural networks.  

PubMed

This paper presents a new robust adaptive control method for a class of nonlinear systems subject to uncertainties. The proposed approach is based on an adaptive dynamic surface control, where the system uncertainties are approximately modeled by interval type-2 fuzzy neural networks. In this paper, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all error signals are shown to be uniformly ultimately bounded. In addition to simulations, the proposed method is applied to a real ball-and-beam system for performance evaluations. To highlight the system robustness, different initial settings of ball-and-beam parameters are considered. Simulation and experimental results indicate that the proposed control scheme has superior responses, compared to conventional dynamic surface control. PMID:23757550

Chang, Yeong-Hwa; Chan, Wei-Shou

2014-02-01

101

Neuro-fuzzy modeling and control  

Microsoft Academic Search

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

JYH-SHING ROGER JANG; Chuen-Tsai Sun

1995-01-01

102

Adaptive Approximation Based Control-Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches-  

Microsoft Academic Search

This book, by two of the key people who have developed rigorous methods in the arena of Intelligent Control, provides a long-awaited overall perspective that unifies nonlinear network approximators and adaptive control techniques. The focus of the book is continuous-time adaptive systems. The text consists of eight chapters. Some of the topics discussed include: approximation theory; approximation structures used in

Frank Lewis; M. M. Polycarpou

2007-01-01

103

Modeling and control compensation of nonlinear friction using adaptive fuzzy systems  

NASA Astrophysics Data System (ADS)

System performance in terms of control accuracy and stability is usually negatively affected by friction occurrences in mechanical systems. Thus, it is important to model the friction properly so that it can be used in controller design. This paper employs adaptive fuzzy systems to approximate unknown nonlinear friction functions, and applies the estimation of friction in proportional-derivative (PD) control law to enhance the control performance. On the basis of Lyapunov stability theory, a bound of tracking errors of the closed-loop control system is derived. Techniques proposed in this paper have been applied to a typical motion control system for simulation studies. The results obtained demonstrate that our proposed method in this paper has good potential in controlling many mechanical systems with unknown nonlinear friction.

Wang, Y. F.; Wang, D. H.; Chai, T. Y.

2009-11-01

104

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

PubMed

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

Savran, Aydogan; Kahraman, Gokalp

2014-03-01

105

Cascade direct adaptive fuzzy control design for a nonlinear two-axis inverted-pendulum servomechanism.  

PubMed

This paper presents and analyzes a cascade direct adaptive fuzzy control (DAFC) scheme for a two-axis inverted-pendulum servomechanism. Because the dynamic characteristic of the two-axis inverted-pendulum servomechanism is a nonlinear unstable nonminimum-phase underactuated system, it is difficult to design a suitable control scheme that simultaneously realizes real-time stabilization and accurate tracking control, and it is not easy to directly apply conventional computed torque strategies to this underactuated system. Therefore, the cascade DAFC scheme including inner and outer control loops is investigated for the stabilizing and tracking control of a nonlinear two-axis inverted-pendulum servomechanism. The goal of the inner control loop is to design a DAFC law so that the stick angle vector can fit the stick angle command vector derived from the stick angle reference model. In the outer loop, the reference signal vector is designed via an adaptive path planner so that the cart position vector tracks the cart position command vector. Moreover, all adaptive algorithms in the cascade DAFC system are derived using the Lyapunov stability analysis, so that system stability can be guaranteed in the entire closed-loop system. Relying on this cascade structure, the stick angle and cart position tracking-error vectors will simultaneously converge to zero. Numerical simulations and experimental results are given to verify that the proposed cascade DAFC system can achieve favorable stabilizing and tracking performance and is robust with regard to system uncertainties. PMID:18348926

Wai, Rong-Jong; Kuo, Meng-An; Lee, Jeng-Dao

2008-04-01

106

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

PubMed

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

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

2012-07-01

107

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

Microsoft Academic Search

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

Yongming Li; Chang’e Ren; Shaocheng Tong

108

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

109

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

PubMed

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

Favieiro, Gabriela W; Balbinot, Alexandre

2011-01-01

110

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control  

NASA Astrophysics Data System (ADS)

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

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

111

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

112

Adaptive fuzzy PID composite control with hysteresis-band switching for line of sight stabilization servo system  

Microsoft Academic Search

The line of sight (LOS) stabilization control based on gyro stabilized platform is required to isolate the LOS from the disturbance and vibration of carrier and ensure pointing and tracking for target in electro-optical tracking system. A composite adaptive fuzzy proportional-integral-derivative (PID) control with hysteresis-band switching is developed to achieve real-time and high stabilization precision for this nonlinear uncertainty servo

Wei Ji; Qi Li; Bo Xu; Dean Zhao; Shixiong Fang

2011-01-01

113

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

PubMed Central

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

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

2013-01-01

114

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

PubMed

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

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

2013-01-01

115

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

116

Model-Independent Vibration Control of Flexible Beam-Like Structures Using a Fuzzy Based Adaptation Strategy  

Microsoft Academic Search

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

K. Cohen; T. Weller; J. Levitas; H. Abramovich

1997-01-01

117

A self-adaptive fuzzy PI controller of power conditioning system for hybrid fuel-cell\\/turbine power plant  

Microsoft Academic Search

Power conditioning system (PCS) is an interface between distributed generation and utility grid. It regulates voltage, current and power transmitted from the hybrid direct fuel-cell\\/turbine (DFC\\/T) power plant to utility grid. This paper presents a self-adaptive fuzzy PI controller of three phase inverter in PCS for the DFC\\/T power plant. One of the main tasks of PCS for distributed generations

Zhitong Guo; Kwang Y. Lee

2011-01-01

118

Discrete event fuzzy airport control  

Microsoft Academic Search

A discrete event simulation that uses a modified expert system as a controller is described. Fuzzy logic concepts from analog controllers are applied in the expert system controller to mimic human control of an airport, modeled with a combined discrete and continuous state space. The controller is adaptive so rule confidences are automatically varied to achieve near optimum system performance.

John R. Clymer; Philip D. Corey; Judith A. Gardner

1992-01-01

119

Predicting Spring rainfall Based on Remote Linkage controlling using Adaptive Neural-Fuzzy Inference System (ANFIS)  

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

120

Fuzzy and neural control  

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

121

Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification.  

PubMed

This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic. PMID:19497816

Xia, Yonghui; Yang, Zijiang; Han, Maoan

2009-07-01

122

Fuzzy control strategies in human operator and sport modeling  

Microsoft Academic Search

The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex\\u000a dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for\\u000a human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal\\u000a control. As an application of the presented fuzzy strategies,

Tijana T. Ivancevic; Bojan Jovanovic; Sasa Markovic

2010-01-01

123

Design of a neuro-fuzzy controller for speed control applied to AC servo motor  

Microsoft Academic Search

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and an artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules that are created by an expert. To adapt the more precise modeling, error backpropagation learning of adjusting the link-weight of fuzzy

Sang Hoon Kim; Lark Kyo Kim

2001-01-01

124

Fuzzy Control Engineering.  

National Technical Information Service (NTIS)

'Fuzzy' control engineering is an application of fuzzy theory, which was first proposed by L.A. Zadeh at the University of California in 1964, to engineering problems possessing some degree of ambiguity. It can, in fact, be applied to the automation of sy...

1988-01-01

125

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls  

PubMed Central

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

Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

2014-01-01

126

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls.  

PubMed

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

Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

2014-01-01

127

Neuro-fuzzy control of a steam boiler-turbine unit  

Microsoft Academic Search

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

Fahd A. Alturki; Adel Ben Abdennour

1999-01-01

128

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

129

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

NASA Astrophysics Data System (ADS)

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

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

2010-07-01

130

The application of a neural-fuzzy logic controller to process control  

Microsoft Academic Search

A neural-fuzzy controller is an intelligent system that allows for the combination of qualitative knowledge in fuzzy rules and the learning capabilities of neural networks. This paper examines the suitability of one particular neural-fuzzy model, the adaptive network fuzzy interference system (ANFIS) proposed by J.-S.R. Jang, for use as part of control systems. The adaptive neural-fuzzy controller developed uses the

D. J. Kelly; P. D. Burton; M. A. Rahman

1994-01-01

131

Dryer surface temperature control system based on improved self-adaptive fuzzy Smith prediction controller  

Microsoft Academic Search

In bank-note paper making process, the paper moisture is controlled mainly by regulating the steam traffic of dryers to change the surface temperature of dryers. The heat transfer process of drying bank-note paper is very complicated, and the process has serious nonlinearity, parameters drift, lag and inertia characteristic etc, so the accurate mathematical model of the process can not be

Dejun Ren; Jin Yao; Yonggui Wang

2005-01-01

132

FPGA-Based Speed Control IC for PMSM Drive With Adaptive Fuzzy Control  

Microsoft Academic Search

The new generation of field programmable gate array (FPGA) technologies enables an embedded processor intellectual property (IP) and an application IP to be integrated into a system-on-a-programmable-chip (SoPC) developing environment. Therefore, this study presents a speed control integrated circuit (IC) for permanent magnet synchronous motor (PMSM) drive under this SoPC environment. First, the mathematic model of PMSM is defined and

Ying-Shieh Kung; Ming-Hung Tsai

2007-01-01

133

An FPGA-Based Adaptive Fuzzy Coprocessor  

Microsoft Academic Search

\\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

134

Adaptive fuzzy decentralized control for large-scale nonlinear systems with time-varying delays and unknown high-frequency gain sign.  

PubMed

In this paper, an adaptive fuzzy decentralized robust output feedback control approach is proposed for a class of large-scale strict-feedback nonlinear systems without the measurements of the states. The nonlinear systems in this paper are assumed to possess unstructured uncertainties, time-varying delays, and unknown high-frequency gain sign. Fuzzy logic systems are used to approximate the unstructured uncertainties, K-filters are designed to estimate the unmeasured states, and a special Nussbaum gain function is introduced to solve the problem of unknown high-frequency gain sign. Combining the backstepping technique with adaptive fuzzy control theory, an adaptive fuzzy decentralized robust output feedback control scheme is developed. In order to obtain the stability of the closed-loop system, a new lemma is given and proved. Based on this lemma and Lyapunov-Krasovskii functions, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded and that the tracking errors can converge to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated from simulation results. PMID:20716504

Tong, Shaocheng; Liu, Changliang; Li, Yongming; Zhang, Huaguang

2011-04-01

135

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.  

PubMed

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper. PMID:20858584

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

136

Unknown nonlinear chaotic gyros synchronization using adaptive fuzzy sliding mode control with unknown dead-zone input  

NASA Astrophysics Data System (ADS)

In this paper, the problem of synchronizing two chaotic gyros in the presence of uncertainties, external disturbances and dead-zone nonlinearity in the control input is studied while the structure of the gyros, parameters of the dead-zone and the bounds of uncertainties and external disturbances are unknown. The dead-zone nonlinearity in the control input might cause the perturbed chaotic system to show unpredictable behavior. This is due to the high sensitivity of these systems to small changes in their parameters. Thereby, the effect of these issues should not be ignored in the control design for these systems. In order to eliminate the effects from the dead-zone nonlinearity, in this paper, a robust adaptive fuzzy sliding mode control scheme is proposed to overcome the synchronization problem for a class of unknown nonlinear chaotic gyros. The main contribution of our paper in comparison with other works that attempt to solve the problem of dead-zone in the synchronization of chaotic gyros is that we assume that the structure of the system, uncertainties, external disturbances, and dead-zone are fully unknown. Simulation results are provided to illustrate the effectiveness of the proposed method.

Roopaei, Mehdi; Zolghadri Jahromi, Mansoor; John, Robert; Lin, Tsung-Chih

2010-09-01

137

Adaptive and cooperative multi-agent fuzzy system architecture  

Microsoft Academic Search

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in intelligent transportation system (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a

Fatemeh Daneshfar; Fardin Akhlaghian; Fathollah Mansoori

2009-01-01

138

Alternative Adaptive Fuzzy C-Means Clustering  

Microsoft Academic Search

Fuzzy C-Means (FCM) clustering algorithm is used in a variety of application domains. Fundamentally, it cannot be used for the subsequent data (adaptive data). A complete dataset has to be static prior to implementing the algorithm. This paper presents an alternative adaptive FCM which is able to cope with this limitation. The adaptive FCM using Euclidean and Mahalanobis distances were

SOMCHAI CHAMPATHONG; SARTRA WONGTHANAVASU; KHAMRON SUNAT

2006-01-01

139

Adapting Applications in Mobile Terminals Using Fuzzy Context Information  

Microsoft Academic Search

Context-aware appliances are able to take advantage of fusing sensory and application specific information to provide proper\\u000a information for situation, more flexible services, and adaptive user interfaces. Characteristic for mobile devices and their\\u000a users is that they are continuously moving in several simultaneous fuzzy contexts. We present an approach for controlling\\u000a context aware applications in the case of multiple fuzzy

Jani Mäntyjärvi; Tapio Seppänen

2002-01-01

140

Adaptive motion washout filter design by using self-tuning fuzzy control  

Microsoft Academic Search

The purpose of this research is to improve the motion-cueing fidelity of the simulator by using fuzzy-tuning technique to tune the parameters of washout filter in real-time. In order to enhance the motion-cueing fidelity within the limited working space on simulator, the limited working space and the frequency of input dynamic signal must be taken into account for the parameter

Thong-Shing Hwang; Shen-Kai Yeh; Jr-Ruei Lin; Wen-Pin Su

2009-01-01

141

Intelligent Fuzzy Controllers Laboratory  

Microsoft Academic Search

The Intelligent Fuzzy Controllers Laboratory has been developed in the Department of Electrical and Computer Engineering of Western Michigan University with the help of a DURIP grant by the Department of Defense (6) and generous donations by ABB Automation Technology Products. This new lab is to support research, the development of advanced courses, and graduate projects in the area of

Janos L Grantner; Ramakrishna Gottipati; George A Fodor

2004-01-01

142

Learning laws for neural-network implementation of fuzzy control systems  

Microsoft Academic Search

A method of designing adaptive fuzzy control systems using structured neural networks is discussed. The basic idea is to implement a rule-based fuzzy control system with a neural network consisting of two subnetworks of pattern recognition, and fuzzy reasoning and control synthesis. The neural network is arranged such that the structure and operations of the original fuzzy control system can

Fei-Yue Wang; Deqian David Chen

1994-01-01

143

A fuzzy-logic based dual-purpose adaptive circuit for vibration control and energy harvesting using piezoelectric transducer  

NASA Astrophysics Data System (ADS)

Due to their two-way electromechanical coupling effect, piezoelectric transducers can be used to synthesize passive vibration control schemes, e.g., RLC circuit with the integration of inductance and resistance elements that is conceptually similar to damped vibration absorber. Meanwhile, the wide usage of wireless sensors has led to the recent enthusiasm of developing piezoelectric-based energy harvesting devices that can convert ambient vibratory energy into useful electrical energy. It can be shown that the integration of circuitry elements such as resistance and inductance can benefit the energy harvesting capability. Here we explore a dual-purpose circuit that can facilitate simultaneous vibration suppression and energy harvesting. It is worth noting that the goal of vibration suppression and the goal of energy harvesting may not always complement each other. That is, the maximization of vibration suppression doesn't necessarily lead to the maximization of energy harvesting, and vice versa. In this research, we develop a fuzzy-logic based algorithm to decide the proper selection of circuitry elements to balance between the two goals. As the circuitry elements can be online tuned, this research yields an adaptive circuitry concept for the effective manipulation of system energy and vibration suppression. Comprehensive analyses are carried out to demonstrate the concept and operation.

Liu, Zhe Peng; Li, Qing

2013-04-01

144

An optimal fuzzy PID controller  

Microsoft Academic Search

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

145

Investigation on Controlling Techniques of Moving Contact Behaviors for Vacuum Circuit Breaker Based on Fuzzy Control  

Microsoft Academic Search

To improve the switching property of the vacuum circuit breaker, a scheme of position feedback control for permanent magnetic actuator vacuum circuit breaker is presented based on fuzzy control. An adaptive fuzzy controller is designed. With Matlab fuzzy and simulink software package, a model is built to simulate the control scheme. The result indicates that the permanent magnetic actuator can

Li Tian-Hui; Fang Chun-en; Li Wei; Zhou Li-li

2010-01-01

146

The fuzzy controller by programmable logic circuit  

Microsoft Academic Search

The paper introduces a fuzzy controller based on an in-system programmable logic circuit. The fuzzy transformation of accurate input data, fuzzy control rules based on observing the control action and the user's experience, fuzzy decision of output can be carried out by the programmable device. Owing to using VHDL language to write fuzzy rules, the characteristic of this controller is

Luyi Xia; Yuntian Qu

2002-01-01

147

Fuzzy control of integrating processes  

Microsoft Academic Search

The paper deals with tuning of fuzzy controllers for integrating plants with time delay. The designed approach is verified on three examples by simulations and compared with classical PID control. Obtained results are measured calculating integral performance indexes ISE (integral squared value of error) and IAE (integral absolute value of error). Designed fuzzy controllers lead to better closed-loop control responses

Anna Vasickaninova; Monika Bakosova

2011-01-01

148

AFRED: An Adaptive Fuzzy-based Control Algorithm for Active Queue Management  

Microsoft Academic Search

This paper studies the active queue management (AQM) in high-speed routers. One of the original and popularly implemented AQM algorithms is random early detection (RED). But it is hard to configure the parameters involved in RED. Although there are some new TCP-modeling based approaches to overcome this, they really show some instability under such dynamical environments with diverse connections (adaptive

Chonggang Wang; Bo Li; Kazem Sohraby; Yong Peng

2003-01-01

149

Fuzzy adaptive Kalman filtering for INS\\/GPS data fusion  

Microsoft Academic Search

Presents a method for sensor fusion based on adaptive fuzzy Kalman filtering. The method is applied in fusing position signals from Global Positioning Systems (GPS) and inertial navigation systems (INS) for autonomous mobile vehicles. The presented method has been validated in a 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The extended Kalman

J. Z. Sasiadek; Q. Wang; M. B. Zeremba

2000-01-01

150

Neuro-fuzzy intelligent controller for ship roll motion stabilization  

Microsoft Academic Search

In this paper, we present an Adaptive Network-based Fuzzy Inference System (ANFIS), based on a neuro-fuzzy controller, as a possible control mechanism for a ship stabilizing fin system. Simulation results show that ANFIS can effectively improve the ship stabilizing performance against roll motion in cases of rough sea conditions. it is a promising alternative to conventional PID controllers.

