Fuzzy Neural Cast 2

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Fuzzy neural controller- druh st

Databze:

www.springerlink.com

www.dialog.cvut.cz

www.acm.org/dl

Reere:

Encyklopedie:

The educational encyclopedia, automatisation and control systems

HP:http://users.telenet.be/educypedia/index.htm

http://users.telenet.be/educypedia/electronics/control-systems.htm

Na tchto stranch:

http://vorlon.cwru.edu/~vxl11/NetBots/thesisc.pdf

je studie (80 stran):

Abstrakt:
http://www.springerlink.com/http://www.dialog.cvut.cz/http://www.acm.org/dlhttp://users.telenet.be/educypedia/electronics/control-systems.htmhttp://users.telenet.be/educypedia/index.htmhttp://users.telenet.be/educypedia/index.htmhttp://users.telenet.be/educypedia/electronics/control-systems.htmhttp://vorlon.cwru.edu/~vxl11/NetBots/thesisc.pdfhttp://www.springerlink.com/http://www.dialog.cvut.cz/http://www.acm.org/dlhttp://users.telenet.be/educypedia/electronics/control-systems.htmhttp://users.telenet.be/educypedia/index.htmhttp://users.telenet.be/educypedia/electronics/control-systems.htmhttp://vorlon.cwru.edu/~vxl11/NetBots/thesisc.pdf


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Na stranch:

http://vorlon.cwru.edu/~wsn/theses/Hao_Zhang_thesis.pdf

je studie (str.):
http://vorlon.cwru.edu/~wsn/theses/Hao_Zhang_thesis.pdfhttp://vorlon.cwru.edu/~wsn/theses/Hao_Zhang_thesis.pdfhttp://vorlon.cwru.edu/~wsn/theses/Hao_Zhang_thesis.pdf


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Anotace: vstupy s databz WOS.CUNI.CZ a INOV. pi zadn rznch kombinac

klovch slov.

Study of learning fuzzy controllersKazemian HB

EXPERT SYSTEMS18: (4) 186-193 SEP 2001

Document type: Article Language: English Cited References: 29 Times Cited: 1

Abstract:

This paper compares two types of learning fuzzy controllers, the self-organizing fuzzy (SOF)controller and the hybrid self-organizing fuzzy proportional-integral-derivative (SOF-PID)

controller. The SOF is an extension of the rule-based fuzzy controller, with additional rule

creation and rule modification mechanisms. The hybrid SOF-PID comprises the SOF as a

learning supervisory controller readjusting the proportional gain of the PID controller at the

actuator section, when the system is on line. The structures of the SOF controller and the

hybrid SOF-PID controller are studied. The performances of the SOF controller and the

hybrid SOF-PID controller are compared by applying them to a two-link non-linear revolute-

joint robot arm. For the path tracking experiments, the hybrid SOF-PID controller followed

the required path more closely and smoothly than the SOF controller. The results of the

experiments for the SOF controller and the hybrid SOF-PID controller are also compared with

those obtained with a conventional PID controller, using the same values supplied at the

setpoint.
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000171297100003&PW=2001&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000171297100003&PW=2001&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000171297100003&PW=2001&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.81888976&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.81888976&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.81888976&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000171297100003&PW=2001&doc=935/1&PR=935/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=935/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.81888976&doc=935/1&PR=935/1


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Author Keywords:self-organizing fuzzy controller, hybrid self-organizing fuzzy PIDcontroller, robot arm

KeyWords Plus: PID CONTROL, DESIGN, ROBOT

Addresses: Kazemian HB, London Guildhall Univ, Comp Informat Syst & Math Dept, 100Minories, London EC3N 1JY, England.

Publisher: BLACKWELL PUBL LTD, OXFORD

IDS Number: 477RB ISSN: 0266-4720

Symbolic synthesis of finite-state controllers for request-response specificationsWallmeier N, Hutten P, Thomas W

IMPLEMENTATION AND APPLICATION OF AUTOMATA, PROCEEDINGS

LECTURE NOTES IN COMPUTER SCIENCE

2759: 11-22 2003


Abstract:

We present a method to solve certain infinite games over finite state spaces and apply this for

the automatic synthesis of finite-state controllers. A lift-controller problem serves as an

example for which the implementation of our algorithm has been tested. The specifications

consist of safety conditions and so-called request-response-conditions (which have the form

after visiting a state of P later a state of R is visited). Many real-life problems can be

modeled in this framework. We sketch the theoretical solution which synthesizes a finite- state

controller for satisfiable specifications. The core of the implementation is a convenient inputlanguage (based on enriched Boolean logic) and a realization of the abstract algorithms withOBDDs (ordered binary decision diagrams).

Addresses:

Wallmeier N, Rhein Westfal TH Aachen, Lehrstuhl Informat 7, D-52056 Aachen, Germany.

Rhein Westfal TH Aachen, Lehrstuhl Informat 7, D-52056 Aachen, Germany.

Publisher: SPRINGER-VERLAG BERLIN, BERLIN

IDS Number: BX41A ISSN: 0302-9743

______

Hierarchical fuzzy logic controller for a flexible linkrobotarm performing constrained

motion tasksLin J

IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS150: (4) 355-364 JUL 2003


Abstract:An examination is performed on the dynamics and control issues for a robotic manipulator

with link structural flexibility modelled during the execution of a task that requires the robottip to contact fixed rigid objects. A multi-time-scale fuzzy logic controller is applied to this
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000185178700003&PW=2003&doc=0/1&PR=0/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000185178700003&PW=2003&doc=0/1&PR=0/1http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000185178700003&PW=2003&doc=0/1&PR=0/1http://wos.cuni.cz/CIW.cgi?120024_BE8AB2D9&Func=Abstract&doc=244/11&PR=244/CIW.cgi?120024_BE8AB2D9&Func=DispCitedRef&UT=000185484600005&PW=2003&doc=244/11&PR=244/11http://wos.cuni.cz/CIW.cgi?120024_BE8AB2D9&Func=Abstract&doc=244/11&PR=244/CIW.cgi?120024_BE8AB2D9&Func=DispCitedRef&UT=000185484600005&PW=2003&doc=244/11&PR=244/11http://wos.cuni.cz/CIW.cgi?120024_BE8AB2D9&Func=Abstract&doc=244/11&PR=244/CIW.cgi?120024_BE8AB2D9&Func=DispCitedRef&UT=000185484600005&PW=2003&doc=244/11&PR=244/11http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000185178700003&PW=2003&doc=0/1&PR=0/1http://wos.cuni.cz/CIW.cgi?120024_BE8AB2D9&Func=Abstract&doc=244/11&PR=244/CIW.cgi?120024_BE8AB2D9&Func=DispCitedRef&UT=000185484600005&PW=2003&doc=244/11&PR=244/11