Chen Guo; Marwan A. Simaan; Zengqi Sun

2003-01-01

151

Fuzzy Traffic Signal Control. Principles and Applications.  

National Technical Information Service (NTIS)

The FUSICO (Fuzzy Signal Control)-research project was started in 1996 at the Helsinki University of Technology. The main goals of the project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules using linguistic variables, va...

J. Niittymaki

2002-01-01

152

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

153

Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia  

Microsoft Academic Search

Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller

Jiann-Shing Shieh; Ming-Hsien Kao; Chien-Chiang Liu

2006-01-01

154

Vibration suppression for a beam-cart system using adaptive fuzzy controller  

Microsoft Academic Search

Motion and control of a Bernoulli-Euler beam fixed on a moving cart will be analysis in this study. The moving cart is mounted on the ball-screw mechanism system. Dynamic formulation for control purposes is first investigated for such beam-cart system in this research. The controller has two separate feedback loops for positioning and damping, and the vibration suppression controller is

J. Lin; W.-S. Chao

2008-01-01

155

An adaptive neuro fuzzy power system stabilizer for damping inter-area oscillations in power systems  

Microsoft Academic Search

An adaptive neuro-fuzzy inference system (ANFIS) based PSS is proposed in this paper. The controller is essentially divided into two sub-systems, a recursive least square identifier for the generator and an adaptive neuro fuzzy PSS to damp the oscillations. The PSS is coupled to a single machine in every area and the parameters of this PSS are tuned online in

As. Venugopal; G. Radman; M. Abdelrahman

2004-01-01

156

Fuzzy adaptive PI control for near space aircraft electric propulsion system  

Microsoft Academic Search

As the near space atmospheric environment is complicated and variational, aircraft electric propulsion system parameters and load characteristics of the propeller will be changed, if the control method of electric propulsion system shall not be treated, the system stability and reliability will be affected. A novel double close loops control method with current and speed was presented in this paper,

Lei Jinli; Dou Manfeng

2010-01-01

157

An approximation of interval type-2 fuzzy controllers using fuzzy ratio switching type-1 fuzzy controllers.  

PubMed

In this paper, the interval type-2 fuzzy controllers (FC(IT2)s) are approximated using the fuzzy ratio switching type-1 FCs to avoid the complex type-reduction process required for the interval type-2 FCs. The fuzzy ratio switching type-1 FCs (FC(FRST1)s) are designed to be a fuzzy combination of the possible-leftmost and possible-rightmost type-1 FCs. The fuzzy ratio switching type-1 fuzzy control technique is applied with the sliding control technique to realize the hybrid fuzzy ratio switching type-1 fuzzy sliding controllers (HFSC(FRST1)s) for the double-pendulum-and-cart system. The simulation results and comparisons with other approaches are provided to demonstrate the effectiveness of the proposed HFSC(FRST1)s. PMID:21189244

Tao, C W; Taur, Jinshiuh; Chuang, Chen-Chia; Chang, Chia-Wen; Chang, Yeong-Hwa

2011-06-01

158

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

159

Fuzzy control of an underactuated robot with a fuzzy microcontroller  

Microsoft Academic Search

In this work the position control of a planar underactuated manipulator with two revolute joints is considered. A dynamic model of the system is presented and a fuzzy control strategy is proposed. Fuzzy logic allows empirical rules to be translated into a control algorithm. A fuzzy microcontroller is adopted for the practical implementation of the system. The results of several

G. Muscato

1999-01-01

160

An improved flush material belt weigh feeder system via fuzzy logic controller and adaptive neural networks  

Microsoft Academic Search

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

161

Fuzzy Model Reference Adaptive Control of power converter for unity power factor and harmonics minimization  

Microsoft Academic Search

The absorbed current by the converter is rich in harmonics. This provokes the disruption of the network and influences on the consumers joined to the same node. On the other hand, because of its structure and of the absence of the degree of liberty in the control, the converter consummates a reactive power. In order to minimize the harmonics of

M. T. Benchouia; A. Ghamri; M. E. H. Benbouzid; A. Golea; S. E. Zouzou

2007-01-01

162

MRAS Sensorless Vector Control of an Induction Motor Using New Sliding-Mode and Fuzzy-Logic Adaptation Mechanisms  

Microsoft Academic Search

In this paper, two novel adaptation schemes are proposed to replace the classical PI controller used in model reference adaptive speed-estimation schemes that are based on rotor flux. The first proposed adaptation scheme is based on sliding-mode theory. A new speed-estimation adaptation law is derived using Lyapunov theory to ensure estimation stability, as well as fast error dynamics. The other

Shady M. Gadoue; Damian Giaouris; John W. Finch

2010-01-01

163

SIMULATION OF SELF-TUNING PID-TYPE FUZZY ADAPTIVE CONTROL OF A HVAC SYSTEM  

Microsoft Academic Search

The modeling, numerical simulation and control of a HVAC (heating, ventilating and air-conditioning) system having two different zones with variable flow-rate were performed by considering the ambient temperature in this study. The sub-models of the system were obtained by deriving to heat transfer equations of heat loss of two zones by conduction and convection, cooling unit and fan. All models

Servet Soygüder; Mehmet Karaköse; Hasan Alli

164

Performance analysis of a fuzzy logic based adaptive call admission control over heterogeneous wireless networks  

Microsoft Academic Search

This paper propose a novel call admission control (CAC) scheme that considers real-time and non-real-time nature of a call before allocating resources for its progress in a network. The scheme accumulates the benefits of different CAC schemes for best optimization of the available resources. It has combined the three major schemes- reserve guard channel, buffer based and prioritization. Application of

Asish K Mukhopadhyay; Sajal Saha

2010-01-01

165

Frequency \\/ duty cycle current-mode fuzzy control for LCC resonant converter  

Microsoft Academic Search

A novel current-mode nonlinear fuzzy controller with adaptive gain is developed for the LCC resonant converter applied in very low frequency high voltage generators using switching frequency and duty cycle as control variables. The designed fuzzy controller is verified through simulations. Comparison between the fuzzy controller and a standard linear controller indicates the correctness and effectiveness of the novel approach.

Manli Hu; Joachim Bocker; Norbert Frohleke

2011-01-01

166

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

167

Fuzzy control system design via fuzzy Lyapunov functions.  

PubMed

This correspondence deals with the problems of analysis and design for a class of continuous-time Takagi-Sugeno fuzzy control systems. Sufficient conditions for the stability of fuzzy control systems are derived based on a fuzzy Lyapunov function. Both parallel and nonparallel distributed compensation controllers are considered. Sufficient conditions for the solvability of the controller design problem are given in the form of linear matrix inequalities. Unlike the fuzzy Lyapunov function approaches reported in the literature, the bound of the time derivatives of the fuzzy basis functions is not required in the proposed approaches. The effectiveness of the proposed approaches is shown through a numerical example. PMID:19022736

Li, J; Zhou, S; Xu, S

2008-12-01

168

Design and Implementation of Fuzzy Logic Controllers.  

National Technical Information Service (NTIS)

The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real syst...

O. A. Abihana O. R. Gonzalez

1993-01-01

169

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

170

Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Farrell, J.A. and Polycarpou, M.M. [Book review  

Microsoft Academic Search

The goal of this book is to present different methodologies of the adaptive-approximation-based control in a unified context to facilitate discussions and comparison. Some of the topics covered include: a review of typical terminology used in nonlinear system control; approximation theory; approximation structures; learning methodologies used for adaptive function approximation; the design of nonlinear control systems; analysis and synthesis of

Edgar N. Sanchez; Alma Y. Alanis

2008-01-01

171

Fuzzy-PID controllers vs. fuzzy-PI controllers  

Microsoft Academic Search

The synthesis of a control system includes both the controller selection and the adjustment of its parameters. In some cases, the type of controller might be more complex or more general, like PID instead PI or PD, to improve the control system performance. In all cases, the tuning problem must be satisfactorily solved. On the other hand, fuzzy control has

M. Santos; S. Dormido; J. M. de la Cruz

1996-01-01

172

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

173

Fuzzy scheduled RTDA controller design.  

PubMed

In this paper, the design and development of fuzzy scheduled robustness, tracking, disturbance rejection and overall aggressiveness (RTDA) controller design for non-linear processes are discussed. pH process is highly non-linear and the design of good controller for this process is always a challenging one due to large gain variation. Fuzzy scheduled RTDA controller design based on normalized integral square error (N_ISE) performance criteria for pH neutralization process is developed. The applicability of the proposed controller is tested for other different non-linear processes like type I diabetic process and conical tank process. The servo and regulatory performance of fuzzy scheduled RTDA controller design is compared with well-tuned internal model control (IMC) and dynamic matrix control (DMC)-based control schemes. PMID:23317662

Srinivasan, K; Anbarasan, K

2013-03-01

174

Fuzzy stochastic automata for intelligent vehicle control  

Microsoft Academic Search

Fuzzy stochastic automata (FSA) are proposed for the control of autonomous vehicles. FSA merge the concept of sliding-mode control with fuzzy logic and have interesting robustness properties. Sufficient conditions for the convergence of the FSA control are provided.

G. G. Rigatos

2003-01-01

175

Fuzzy Control of Small Servo Motors.  

National Technical Information Service (NTIS)

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 mot...

R. Maor Y. Jani

1993-01-01

176

Tuning fuzzy logic controllers by genetic algorithms  

Microsoft Academic Search

The performance of a fuzzy logic controller depends on its control rules and membership functions. Hence, it is very important to adjust these parameters to the process to be controlled. A method is presented for tuning fuzzy control rules by genetic algorithms to make the fuzzy logic control systems behave as closely as possible to the operator or expert behavior

Francisco Herrera; Manuel Lozano; José L. Verdegay

1995-01-01

177

Expert system driven fuzzy control application to power reactors  

SciTech Connect

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

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

1990-12-31

178

MPPT Strategy of PV System Based on Adaptive Fuzzy PID Algorithm  

Microsoft Academic Search

\\u000a To further improve the control quality of photovoltaic generation MPPT system, dual-mode adaptive fuzzy PID control strategy\\u000a was proposed in this paper. On the basis of conventional fuzzy tracking algorithm, the principle of control algorithm was\\u000a analyzed and the control system was designed. The results show that dual-mode control algorithms can quickly sense the changes\\u000a in the external environment, and

Jing Hui; Xiaoling Sun

179

Toshiba Review, Vol. 43, No. 4, 1988. Special Issue: Fuzzy Control Engineering, Power Electronics.  

National Technical Information Service (NTIS)

Special features: Fuzzy control engineering (Preface to special issue on fuzzy control engineering, Recent trends in fuzzy control engineering, Shell system for fuzzy control, Fuzzy control of autonomous robot, Fuzzy-control system for transportation, Fuz...

1988-01-01

180

Fuzzy-adaptive-thresholding-based exon prediction.  

PubMed

Thresholding is always critical and decisive in many bioinformatics problems. In this paper, we propose and apply a fuzzy-logic-based adaptive thresholding approach to a well-known solution for the exon prediction problem, which uses a threshold on the frequency component at f = 1/3 in the nucleotide sequence. The proposed approach allows the thresholds to vary along the data set based on the local statistical properties. Experiments and results on the nucleotide data of Saccharomyces cerevisiae (Bakers yeast) illustrate the advantage of our approach. A user-friendly GUI in MATLAB is freely available for academic use at www.cs.iastate.edu/˜ankitag/FATBEP.html. PMID:21297230

Agrawal, Ankit; Mittal, Ankush; Jain, Rahul; Takkar, Raghav

2010-01-01

181

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

182

A Fuzzy Sliding Mode Control Based on Model Reference Adaptive Control for Permanent Magnet Synchronous Linear Motor  

Microsoft Academic Search

In order to meet the demands of high-acceleration\\/high-deceleration and high-precision motion control system, the direct-drive linear motor as the prime motion actuator is widely used in modern motion control systems such as machine tools, ranging from mass transportation to factory automation etc. Among these applications, permanent magnet synchronous linear motor (PMSLM) owing to their simple structure, easy of manufacture and

Yujie Zhao; Qingli Wang; Jinxue Xu; Chengyuan Wang

2007-01-01

183

Fuzzy logic control of an AGV  

NASA Astrophysics Data System (ADS)

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 expert system. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised by a 486 computer through a multi-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. This micro- controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected by a vision tracking device that transmits the X, Y coordinates of the lane marker to the control computer. Simulation and testing of these systems yielded promising results. This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.

Kelkar, Nikhal; Samu, Tayib; Hall, Ernest L.

1997-09-01

184

Fuzzy control method of the intelligent hydraulic impactor  

Microsoft Academic Search

This paper introduces a type of intelligent hydraulic impactor control system based on fuzzy control method, gives the fuzzy control strategy of intelligent hydraulic impactor including fuzzy controller frame, input, output and fuzzifying, anti-fuzzifying, fuzzy control method. In order to validate the correctness of the fuzzy control strategy for intelligent hydraulic impactor, we design the control programs of intelligent hydraulic

Guoping Yang; Cuiping Liang; Liang Wang; Chongchong Ding

2010-01-01

185

Fuzzy control of multivariable process by modified error decoupling.  

PubMed

In this paper, a control concept for the squared (equal number of inputs and outputs) multivariable process systems is given. The proposed control system consists of two parts, single loop fuzzy controllers in each loop and a centralized decoupling unit. The fuzzy control system uses feedback control to minimize the error in the loop and the decoupler uses an adaptive technique to mitigate loop interactions. The decoupler predicts the interacting loop changes and modifies the input (error) of the loop controller. The controller was tested on the simulation model of "single component vaporizer" process. The results indicate that the decoupling controller shows better performance for set point and load changes. PMID:12398275

Saravanan, S; Kher, Shubhalaxmi

2002-10-01

186

Synthesis of Nonlinear Control Strategies from Fuzzy Logic Control Algorithms.  

National Technical Information Service (NTIS)

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 ...

R. Langari

1993-01-01

187

A fuzzy call admission control scheme in wireless networks  

NASA Astrophysics Data System (ADS)

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy scheme with an adaptive channel reservation scheme. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

Ma, Yufeng; Gong, Shenguang; Hu, Xiulin; Zhang, Yunyu

2007-11-01

188

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

189

Fuzzy logic control and optimization system  

DOEpatents

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

190

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

191

Improved catfish particle swarm optimization with fuzzy adaptation  

Microsoft Academic Search

Catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization, which inspired by the behavior between sardines and catfish, i.e. the so-called catfish effect is applied to improve the performance of particle swarm optimization (PSO). In this paper, we propose an improved CatfishPSO with fuzzy adaptive (F-CatfishPSO), which a fuzzy system is implemented to dynamically adapt the inertia

Li-Yeh Chuang; Sheng-Wei Tsai; Cheng-Hong Yang

2009-01-01

192

Fuzzy switching control law design for helicopters  

Microsoft Academic Search

With handling qualities requirements ADS-33 taken as design criteria, the design problem of fuzzy switching control law for a helicopter in the whole flight envelope is investigated by using the fuzzy logic theory and H? control approach. For every linearized model in the fuzzy state-space model of a nonlinear system, a robust controller is designed using the GA-based H? controller

Jiyang Dai; Jiangqin Mao; Weiqiang Wan; Yanxia Li; Er Meng Joo

2002-01-01

193

Fuzzy-immune control strategy of a hydro-viscous soft start device of a belt conveyor  

Microsoft Academic Search

Considering the control difficulties of a hydro-viscous soft start (HVSS) device of a belt conveyor, a fuzzy-immune control algorithm was derived. A fuzzy-immune PID controller was designed based on immune feedback regulations and adaptability of the fuzzy logic inference. Using MATLAB software, we simulated the controller and compared the HVSS device with a conventional PID controller and a fuzzy PID

Fang-wei XIE; You-fu HOU; Zhi-peng XU; Rui ZHAO

2009-01-01

194

Neuro-fuzzy techniques for traffic control  

Microsoft Academic Search

Neuro-fuzzy techniques are proposed here to control each light of an intersection, at one-second intervals. Rules, fuzzification and inference are modeled by a neural network. For each signal, the neuro-fuzzy control selects between ‘switch on’ and ‘switch off’, and presents the required action to a Petri net. A neuro-fuzzy acceleration of Forward Dynamic Programming (FDP) is obtained by enumerating controls

J. J. Henry; J. L. Farges; J. L. Gallego

1998-01-01

195

Genetic multi-stage fuzzy PID controller with a fuzzy switch  

Microsoft Academic Search

A genetic algorithm is used to optimize the membership functions and rule bases of a multi-stage fuzzy PID controller with a fuzzy switch. The multistage controller uses the fuzzy switch to blend a proportional-plus-derivative fuzzy logic controller with an integral fuzzy logic input. The multi-stage structure operates on fuzzy values by passing the consequence of a prior stage onto the

JAMES M. ADAMS; KULDIP S. RATTAN

2001-01-01

196

A stability analysis of fuzzy control system using a generalized fuzzy Petri net model  

Microsoft Academic Search

Stability analysis of a fuzzy control system has been one of the main topics of fuzzy control. A Petri net, representing a discretized fuzzy control system, has been applied to the stability analysis. A theory of asymptotic stability has been derived for the approximated control system. However, this theory guarantees the stability of nominal behavior of a fuzzy control system.

Takeshi Furuhashi; H. Kakami; J. Peters; W. Pedrycz

1998-01-01

197

Fuzzy Control Of A Fluidized Bed Dryer  

Microsoft Academic Search

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and

A. V. Taprantzis; C. I. Siettos; G. V. Bafas

1997-01-01

198

Fuzzy control for robotic power grasp  

Microsoft Academic Search

This paper presents a fuzzy controller for a robotic power grasp system. The controller works to achieve three objectives: obtaining contact with all finger links and the palm, centering the object in the grasp, and controlling the link and palm normal forces such that they lie within a specified clinch range. To perform the control, fuzzy rules which exploit the

Matthew J. Sheridan; Stanley C. Ahalt; David E. Orin

1994-01-01

199

An improved robust fuzzy-PID controller with optimal fuzzy reasoning  

Microsoft Academic Search

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

200

Comments on fuzzy control systems design via fuzzy Lyapunov functions.  

PubMed

This paper considers the work entitled "Fuzzy control systems design via fuzzy Lyapunov functions" and published in IEEE Transactions on Systems, Man, and Cybernetics-Part B , where the authors try to extend the work of Rhee and Won. Nevertheless, the results proposed by Li have been obtained without taking into account a necessary path independency condition to ensure the line integral function to be a Lyapunov function candidate, and consequently, the proposed global asymptotic stability and stabilization conditions are unsuitable. PMID:19900850

Guelton, Kevin; Guerra, Thierry-Marie; Bernal, Miguel; Bouarar, Tahar; Manamanni, Noureddine

2010-06-01

201

Fuzzy control of a fluidized bed dryer  

SciTech Connect

Fluidized bed dryers are utilized in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for process nonlinearities and exhibits more robust behavior. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behavior, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.

Taprantzis, A.V.; Siettos, C.I.; Bafas, G.V. [National Technical Univ., Athens (Greece). Dept. of Chemical Engineering

1997-05-01

202

IPMSM sensorless control based on fuzzy active-disturbance rejection controller for electric vehicle  

Microsoft Academic Search

In the interior permanent magnet synchronous motor (IPMSM) sensorless control system for electric vehicle (EV), in order to improve system robustness and adaptive ability, a novel active-disturbance rejection controller (ADRC) is presented in this paper. In practice, the ADRC parameters are difficult to operate and adjust, so we introduce the fuzzy control, combined with their own characteristics, a self-adapted active

Shoudao Huang; Jiangchuan Kuang; Qing Huang; Keyuan Huang; Jian Gao; Ting Liu

2011-01-01

203

Analysis and design of fuzzy control systems using dynamic fuzzy-state space models  

Microsoft Academic Search

A discrete-time fuzzy control system which is composed of a dynamic fuzzy model and a fuzzy-state feedback controller is proposed. Stability of the fuzzy control system is discussed and two sufficient conditions to guarantee the stability of the system are given in terms of uncertain linear system theory. An algorithm is developed to check the stability condition. The controller design

Shu-Guang Cao; Neville W. Rees; Gang Feng

1999-01-01

204

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

205

H? fuzzy control with missing data  

Microsoft Academic Search

This paper investigates the problem of Hinfin fuzzy control of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the control strategy takes the form of parallel distributed compensation. The communication links, existing between the plant and controller, are assumed to be imperfect (that is, data-packet dropouts occur intermittently, which appear typically

Huijun Gao; Yan Zhao; Tongwen Chen

2007-01-01

206

The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor  

NASA Astrophysics Data System (ADS)

Based on analyzing mathematical mode of the permanent magnet linear synchronous motor (PMLSM), three-closed-loop control system is presented in this paper. Combined the advantages of traditional PID control algorithm and fuzzy control algorithm, according to the characteristics of linear motor and the possible factors of uncertainty, a set of adaptive fuzzy PID control system is designed for the speed loop of the proposed control system, moreover, fuzzy inference rules is established to realize the Fuzzy PID controlling of the speed loop. In the end, the simulation models of the motor and the whole control system are built on Matlab/Simulink platform to compare and analyze the fuzzy PID control and conventional PID control. Simulation results show that the designed fuzzy PID speed loop controller can significantly improve the response performance of linear motor.

Wu, Ying; Jiang, Hang; Zou, Min

207

Application of fuzzy predictive control in tubular heating furnace system  

Microsoft Academic Search

Tubular heating furnace system is a complicated controlled object. In this paper, based on the fuzzy control fuzzy predictive control (FPC) is proposed and applied to the oil temperature control of the tube furnace. The results show that the stability and response speed of the control system applying fuzzy predictive control has improved compared with a simple fuzzy control system.

Wei Sun; Xin Li; Jialiang Ye

2010-01-01

208

A fuzzy adaptive resonance theory—supervised predictive mapping neural network applied to the classification of multivariate chemical data  

Microsoft Academic Search

A fuzzy adaptive resonance theory—supervised predictive mapping (Fuzzy ARTMAP) neural network has been studied for the classification of multivariate chemical data. Fuzzy ARTMAP achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) by exploiting the close formal similarity between the computations of fuzzy subset membership and ART category choice, resonance, and learning. To examine the properties of Fuzzy

Xin-Hua Song; Philip K Hopke; MaryAnn Bruns; Deborah A Bossio; Kate M Scow

1998-01-01

209

Adaptive fuzzy approach for a class of uncertain nonlinear systems in strict-feedback form.  

PubMed

Adaptive fuzzy control is proposed for a class of affine nonlinear systems in strict-feedback form with unknown nonlinearities. The unknown nonlinearities include two types of nonlinear functions: one satisfies the "triangularity condition" and can be directly approximated by fuzzy logic system, while the other is assumed to be partially known and consists of parametric uncertainties. Takagi-Sugeno type fuzzy approximators are used to approximate unknown system nonlinearities and the design procedure is a combination of adaptive backstepping and generalized small gain design techniques. It is proved that the proposed adaptive control scheme can guarantee the uniformly ultimately bounded (UBB) stability of the closed-loop systems. Simulation studies are shown to illustrate the effectiveness of the proposed approach. PMID:18482726

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

2008-07-01

210

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

211

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

212

Adaptive fuzzy k-NN classifier for EMG signal decomposition  

Microsoft Academic Search

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

Sarbast Rasheed; Daniel Stashuk; Mohamed Kamel

2006-01-01

213

VSS Theory Based Training of a Fuzzy Motion Control System  

Microsoft Academic Search

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

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

214

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

215

Stability of fuzzy linguistic control systems  

Microsoft Academic Search

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

Gholamreza Langari; M. Tomizuka

1990-01-01

216

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

Microsoft Academic Search

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

Mandar Shriram Ambre

2004-01-01

217

A fuzzy control design case: The fuzzy PLL  

NASA Technical Reports Server (NTRS)

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

Teodorescu, H. N.; Bogdan, I.

1992-01-01

218

Systematic methods for the design of a class of fuzzy logic controllers  

NASA Astrophysics Data System (ADS)

Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental data, and a conversion algorithm, to develop a fuzzy-based control algorithm. Results were similar to those obtained by recently published conventional control based studies. The influence of the fuzzy inference operators and parameters on performance and stability of the fuzzy logic controller was studied Results indicated that, the selections of certain parameters or combinations of parameters, affect greatly the performance and stability of the fuzzy controller. Diagnostic guidelines used to tune or change certain factors or parameters to improve controller performance were developed based on knowledge gained from conventional control methods and knowledge gained from the experimental and the simulation results of this study.

Yasin, Saad Yaser

2002-09-01

219

Fuzzy Classifier System for Process Control.  

National Technical Information Service (NTIS)

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 classifie...

C. L. Karr J. C. Phillips

1994-01-01

220

Fuzzy technique based close formation flight control  

Microsoft Academic Search

This paper studies a fuzzy logic method for close formation control of multi-UAVs. To account for the aerodynamic impacts of leading UAV on the trailing UAV, a fuzzy algorithm is developed to track and maintain close relative planar spacing during close formation maneuver. The algorithm is refined using a self tuning technique without communication link to provide robust positioning of

Y. Li; Bin Li; Zhao Sun; Y. D. Song

2005-01-01

221

Adaptively Managing Wildlife for Climate Change: A Fuzzy Logic Approach  

NASA Astrophysics Data System (ADS)

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

Prato, Tony

2011-07-01

222

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

PubMed

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

Prato, Tony

2011-07-01

223

Type2 fuzzy airplane altitude control: A comparative study  

Microsoft Academic Search

The standard fuzzy logic controllers, also known as type-1 fuzzy logic controllers, have often been criticized for their inability to handle uncertainties in the control processes. Therefore, a lot of attention is being focused on type-2 fuzzy logic controllers, especially, the interval type-2 fuzzy logic controllers. This paper aims at developing both type-1 and type-2 fuzzy logic controllers for an

Sheir Afgen Zaheer; Jong-Hwan Kim

2011-01-01

224

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

Microsoft Academic Search

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

225

Decoupled fuzzy sliding-mode control  

Microsoft Academic Search

A decoupled fuzzy sliding-mode controller design is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear systems with only five fuzzy control rules. The ideas behind the controller are as follows. First, decouple the whole system into two second-order systems such that each subsystem has a separate control target expressed in

Ji-Chang Lo; Ya-Hui Kuo

1998-01-01

226

Implementation of a fuzzy logic/neural network multivariable controller  

SciTech Connect

This paper describes a multivariable controller developed at the Idaho National Engineering Laboratory (INEL) that incorporates both fuzzy logic rules and a neural network. The controller was implemented in a laboratory demonstration and was robust, producing smooth temperature and water level response curves with short time constants. In the future, intelligent control systems will be a necessity for optimal operation of autonomous reactor systems located on earth or in space. Even today, there is a need for control systems that adapt to the changing environment and process. Hybrid intelligent control systems promise to provide this adaptive capability. Fuzzy logic implements our imprecise, qualitative human reasoning. The values of system variables (controller inputs) and control variables (controller outputs) are described in linguistic terms and subdivided into fully overlapping value ranges. The fuzzy rule base describes how combinations of input parameter ranges determine the output control values. Neural networks implement our human learning. In this controller, neural networks were embedded in the software to explore their potential for adding adaptability.

Cordes, G.A.; Clark, D.E.; Johnson, J.A.; Smartt, H.B.; Wickham, K.L.; Larson, T.K. (Idaho National Engineering Lab., Idaho Falls (United States))

1992-01-01

227

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

228

Flexible complexity reduced PID-like fuzzy controllers  

Microsoft Academic Search

In this paper, a flexible complexity reduced design approach for PID-like fuzzy controllers is proposed. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controllers is significantly reduced. However, the performance of the complexity reduced fuzzy PID controller may be degraded since the degree of freedom is decreased

Chin-wang Tao; Jin-shiuh Taur

2000-01-01

229

An improved robust fuzzy-PID controller with optimal fuzzy reasoning.  

PubMed

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 reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller. PMID:16366252

Li, Han-Xiong; Zhang, Lei; Cai, Kai-Yuan; Chen, Guanrong

2005-12-01

230

A robust control of electro hydrostatic actuator using the adaptive back-stepping scheme and fuzzy neural networks  

Microsoft Academic Search

In general, the position control of electro hydrostatic actuator(EHA) systems is difficult because of the large variation\\u000a of the effective bulk modulus of the working fluid, which is due to the absence of a heat exchanger like a reservoir tank,\\u000a the friction between the cylinder and piston, and the external disturbance force. Moreover, it is difficult to identify the\\u000a values

Han Me Kim; Sung Hwan Park; Ji Min Lee; Jong Shik Kim

2010-01-01

231

Fuzzy rule-based controller for binary distillation column  

Microsoft Academic Search

In this paper a fuzzy logic based control scheme has been proposed for distillation column. Fuzzy Inference Systems (FIS) is proposed to adjust the manipulated variables to get the desired composition of products for a binary distillation column. To control the top and bottom product composition two separate fuzzy inference systems has been designed. The scheme uses fuzzy rules and

Amit Kumar Singh; Barjeev Tyagi; Vishal kumar

2011-01-01

232

A new fuzzy Lyapunov function approach for a Takagi-Sugeno fuzzy control system design  

Microsoft Academic Search

In this paper, a new fuzzy Lyapunov function approach is presented for a class of continuous-time Takagi–Sugeno fuzzy control system. The proposed fuzzy Lyapunov function is formulated as a line-integral of a fuzzy vector which is a function of the state, and it can be regarded as the work done from the origin to the current state in the fuzzy

Bong-jae Rhee; Sangchul Won

2006-01-01

233

Fuzzy controllers in nuclear material accounting  

SciTech Connect

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

Zardecki, A.

1994-10-01

234

Fuzzy Control/Space Station Automation.  

National Technical Information Service (NTIS)

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 g...

M. Gersh

1990-01-01

235

A proposal of intelligent vehicle control system by predictive fuzzy control with hierarchical temporary target setting  

Microsoft Academic Search

Adaptation is difficult due to complex obstacles. In this research, an intelligent vehicle control system using predictive fuzzy control is proposed by hierarchically combining temporary target settings based on a skilled driver. A hierarchical temporary target considers the dynamic characteristic of the vehicle and is based on the route. The effectiveness of the intelligent vehicle control system is shown by

Seiji Yasunobu; Naoki Minamiyama

1996-01-01

236

Fuzzy Modeling and Control of HIV Infection  

PubMed Central

The present study proposes a fuzzy mathematical model of HIV infection consisting of a linear fuzzy differential equations (FDEs) system describing the ambiguous immune cells level and the viral load which are due to the intrinsic fuzziness of the immune system's strength in HIV-infected patients. The immune cells in question are considered CD4+ T-cells and cytotoxic T-lymphocytes (CTLs). The dynamic behavior of the immune cells level and the viral load within the three groups of patients with weak, moderate, and strong immune systems are analyzed and compared. Moreover, the approximate explicit solutions of the proposed model are derived using a fitting-based method. In particular, a fuzzy control function indicating the drug dosage is incorporated into the proposed model and a fuzzy optimal control problem (FOCP) minimizing both the viral load and the drug costs is constructed. An optimality condition is achieved as a fuzzy boundary value problem (FBVP). In addition, the optimal fuzzy control function is completely characterized and a numerical solution for the optimality system is computed.

Zarei, Hassan; Kamyad, Ali Vahidian; Heydari, Ali Akbar

2012-01-01

237

Fuzzy modeling and control of HIV infection.  

PubMed

The present study proposes a fuzzy mathematical model of HIV infection consisting of a linear fuzzy differential equations (FDEs) system describing the ambiguous immune cells level and the viral load which are due to the intrinsic fuzziness of the immune system's strength in HIV-infected patients. The immune cells in question are considered CD4+ T-cells and cytotoxic T-lymphocytes (CTLs). The dynamic behavior of the immune cells level and the viral load within the three groups of patients with weak, moderate, and strong immune systems are analyzed and compared. Moreover, the approximate explicit solutions of the proposed model are derived using a fitting-based method. In particular, a fuzzy control function indicating the drug dosage is incorporated into the proposed model and a fuzzy optimal control problem (FOCP) minimizing both the viral load and the drug costs is constructed. An optimality condition is achieved as a fuzzy boundary value problem (FBVP). In addition, the optimal fuzzy control function is completely characterized and a numerical solution for the optimality system is computed. PMID:22536298

Zarei, Hassan; Kamyad, Ali Vahidian; Heydari, Ali Akbar

2012-01-01

238

Using building blocks to design analog neuro-fuzzy controllers  

Microsoft Academic Search

We present a parallel architecture for fuzzy controllers and a methodology for their realization as analog CMOS chips for low- and medium-precision applications. These chips can be made to learn through the adaptation of electrically controllable parameters guided by a dedicated hardware-compatible learning algorithm. Our designs emphasize simplicity at the circuit level-a prerequisite for increasing processor complexity and operation speed.

Fernando Vidal-Verdu; A. Rodriguez-Vazquez

1995-01-01

239

Adaptive fuzzy observer based synchronization design and secure communications of chaotic systems  

Microsoft Academic Search

This paper proposes a synchronization design scheme based on an alternative indirect adaptive fuzzy observer and its application to secure communication of chaotic systems. It is assumed that their states are unmeasurable and their parameters are unknown. Chaotic systems and the structure of the fuzzy observer are represented by the Takagi–Sugeno fuzzy model. Using Lyapunov stability theory, an adaptive law

Chang-Ho Hyun; Jae-Hun Kim; Euntai Kim; Mignon Park

2006-01-01

240

Indirect Adaptive Control  

Microsoft Academic Search

\\u000a Indirect adaptive control is a widely applicable adaptive control strategy. In real-time, it combines plant model parameter\\u000a estimation in closed loop with the redesign of the controller. Adaptive pole placement and its robustified version, together\\u000a with adaptive generalized predictive control constitute the core of the chapter. Adaptive linear quadratic control is also\\u000a presented. Application of various strategies for the indirect

Ioan Doré Landau; Rogelio Lozano; Mohammed M’Saad; Alireza Karimi

241

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

242

Learning and tuning fuzzy logic controllers through reinforcements  

NASA Technical Reports Server (NTRS)

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, 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 neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates 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

243

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

NASA Astrophysics Data System (ADS)

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

Smith, James F., III

2004-08-01

244

COMPARATIVE ANALYSIS OF CLASSICAL AND FUZZY PID CONTROL ALGORITHMS  

Microsoft Academic Search

A fuzzy PID controllers are physically related to classical PID controller. The settings of classical controllers is based on deep common physical background. Fuzzy controller can embody better behaviour comparing with classical linear PID controller because of its non linear characteristics. Well tuned fuzzy controller can be also more stable and more robust for the time varying systems. On the

Petr BLAHA

245

Fuzzy Controller Design and Stability Analysis for an Aircraft Model  

Microsoft Academic Search

Fuzzy control systems are generally considered applicable to processes that are mathematically ill-understood and where human experience is available for control rule synthesis. An equally important characteristic of fuzzy control is the ability to encompass qualitative and highly nonlinear control objectives. This ability makes fuzzy control applicable to processses for which the control objective cannot be adequately expressed in a

Stephen Chiu; Sujeet Chand

1991-01-01

246

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

247

Seizure prediction using adaptive neuro-fuzzy inference system.  