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system. The large-scale system is decomposed into a finite number of reduced-order

subsystems using the singular perturbation approach. A hierarchical ordering of fuzzy rules is

used to reduce the size of the inference engine. Real-time implementation of fuzzy controllers

can help reduce the burden of large-sized rule sets by fusing sensory data before input and the

systems output to the inference engine. Using this approach the control of the force and

position of the robot end point is possible while the end-effector moves on the constraintsurface.

KeyWords Plus:

SINGULAR PERTURBATION APPROACH, JOINT ROBOTS, MANIPULATORS,

FORCE, STABILITY, TRACKING

Addresses: Lin J, Ching Yun Inst Technol, Dept Mech Engn, 229 Chien Hsin Rd, Jing Li320, Taiwan. Ching Yun Inst Technol, Dept Mech Engn, Jing Li 320, Taiwan.

Publisher: IEE-INST ELEC ENG, HERTFORD

IDS Number: 724JN ISSN: 1350-2379

A new approach to global optimization using a closed loop control system with fuzzy

logic controllerUstundag B, Eksin I, Bir A

ADVANCES IN ENGINEERING SOFTWARE

33: (6) 309-318 JUN 2002


Abstract:In this study, a new global optimization method that uses a closed loop control system isproposed. If a plant, in a feedback control system with a reference input r, is replaced by the

objective function f ((x) under bar) then the output of a properly designed controller

approaches the solution of the equation f((x) under bar) - (r) under bar = 0 at the steady state.

An algorithm is then designed such that the reference point and the objective function

representing the plant are continuously changed within the control loop. This change is done

in accordance with the result of the steady-statecontroller output. This algorithm can find

the global optimum point in a bounded feasible region. Even though the new approach is

applicable to the optimization of single and multivariable non-linear objective functions,- only

the results related to some test functions with single variable are presented. The results of the

new algorithm are compared with some well-known global optimization algorithms. (C) 2002Elsevier Science Ltd. All rights reserved.

Author Keywords:global optimization, feedback control system, fuzzy logic controller, root search algorithm

Addresses: Ustundag B, Istanbul Tech Univ, Elect & Elect Engn Fac, TR-80626 Istanbul,

Turkey. Istanbul Tech Univ, Elect & Elect Engn Fac, TR-80626 Istanbul, Turkey.

Publisher: ELSEVIER SCI LTD, OXFORD

IDS Number: 604WH ISSN: 0965-9978
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/7&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000178643700001&PW=2002&doc=0/7&PR=0/7http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/7&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000178643700001&PW=2002&doc=0/7&PR=0/7http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/7&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000178643700001&PW=2002&doc=0/7&PR=0/7http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/7&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000178643700001&PW=2002&doc=0/7&PR=0/7


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Hybrid vision/force control at corners in planar robotic-contour followingBaeten J, De Schutter J

IEEE-ASME TRANSACTIONS ON MECHATRONICS7: (2) 143-151 JUN 2002

Document type: Article Language: English Cited References: 21 Times Cited: 1Abstract:The accuracy and execution speed of a force controlled contour-following task is limited if

the shape of the work-piece is unknown. This is even more true when the workpiece contour

contains corners. This paper shows how a hybrid vision/force control approach at corners in

planar-contour following results in a more accurate and faster task execution. The vision

system is used to measure online the contour and to watch out for corners. The edge is

correctly located by compensating the compliance of the tool/camera setup which affects the

contour measurement. A simple corner-detection algorithm is presented. Once a corner is

detected, the finite-statecontroller is activated to take the corner in the best conditions.

Experimental results are presented to validate the approach.

Author Keywords:corner detection, force control, hybrid position/force, machine vision, robotic control, sensor

fusion, visual servoing

KeyWords Plus: FORCE, ENVIRONMENT, FORMALISM, MOTION

Addresses: Baeten J, Katholieke Univ Leuven, Dept Engn Mech, B-3001 Heverlee, Belgium.

Katholieke Univ Leuven, Dept Engn Mech, B-3001 Heverlee, Belgium.

Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, NEW YORK

IDS Number: 565JM ISSN: 1083-4435

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Direct adaptive fuzzy output tracking control of nonlinear systemsTong SC, Li HX

FUZZY SETS AND SYSTEMS

128: (1) 107-115 MAY 16 2002


Abstract:A stable direct adaptive fuzzy output tracking control scheme is developed for the single-

input-single-output (SISO) unknown nonlinear systems, Using a high-gain observer. the

proposed adaptive fuzzy algorithm does not require the state variables to be measurable, First,

a direct adaptive fuzzy statecontroller is constructed with the aid of its H-infinity control

technique to achieve the H-infinity tracking performance. After-wards, a high-gain observer is

used to estimate the system states, by which the above adaptive fuzzy statecontrollerbecomes an adaptive fuzzy output feedback control. The proposed control scheme can

guarantee the stability of the closed-loop system and the good tracking performance as well.

(C) 2002 Elsevier Science B.V. All rights reserved.

Author Keywords: fuzzy control, high-gain observer, nonlinear systems, adaptive control,stability
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176366400006&PW=2002&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176366400006&PW=2002&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176366400006&PW=2002&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88548156&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88548156&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88548156&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/9&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000175672900009&PW=2002&doc=0/9&PR=0/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/9&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000175672900009&PW=2002&doc=0/9&PR=0/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/9&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000175672900009&PW=2002&doc=0/9&PR=0/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176366400006&PW=2002&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/8&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88548156&doc=0/8&PR=0/8http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/9&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000175672900009&PW=2002&doc=0/9&PR=0/9


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Addresses: Tong SC, Liaoning Inst Technol, Dept Basic Math, Jinzhou 121001, Peoples RChina. Liaoning Inst Technol, Dept Basic Math, Jinzhou 121001, Peoples R China.