PubMed

In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. PMID:24110134

Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza

2013-01-01

248

The Study on Fuzzy\\/PID Composite Serial Control System with Smith Preditive Estimate for Main Steam Temperature in Thermal Power Plant  

Microsoft Academic Search

For the characteristics of the main steam temperature in thermal power station, the novel intelligent serial control system was proposed. The parallel cascade of fuzzy controller and PID controller was used in the main controller of system, the adaptive section was added in the general PD fuzzy control module, the gain adaptive Smith predictive estimator was adopted in the main

Qiming Cheng; Yonghao Wang

2006-01-01

249

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

250

ANFIS Controller with Fuzzy Subtractive Clustering Method to Reduce Coupling Effects in Twin Rotor MIMO System (TRMS) with Less Memory and Time Usage  

Microsoft Academic Search

In this paper, adaptive neural fuzzy inference system (ANFIS) and fuzzy subtractive clustering method (FSCM) were used to solve non-linearity, trajectory, and interaction problems of twin rotor MIMO system (TRMS). Basically, four fuzzy logic controllers (FLC) have been proposed to match the control objectives on TRMS. The four FLCs are considered as high consumers of memory and processing time relatively.

T. S. Mahmoud; Mohammed H. Marhaban; Tang S. Hong; Sokchoo Ng

2009-01-01

251

Uncovering transcriptional interactions via an adaptive fuzzy logic approach  

PubMed Central

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

2009-01-01

252

A transductive neuro-fuzzy controller: application to a drilling process.  

PubMed

Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. PMID:20659865

Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R

2010-07-01

253

Neural-Network-Based Fuzzy Logic Control and Decision System  

Microsoft Academic Search

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training

Chin-teng Lin; C. S. George Lee

1991-01-01

254

Application of adaptive fuzzy logic systems to model electric arc furnaces  

Microsoft Academic Search

Presents the application of adaptive fuzzy logic systems to modelling electric arc furnaces. The main objectives are to provide the rationale and to justify the use of fuzzy modeling for electric furnaces. This is done with reference to three important properties of fuzzy logic systems, namely their nonlinear black-box modeling capability, universal approximation ability and their functional equivalence to radial

A. R. Sadeghian; J. D. Lavers

1999-01-01

255

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

Microsoft Academic Search

Gunshots produce bruise patterns on persons who wear soft body armor when shot even though the armor stops the bullets. An adaptive fuzzy system modeled these bruise patterns based on the depth and width of the deformed armor given a projectile's mass and momentum. The fuzzy system used rules with sinc-shaped if-part fuzzy sets and was robust against random rule

Ian Lee; Bart Kosko; W. French Anderson

2005-01-01

256

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

Microsoft Academic Search

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

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

1991-01-01

257

Fuzzy sliding mode direct torque control for PMSM  

Microsoft Academic Search

A fuzzy sliding mode controller for permanent magnet synchronous machine (PMSM) is investigated in this paper, in which direct torque control (DTC) concept, fuzzy variable structure control and space vector modulation (SVM) are integrated to achieve high performance. It features in very low flux and torque ripple, strong robustness and fixed switching frequency. The theoretical analyses for the proposed fuzzy

Rui Guo; Xiuping Wang; Junyou Zhao; Wenbo Yu

2011-01-01

258

Optimization of fuzzy controllers for industrial manipulators via genetic algorithms  

Microsoft Academic Search

This paper describes a design procedure for the decentralized fuzzy control of a 5-dof robotic manipulator based on Genetic Algorithms (GAs). Compared to traditional PID, fuzzy controllers better lend themselves to the nonlinear, coupled dynamics of industrial manipulators, thanks to their universal approximation capabilities. In addition, GAs allow a full exploitation of the potentialities of fuzzy control, being able to

F. Cupertino; Vincenzo Giordano; David Naso; Luigi Salvatore; Biagio Turchiano

2003-01-01

259

Airplane level changes using fuzzy control  

Microsoft Academic Search

Decision making systems in air traffic management have become necessary over the last few years due to the increase in flights. This paper shows how fuzzy control sets the flight change levels that can be used to route flight or approximation area on the airports, which implies in a decrease of air traffic controller workload. The description of the problem

Agnaldo Volpe Lovato; J. C. M. Oliveira

2010-01-01

260

Fuzzy Decision in Airplane Speed Control  

Microsoft Academic Search

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

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

2006-01-01

261

Fuzzy Sequential Control Based On Petri Nets  

Microsoft Academic Search

Programmable Logic Controllers are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In case of simple regulation problems it could be of particular interest to introduce a notion of fuzzy events. Such an event corresponds to a continuous evolution from one state to another and results

J. C. Pascal; R. Valette; D. Andreu

1992-01-01

262

Fuzzy learning control for antiskid braking systems  

Microsoft Academic Search

Although antiskid braking systems (ABS) are designed to optimize braking effectiveness while maintaining steerability, their performance often degrades under harsh road conditions (e.g. icy\\/snowy roads). The use of the fuzzy model reference learning control (FMRLC) technique for maintaining adequate performance even under such adverse road conditions is proposed. This controller utilizes a learning mechanism that observes the plant outputs and

Jeffery R. Layne; Kevin M. Passino; Stephen Yurkovich

1993-01-01

263

Toward intelligent machining: hierarchical fuzzy control for the end milling process  

Microsoft Academic Search

The difficulties in implementing adaptive and other advanced control schemes in industrial machining processes have encouraged researchers to combine the utilization of one hierarchical level, a fuzzy control algorithm, and robust sensing systems. The main idea of this paper deals with self-regulating controllers (SRCs). The control signal's scaling factor (output scaling factor) is self-regulated during the control process, and it

R. E. Haber; C. R. Peres; A. Alique; S. Ros; C. Gonzalez; J. R. Alique

1998-01-01

264

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

265

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

266

An application of fuzzy set theory to inventory control models  

Microsoft Academic Search

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

Mitsuo Gen; Yasuhiro Tsujimura; Dazhong Zheng

1997-01-01

267

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

268

Adaptive inverse control  

Microsoft Academic Search

Methods for adaptive control of plant dynamics and for control of plant disturbance for unknown linear plants are described. In addition, extension of control of plant dynamics to nonlinear plants using neural networks is presented. For their proper application, the plant must be stable. An unstable plant could first be stabilized with feedback, then adaptively controlled

Bernard Widrow; Michel Bilello

1993-01-01

269

Autonomous Robot Motion Control Using Fuzzy PID Controller  

Microsoft Academic Search

\\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

270

A Laboratory Testbed for Embedded Fuzzy Control  

ERIC Educational Resources Information Center

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

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

2011-01-01

271

FUZZY LOGIC CONTROL OF AC INDUCTION MOTORS  

EPA Science Inventory

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

272

Fuzzy control of water desalination plants.  

National Technical Information Service (NTIS)

In this report we have chosen a sub-system of an MSF water desalination plant, the brine heater, for analysis, synthesis, and simulation. This system has been modelled and implemented on computer. A fuzzy logic controller (FLC) for the top brine temperatu...

A. Titli M. Jamshidi F. Olafsson

1995-01-01

273

Fuzzy Logic Control of AC Induction Motors.  

National Technical Information Service (NTIS)

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. Electric motors use 60% of the electr...

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

1992-01-01

274

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

275

The cognitive bases for the design of a new class of fuzzy logic controllers: The clearness transformation fuzzy logic controller  

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

276

A combined neural network and fuzzy systems based adaptive digital predistortion for RF power amplifier linearization  

Microsoft Academic Search

Linearization of nonlinear RF power amplifiers (PA) is an important issue when spectrally efficient modulation signals are used in mobile communications. Adaptive digital predistortion (ADP) is one promising linearization technique that can be employed. This paper presents a combined neural network and fuzzy systems based ADP for RF PA linearization. This hybrid approach employed is called adaptive neuro-fuzzy inference system

K. C. Lee; P. Gardner

2004-01-01

277

Adaptive nonlinear flight control  

Microsoft Academic Search

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

278

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

279

Fuzzy logic based position control of permanent magnet synchronous motor  

Microsoft Academic Search

In this paper, the performance of a fuzzy position controller of an interior permanent magnet synchronous motor for robotic applications is investigated. A fuzzy logic controller is employed as an outer position loop. Moreover, a synchronous frame PI controller is employed as an inner speed control loop. The controllers are designed based on the indirect field oriented control. The control

M. N. Uddin; T. S. Radwan; M. A. Rahman; G. H. George

2000-01-01

280

Memory control of tabu search with genetic fuzzy systems  

Microsoft Academic Search

This paper introduces a genetic fuzzy system to control short and long term memory of tabu search algorithms. The genetic fuzzy system involves learning of the knowledge base and a rule selection procedure. The aim is to trade-off exploration and exploitation behavior of the search, and to handle high dimensional optimization problems. The genetic fuzzy system approach introduces a high

Vitor Marques; Fernando Gomide

2010-01-01

281

Robust fuzzy control of nonlinear systems with parametric uncertainties  

Microsoft Academic Search

Addresses the robust fuzzy control problem for nonlinear systems in the presence of parametric uncertainties. The Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the nonlinear system. Two cases of the T-S fuzzy system with parametric uncertainties, both continuous-time and discrete-time cases are considered. In both continuous-time and discrete-time cases, sufficient conditions are derived for robust stabilization in

Ho Jae Lee; Jin Bae Park; Guanrong Chen

2001-01-01

282

Design of fuzzy system by NNs and realization of adaptability  

NASA Technical Reports Server (NTRS)

The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

Takagi, Hideyuki

1993-01-01

283

Induction Motor Speed Control via Fuzzy Logic Modification of Reference Model  

Microsoft Academic Search

A novel model reference adaptive control scheme using reference model modification through fuzzy logic to control the speed of an induction motor is presented. This scheme is developed to cope with uncertainties and disturbances that the plant under control might undergo where a single reference model can not handle. The main concept of the proposed philosophy is to ensure automatic

Khalid S. Al-Olimat; Adel A. Ghandakly; Sukumar Kamalasadan

2007-01-01

284

Tuning of an industrial fuzzy logic controller  

Microsoft Academic Search

This paper presents an experimental study on the tuning of an industrial fuzzy logic controller (FLC) for a shell-and-tube heat exchanger system. The objective of the experiment is to study the effect of FLC's scaling factors (e, ?e ?u) to temperature controllability of the heat-exchanger system. Various values for e, ?e and ?u were set to FLC, and the process

Abdul Aziz Ishak; Dinah Fadhilah Nasir; Sharliza Mohamad

2010-01-01

285

Simulation and Evaluation of PID Control and Fuzzy Control  

Microsoft Academic Search

Under the control object and the structure of control system built, we set the range of parameters and the objective function and get the best parameters of PID control of the system by genetic algorithm, making use of MATLAB we can do the modeling and simulation of the PID control and fuzzy control. The results of simulation show that the

Haixia Zhang; Changqing Li

2010-01-01

286

Hybrid compensation control for affine TSK fuzzy control systems.  

PubMed

The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models. In the approach, by combining two different control design methodologies, the proposed controller is designed to compensate all rules so that the desired control performance can appear in the overall system. Our approach treats all fuzzy rules as variations of a nominal rule and such variations are individually dealt with in a Lyapunov sense. Previous approaches have proposed a similar idea but the variations are dealt with as a whole in a robust control sense. As a consequence, when fuzzy rules are distributed in a wide range, the stability conditions may not be satisfied. In addition, the control performance of the closed-loop system cannot be anticipated in those approaches. Various examples were conducted in our study to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired. PMID:15462451

Hsiao, Chih-Ching; Su, Shun-Feng; Lee, Tsu-Tian; Chuang, Chen-Chia

2004-08-01

287

Fuzzy control for closed-loop, patient-specific hypnosis in intraoperative patients: a simulation study.  

PubMed

Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzy controller demonstrated clinically acceptable performance, suggesting that further study is warranted. PMID:19963562

Moore, Brett L; Pyeatt, Larry D; Doufas, Anthony G

2009-01-01

288

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

289

Control Law for Automatic Landing Using Fuzzy Logic Control  

Microsoft Academic Search

The effectiveness of fuzzy logic control law for automatic landing of aircraft, which cover both of control to lead aircraft from level flight at an altitude of 500m to the flight on the glide-path course near the runway and control for the aircraft to land smoothly on a runway, was studied. The control law of the automatic landing was designed

Akio Kato; Yoshiki Inagaki

2007-01-01

290

Control Law for Automatic Landing Using Fuzzy-Logic Control  

Microsoft Academic Search

The effectiveness of a fuzzy-logic control law for automatically landing an aircraft that handles both the control to lead an aircraft from horizontal flight at an altitude of 500 meters to flight along the glide-path course near the runway, as well as the control to direct the aircraft to land smoothly on a runway, was investigated. The control law for

Akio Kato; Yoshiki Inagaki

2008-01-01

291

TS fuzzy model identification and the fuzzy model based controller design  

Microsoft Academic Search

This paper presents an algorithm to identify T-S fuzzy models and design fuzzy model based controllers (FMBC) for a class of nonlinear plant. First, the algorithm using fuzzy c-regression models (FCRM) clustering to find the functional relationships in the product space of the input-output data. A new cluster validity criterion is proposed to calculate overall compactness and separateness of the

Chung-chun Kung; Jui-yiao Su

2007-01-01

292

Gain-phase margin analysis of dynamic fuzzy control systems.  

PubMed

In this paper, we apply some effective methods, including the gain-phase margin tester, describing function and parameter plane, to predict the limit cycles of dynamic fuzzy control systems with adjustable parameters. Both continuous-time and sampled-data fuzzy control systems are considered. In general, fuzzy control systems are nonlinear. By use of the classical method of describing functions, the dynamic fuzzy controller may be linearized first. According to the stability equations and parameter plane methods, the stability of the equivalent linearized system with adjustable parameters is then analyzed. In addition, a simple approach is also proposed to determine the gain margin and phase margin which limit cycles can occur for robustness. Two examples of continuous-time fuzzy control systems with and without nonlinearity are presented to demonstrate the design procedure. Finally, this approach is also extended to a sampled-data fuzzy control system. PMID:15503509

Perng, Jau-Woei; Wu, Bing-Fei; Chin, Hung-I; Lee, Tsu-Tian

2004-10-01

293

Takagi-Sugeno fuzzy control of a synchronous machine  

Microsoft Academic Search

This paper describes the results of an application of a PDC type T-S fuzzy control to synchronous machine. The non linear mathematical model for the proposed synchronous machine adopted in this work is described by T-S continuous fuzzy models. Next, the control law for T-S fuzzy control based on PDC design is studied. The stability and the stabilisation of the

OUAALINE Najat; ELALAMI Noureddine

2010-01-01

294

Fuzzy control of ionic polymer-metal composites.  

PubMed

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

295

Study on the application of the fuzzy logic controllers (II).  

National Technical Information Service (NTIS)

We have studied how to generate a fuzzy logic controller whose output is identical to that of a given PI controller. Based on this study, we have analyzed what makes the fuzzy logic controller perform better than the PI controller. We have also designed a...

B. S. Moon B. S. Lee K. S. Han J. S. Moon S. B. Hong

1993-01-01

296

Neuro-fuzzy synthesis of flight control electrohydraulic servo  

Microsoft Academic Search

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

Ioan Ursu; Felicia Ursu; Lucian Iorga

2001-01-01

297

Adaptive Cruise Control  

NASA Astrophysics Data System (ADS)

Mit Adaptive Cruise Control, abgekürzt ACC, wird eine Fahrgeschwindigkeitsregelung bezeichnet, die sich an die Verkehrssituation anpasst. Synonyme Bezeichnungen sind Aktive Geschwindigkeitsregelung, Automatische Distanzregelung oder Abstandsregeltempomat. Im englischen Sprachraum fnden sich die weiteren Bezeichnungen Active Cruise Control, Automatic Cruise Control oder Autonomous Intelligent Cruise Control. Als markengeschützte Bezeichnungen sind Distronic und Automatische Distanz-Regelung (ADR) eingetragen.

Winner, Hermann; Danner, Bernd; Steinle, Joachim

298

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

299

A fuzzy logic controller for aircraft flight control  

Microsoft Academic Search

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

300

Designing fuzzy logic controllers for DC servomotors supported by fuzzy logic control development environment  

Microsoft Academic Search

The design of advanced controllers for many industrial processes is heavily dependent on the availability of a model for the process. Construction of appropriate models is often not possible due to the complexity and nonlinearity of the process. Fuzzy logic can be used for different tasks within intelligent control systems because they represent general, nonlinear relationships that can be initialized

Maki K. Habib

2001-01-01

301

Intelligent fuzzy controller for event-driven real time systems  

NASA Technical Reports Server (NTRS)

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

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

1992-01-01

302

Random fuzzy chance-constrained programming based on adaptive chaos quantum honey bee algorithm and robustness analysis  

Microsoft Academic Search

This paper proposes an adaptive chaos quantum honey bee algorithm (CQHBA) for solving chance-constrained programming in random\\u000a fuzzy environment based on random fuzzy simulations. Random fuzzy simulation is designed to estimate the chance of a random\\u000a fuzzy event and the optimistic value to a random fuzzy variable. In CQHBA, each bee carries a group of quantum bits representing\\u000a a solution.

Han Xue; Xun Li; Hong-Xu Ma

2010-01-01

303

An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images  

Microsoft Academic Search

The paper presents a novel adaptive fuzzy evidential nearest neighbor formulation for classifying remotely sensed images. The formulation combines the generalized fuzzy version of the Dempster-Shafer evidence theory (DSET) and the K-nearest neighbor (KNN) algorithm. Each of the K nearest neighbors provides evidence on the belongingness of the input pattern to be classified, and it is evaluated based on a

Hongwei Zhu; Otman Basir

2005-01-01

304

Analysis of complex radar data sets using fuzzy adaptive resonance theory map  

Microsoft Academic Search

This paper will evaluate one promising method used to solve one of the main problems in electronic warfare. This problem is the identification of radar signals in a tactical environment. The identification process requires two steps: clustering of collected radar pulse descriptor words and the classification of clustered results. The method described here, Fuzzy Adaptive Resonance Theory Map (Fuzzy ARTMAP)

Michael J. Thompson; John C. Sciortino Jr.

2004-01-01

305

Synchronization and secure communication of chaotic systems via robust adaptive high-gain fuzzy observer  

Microsoft Academic Search

This paper proposes an alternative robust adaptive high-gain fuzzy observer design scheme and its application to synchronization and secure communication of chaotic systems. It is assumed that their states are immeasurable and their parameters are unknown. The structure of the proposed observer is represented by Takagi–Sugeno fuzzy model and has the integrator of the estimation error. It improves the performance

Chang-Ho Hyun; Chang-Woo Park; Jae-Hun Kim; Mignon Park

2009-01-01

306

Fuzzy Stochastic Automata for Reactive Learning and Hybrid Control  

Microsoft Academic Search

Fuzzy Stochastic Automata (FSA) are suitable for the modelling of the reactive (memoryless) learning and for the control of\\u000a hybrid systems. The concept of FSA is to switch between a fuzzy increase and a fuzzy decrease of the control action according\\u000a to the sign of the product e e, where e = x-x\\u000a d is the error of the system’s

Gerasimos G. Rigatos; Rion Patras

2002-01-01

307

A fuzzy-logic-based controller for active rectifier  

Microsoft Academic Search

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

308

Mixed analog-digital fuzzy logic controller with continuous-amplitude fuzzy inferences and defuzzification  

Microsoft Academic Search

A fuzzy logic controller has been realized using mixed analog-digital CMOS very large scale integration (VLSI) circuits for application in cases where the input and output variables are in analog form. It employs a new architecture where time sweeping of variables allows continuous-amplitude evaluation of fuzzy inferences and defuzzification during each evaluation cycle without having to discretize input and output

Stamatis Bouras; Manousos Kotronakis; Ken Suyama; Yannis Tsividis

1998-01-01

309

Design of robust fuzzy-model-based controller with sliding mode control for SISO nonlinear systems  

Microsoft Academic Search

In this paper, we present the design of a new type of fuzzy controllers for controlling complex single-input–single-output systems by incorporating sliding mode control theory with fuzzy control technology. First, a fuzzy model of the given nonlinear system is constructed to represent the local dynamic behaviors of the given nonlinear system. A global controller is then constructed by combining all

Wook Chang; Jin Bae Park; Young Hoon Joo; Guanrong Chen

2002-01-01

310

Application of fuzzy control in direct torque control of permanent magnet synchronous motor  

Microsoft Academic Search

For the problem of big torque ripple in direct torque control of permanent magnetic synchronous motor (PMSM DTC), a PMSM DTC method based on fuzzy control is proposed. The hysteresis controller in conventional PMSM DTC is replaced by a two-input fuzzy controller. The output of the fuzzy controller is added to a vector table to decide which vector should be

Jun Liu; PuSheng Wu; HuaYu Bai; Xiping Huang

2004-01-01

311

Economic load dispatch using intelligent optimization with fuzzy control  

Microsoft Academic Search

In this paper, Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm is proposed to tackle the economic load dispatch problem. To provide the self-terminating ability, a technique called Iteration Windows (IW) is introduced to govern the number of iteration in each searching stage during the optimization. The size of IW is controlled by a fuzzy controller, which

Johnny C. Y. Lai; Frank H. F. Leung; Sai-Ho Ling; Edwin C. Shi

2011-01-01

312

Learning and tuning fuzzy logic controllers through reinforcements  

Microsoft Academic Search

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths

Hamid R. Berenji; Pratap Khedkar

1992-01-01

313

Self-Learning Fuzzy Controllers Based on Temporal Back Propagation  

Microsoft Academic Search

This paper presents a generalized control strategy that enhances fuzzy controllers with selflearningcapability for achieving prescribed control objectives in a near-optimal manner. Thismethodology, termed temporal back propagation, is model-insensitive in the sense that it candeal with plants that can be represented in a piecewise differentiable format, such as differenceequations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of

J. sr. Jang

1992-01-01

314

Fuzzy logic surge control in constant speed centrifugal compressors  

Microsoft Academic Search

This paper presents the application of fuzzy logic active control of surge in constant speed centrifugal compressors based on the Moore-Greitzer (MG) model. A compression system equipped with a close-coupled valve (CCV) and a throttle control valve (TCV) is investigated. Two fuzzy controllers are developed, one for each valve. The combination of the two valves proves helpful in suppressing surge

Raef S. Shehata; Hussein A. Abdullah; Fayez F. G. Areed

2008-01-01

315

Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.  

PubMed

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently. PMID:23757441

Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

2013-06-01

316

Modal control of a plate using a fuzzy logic controller  

NASA Astrophysics Data System (ADS)

This paper presents fuzzy logic based independent modal space control (IMSC) and fuzzy logic based modified independent modal space control (MIMSC) of vibration. The rule base of the controller consists of nine rules, which have been derived based upon simple human reasoning. Input to the controller consists of the first two modal displacements and velocities of the structure and the output of the controller is the modal force to be applied by the actuator. Fuzzy logic is used in such a way that the actuator is never called to apply effort which is beyond safe limits and also the operator is saved from calculating control gains. The proposed fuzzy controller is experimentally tested for active vibration control of a cantilevered plate. A piezoelectric patch is used as a sensor to sense vibrations of the plate and another piezoelectric patch is used as an actuator to control vibrations of the plate. For analytical formulation, a finite element method based upon Hamilton's principle is used to model the plate. For experimentation, the first two modes of the plate are observed using a Kalman observer. Real-time experiments are performed to control the first mode, the second mode and both modes simultaneously. Experiments are also performed to control the first mode by IMSC, the second mode by IMSC and both modes simultaneously by MIMSC. It is found that for the same decibel reduction in the first mode, the voltage applied by the fuzzy logic based controller is less than that applied by IMSC. While controlling the second mode by IMSC, a considerable amount of spillover is observed in the first mode and region just after the second mode, whereas while controlling the second mode by fuzzy logic, spillover effects are much smaller. While controlling two modes simultaneously, with a single sensor/actuator pair, appreciable resonance control is observed both with fuzzy logic based MIMSC as well as with direct MIMSC, but there is a considerable amount of spillover in the off-resonance region. This may be due to the sub-optimal location and/or an insufficient number of actuators. So, another smart plate with two piezoelectric actuators and one piezoelectric sensor is considered. Piezoelectric patches are fixed in an area where modal strains are high. With this configuration of the smart plate, experiments are conducted to control the first three modes of the plate and it is found that spillover effects are greatly reduced.

Sharma, Manu; Singh, S. P.; Sachdeva, B. L.

2007-08-01

317

Fuzzy Delay Compensation Control for T-S Fuzzy Systems Over Network.  

PubMed

This paper is concerned with the network delay compensation problem for nonlinear networked control systems (NCSs). By taking full advantage of the characteristics of the packet-based transmission in NCSs, new network delay compensation approaches are proposed to actively compensate the network communication delay under the fuzzy control framework. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the predictive control input packets are constructed based on parallel distributed compensation technique. Both state and output feedback fuzzy delay compensation controllers are designed. Finally, two examples are provided to illustrate the effectiveness and applicability of the developed techniques. PMID:22801520

Zhang, Jinhui; Shi, Peng; Xia, Yuanqing

2012-07-11

318

Stability analysis of fuzzy control systems subject to uncertain grades of membership.  

PubMed

This paper presents relaxed stability conditions for fuzzy control systems subject to parameter uncertainties. As the parameter uncertainties introduce uncertain grades of membership to the fuzzy control systems, the favorable property offered by sharing the same premises in the fuzzy plant models and fuzzy controllers cannot be employed to enhance the stabilization ability of the fuzzy control systems. To widen the applicability of the fuzzy control approach, fuzzy control systems subject to uncertain grades of membership will be investigated. New relaxed stability conditions will be derived to guarantee the stability of this class of fuzzy control systems. A numerical example will be given to show the effectiveness of the proposed approach. PMID:16366257

Lam, H K; Leung, F H F

2005-12-01

319

Fuzzy logic control for an automated guided vehicle  

NASA Astrophysics Data System (ADS)

This paper describes the use of fuzzy logic control for the high level control systems of a mobile robot. The advantages of the fuzzy logic system are that multiple types of input such as that from vision and sonar sensors as well as stored map information can be used to guide the robot. Sensor fusion can be accomplished between real time sensed information and stored information in a manner similar to a human decision maker. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected through a commercial tracking device, communicating to the computer the X,Y coordinates of a lane marker. Testing of these systems yielded positive results by showing that at five miles per hour, the vehicle can follow a line and avoid obstacles. The obstacle detection uses information from Polaroid sonar detection system. The motor control system uses a programmable Galil motion control system. This design, in its modularity, creates a portable autonomous controller that could be used for any mobile vehicle with only minor adaptations.

Cao, Ming; Hall, Ernest L.

1998-10-01

320

Robust Fuzzy Controllers Using FPGAs.  

National Technical Information Service (NTIS)

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 techniqu...

A. G. S. Monroe

2007-01-01

321

Fuzzy logic control of AC induction motors  

NASA Astrophysics Data System (ADS)

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

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

322

Reduced order fuzzy sliding mode control for linear synchronous motor systems  

Microsoft Academic Search

This paper proposed a new fuzzy sliding mode controller that integrates the fuzzy logic control and the sliding mode control method. The proposed controller allows designers to employ switching function as the fuzzy logic control input directly in permanent magnet linear synchronous motor systems. Furthermore, the fuzzy logic control rule is independent of the number of system state variables, and

Yi-Sheng Huang; Cheng-Chung Sung; Chi-Shan Yu

2010-01-01

323

Fuzzy Control for Closed-Loop, Patient-Specific Hypnosis in Intraoperative Patients: A Simulation Study  

Microsoft Academic Search

Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the

Brett L. Moore; Larry D. Pyeatt; Anthony G. Doufas

2009-01-01

324

Resilient Adaptive Control  

Microsoft Academic Search

This Chapter takes on some steps further along the way towards the controller fragility and performance deterioration issues due to inaccuracies in controller implementation. Interestingly enough, it addresses the problem of resilient adaptive control problem for classes of uncertain continuous-time and discrete-time systems with state-delays against controller gain variations. In the continuous case, design results on both norm-bounded and convexbounded

Magdi S. Mahmoud

325

Adaptive Cruise Control  

Microsoft Academic Search

Mit Adaptive Cruise Control, abgekürzt ACC, wird eine Fahrgeschwindigkeitsregelung bezeichnet, die sich an die Verkehrssituation\\u000a anpasst. Synonyme Bezeichnungen sind Aktive Geschwindigkeitsregelung, Automatische Distanzregelung oder Abstandsregeltempomat.\\u000a Im englischen Sprachraum fnden sich die weiteren Bezeichnungen Active Cruise Control, Automatic Cruise Control oder Autonomous\\u000a Intelligent Cruise Control. Als markengeschützte Bezeichnungen sind Distronic und Automatische Distanz-Regelung (ADR) eingetragen.

Hermann Winner; Bernd Danner; Joachim Steinle

2009-01-01

326

A Fuzzy Controller for a Health Services Mobile Robot  

Microsoft Academic Search

The purpose of this paper is to present the devolvement of a fuzzy control applied for a hospitals and health care centres mobile concept robot, the i-MERC. The robot and fuzzy controller models are presented as well as simulation results. The simulation application that was developed included the possibility to change de bending radius of the curves. To analyze the

Fernando Carreira; Tomé Canas; Joao Sousa; Carlos Cardeira

2007-01-01

327

Fuzzy Control for Cyclist Robot Stability Using FPGAs  

Microsoft Academic Search

This paper presents a fuzzy controller implementation in FPGA (Field Programmable Gate Array) for a robot that rides a bicycle using the well-known Acrobot model. The overall system presents a hardware\\/software codesign approach and it was achieved by means of a Microblaze FPGA embedded processor and a fuzzy controller, which was implemented directly in hardware. Both the microprocessor and the

C. Yesid E. Castro; Carlos H. Llanos; Walter de Britto Vidal Filho; Leandro dos Santos Coelho

2009-01-01

328

Fuzzy successive modelling and control for time-delay systems  

Microsoft Academic Search

In this paper, a procedure for constructing a fuzzy model of a given system associated with a digital fuzzy filter is proposed. Based on the structure of the Smith predictor, a hybrid model predictive control approach is also presented. Two experimental results using (1) an analogue simulator and (2) a blower temperature control rig have been illustrated. It shows that

MING-JYI JANG; CHIEH-LI CHEN

1996-01-01

329

Longitudinal stability augmentation using a fuzzy logic based PID controller  

Microsoft Academic Search

We develop a PID based fuzzy logic pitch attitude hold system for a typical fighter jet under a variety of performance conditions that include approach, subsonic cruise and supersonic cruise. In this approach, the gains found in a classic PID controller are replaced with fuzzy systems, still contributing the overall effects of a proportional, integral, and derivative controller. The response

Andrew Vick; Kelly Cohen

2009-01-01

330

A FUZZY APPROACH TO ACTIVE SURGE CONTROL OF CENTRIFUGAL COMPRESSORS  

Microsoft Academic Search

The operating range of aerodynamic compressors is usually limited by a phenomenon known as surge. Active surge control has showed the ability to extend the operating range significantly. This study presents a solution to this problem based on the fuzzy logic approach. A simple fuzzy controller is designed to suppress the surge instability on a given compressor model. Simulation studies

Salim Hamed; Thunaiyan Al-Mawali; Jie Zhang

331

Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking  

Microsoft Academic Search

In this paper, a genetic tracker with adaptive neuro-fuzzy inference system (GT-ANFIS) is presented for multiple target tracking (MTT). First, the data association problem, formulated as an N-dimensional assignment problem, is solved using the genetic algorithm (GA), and then the inaccuracies in the estimation are corrected by the adaptive neuro-fuzzy inference system (ANFIS). The performances of the GT-ANFIS, the joint

Ilke Turkmen; Kerim Guney

2008-01-01

332

Adaptive neuro-fuzzy inference system (ANFIS) digital predistorter for RF power amplifier linearization  

Microsoft Academic Search

This paper describes an adaptive digital predistorter (ADP) for RF power amplifier (PA) linearization using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS predistorter (PD) employs the advantage of real-time modeling of the PA's responses in determining the PD's functions. The amplitude and phase corrections for the PD are represented in an easy-to-understand fuzzy if-then rule, while the parameters involved

Kok Chew Lee; Peter Gardner

2006-01-01

333

Application of fuzzy logic operation and control to BWRs  

SciTech Connect

Fuzzy logic control schemes employing linguistic decision rules for flexible operator control strategies have undergone application tests in dynamic systems. The advantages claimed for fuzzy logic control are its abilities: (a) to facilitate direct use of skillful operator know-how for automatic operation and control of the systems and (b) to provide robust multivariable control for complex plants. The authors have also studied applications of fuzzy logic control to automatic startup operations and load-following control in boiling water reactors, pursuing these same advantages.

Junichi Tanji; Mitsuo Kinoshita; Takaharu Fukuzaki; Yasuhiro Kobayashi (Hitachi Ltd., Energy Research Lab., Ibaraki (Japan))

1993-01-01

334

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

335

Fuzzy Logic Application-Specific Processor for Traffic Control in ATM Network  

Microsoft Academic Search

Fuzzy logic appears a promising approach to address many important aspects of networks, particularly the traffic control in ATM (Asynchronous Transfer Mode) network. In this paper we first investigate a fuzzy logic based model for traffic control in ATM. ATM traffic model and traffic control using fuzzy controllers are first simulated using MatLab. Then, an application specific fuzzy controller is

Zoran Salcic

336

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

Microsoft Academic Search

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

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

2008-01-01

337

Design of Energy Management Strategy in Hybrid Vehicles by Evolutionary Fuzzy System Part I: Fuzzy Logic Controller Development  

Microsoft Academic Search

In this two-part paper presents an evolutionary fuzzy design method based energy management strategy for parallel hybrid electric vehicles. The proposed method consists of two steps. In the first phase, a fuzzy logic controller was employed to quantify the energy management system behavior. In the second stage, a genetic algorithm was used for fine-tuning the parameters of the fuzzy controller

Aihua Wang; Weizi Yang

2006-01-01

338

Fuzzy Decision Making with Control Applications.  