City Univ Hong Kong, Dept Mfg Engn & Engn Management, Hong Kong, Peoples R China.

Publisher: ELSEVIER SCIENCE BV, AMSTERDAM

IDS Number: 553JW ISSN: 0165-0114

State controllability and optimal regulator control of time-delayed systems

Klein EJ, Ramirez WF

INTERNATIONAL JOURNAL OF CONTROL

74: (3) 281-289 FEB 2001


Abstract:

An important recent advance in the solution of the optimal regulator control problem for time-

delayed systems is extended here to multivariable systems and to systems which exhibitmultiple time delays. The state equations are partitioned into discrete and continuous portions

through a state transformation such that the solution of the optimal regulator problem reduces

to finding a steady-statecontroller gain based on both a discrete and continuous Riccati

matrix. The discrete Ricatti matrix is found independently of the continuous solution due to

the partitioning of the state equations, and it is not necessary to solve the system of partial

differential Riccati equations which arise in the traditional solution of the linear quadratic

regulator (LQR) problem for time-delayed systems. In addition, through this state

transformation it becomes possible to extend the standard state controllability tests to time-

delayed systems. It is shown that the controllability of the transformed state space is necessary

for a feasible solution to the optimal regulator problem for time-delayed systems. This is an

important test to determine the practicality of various time-delayed system realizations.

Numerical examples illustrate the application of the technique to systems exhibiting multiple

time delays, multivariable systems and time-series models. It is shown that the classic Wood-

Berry distillation model realization does not possess state controllability properties which

explains why this system has been historically di? cult to control using feedback techniques.

KeyWords Plus:STABILIZATION, EQUATION

Addresses: Ramirez WF, Univ Colorado, Dept Chem Engn, Campus Box 424, Boulder, CO80309 USA. Univ Colorado, Dept Chem Engn, Boulder, CO 80309 USA.

Publisher: TAYLOR & FRANCIS LTD, LONDON

IDS Number: 386BU

ISSN: 0020-7179

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Fuzzy network model-based fuzzy statecontrollerdesignKroll A, Bernd T, Trott S

IEEE TRANSACTIONS ON FUZZY SYSTEMS

8: (5) 632-644 OCT 2000
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000166040900007&PW=2001&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000166040900007&PW=2001&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000166040900007&PW=2001&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.78944838&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.78944838&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.78944838&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000166040900007&PW=2001&doc=0/13&PR=0/13http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/13&PR=0/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.78944838&doc=0/13&PR=0/13


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Abstract:The performance of model-based controller design relies heavily on the quality and suitability

of the utilized process model. This contribution proposes a fuzzy network based nonlinear

controller design methodology. Fuzzy networks are a model approach combining high

approximation quality with high interpretability. The input/output (I/O) models commonlyused for identification are transformed to fuzzy state-space models, Transferring and adjusting

methods from linear state-space theory a control concept consisting of a fuzzy statecontroller and an adaptive set-point filter for nonlinear dynamic processes is deduced. Thecapability of the method is demonstrated for a hydraulic drive.

Author Keywords: adaptive set-point filtering, fuzzy control, fuzzy models, fuzzy networks,

fuzzy state-space controller, hydraulic drives, nonlinear control

KeyWords Plus: CONTROL-SYSTEMS, STABILITY, PERFORMANCE

Addresses: Kroll A, ABB Corp Res Ctr, D-69115 Heidelberg, Germany. ABB Corp Res Ctr,D-69115 Heidelberg, Germany. Rexroth Indramat GMBH, Machine Tool Syst Dev, D-97816

Lohr Am Main, Germany. Alice Software Serv GMBH, D-40699 Erkrath, Germany.


IDS Number: 363DU ISSN: 1063-6706

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KS: fuzzy control

Multiple-valued logic and artificial intelligence fundamentals offuzzycontrol revisited

Moraga C, Trillas E, Guadarrama S

ARTIFICIAL INTELLIGENCE REVIEW

20: (3-4) 169-197 DEC 2003


Abstract:This paper reviews one particular area of Artificial Intelligence, which roots may be traced

back to Multiple- valued Logic: the area offuzzycontrol. After an introduction based on an

experimental scenario, basic cases offuzzycontrol are presented and formally analyzed.Their capabilities are discussed and their constraints are explained. Finally it is shown that a

parameterization of either the fuzzy sets or the connectives used to express the rules

governing a fuzzy controller allows the use of new optimization methods to improve the

overall performance.

Author Keywords: approximate reasoning, fuzzycontrol, fuzzy if-then rules

KeyWords Plus: CONTROL-SYSTEMS, CONNECTIVES

Addresses: Moraga C, Univ Politecn Madrid, Dept Artificial Intelligence, Campus

Montegancedo, E-28660 Madrid, Spain.Univ Politecn Madrid, Dept Artificial Intelligence, E-28660 Madrid, Spain.
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/14&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000089820400014&PW=2000&doc=0/14&PR=0/14http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/14&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000089820400014&PW=2000&doc=0/14&PR=0/14http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/14&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000089820400014&PW=2000&doc=0/14&PR=0/14http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186971300002&PW=2003&doc=62/2&PR=62/2http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186971300002&PW=2003&doc=62/2&PR=62/2http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186971300002&PW=2003&doc=62/2&PR=62/2http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=0/14&PR=0/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000089820400014&PW=2000&doc=0/14&PR=0/14http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186971300002&PW=2003&doc=62/2&PR=62/2


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Publisher: KLUWER ACADEMIC PUBL, DORDRECHT

IDS Number: 750BK

ISSN: 0269-2821

Stable direct adaptive fuzzycontrol for a class of MIMO non-linear systemsZhang TP

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

34: (6) 375-388 MAY 15 2003


Abstract:Two new schemes of direct adaptive fuzzy controller for a class of multi-input multi-output

non-linear systems with unknown constant gains or function gains are proposed in this paper.

The design is based on a modified Lyapunov function and the approximation capability of the

first type fuzzy system. The approach is able to avoid the requirement of the upper bound ofthe first-time derivative of the control gain, which is assumed to know a priori in some of the

existing adaptive fuzzy/neural network control schemes. In addition, it is also able to avoid

the controller singularity problem. By theoretical analysis, the closed-loop fuzzycontrol

system is proven to be globally stable in the sense that all signals involved are bounded, with

tracking errors converging to zero. The simulation results verify the effectiveness the

proposed controllers and the theoretical discussion.

KeyWords Plus:NETWORKS, DESIGN

Addresses: Zhang TP, Yangzhou Univ, Coll Informat Engn, Dept Comp Sci, Yangzhou225009, Peoples R China.Yangzhou Univ, Coll Informat Engn, Dept Comp Sci, Yangzhou 225009, Peoples R China.

Publisher: TAYLOR & FRANCIS LTD, ABINGDON

IDS Number: 749PQ ISSN: 0020-7721

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PI predictive fuzzy controllers for electrical drive speed control: methods and softwarefor stable development

Precup RE, Preitl S, Faur GCOMPUTERS IN INDUSTRY

52: (3) 253-270 DEC 2003


Abstract: The paper presents two structures of PI predictive fuzzy controllers (PI-P-FCs)with first- and second-order prediction. The PI-P-FCs are meant for the speed control of

electrical drives with variable inertia used in several industrial applications. The new

development method for these PI-P-FCs is based on guaranteeing a desired domain for the

phase margin of the fuzzycontrol systems (FCSs) with PI-P-FCs when they are considered

to be equivalent to the linear control systems with linear (conventional) PI controllers. It is

also proposed a stability analysis method for the FCSs with PI-P-FCs employing Popovshyperstability theory and a discrete-time state space mathematical model of the controlled
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/5&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186929500001&PW=2003&doc=62/5&PR=62/5http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/5&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186929500001&PW=2003&doc=62/5&PR=62/5http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/5&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186929500001&PW=2003&doc=62/5&PR=62/5http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/6&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186771300005&PW=2003&doc=62/6&PR=62/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/6&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186771300005&PW=2003&doc=62/6&PR=62/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/6&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186771300005&PW=2003&doc=62/6&PR=62/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/5&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186929500001&PW=2003&doc=62/5&PR=62/5http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=62/6&PR=62/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000186771300005&PW=2003&doc=62/6&PR=62/6


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plant. A software tool for the stability analysis of the FCSs, developed in the Matlab and

Simulink environment, is presented. (C) 2003 Elsevier B.V. All rights reserved.

Author Keywords: fuzzycontrol, PI predictive fuzzy controllers, phase margin, electrical

drives with variable inertia, stability analysis software tool

KeyWords Plus: CONTROL-SYSTEMS, STABILITY, LOGIC

Addresses: Precup RE, Politehn Univ Timisoara, Dept Automat & Ind Informat, Fac

Automat & Comp, Bd V Parvan 2, RO-1900 Timisoara, Romania. Politehn Univ Timisoara,

Dept Automat & Ind Informat, Fac Automat & Comp, RO-1900 Timisoara, Romania.

W Bank Arad Sann Paolo Imi, Dept Informat Technol, RO-2900 Arad, Romania.


IDS Number: 746WU ISSN: 0166-3615

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Neuro-fuzzy chip to handle complex tasks with analog performanceNavas-Gonzalez RD, Vidal-Verdu F, Rodriguez-Vazquez A

IEEE TRANSACTIONS ON NEURAL NETWORKS14: (5) 1375-1392 SEP 2003


Abstract: This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms ofpower consumption, input-output delay, and precision, performs as a fully analog

implementation. However, it has much larger complexity than its purely analog counterparts.This combination of performance and complexity is achieved through the use of a mixed-

signal architecture consisting of a programmable analog core of reduced complexity, and a

strategy, and the associated mixed-signal circuitry, to cover the whole input space through the

dynamic programming of this core. Since errors and delays are proportional to the reduced

number of fuzzy rules included in the analog core, they are much smaller than in the case

where the whole rule set is implemented by analog circuitry. Also, the area and the power

consumption of the new architecture are smaller than those of its purely analog counterparts

simply because most rules are implemented through programming. The Paper presents a set of

building blocks associated to this architecture, and gives results for an exemplary prototype.

This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS

0.7 mum standard technology. It has two inputs, implements 64 rules, and features 500 ns ofinput to output delay with 16-mW of power consumption. Results from the chip in a control

application with a dc motor are also provided.

Author Keywords: fuzzy-control, fuzzy-hardware, mixed-signal

KeyWords Plus: CONTROLLERS, DESIGN, SYSTEMS, LOGIC, IMPLEMENTATION,

COMPILER

Addresses:Navas-Gonzalez RD, Univ Malaga, Dept Elect, E-29071 Malaga, Spain.

Univ Malaga, Dept Elect, E-29071 Malaga, Spain.

Univ Sevilla, Dept Analog & Mixed Circuit Design, E-41012 Seville, Spain.


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Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, PISCATAWAY

IDS Number: 741UN ISSN: 1045-9227

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An analytic approach to fuzzy robot control synthesisNovakovic B, Scap D, Novakovic D

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE13: (1) 71-83 FEB 2000


Abstract: The main advantage of a fuzzy control system is the fact that no mathematical

model of the controlled plant is required. Instead of that model, it is necessery to construct a

fuzzy rule base for each particular application case. A vexing problem in fuzzy control,

however, is the exponential growth in rules as the number of variables increases. This

problem is avoided here by the introduction of a new, nonconventional analytic method for

synthesising the fuzzy control. For this purpose a new analytic function is defined that

determines the positions of the centres of the output fuzzy sets, instead of the definition of afuzzy rule base. This function can be adapted to each concrete application case by changing

the free fuzzy-set parameters. The proposed analytic approach to the synthesis of fuzzy

control, has been tested by a numerical simulation of an analytic fuzzy control system for a

robot with four degrees of freedom. (C) 2000 Elsevier Science Ltd. All rights reserved.