National Technical Information Service (NTIS)

Since Lotfi Zadeh's introductory paper in 1965, the fuzzy set theory and the applications of fuzzy systems have come a long way. The initial hesitation, even the hostile reaction to fuzzy set theory has left its place to enthusiasm, or at least tolerance ...

U. Kaymak

1998-01-01

339

On fuzzy logic speed control for vector controlled AC motors  

Microsoft Academic Search

The paper deals with the fuzzy logic control(ler) (FLC) application to the field-oriented AC motor drive. Some fundamentals of the FLC are illustrated. The aspects of major importance in the application to field-oriented AC motor drives are pointed out and discussed. A FLC field-oriented drive is designed, simulated and experimented in a speed control loop. The results are compared with

D. Fodor; Z. Katona; J. Vass

1996-01-01

340

Variable Universe Fuzzy Controller with Correction Factors for Ball and Beam System  

Microsoft Academic Search

Traditional fuzzy controller is a interpolator in mathematic essence. The ball and beam system has defects of steady state error and low respond speed under traditional fuzzy control method. Therefore, a variable universe fuzzy control method with correction factors is proposed in this paper to solve those problems. By adjusting the fuzzy control rule and changing the universe on real

Beibei Hou; Yan Gao

2011-01-01

341

A fuzzy call admission control scheme in wireless networks  

Microsoft Academic Search

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy

Yufeng Ma; Shenguang Gong; Xiulin Hu; Yunyu Zhang

2007-01-01

342

Fuzzy logic control of a solar power plant  

Microsoft Academic Search

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

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

1995-01-01

343

Fuzzy control of the compressor speed in a refrigeration plant  

Microsoft Academic Search

In this paper, referring to a vapor compression refrigeration plant subjected to a commercially available cold store, a control algorithm, based on the fuzzy logic and able to select the most suitable compressor speed in function of the cold store air temperature, is presented. The main aim is to evaluate the energy saving obtainable when the fuzzy algorithm, which continuously

C. Aprea; R. Mastrullo; C. Renno

2004-01-01

344

Fuzzy Petri net Implementation for Programmable Logic Controllers  

Microsoft Academic Search

Abstract The concept of fuzzy reasoning has been extended to Petri nets and has been ,applied for modeling ,of discrete event systems [1]. However, the theory was not extended to supervisory,control of discrete event systems,because ,there is no ,valid translation for fuzzy Petri nets into ladder logic diagrams. Discrete event systems ,are time and event ,dependent ,and hence digital in

P. r. Venkateswaran; Jayadev Bhat; V. i. George

2006-01-01

345

Adaptive sequential controller  

DOEpatents

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

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

1994-01-01

346

Adaptive vehicle traction force control for intelligent vehicle highway systems (IVHSs)  

Microsoft Academic Search

This paper is concerned with robust longitudinal control of vehicles in intelligent vehicle highway systems by adaptive vehicle traction force control. Two different traction force controllers, adaptive fuzzy logic control and adaptive sliding-mode control, are proposed and applied to the fastest stable acceleration\\/deceleration and robust vehicle platooning problems. The motivation for investigating adaptive techniques arises from the unknown time-varying nature

Hyeongcheol Lee; M. Tomizuka

2003-01-01

347

Type-2 fuzzy model based controller design for neutralization processes.  

PubMed

In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures. PMID:22036014

Kumbasar, Tufan; Eksin, Ibrahim; Guzelkaya, Mujde; Yesil, Engin

2012-03-01

348

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation  

NASA Astrophysics Data System (ADS)

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

Ibrahim, Wael Refaat Anis

349

Adaptive Cruise Control (ACC)  

NASA Astrophysics Data System (ADS)

Die adaptive Fahrgeschwindigkeitsregelung (ACC, Adaptive Cruise Control) ist eine Weiterentwicklung der konventionellen Fahrgeschwindigkeitsregelung, die eine konstante Fahrgeschwindigkeit einstellt. ACC überwacht mittels eines Radarsensors den Bereich vor dem Fahrzeug und passt die Geschwindigkeit den Gegebenheiten an. ACC reagiert auf langsamer vorausfahrende oder einscherende Fahrzeuge mit einer Reduzierung der Geschwindigkeit, sodass der vorgeschriebene Mindestabstand zum vorausfahrenden Fahrzeug nicht unterschritten wird. Hierzu greift ACC in Antrieb und Bremse ein. Sobald das vorausfahrende Fahrzeug beschleunigt oder die Spur verlässt, regelt ACC die Geschwindigkeit wieder auf die vorgegebene Sollgeschwindigkeit ein (Bild 1). ACC steht somit für eine Geschwindigkeitsregelung, die sich dem vorausfahrenden Verkehr anpasst.

Reif, Konrad

350

Fuzzy control of a hand rehabilitation robot to optimize the exercise speed in passive working mode.  

PubMed

The robotic rehabilitation devices can undertake the difficult physical therapy tasks and provide improved treatment procedures for post stroke patients. During passive working mode, the speed of the exercise needs to be controlled continuously by the robot to avoid excessive injurious torques. We designed a fuzzy controller for a hand rehabilitation robot to adjust the exercise speed by considering the wrist angle and joint resistive torque, measured continuously, and the patient's general condition, determined by the therapist. With a set of rules based on an expert therapist experience, the fuzzy system could adapt effectively to the neuromuscular conditions of the patient's paretic hand. Preliminary clinical tests revealed that the fuzzy controller produced a smooth motion with no sudden change of the speed that could cause pain and activate the muscle reflexive mechanism. This improves the recovery procedure and promotes the robot's performance for wide clinical usage. PMID:21335755

Baniasad, Mina Arab; Akbar, Mohammad; Alasty, Aria; Farahmand, Farzam

2011-01-01

351

Fuzzy logic control: a knowledge-based system perspective  

NASA Astrophysics Data System (ADS)

We view fuzzy logic control technology as a high level language in which we can efficiently define and synthesize non-linear controllers for a given process. We contrast fuzzy proportional integral (PI) controllers with conventional PI and 2D sliding mode controllers. Then we compare the development of fuzzy logic controllers (FLC) with that of knowledge-based system (KBS) applications. We decompose the comparison into reasoning tasks (representation, inference, and control) and application tasks (acquisition, development, validation, compilation and deployment). After reviewing the reasoning tasks, we focus on the compilation of fuzzy rule bases into fast access lookup tables. These tables can be used by a simplified run-time engine to determine the FLC's crisp output for a given input. Finally we illustrate the application of FLC technology in a hierarchical architecture to control a complex power plant for heavy vehicles.

Bonissone, Piero P.; Chiang, Kenneth H.

1993-12-01

352

Fuzzy explicit marking: A unified congestion controller for Best-Effort and DiffServ networks  

Microsoft Academic Search

This paper proposes a generic AQM (Active Queue Management) control methodology in TCP\\/IP networks, based on fuzzy logic control. A simple, effective and efficient nonlinear control law is built, using a linguistic model of the system, rather than a traditional mathematical model, which is easily adapted in different network environments (e.g. Best-Effort and Differentiated-Services architectures). We demonstrate, via extensive simulative

Chrysostomos Chrysostomou; Andreas Pitsillides; Y. Ahmet Sekercioglu

2009-01-01

353

Adaptive Pruefmaschinenkorrektur (Adaptive Correction of Control Machines).  

National Technical Information Service (NTIS)

A data reduction method which is under adaptive correction of an error control value of the analog control system of a servohydraulic control machine was examined. For the use of the analog control no special care is needed. The composition of the correct...

J. H. Argyris W. Aicher J. Ertelt

1983-01-01

354

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

355

Automatic control of biomass gasifiers using fuzzy inference systems.  

PubMed

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated. PMID:16697183

Sagüés, C; García-Bacaicoa, P; Serrano, S

2007-03-01

356

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

PubMed Central

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.

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

357

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

PubMed

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

358

Helicopter flight control with fuzzy logic and genetic algorithms  

Microsoft Academic Search

Researchers at the U.S. Bureau of Mines, in conjunction with researchers at the University of Alabama and the U.S. Army, have developed a fuzzy system for controlling the flight of UH-1 helicopters through various maneuvers. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over

Greg Walker

1996-01-01

359

Implementation of an Intelligent Control System Using Fuzzy ITI  

Microsoft Academic Search

This paper proposes a new multi-strategy (hybrid) intelligent control technique whose concept is applicable to the control\\u000a of a wide range of processes. The proposed technique uses Incremental Tree Induction (ITI) as the learning algorithm, and\\u000a incorporates fuzzy logic to deal with uncertainties apparent in the process to be controlled. ITI operates solely on symbolic\\u000a fuzzy knowledge, as both the

G. H. Shah Hamzei; D. J. Mulvaney

2000-01-01

360

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

NASA Astrophysics Data System (ADS)

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

Zhang, Mei; Zheng, Meng; Li, Yanqiu

2013-12-01

361

Damping Local and InterArea Oscillations with Adaptive Neuro-Fuzzy Power System Stabilizer  

Microsoft Academic Search

In this paper, the authors report on the design, simulation and validation of an adaptive neuro-fuzzy inference system (ANFIS) based power system stabilizer (PSS) for a single-machine-infinite-bus (SMIB) and a multi-machine power system and investigate its performance in damping low frequency local and inter-area oscillations. The design employs a first order Sugeno fuzzy model, whose parameters are tuned off-line through

P. Mitra; S. Chowdhury; S. K. Pal; Y. H. Song; G. A. Taylor

2006-01-01

362

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

PubMed

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

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

2010-07-01

363

Turbine speed control system based on a fuzzy-PID  

NASA Astrophysics Data System (ADS)

The flexibility demand of marine nuclear power plant is very high, the multiple parameters of the marine nuclear power plant with the once-through steam generator are strongly coupled, and the normal PID control of the turbine speed can’t meet the control demand. This paper introduces a turbine speed Fuzzy-PID controller to coordinately control the steam pressure and thus realize the demand for quick tracking and steady state control over the turbine speed by using the Fuzzy control’s quick dynamic response and PID control’s steady state performance. The simulation shows the improvement of the response time and steady state performance of the control system.

Sun, Jian-Hua; Wang, Wei; Yu, Hai-Yan

2008-12-01

364

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

PubMed

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

365

Fuzzy control in image qualities of holographic optical elements (HOE)  

NASA Astrophysics Data System (ADS)

A fuzzy control model in image formation qualities of holograms manufactured in dichromated gelatin; Agfa 8E75 and other holographic recording material are described. A new model based on the fuzzy set theory is presented to control spectral shifting from the frequency of construction laser beam to the frequency of request. We are concerned with new simplified procedures inserted in ordinary hologram manufacturing process. From the modulation mechanism in these holograms we seize the intrinsic qualities and our model of fuzzy set analysis show to produce good general agreement with the experimentally measured image formation qualities.

Chang, Rong-Seng; Lin, Chern-Sheng

1994-05-01

366

Fuzzy Petri net-based programmable logic controller.  

PubMed

Programmable logic controllers (PLCs) are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In the case of simple regulation problems between two steps it could be of great interest to introduce a notion of "fuzzy events" in order to denote a continuous evolution from one state to another. This could result from a linear interpolation between the commands attached to two control steps represented by two Petri net (PN) places. This paper is an attempt to develop fuzzy PN-based PLCs in a similar way as fuzzy controllers (regulators). Our approach is based on a combination of Petri nets with possibility theory (Petri nets with fuzzy markings). PMID:18263103

Andreu, D; Pascal, J C; Valette, R

1997-01-01

367

Knowledge-Based Fuzzy Control of Pilot-Scale SBR for Wastewater Treatment  

Microsoft Academic Search

\\u000a A fuzzy controller to optimize oxic phase of sub-cycle in pilot scale SBR (working volume, 20m3) located at public swine wastewater treatment plant was investigated. The operation mode of intermittent feeding of raw water\\u000a and sub-cycle with repeating anoxic-aeration conditions were adapted to avoid the high-strength nitrogen inhibition. In sub-cycle,\\u000a aeration time for ammonium oxidation was tried to control by

Byong-hee Jun; Jang-hwan Park; Myung-geun Chun

2005-01-01

368

Fuzzy-neural based multi-agent strategy for biped motion control  

Microsoft Academic Search

In this paper the problem of motion control of biped is considered. We develop a new method based on multi-agent associated Fuzzy-Neural and Adaptive Stochastic Petri Nets strategy. This method deals with organization and coordination aspects in an intelligent modeling of human motion. We propose a cooperative multi-agent model. Based on this model, we develop a control kernel named Ih4COK

A. Khoukhi; L. Khoukhi

1999-01-01

369

Adaptive control for accelerators  

DOEpatents

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

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

1991-01-01

370

Hardware design of programmable fuzzy controller on FPGA  

Microsoft Academic Search

This paper presents a hardware approach for the design of a programmable fuzzy controllers. The architecture of the design is integrated on XILINX FPGA employing a first order Takagi-Sugeno's approach. To demonstrate the performances of the programmable fuzzy controllers, a system non-linear pendulum is used. This first version has been implemented by means of three blocks: D\\/A and A\\/D converters,

N. Masmoudi; M. Hachicha; L. Kamoun

1999-01-01

371

Nonlinear rescaling of control values simplifies fuzzy control  

NASA Technical Reports Server (NTRS)

Traditional control theory is well-developed mainly for linear control situations. In non-linear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts' knowledge is usually represented in non-numeric terms, namely, in terms of uncertain statements of the type 'if the obstacle is straight ahead, the distance to it is small, and the velocity of the car is medium, press the brakes hard'. Fuzzy control is a methodology that translates such statements into precise formulas for control. The necessary first step of this strategy consists of assigning membership functions to all the terms that the expert uses in his rules (in our sample phrase these words are 'small', 'medium', and 'hard'). The appropriate choice of a membership function can drastically improve the quality of a fuzzy control. In the simplest cases, we can take the functions whose domains have equally spaced endpoints. Because of that, many software packages for fuzzy control are based on this choice of membership functions. This choice is not very efficient in more complicated cases. Therefore, methods have been developed that use neural networks or generic algorithms to 'tune' membership functions. But this tuning takes lots of time (for example, several thousands iterations are typical for neural networks). In some cases there are evident physical reasons why equally space domains do not work: e.g., if the control variable u is always positive (i.e., if we control temperature in a reactor), then negative values (that are generated by equal spacing) simply make no sense. In this case it sounds reasonable to choose another scale u' = f(u) to represent u, so that equal spacing will work fine for u'. In the present paper we formulate the problem of finding the best rescaling function, solve this problem, and show (on a real-life example) that after an optimal rescaling, the un-tuned fuzzy control can be as good as the best state-of-art traditional non-linear controls.

Vanlangingham, H.; Tsoukkas, A.; Kreinovich, V.; Quintana, C.

1993-01-01

372

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

373

Fuzzy adaptive management of social and ecological carrying capacities for protected areas.  

PubMed

Commonly used methods of evaluating the degree of consistency of protected area ecosystems with social and ecological carrying capacities are likely to result in decision errors. This occurs because such methods do not account for imprecision and uncertainty in inferring the degree of ecosystem consistency from an observed ecosystem indicator. This paper proposes a fuzzy adaptive management approach to determine whether a protected area ecosystem is consistent with ecological and social carrying capacities and, if not, to identify management actions that are most likely to achieve consistency when there is uncertainty about the current degree of consistency and how alternative management actions are likely to influence that consistency. The proposed approach is illustrated using a hypothetical example that uses an ecosystem indicator that reflects combinations of different levels of user satisfaction and conservation of threatened and endangered species. Application of the proposed fuzzy adaptive management approach requires a protected area manager to: (1) identify alternative management actions for achieving ecosystem consistency with social and ecological carrying capacities in each of several management zones in a protected area; (2) randomly assign alternative management actions to management zones; (3) define fuzzy sets for the ecosystem indicator and degree of ecosystem consistency, and fuzzy relations between the ecosystem indicator and the degree of ecosystem consistency; (4) monitor the indicator in each management zone; (5) define fuzzy sets based on the observed indicator in each management zone; and (6) combine the fuzzy sets defined on the observed indicator and the fuzzy relations between the indicator and the degree of ecosystem consistency to reach conclusions about the most likely degree of consistency for alternative management actions in each management zone. The fuzzy adaptive management approach proposed here is advantageous when the benefits of avoiding the decision errors inherent with crisp and stochastic decision rules outweigh the added cost of implementing the approach. PMID:19233541

Prato, Tony

2009-06-01

374

Identification of Restoring Forces in Non-Linear Vibration Systems Using Fuzzy Adaptive Neural Networks  

NASA Astrophysics Data System (ADS)

The fuzzy adaptive back-propagation (FABP) algorithm which combines fuzzy theory with artificial neural network techniques is applied to the identification of restoring forces in non-linear vibration systems. Simulated results show that the FABP algorithm is effective for the identification of dynamic systems. The FABP algorithm not only increases the training speed of the network, but also decreases the artificial interference of network parameters to a certain extent. Based upon the FABP algorithm, an improved scheme with a mutation mechanism is presented in this paper. The improved fuzzy adaptive BP (IFABP) algorithm extends the effectiveness and adaptivity of the FABP algorithm still further. The successful estimation of simulated systems show that a feasible method of identification is provided, which can be used to estimate the restoring forces in non-linear vibrating systems quickly and effectively.

LIANG, Y. C.; FENG, D. P.; COOPER, J. E.

2001-04-01

375

Neuro-fuzzy controller to navigate an unmanned vehicle.  