Author Keywords: analytic fuzzy control, adaptive fuzzycontrol, robot control, controlsynthesis, discrete-time domain

KeyWords Plus: SYSTEMS, LOGIC, DESIGN, MODEL

Addresses:Novakovic D, Univ Zagreb, FSB, Luciceva 5, POB 509, Zagreb 10000, Croatia.Univ Zagreb, FSB, Zagreb 10000, Croatia.

Publisher: PERGAMON-ELSEVIER SCIENCE LTD, OXFORD

IDS Number: 276MT ISSN: 0952-1976

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Robust adaptive control of robot manipulators using generalized fuzzy neural networks

Er MJ, Gao YIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

50: (3) 620-628 JUN 2003


Abstract: This paper presents a robust adaptive fuzzyneuralcontroller (AFNC) suitable for

motion control of multilink robot manipulators. The proposed controller has the following

salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be

generated or deleted automatically according to their significance to the control system and

the complexity of the mapped system and no predefined fuzzy rules are, required; 2) fast

online learning ability, i.e., no prescribed training models are needed for online learning and

weights of the fuzzyneuralcontroller are modified without any iterations; 3) fast

convergence of tracking errors, i.e., manipulator joints can track the desired trajectories veryquickly; 4) adaptive control, i.e., structure and parameters of the AFNC can be self-adaptive
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in the presence of disturbances to maintain high control performance; and 5) robust control,

where asymptotic stability of the control system is established using the Lyapunov theorem.

Experimental evaluation conducted on an industrial selectively compliant assembly robot arm

demonstrates, that excellent. tracking performance can be achieved under time-varying

conditions.

Author Keywords: adaptive control, fuzzy logic, Lyapunov methods, manipulators, neuralnetworks (NNs)

Addresses: Er MJ, Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798,

Singapore. Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore.


IDS Number: 686VK ISSN: 0278-0046

----Modelling, control, and stability analysis of non-linear systems using generalized fuzzy

neural networks

Gao Y, Er MJ

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

34: (6) 427-438 MAY 15 2003


Abstract: This paper presents an adaptive fuzzyneuralcontroller (AFNC) suitable for

modelling and control of MIMO non-linear dynamic systems. The proposed AFNC has the

following salient features: (1) fuzzy neural control rules can be generated or deleted

dynamically and automatically; (2) uncertain MIMO non-linear systems can be adaptivelymodelled on line; (3) adaptation and learning speed is fast; (4) expert knowledge can be easily

incorporated into the system; (5) the structure and parameters of the AFNC can be self-

adaptive in the presence of uncertainties to maintain a high control performance; and (6) the

asymptotical stability of the system is established using the Lyapunov approach. Simulation

studies on a two-link robot manipulator show that the performance of the proposed controller

is better than that of some existing fuzzy/neural methods.

KeyWords Plus: ROBOT MANIPULATORS

Addresses: Gao Y, Nanyang Technol Univ, Sch Elect & Elect Engn, Instrumentat & Syst

Engn Lab, Singapore 639798, Singapore. Nanyang Technol Univ, Sch Elect & Elect Engn,Instrumentat & Syst Engn Lab, Singapore 639798, Singapore.

Publisher: TAYLOR & FRANCIS LTD, ABINGDON

IDS Number: 749PQ ISSN: 0020-7721

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Output-feedback control of nonlinear systems using direct adaptive fuzzy-neural

controller

Wang WY, Leu YG, Lee TT

FUZZY SETS AND SYSTEMS140: (2) 341-358 DEC 1 2003


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Abstract: In this paper, a direct adaptive fuzzy-neural output-feedback controller (DAFOC)

for a class of uncertain nonlinear systems is developed under the constraint that only the

system output is available for measurement. An output feedback control law and an update

law are derived for on-line tuning the weighting factors of the DAFOC. By using strictlypositive-real Lyapunov theory, the stability of the closed-loop system compensated by the

DAFOC can be verified. Moreover, the proposed overall control scheme guarantees that all

signals involved are bounded and the output of the closed-loop system asymptotically tracks

the desired output trajectory. To demonstrate the effectiveness of the proposed method,

simulation results are illustrated in this paper. (C) 2002 Elsevier B.V. All rights reserved.

Author Keywords:fuzzy-neural control, direct adaptive control, output feedback control, nonlinear systems

KeyWords Plus: DYNAMICAL-SYSTEMS, SISO SYSTEMS, NETWORKS, OBSERVER

Addresses: Wang WY, Fu Jen Cathiolic Univ, Dept Elect Engn, 510, Chung Cheng Rd,Taipei 24205, Taiwan. Fu Jen Cathiolic Univ, Dept Elect Engn, Taipei 24205, Taiwan.

Hwa Hsia Coll Technol, Dept Elect Engn, Taipei, Taiwan.

Natl Chiao Tung Univ, Dept Elect & Control Engn, Hsinchu, Taiwan.


IDS Number: 742FZ ISSN: 0165-0114

---Online adaptive fuzzy neural identification and control of a class of MIMO nonlinearsystems

Gao Y, Er MJ


11: (4) 462-477 AUG 2003


Abstract: This paper presents a robust adaptive fuzzyneuralcontroller (AFNC) suitable for

identification and control of a class of uncertain multiple-input-multiple-output (MIMO)

nonlinear systems. The proposed controller has the following salient features: 1) self-

organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted

automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fastlearning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure

and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain

high control performance; 6) robust control, where global stability of the system is established

using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link

robot manipulator show that the performance of the proposed controller is superior.

Author Keywords: fuzzy logic, Lyapunov theorem, multiple-input-multiple-output, (MIMO)

nonlinear systems, neural networks, robust adaptive control

KeyWords Plus: ROBOT MANIPULATORS, TRACKING CONTROL, NETWORKS


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Addresses: Gao Y, Nanyang Technol Univ, Sch Elect & Elect Engn, Instrumentat & SystEngn Lab, Singapore 639798, Singapore. Nanyang Technol Univ, Sch Elect & Elect Engn,

Instrumentat & Syst Engn Lab, Singapore 639798, Singapore.


IDS Number: 712GP ISSN: 1063-6706

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Stable fuzzy neural tracking control of a class of unknown nonlinear systems based on

fuzzy hierarchy error approachWu A, Tam PKS


10: (6) 779-789 DEC 2002


Abstract: In this paper, a stable fuzzy neural tracking control of a class of unknown nonlinear

systems based on the fuzzy hierarchy approach is proposed. The adaptive fuzzyneuralcontroller is constructed from the fuzzy neural network with a set of fuzzy rules. Thecorresponding network parameters are adjusted online according to the control law and update

law for the purpose of controlling the plant to track a given trajectory. A stability analysis of

the unknown nonlinear system is discussed based on the Lyapunov principle. In order to

improve the convergence of the nonlinear dynamical systems, a fuzzy hierarchy error

approach (FHEA) algorithm is incorporated into the adaptive update and control scheme. The

simulation results for an unstable nonlinear plant demonstrate the control effectiveness of the

proposed adaptive fuzzyneuralcontroller and are consistent with the theoretical analysis.

Author Keywords: adaptive fuzzy neural control, fuzzy hierarchy approach, fuzzy neuraltracking control, unknown nonlinear systems

KeyWords Plus: ADAPTIVE-CONTROL, TIME-SYSTEMS, NETWORKS, DESIGN

Addresses: Wu A, Huazhong Univ Sci & Technol, Sch Mech Engn, Ctr Numer ControlEngn, Wuhan 430074, Peoples R China.


IDS Number: 623HE ISSN: 1063-6706

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Adaptive control of robot manipulators using fuzzy neural networks

Gao Y, Er MJ, Yang S

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

48: (6) 1274-1278 DEC 2001


Abstract: This letter presents an adaptive fuzzyneuralcontroller suitable for multilink

manipulators motion control. The proposed controller has the following salient features: 1)

self-organizing fuzzy neural structure; 2) online learning of the robot dynamics; 3) fast

convergence of tracking error; and 4) adaptive control. Computer simulation results of a two-

link manipulator demonstrate that excellent tracking performance can be achieved underexternal disturbances.
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000179696600009&PW=2003&doc=117/6&PR=117/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000179696600009&PW=2003&doc=117/6&PR=117/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000179696600009&PW=2003&doc=117/6&PR=117/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000172569400029&PW=2001&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000172569400029&PW=2001&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000172569400029&PW=2001&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.82622657&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.82622657&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.82622657&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000179696600009&PW=2003&doc=117/6&PR=117/6http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000172569400029&PW=2001&doc=117/9&PR=117/9http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=117/9&PR=117/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.82622657&doc=117/9&PR=117/9


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Author Keywords: adaptive control, fuzzy neural networks, multilink manipulators

Addresses: Gao Y, Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798,

Singapore. Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore.


IDS Number: 499KB ISSN: 0278-0046

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Fuzzy and neuro-fuzzy approaches to control a flexible single-link manipulatorSubudhi B, Morris AS

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-

JOURNAL OF SYSTEMS AND CONTROL ENGINEERING

217: (I5) 387-399 2003


Abstract: In this paper, new fuzzy and neuro-fuzzy approaches to tip position regulation of aflexible-link manipulator are presented. Firstly, a non-collocated, proportional-derivative (PD)

type, fuzzylogiccontroller (FLC) is developed. This is shown to perform better than typicalmodel-based controllers (LQR and PD). Following this, an adaptive neuro-fuzzy controller

(NFC) is described that has been developed for situations where there is payload variability.

The proposed NFC tunes the input and output scale parameters of the fuzzy controller on-line.

The efficacy of the NFC has been evaluated by comparing it with a fuzzy model reference

adaptive controller (FMRC).

Author Keywords: fuzzy logic, neural network, flexible link, manipulator, tip position

KeyWords Plus: DESIGN

Addresses: Morris AS, Univ Sheffield, Dept Automat Control & Syst Engn, Mappin St,

Sheffield S1 3JD, S Yorkshire, England.

Publisher: PROFESSIONAL ENGINEERING PUBLISHING LTD, BURY ST EDMUNDS

IDS Number: 742RJ ISSN: 0959-6518

----A quantitative analysis of evolvability for an evolutionary fuzzylogiccontroller

Lee SI, Cho SB

INTEGRATED COMPUTER-AIDED ENGINEERING

10: (4) 369-385 2003


Abstract: This paper presents a quantitative analysis of evolvability with evolutionary

activity statistics in an evolutionary fuzzy system. In general, one can estimate the

performance of an evolved fuzzy controller by its fitness. However, it is difficult to explain

how its fitness or adaptability has been obtained. Evolutionary activity is used to measure the

evolvability of fuzzy rules and explain why salient rules have higher evolvability. A genetic

algorithm is used to construct a fuzzylogiccontroller for a mobile robot in simulation


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environments. The quantitative analysis shows that sufficient evolvability is maintained

during the evolution and that it contributes to the construction of the optimal controller.

KeyWords Plus: CONTROL-SYSTEMS

Addresses: Lee SI, ATR Int, 2-2-2 Hikaridai, Seika, Kyoto 6190288, Japan.ATR Int, Seika, Kyoto 6190288, Japan.

Publisher: IOS PRESS, AMSTERDAM

IDS Number: 740DN ISSN: 1069-2509

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Self tuning fuzzy PID type load and frequency controller

Yesil E, Guzelkaya M, Eksin I

ENERGY CONVERSION AND MANAGEMENT

45: (3) 377-390 FEB 2004Document type: Article Language: English Cited References: 31 Times Cited: 0

Abstract: In this paper, a self tuning fuzzy PID type controller is proposed for solving the

load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set

of control rules, and the control signal is directly deduced from the knowledge base and the

fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling

factor corresponding to the derivative coefficient and the output scaling factor corresponding

to the integral coefficient of the PID type fuzzylogiccontroller in an on-line manner. The

self tuning mechanism depends on the peak observer idea, and this idea is modified and

adapted to the LFC problem. A two area interconnected system is assumed for

demonstrations. The proposed self tuning fuzzy PID type controller has been compared withthe fuzzy PID type controller without a self tuning mechanism and the conventional integral

controller through some performance indices. (C) 2003 Elsevier Ltd. All rights reserved.