PubMed

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

Selma, Boumediene; Chouraqui, Samira

2013-12-01

376

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

NASA Astrophysics Data System (ADS)

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

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

2010-09-01

377

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

NASA Astrophysics Data System (ADS)

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

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

2011-01-01

378

Research of Hybrid Fuzzy-PID Control Technology based on the Temperature and Humidity Control  

Microsoft Academic Search

Aim at such drawbacks of conventional PID control as nonlinearity, time-change uncertainty, design depending on math model of controlled object, difficulty in PID parameter ascertaining, fuzzy technology is introduced into the conventional PID control in this study. Such hybrid fuzzy-PID control technology is applied in a container fruit process system for controlling its temperature and humidity, and application results demonstrate

Liu Hong; Xu Jinhua

2008-01-01

379

The Research of Photovoltaic Charging System Based on Fuzzy Controller  

Microsoft Academic Search

In a photovoltaic system, the output of solar cells, load and the discharge of accumulators are indefinite quantities, generally, it can not obtain a satisfied effect with controlling the charge of accumulators by using the classical control methods. Therefore, in the paper, a fuzzy control theory is applied to the charge controller in PV system. A solar photovoltaic charging controller

Weiping Luo

2009-01-01

380

Altitude control system of autonomous airship based on fuzzy logic  

Microsoft Academic Search

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

381

A robust speed control of Ac motor drives based on fuzzy reasoning  

Microsoft Academic Search

Fuzzy-logic based control of an induction motor is discussed. The controller uses the indirect vector control method to decouple the motor current components. The fuzzy controller uses a simple rule set obtained by a modification of the parameters of a linear controller. Results obtained via simulation are reported, showing the excellent behavior of the fuzzy controller

E. Galvan; F. Barrero; M. A. Aguirre; A. Torralba; L. G. Franquelo

1993-01-01

382

Control synthesis of continuous-time T-S fuzzy systems with local nonlinear models.  

PubMed

This paper is concerned with the problem of designing fuzzy controllers for a class of nonlinear dynamic systems. The considered nonlinear systems are described by T-S fuzzy models with nonlinear local models, and the fuzzy models have fewer fuzzy rules than conventional T-S fuzzy models with local linear models. A new fuzzy control scheme with local nonlinear feedbacks is proposed, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). In contrast to the existing methods for fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Moreover, the local nonlinear feedback laws in the new fuzzy controllers are also helpful in achieving good control effects. Numerical examples are given to illustrate the effectiveness of the proposed method. PMID:19336311

Dong, Jiuxiang; Wang, Youyi; Yang, Guang-Hong

2009-10-01

383

Identification of uncertain nonlinear systems for robust fuzzy control.  

PubMed

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

384

Terminal sliding mode fuzzy control based on multiple sliding surfaces for nonlinear ship autopilot systems  

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

385

Fuzzy controller design for passive continuous-time affine T-S fuzzy models with relaxed stability conditions.  

PubMed

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

386

Reduced order adaptive controller studies  

NASA Technical Reports Server (NTRS)

The use of a reduced-order adaptive controller, can arise from a desire to reduce control complexity with a commensurate reduction in controller sensitivity or from necessity in an attempt to use a finite dimensional controller on an infinite dimensional system. An interest in developing adaptive controllers for flexible structures by application of existing lumped-parameter system (LPS) adaptive controller strategies to truncated expansion descriptions of the distributed parameter system (DPS) behavior of flexible structures has led to two qualitative descriptions of the misbehavior of reduced-order adaptive controllers. A summary is provided of these interpretations of the additional difficulties facing reduced-order adaptive controllers, which are bypassed by exact-order adaptive controllers. A test problem, which initializes attempts to quantify the qualitative insights, is also formulated.

Johnson, C. R., Jr.; Balas, M. J.

1980-01-01

387

Implementation of a new fuzzy vector control of induction motor.  

PubMed

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

388

Adaptive control of continuous pulp digesters based on radial basis function neural network models  

Microsoft Academic Search

In this paper, an adaptive nonlinear Model Predictive Control (MPC) configuration is proposed, where the model used for predicting the future behavior of the process is a discrete dynamic Radial Basis Function (RBF) network. The innovative fuzzy clustering algorithm allows the continuous and easy adaptation of the model, thus making it suitable for controlling time-varying processes, such as the continuous

Alex Alexandridis; Haralambos Sarimveis; George Bafas

2003-01-01

389

Backstepping based fuzzy logic control of active vehicle suspension systems  

Microsoft Academic Search

In this paper, we present a backstepping based fuzzy logic (FL) scheme for the active control of vehicle suspension systems using the two-degrees of freedom or 1\\/4 car model. The full dynamics of a novel hydraulic strut are considered. The servo-valve dynamics are also included. A fuzzy logic system is used to estimate the nonlinear hydraulic strut dynamics. The backstepping

J. Campos; F. L. Lewis; L. Davis; S. Ikenaga

2000-01-01

390

BACKSTEPPING BASED FUZZY LOGIC CONTROL OF ACTIVE VEHICLE SUSPENSION SYSTEMS  

Microsoft Academic Search

In this paper, we present a backstepping based fuzzy logic (FL) scheme for the active control of vehicle suspension systems using the two-degrees of freedom or 1\\/4 car model. The full dynamics of a novel hydraulic strut are considered. The servo-valve dynamics are also included. A fuzzy logic system is used to estimate the nonlinear hydraulic strut dynamics. The backstepping

J. Campos; F. L. Lewis; L. Davis; S. Ikenaga

1999-01-01

391

Adaptive fuzzy sliding mode active queue management algorithms  

Microsoft Academic Search

Active queue management (AQM) is aimed at achieving the tradeoff between link utilization and queuing delay to enhance TCP\\u000a congestion control and is expected to perform well for a wider-range of network conditions. Static AQM schemes despite their\\u000a simplicity, often suffer from long response time due to conservative parameter setting to ensure stability. Adaptive parameter\\u000a settings, which might solve this

Xinping Guan; Bo Yang; Bin Zhao; Gang Feng; Cailian Chen

2007-01-01

392

A decentralized adaptive robust method for chaos control  

NASA Astrophysics Data System (ADS)

This paper presents a control strategy, which is based on sliding mode control, adaptive control, and fuzzy logic system for controlling the chaotic dynamics. We consider this control paradigm in chaotic systems where the equations of motion are not known. The proposed control strategy is robust against the external noise disturbance and system parameter variations and can be used to convert the chaotic orbits not only to the desired periodic ones but also to any desired chaotic motions. Simulation results of controlling some typical higher order chaotic systems demonstrate the effectiveness of the proposed control method.

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-09-01

393

A decentralized adaptive robust method for chaos control.  

PubMed

This paper presents a control strategy, which is based on sliding mode control, adaptive control, and fuzzy logic system for controlling the chaotic dynamics. We consider this control paradigm in chaotic systems where the equations of motion are not known. The proposed control strategy is robust against the external noise disturbance and system parameter variations and can be used to convert the chaotic orbits not only to the desired periodic ones but also to any desired chaotic motions. Simulation results of controlling some typical higher order chaotic systems demonstrate the effectiveness of the proposed control method. PMID:19791991

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-09-01

394

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

Microsoft Academic Search

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

395

Automatic Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device  

Microsoft Academic Search

A hybrid track seeking fuzzy controller for the optical data storage device is proposed in this paper. It was shown that the proposed hybrid fuzzy controller smoothes the applied voltage to the sled motor and improves the track seeking efficiency. The proposed hybrid fuzzy controller consists of two subsystems including parking time controller and driving force controller. Both subsystems are

Leehter Yao; Po-Zhao Huang

2006-01-01

396

A PID type fuzzy controller with self-tuning scaling factors  

Microsoft Academic Search

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

397

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

Microsoft Academic Search

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

398

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

399

Analysis of Fuzzy Control for Permanent Magnet Synchronous Motor  

Microsoft Academic Search

This paper presents the design, analysis and speed control of a permanent magnet synchronous (PMSM) motor with a speed controller to improve the performances of the motor. To reduce torque ripples and improve dynamic performance, a fuzzy controller for permanent magnet synchronous motor drive is presented, which replaces the conventional hysteresis controller. Results of simulation are provided to demonstrate that

H. Soleimani Bidgoli

400

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

401

Adaptive servo visual robot control  

Microsoft Academic Search

Adaptive controllers for robot positioning and tracking using direct visual feedback with camera-in-hand configuration are proposed in this paper. The controllers are designed to compensate for full robot dynamics. Adaptation is introduced to reduce the design sensitivity due to robot and payload dynamics uncertainties. It is proved that the control system achieves the motion control objective in the image coordinate

Oscar Nasisi; Ricardo Carelli

2003-01-01

402

Soft-computing techniques for the development of adaptive helicopter flight controller  

Microsoft Academic Search

In this paper we design an on-line controller which is able to modify and adapt the rule base of the system with just only qualitative knowledge about the plant to be controlled. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over four individual control

I. Rojas; H. Pomares; J. Gonzalez; L. J. Herrera; A. Guillen; F. Rojas

2006-01-01

403

Fuzzy Kalman Filter based trajectory estmation  

Microsoft Academic Search

This paper presents an algorithm of fuzzy based Kalman filter for trajectory estimation of dynamical objects. The Fuzzy subsystem is designed to tune dynamically the process noise covariance matrix of the discrete time Kalman Filter. The main adaptation strategy is based on the heuristic knowledge\\/practical expertise of the human observer\\/control engineer. The Fuzzy Kalman Filter attempts to offset some of

N. Yadaiah; Tirunagari Srikanth; V. Seshagiri Rao

2011-01-01

404

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

405

Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.  

PubMed

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

Nagarale, Ravindrakumar M; Patre, B M

2014-05-01

406

Fuzzy sampled-data control for uncertain vehicle suspension systems.  

PubMed

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

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

2014-07-01

407

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

408

Simulation Research of Fuzzy-PID Synthesis Yaw Vector Control System of Wind Turbine  

Microsoft Academic Search

Enhancing the stability and robust of yawing system effectively, carrying out the simulation research of fuzzy-PID synthesis control. Designing the yawing vector control system with the synthesis controller of fuzzy-PID, modeling the system with Matlab simulation software, and taking simulation test. Comparing the simulation curves with common PID control and fuzzy PID subsection control, the result indicate that the fuzzy-PID

PIAO HAIGUO; WANG ZHIXIN

409

Fuzzy Control of Flexible-Link Manipulators: A Review  

NASA Technical Reports Server (NTRS)

Several recent research efforts are reviewed here which have applied fuzzy logic in control of flexible-link manipulators. A flexible robot is a distributed parameter system represented by complex nonlinear dynamics, its actuator and the control parameters are non-colocated, and lastly, unstructured/unknown parameters play a significant role in model dynamics of a flexible robot operating in the real world. As a result, control of flexible robots is considered a promising area for application of intelligent control methodologies such as fuzzy logic, genetic algorithms, and neural networks.

Akbarzadeh-T, M.-R.; Quintana, S.; Jamshidi, M.

1998-01-01

410

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system  

Microsoft Academic Search

Eighteen anthropometric measurements were taken in standing and sitting positions, from 387 subjects between 15 and 17 years old. “Adaptive Neuro-Fuzzy Inference System (ANFIS)” was used to estimate anthropometric measurements as an alternative to stepwise regression analysis. Six outputs (shoulder width, hip width, knee height, buttock-popliteal height, popliteal height, and height) were selected for estimation purpose. The results showed that

M. Dursun Kaya; A. Samet Hasiloglu; Mahmut Bayramoglu; Hakki Yesilyurt; A. Fahri Ozok

2003-01-01

411

Total margin based adaptive fuzzy support vector machines for multiview face recognition  

Microsoft Academic Search

Multiview face recognition is a very difficult pattern recognition problem due to its large variation. And support vector machine (SVM) can serve as a robust classifier for its excellent generalization ability. This paper proposes a new class called total margin based adaptive fuzzy support vector machines (TAF-SVM) to deal with the some problems that may occur in SVM when applied

Yi-Hung Liu; Yen-Ting Chen

2005-01-01

412

Adaptive neuro-fuzzy inference system for oscillometric blood pressure estimation  

Microsoft Academic Search

This paper presents a novel approach using principal component analysis (PCA) and adaptive neuro-fuzzy inference system (ANFIS) for estimation of blood pressure (BP) from oscillometric waveforms. The proposed method consists of three stages. In the first stage, the oscillation amplitudes (OAs) of the oscillometric waveforms are represented as a function of the cuff pressure. In the second stage, the PCA

Mohamad Forouzanfar; Hilmi R. Dajani; Voicu Z. Groza; Miodrag Bolic; Sreeraman Rajan

2010-01-01

413

Adaptive neuro-fuzzy inference system based maximum power point tracking of a solar PV module  

Microsoft Academic Search

This paper presents and analyses the operation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based maximum power point tracker (MPPT) for solar photovoltaic (SPV) energy generation system. The MPPT works on the principle of adjusting the voltage of the solar PV modules by changing the duty ratio of the boost converter. The duty ratio of boost converter is calculated for

A. Iqbal; H. Abu-Rub; S. M. Ahmed

2010-01-01

414

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

415

A demosaicing algorithm based on adaptive edge sensitive and fuzzy assignment in CMOS image sensor  

Microsoft Academic Search

In order to ensure the PSNRs of the demosaiced images of CMOS image sensor and reduce the computational cost at the same time, the proposed demosaicing algorithm in this paper improves on adaptive edge sensitive algorithm and fuzzy assignment algorithm. In order to estimate the direction of edges more accurately, it adds two adjacent pixels of the current pixel as

Ge Zhiwei; Yao Suying; Xu Jiangtao

2010-01-01

416

Adaptive Neuro-Fuzzy Inference System for Computing the Resonant Frequency of Circular Microstrip Antennas.  

National Technical Information Service (NTIS)

A new method for computing the resonant frequency of the circular microstrip antenna, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. A hybrid learning algorithm is used to identify the parameters of ANFIS. The results of the new...

K. Guney N. Sarikaya

2004-01-01

417

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

National Technical Information Service (NTIS)

Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/A...

H. A. Malki R. Langari S. Kottapalli

2003-01-01

418

Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents  

Microsoft Academic Search

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart

Elif Derya Übeyli

2009-01-01

419

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders  

Microsoft Academic Search

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is presented for detection of ophthalmic arterial (OA) disorders. Decision making was performed in two stages: feature extraction using the discrete wavelet transform (DWT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of OA Doppler signals

Elif Derya Übeyli

2008-01-01

420

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series  

Microsoft Academic Search

The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA)

M. EROL KESKIN; DILEK TAYLAN; ÖZLEM TERZI

2006-01-01

421

Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals  

Microsoft Academic Search

In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were

Elif Derya Übeyli; ?nan Güler

2005-01-01

422

Adaptive personalization of multimodal vehicular interfaces using a hybrid recommendation approach with fuzzy preferences  

Microsoft Academic Search

User-adaptive and situation-aware presentation of information on multimodal interfaces is an important research area for coping with the increasing information load on the driver. This paper describes a comprehensive recommendation approach for inferring vague individual preferences under uncertain conditions by using fuzzy preference relations. The approach was applied to rank internet information on a multimodal driver interface in the vehicle.

Philipp Fischer; Michael Cebulla; Andr ´ e Berton; A. Nurnberger; Sandro Rodriguez Garzon

2008-01-01

423

Application of a fuzzy learning algorithm to nuclear steam generator level control  

Microsoft Academic Search

In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the

Gee Yong Park; Poong Hyun Seong

1995-01-01

424

PID-fuzzy logic hybrid controller for grid-connected photovoltaic inverters  

Microsoft Academic Search

This paper presents a novel PID-Fuzzy logic hybrid controller to improve the dynamic response of the output active power and reactive power of a grid connected PV inverter. The combination of PID and flexible fuzzy logic controller makes the control of nonlinear objects, such as the electricity grid, more feasible. The proposed integrated PID- Fuzzy logic controller module automatically varies

Nguyen Gia Minh Thao; Mai Tuan Dat; Tran Cong Binh; Nguyen Huu Phuc

2010-01-01

425

Real time experimental study of truck backer upper problem with fuzzy controller  

Microsoft Academic Search

Truck backer-upper system is a typical problem in nonlinear motion control of nonholonomic systems. This paper recommends a fuzzy control design method based on a hierarchical scheme for this problem. The fuzzy controller is a combination of two fuzzy modules. The rules of each module have been obtained from heuristic knowledge and numerical data. The goal of the control system

P. Shahmaleki; M. Mahzoon; B. Ranjbar

2008-01-01

426

Application of temperature fuzzy controller in an indirect resistance furnace  

Microsoft Academic Search

The paper presents the application results of a fuzzy controller of temperature and its rate of change in indirect resistance chamber furnaces. The method of an initial controller tuning based on the computer simulations is described, where the modelling of the furnace appears as a special problem. Further controller tuning was done based on tests performed on the real furnace.