Author Keywords: fuzzy PID type controller, self tuning, load frequency control

KeyWords Plus: AUTOMATIC-GENERATION CONTROL, NEURAL NETWORKS,POWER-SYSTEM, DESIGN

Addresses: Guzelkaya M, Istanbul Tech Univ, Fac Elect & Elect Engn, Control Engn Div,

TR-80626 Istanbul, Turkey.


IDS Number: 738YU ISSN: 0196-8904

----

Hierarchical fuzzylogiccontrollerfor a flexible link robot arm performing constrainedmotion tasks

Lin J

IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS

150: (4) 355-364 JUL 2003Document type: Article Language: English Cited References: 27 Times Cited: 0


17/44

Abstract:An examination is performed on the dynamics and control issues for a roboticmanipulator with link structural flexibility modelled during the execution of a task that

requires the robot tip to contact fixed rigid objects. A multi-time-scale fuzzylogiccontrolleris applied to this system. The large-scale system is decomposed into a finite number of

reduced-order subsystems using the singular perturbation approach. A hierarchical ordering of

fuzzy rules is used to reduce the size of the inference engine. Real-time implementation offuzzy controllers can help reduce the burden of large-sized rule sets by fusing sensory data

before input and the systems output to the inference engine. Using this approach the control

of the force and position of the robot end point is possible while the end-effector moves on the

constraint surface.

KeyWords Plus: SINGULAR PERTURBATION APPROACH, JOINT ROBOTS,MANIPULATORS, FORCE, STABILITY, TRACKING

Addresses: Lin J, Ching Yun Inst Technol, Dept Mech Engn, 229 Chien Hsin Rd, Jing Li

320, Taiwan Ching Yun Inst Technol, Dept Mech Engn, Jing Li 320, Taiwan.


IDS Number: 724JN ISSN: 1350-2379

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Self-tuning of PID-type fuzzylogiccontrollercoefficients via relative rate observerGuzelkaya M, Eksin I, Yesil E

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE16: (3) 227-236 APR 2003


Abstract: In this study, a new method is proposed for tuning the coefficients of PID-typefuzzy logic controllers (FLCs). The new method adjusts the input scaling factor corresponding

to the derivative coefficient and the output scaling factor corresponding to tile integral

coefficient of the PID-type FLC using a fuzzy inference mechanism in an on-line manner. The

fuzzy inference mechanism that adjusts the related coefficients has two inputs, one of which is

called normalized acceleration and the other one is the classical error. The normalized

acceleration gives the relative rate information about the fastness or slowness of tile

system response. An appropriate rule-base is generated for the adaptation of the derivative

coefficient of the PID-type FLC using these two input variables. The integral coefficient is

then updated as the reciprocal of the derivative coefficient. The robustness and effectiveness

of the new self-tuning algorithm have been compared with the other related tuning methodsproposed in the literature through simulations. The simulations are done on a second-order

system with varying parameters and time delay. (C) 2003 Elsevier Ltd. All rights reserved.

Author Keywords: PID-type fuzzy controller, self-tuning, scaling factors

KeyWords Plus: METHODOLOGY, GAIN

Addresses: Guzelkaya M, Istanbul Tech Univ, Control Syst Div, Elect & Elect Fac, TR-

80626 Istanbul, Turkey.


IDS Number: 720TH ISSN: 0952-1976


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Two-time scale fuzzylogiccontrollerof flexible link robot armLin J, Lewis FL

FUZZY SETS AND SYSTEMS139: (1) 125-149 OCT 1 2003


Abstract: A flexible link arm is a distributed parameter system of infinite order, but due toonboard computer limitations, sensor inaccuracy, and system noise, it must be approximated

by a lower-order model and controlled by a finite-order controller. The main object of this

paper is concentrated on the hierarchical fuzzy logic by the singular perturbation approach for

flexible-link robot arm control. A composite control design is adopted. Therefore, a two-time

scale fuzzylogiccontroller will be applied for such system. In this paper, the fast-subsystem

controller will be damp out the vibration of the flexible structure by two hierarchical fuzzy

logic controllers. Moreover, the other slow-subsystem fuzzy controller dominates the

trajectory tracking. We guarantee the stability of the internal dynamics by adding a boundary-

layer correction based on singular perturbations. In addition, various case studies are given in

illustration to verify the control algorithm. It appears that the fuzzy control method is quite

useful as regards reliability and robustness. (C) 2002 Elsevier B.V. All rights reserved.

Author Keywords: reduced-order, singular perturbation, hierarchical fuzzy logic control

KeyWords Plus: SINGULAR PERTURBATION APPROACH, MANIPULATORS,

SYSTEMS

Addresses: Lin J, Ching Yun Inst Technol, Dept Mech Engn, 229 Chien Hsin Rd, Jung Li

City 320, Taiwan. Ching Yun Inst Technol, Dept Mech Engn, Jung Li City 320, Taiwan.

Univ Texas, Automat & Robot Res Inst, Arlington, TX 76019 USA.


IDS Number: 721FY ISSN: 0165-0114

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Nonlinear internal model control using neural networks and fuzzy logic: Application toan electromechanical process

Haber RE, Alique JR, Alique A, Haber RH

COMPUTATIONAL SCIENCE - ICCS 2003, PT I, PROCEEDINGS

LECTURE NOTES IN COMPUTER SCIENCE

2657: 351-360 2003Document type: Article Language: English Cited References: 13 Times Cited: 0

Abstract: This study explores the use of the internal-model control (IMC) paradigm using

artificial neural networks (ANNs) and fuzzy logic (FL) to consider a force-control problem

involving a complex electromechanical system, represented here by the machining process.