Z. R. Radakovic; V. M. Milosevic; S. B. Radakovic

2002-01-01

427

Fuzzy PID controller used in yaw system of Wind Turbine  

Microsoft Academic Search

Yaw system plays an important role in wind turbine generator because of the direction and intensity of wind is time-varying. Because the model of yaw system is difficult to be set up and some parameters of controller are uncertain. General PID controller is not suit for all operation scope. This paper presents a fuzzy PID controller to deal with the

Fu-qing Chen; Jin-ming Yang

2009-01-01

428

Control of voluntary limb movements by using a fuzzy system  

Microsoft Academic Search

We propose a novel scheme for controlling multiple-joint limb movements based on the neuromuscular system, which dictates biological limb movements. The proposed control scheme generates appropriate control signals to bring the limbs to various desired positions under different velocities and loads. A fuzzy system, representing the nervous system of the neuromuscular system, is developed to generate the motor commands, and

Kuu-Young Young; Cheng-Chu Fan

1993-01-01

429

Adaptive Fuzzy Zone Routing for Wireless Ad Hoc Networks  

Microsoft Academic Search

In this chapter we introduce how to implement fuzzy logic for ad hoc networks. Ad hoc networks are characterized by multi-hop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. Designing ad hoc routing protocols is complicated by the fact that every host is moving, leading to the dynamic nature of the topology. Routing and

Tawan Thongpook

2005-01-01

430

Adaptive control of harmonic drives  

Microsoft Academic Search

In this paper, an adaptive control algorithm is designed for controlling the harmonic drives used to drive robot manipulators. Direct torque measurement is available by using the flexspline mounted strain-gauges. The torque error is added to the required velocity. Adaptive friction compensation and flexspline dynamics based control are the two main contributions in the paper. The L2\\/L? stability and the

Wen-Hong Zhu; Michel Doyon

2004-01-01

431

Design of fuzzy logic controllers for genetic programming  

NASA Astrophysics Data System (ADS)

Genetic programming (GP) is applied to the design of fuzzy logic controllers (FLCs) for mobile robot path tracking. GP is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller. GP was successfully applied to discover FLCs capable of steering a mobile robot to track straight-line paths in the plane. Instances of simultaneous evolution of membership functions and rules showed that GP was capable of evolving a FLC that demonstrated satisfactory responsiveness to various initial conditions while utilizing minimal human interface.

Tong, Ren

2005-12-01

432

Experimental Studies in Nonlinear Discrete-Time Adaptive Prediction and Control  

Microsoft Academic Search

This brief paper presents implementation results using recently introduced discrete-time adaptive prediction and control techniques using on-line function approximators. We consider a process control experiment as our test bed, and develop a discrete-time adaptive predictor for liquid volume and a discrete-time adaptive controller for reference volume tracking. We use Takagi-Sugeno fuzzy systems as our function approximators, and for both prediction

Raúl Ordóñez; Jeffrey T. Spooner; Kevin M. Passino

2006-01-01

433

Mixed H2\\/H? fuzzy output feedback control design for nonlinear dynamic systems: an LMI approach  

Microsoft Academic Search

This study introduces a mixed H2\\/H? fuzzy output feedback control design method for nonlinear systems with guaranteed control performance. First, the Takagi-Sugeno fuzzy model is employed to approximate a nonlinear system. Next, based on the fuzzy model, a fuzzy observer-based mixed H2\\/H? controller is developed to achieve the suboptimal H2 control performance with a desired H? disturbance rejection constraint. A

Bor-Sen Chen; Chung-Shi Tseng; Huey-Jian Uang

2000-01-01

434

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

435

Full design of fuzzy controllers using genetic algorithms  

NASA Technical Reports Server (NTRS)

This paper examines the applicability of genetic algorithms 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

436

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

437

A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot.  

PubMed

The goal of this study was to design a robot system for assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The robot should be able to guide patient's wrist to move along planned linear or circular trajectories. A hybrid position/force controller incorporating fuzzy logic was developed to constrain the movement in the desired direction and to maintain a constant force along the moving direction. The controller was stable in the application range of movements and forces. Offline analyses of data were used to quantitatively assess the progress of rehabilitation. The results show that the robot could guide the upper limbs of subjects in linear and circular movements under predefined external force levels and apply a desired force along the tangential direction of the movements. PMID:16200758

Ju, Ming-Shaung; Lin, Chou-Ching K; Lin, Dong-Huang; Hwang, Ing-Shiou; Chen, Shu-Min

2005-09-01

438

FEM Optimization of Spin Forming Using a Fuzzy Control Algorithm  

NASA Astrophysics Data System (ADS)

Finite element (FE) simulation of the manufacturing of a conical nosing such as a pressure vessel from circular tubes, using the spin forming method, was performed on the commercially available software package, ANSYS/LS-DYNA3D. The finite element method (FEM) provides a powerful tool for evaluating the potential to form the pressure vessel with proposed modifications to the process. The use of fuzzy logic inference as a control system to achieve the designed shape of the pressure vessel was investigated using the FEM. The path of the roller as a process parameter was decided by the fuzzy inference control algorithm from information of the result of deformation of each element respectively. The fuzzy control algorithm investigated was validated from the results of the production process time and the deformed shape using FE simulation.

Yoshihara, S.; Ray, P.; MacDonald, B. J.; Koyama, H.; Kawahara, M.

2004-06-01

439

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device  

Microsoft Academic Search

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences.

Leehter Yao; Po-Zhao Huang

2008-01-01

440

Research of grey predictive fuzzy controller for large time delay system  

Microsoft Academic Search

In order to achieve effectiveness and high control performance of complex processes with large time delay, a predictive fuzzy control method based on grey model is proposed in this paper. By employing grey predictive model, a two-dimensional predictive fuzzy controller is constructed. Its input variables are predictive error and predictive error rate. Fuzzy decision making mechanism according to present state

Pu Han; Hong-Jun Liu; Li-Min Meng; Na Wang

2005-01-01

441

Single Chip Fuzzy Control System Based on Mixed-Signal FPGA  

Microsoft Academic Search

A fuzzy control system is analyzed and designed basing on a new hardware platform mixed-signal FPGA. Besides fuzzy control module, FPGA is also embedded with 8051, PWM, ADC. After analyzing the principle of fuzzy control module, it's divided into several modules according to the functional needs on the basis of this division the logic structure of all modules can be

Wu Liming; Liu Junxiu; Dai Min

2009-01-01

442

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

NASA Astrophysics Data System (ADS)

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

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

2012-06-01

443

Adaptive control of linearizable systems  

NASA Technical Reports Server (NTRS)

Initial results are reported regarding the adaptive control of minimum-phase nonlinear systems which are exactly input-output linearizable by state feedback. Parameter adaptation is used as a technique to make robust the exact cancellation of nonlinear terms, which is called for in the linearization technique. The application of the adaptive technique to control of robot manipulators is discussed. Only the continuous-time case is considered; extensions to the discrete-time and sampled-data cases are not obvious.

Sastry, S. Shankar; Isidori, Alberto

1989-01-01

444

Static Output Feedback Control of Uncertain Dynamic Fuzzy Systems  

Microsoft Academic Search

This paper examines the problem of static output feedback control of a T-S fuzzy system. The existence of an uncertain static output feedback control is given in terms of the solvability of bilinear matrix inequalities (BMI). An iterative algorithm based on linear matrix inequality (LMI) is developed to compute the uncertain static output feedback gain. A numerical simulation example is

Xiao Guang Yang; Qing Ling Zhang; Xiao Dong Liu; Da Qing Zhang

2007-01-01

445

A Framework for Fuzzy Logic based UAV Navigation and Control  

Microsoft Academic Search

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

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

2004-01-01

446

Minimization of combined sewer overflows using fuzzy logic control  

Microsoft Academic Search

The use of fuzzy logic control in minimizing the combined sewer overflows (CSOs) for a small but representative section of the Seattle Metro collection system is discussed. Combined sewers carry both sanitary sewage and storm runoff. The problem considered is the real-time control of the CSOs, which consist of about 160 km of interconnected pipes ranging from 0.3 m to

Sheng-Lu Hou; N. L. Ricker

1992-01-01

447

Design and implementation of a fuzzy logic yaw controller  

Microsoft Academic Search

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

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

1993-01-01

448

Modular fuzzy-neuro controller driven by spoken language commands  

Microsoft Academic Search

We present a methodology of controlling machines using spoken language commands. The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated. The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language

Koliya Pulasinghe; Keigo Watanabe; Kiyotaka Izumi; Kazuo Kiguchi

2004-01-01

449

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

450

Optimization of Fuzzy Controller of Permanent Magnet Synchronous Motor  

Microsoft Academic Search

Present study aims at discussing how to optimize the fuzzy controller of Permanent Magnet Synchronous Motor (PMSM). By reducing the influence of parameter changes of plant using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) of Taguchi Method and Multi-Criteria Decision Making (MCDM), it shall be possible to improve robust characteristics of control system, thus promoting the output

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

2007-01-01

451

Fuzzy Petri net-based programmable logic controller  

Microsoft Academic Search

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

David Andreu; Jean-claude Pascal; Robert Valette

1997-01-01

452

The application research of a fuzzy controller on robot  

Microsoft Academic Search

In robot soccer competition, it is important that the robot can identify and approach the ball quickly and accurately. This paper presents a fuzzy control method which could improve the accuracy and speed to recognize ball. The conventional robot control consists of methods for path generation and following. When a robot moves away the desired track, it must return immediately,

Guoguang Cui; Lei Shi; Qiang Wang; Lijun Wu; Haiou Wen

2011-01-01

453

Fuzzy rule extraction for shooting action controller of soccer robot  

Microsoft Academic Search

A fuzzy logic controller (FLC) for shooting action is proposed which is one of the fundamental actions for soccer robots. Shooting action is viewed as a posture control problem with such a constraint that the robot should not approach the ball so that the ball moves to the home team area. The FLC consists of two levels: one is the

M.-J. Jung; H.-S. Kim; H.-S. Shim; J.-H. Kim

1999-01-01

454

A learning fuzzy system for looper control in rolling mills  

Microsoft Academic Search

In this paper, several issues of looper control in rolling mills are discussed and a fuzzy logic control is proposed to address the enumerated issues. The proposed system incorporated fast tuning algorithms for both off-line and online learning of membership functions and singleton values. Also, it is relatively simple to design and implement. The effectiveness of the proposed system is

F. Janabi-Sharifi; J. Fan

2000-01-01

455

Fuzzy logic controlled landing of a Boeing 747  

Microsoft Academic Search

In this research, the simulation of the landing and descent of a Boeing 747 in its linearized landing configuration model are controlled using fuzzy logic controllers (FLCs). The rule bases for the FLCs are functions of the linearized model's inputs, the Boeing 747's vertical velocity and altitude. The crisp FLC outputs, as determined by the centroid method, are the elevator

Lifford L. L. McLauchlan

2009-01-01

456

A type-2 fuzzy logic controller for dynamic positioning systems  

Microsoft Academic Search

This paper presents an approach to design type- 2 fuzzy logic controllers (FLCs) for dynamic positioning (DP) systems, and the design process can be easily extended to other multi-input multi-output (MIMO) systems with coupling between loops. The combination of FLCs and nonlinear observers removes the need to linearize the kinematic equations of motion when designing control systems for the DP

Xue Tao Chen; Woei Wan Tan

2010-01-01

457

Fuzzy logic direct torque control for permanent magnet synchronous motors  

Microsoft Academic Search

An outstanding feature of the conventional direct torque control (DTC) for permanent magnet synchronous motors (PMSM) is its fast dynamic response. However, large torque and flux linkage ripples are generated because of the use of the hysteresis controllers and the crude position signals based on six 60° angular regions for selecting the space voltage vectors. This paper proposes a fuzzy

Dan Sun; Yikang He; Jian Guo Zhu

2004-01-01

458

A Supervisory Hierarchical Fuzzy logic controller for power system stabilizer  

Microsoft Academic Search

The paper presents a novel design of Supervisory Hierarchical Fuzzy Controller (SHFC) for power system stabilizer to damp the low frequency power system oscillations. The control objective is to enhance the stability and to improve the dynamic response of the Single Machine Infinite Bus (SMIB) system operating in different operating conditions. The Power System Stabilizer (PSS) have the capability of

M. Vijayaraghavan; M. Y. Sanavullah

2011-01-01

459

AN INTELLIGENT ABS CONTROL BASED ON FUZZY LOGIC. AIRCRAFT APPLICATION  

Microsoft Academic Search

Over the past ten years, fuzzy logic, as main component of artificial intelligence, has significantly influenced the design of controlled systems. Focusing on applied mathematics field, the paper proposes an antilock-braking system (ABS) for a Romanian military jet. It is well known that in the ABS brake, the control is considered from a \\

Ioan Ursu; Felicia Ursu

460

Fuzzy learning control for anti-skid braking systems  

Microsoft Academic Search

Although antiskid braking systems (ABSs) are designed to optimize braking effectiveness while maintaining steerability, their performance often degrades for harsh road conditions (e.g., icy\\/snowy roads). The authors introduce the idea of using the fuzzy model reference learning control (FMRLC) technique for maintaining adequate performance even under such adverse road conditions. This controller utilizes a learning mechanism which observes the plant

J. R. Layne; K. M. Passino; Stephen Yurkovich

1992-01-01

461

Fuzzy control is often better than manual control of the very experts whose knowledge it uses - An explanation  

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

462

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

463

Semiautonomous adaptive cruise control systems  

Microsoft Academic Search

The concept of a semi-autonomous adaptive cruise control (SAACC) system is developed, which enjoys significant advantages over present day adaptive cruise control (ACC) systems in terms of highway safety and traffic flow capacity. The semi-autonomous systems combine the deployment advantages of autonomous vehicles with the performance advantages of fully automated highway systems (AHSs) in which vehicles operate cooperatively as a

R. Rajamani; C. Zhu

2002-01-01

464

Fuzzy controller design and stability analysis for ship's lift-feedback-fin stabilizer  

Microsoft Academic Search

A Takagi-Sugeno (T-S) fuzzy model of ship's lift-feedback-fin stabilizer control systems is established. Based on character analysis of fuzzy control systems with standard fuzzy partition inputs, a new sufficient condition is proved which guarantees the stability of closed-loop T-S fuzzy control systems via Lyapunov direct method. This approach requires only finding a common positive-definite matrix in each maximal overlapped-rule group.

Zhi-Hong Xiu; Guang Ren

2003-01-01

465

Design and implementation of the tree-based fuzzy logic controller  

Microsoft Academic Search

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search

Bin-da Liu; Chun-yueh Huang

1997-01-01

466

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

467

Adaptive fuzzy leader clustering of complex data sets in pattern recognition  

NASA Technical Reports Server (NTRS)

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

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

1992-01-01

468

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

NASA Technical Reports Server (NTRS)

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

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

2003-01-01

469

Research on fuzzy PID control to electronic speed regulator  

NASA Astrophysics Data System (ADS)

As an important part of diesel engine, the speed regulator plays an important role in stabilizing speed and improving engine's performance. Because there are so many model parameters of diesel-engine considered in traditional PID control and these parameters present non-linear characteristic.The method to adjust engine speed using traditional PID is not considered as a best way. Especially for the diesel-engine generator set. In this paper, the Fuzzy PID control strategy is proposed. Some problems about its utilization in electronic speed regulator are discussed. A mathematical model of electric control system for diesel-engine generator set is established and the way of the PID parameters in the model to affect the function of system is analyzed. And then it is proposed the differential coefficient must be applied in control design for reducing dynamic deviation of system and adjusting time. Based on the control theory, a study combined control with PID calculation together for turning fuzzy PID parameter is implemented. And also a simulation experiment about electronic speed regulator system was conducted using Matlab/Simulink and the Fuzzy-Toolbox. Compared with the traditional PID Algorithm, the simulated results presented obvious improvements in the instantaneous speed governing rate and steady state speed governing rate of diesel-engine generator set when the fuzzy logic control strategy used.

Xu, Xiao-gang; Chen, Xue-hui; Zheng, Sheng-guo

2007-12-01

470

Fuzzy control of nitrogen removal in predenitrification process using ORP.  

PubMed

In order to meet increasingly stringent discharge standards, new applications and control strategies for the sustainable removal of nitrogen from wastewater have to be implemented. In the past years, numerous studies have been carried out dealing with the application of fuzzy logic to improve the control of the activated sludge process. In this paper, fuzzy control strategies of predenitrification systems are presented that could lead to better effluent quality and, in parallel, to a reduction of chemicals consumption. Extensive experimental investigations on lab scale plant studies have shown that there was excellent correlation between nitrate concentration and ORP value at the end of the anoxic zone. Results indicated that ORP could be used as an on-line fuzzy control parameter of nitrate recirculation and external carbon addition. The optimal value of ORP to control nitrate recirculation and external carbon addition was - 86 +/- 2 mV and - 90 +/- 2 mV, respectively. The results obtained with real wastewater also showed the good performance and stability of the fuzzy controllers independently from external disturbances. The integrated control structure of nitrate recirculation and external carbon addition in the predenitrification system is also presented. PMID:16477983

Peng, Y; Ma, Y; Wang, S; Wang, X

2005-01-01

471

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

Microsoft Academic Search

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

472

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

Microsoft Academic Search

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

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

1997-01-01

473

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

Microsoft Academic Search

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

B. McBratney; Inakwu O. A. Odeh

474

Active Vibration Control for Smart Structure Base on the Fuzzy Logic  

Microsoft Academic Search

In the study of active vibration of piezoelectric smart structures, the piezoelectric materials always show non-liner characteristic, so this paper uses the fuzzy logic to control the smart structures vibration. The fuzzy IF-THEN rules are established on analysis of the motion traits of cantilever beam. The fuzzy controller designs on using the displacement and the velocity of the cantilever beams

Zhang Jing-jun; Cao Li-ya; Yuan Wei-ze

2009-01-01

475

Active Vibration Control for Smart Structure Base on the Fuzzy Logic  

Microsoft Academic Search

In the study of active vibration of piezoelectric smart structures, the piezoelectric materials always show nonlinear characteristic, so this paper uses the fuzzy logic to control the smart structures vibration. The fuzzy IF-THEN rules are established on analysis of the motion traits of cantilever beam. The fuzzy logic controller (FLC) designs on using the displacement and the velocity of the

Jingjun Zhang; Liya Cao; Weize Yuan; Ruizhen Gao

2008-01-01

476