The main goal is to control a single output variable, cutting force, by changing a single input

variable, feed rate. This scheme consists of a dynamic model using ANNs to estimate process

output and a fuzzy-logiccontroller (FLC) with the same static gain as the inverse model to

determine the control inputs (feed rate) necessary to keep the cutting force constant. Three

approaches, the fuzzy-logiccontroller (IMC), the internal-model controller (IMC) and a

neuro-fuzzy controller (NFC), are simulated and their performances are assessed in terms of

several performance measurements. The results demonstrate that the NFC strategy providesbetter disturbance rejection than IMC and FLC for the cases analysed.


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KeyWords Plus: FORCE CONTROL

Addresses: Haber RE, CSIC, Inst Automat Ind, Km 22, 800 N-3, Madrid 28500, Spain.

CSIC, Inst Automat Ind, Madrid 28500, Spain.

Publisher: SPRINGER-VERLAG BERLIN, BERLIN

IDS Number: BX28J ISSN: 0302-9743

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Remote fuzzy logic control of networked control system via Profibus-DPLee KC, Lee S, Lee MH

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

50: (4) 784-792 AUG 2003


Abstract: This paper focuses, on the feasibility of fuzzy logic control for networked controlsystems (NCSs). In,order to evaluate its feasibility, a networked control system for servo

motor control is implemented on a Profibus-DP network. The NCS consists of several

independent, but interacting, processes running on two separate stations. By using this NCS,

the network-induced delay is analyzed to find the cause of the delay. Furthermore, the fuzzy

logiccontrollers performance is compared With that of conventional proportional-integral-derivative controllers. Based on the experimental results, it is found. that the fuzzylogiccontroller can be a viable choice for an NCS due to its robustness against parameteruncertainty.

Author Keywords: fieldbus, networked control system (NCS), network-induced delay,Profibus-DP, remote fuzzylogiccontroller (RFLC)

KeyWords Plus: PERFORMANCE MANAGEMENT, COMMUNICATION

Addresses: Lee KC, Univ Ulsan, Network Based Ctr, Ulsan 680749, South Korea.

Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC,PISCATAWAY

IDS Number: 705BM ISSN: 0278-0046

----Various hybrid methods based on genetic algorithm with fuzzylogiccontroller

Yun YS, Gen M, Seo S

JOURNAL OF INTELLIGENT MANUFACTURING14: (3-4) 401-419 JUN-AUG 2003


Abstract: In this paper we propose several efficient hybrid methods based on geneticalgorithms and fuzzy logic. The proposed hybridization methods combine a rough search

technique, a fuzzylogiccontroller, and a local search technique. The rough search technique

is used to initialize the population of the genetic algorithm (GA), its strategy is to make large

jumps in the search space in order to avoid being trapped in local optima. The fuzzylogic

controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithmparameters (crossover ratio and mutation ratio). The local search technique is applied to find a


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better solution in the convergence region after the GA loop or within the GA loop. Five

algorithms including one plain GA and four hybrid Gas along with some conventional

heuristics are applied to three complex optimization problems. The results are analyzed and

the best hybrid algorithm is recommended.

Author Keywords: hybrid algorithm, genetic algorithm, fuzzylogiccontroller, local searchtechnique

KeyWords Plus: OPTIMIZATION, INTEGER

Addresses: Yun YS, Daegu Univ, Mech Engn, Kyungbuk 712714, South Korea.

Publisher:KLUWER ACADEMIC PUBL, DORDRECHT

IDS Number:699DQ ISSN: 0956-5515

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Multiple manipulator control from a human motor-control perspectiveKambhampati C, Rajasekharan S

IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION19: (3) 433-442 JUN 2003


Abstract: The human motor-control system has a hierarchical and decentralized structure,

and building such a control system for a multiple robot system would require a decomposed

model. The difficulty in decomposing complex robotic systems is due to interactions between

robots, and this paper proposes a control architecture that controls the manipulator joints andinteractions separately. A decomposable model for an N - n degrees-of-freedom

multimanipulator/object system handling an object is derived. This model is then used to

design a Lyapunov-based fuzzylogiccontroller for the system by solving linear matrixequalities. It is shown that this controller is closed-loop stable and a stable suboptimal

controller for the system may be designed using bounds.

Author Keywords: cooperative robots, decentralized control, human motor control (HMC),

linear matrix inequalities (LMI), neuro-fuzzy modeling

KeyWords Plus:NONLINEAR-SYSTEMS, FUZZY CONTROL, NETWORKS, DESIGN,

SPACE, LOGIC

Addresses: Kambhampati C, Univ Hull, Dept Comp Sci, Kingston Upon Hull HU6 7RX, N

Humberside, England.


IDS Number: 691HP ISSN: 1042-296X

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Fuzzy logic versus PI speed control in high-performance AC drives: A comparisonIbrahim Z, Levi E

ELECTRIC POWER COMPONENTS AND SYSTEMS31: (4) 403-422 APR 2003


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is based on the equivalence between fuzzy preconditions and on boolean expressions. Using

the fact that fuzzy sets are a generalization of classical subsets, we introduced some

operations on fuzzy sets that are equivalent to those applied in the boolean logic approach.

The paper then discusses the reduction of large scale fuzzy rule bases by the use of a

decoupling approach, and its application to the case of an optimal fuzzylogiccontroller of athree-links robot manipulator using local PID controllers. (C) 2003 Published by Elsevier

Science Ltd.

Author Keywords: fuzzy logic, rule base size reduction, robot manipulator, optimization,

decoupling approach

Addresses: Bezine H, Univ Sfax, ENIS, REGIM, BP W 3038, Sfax, Tunisia.


IDS Number: 658EV ISSN: 0952-1976

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Fuzzy logic based speed controllers for vector controlled induction motor drive

Singh B, Choudhuri SG

IETE JOURNAL OF RESEARCH

48: (6) 441-447 NOV-DEC 2002


Abstract: This paper presents a comparative study of Proportional Integral (PI), Fuzy Logic

(FL), Fuzzy Pre-compensated Proportional Integral (FPPI), Fuzzy Poportional Integral (FPI),

and Hybrid (of FL and PI) speed controllers for vector controlled induction motor drive. An

Indirect Vector Control (VC) strategy is employed for the control of current controlledvoltage source,inverter (CC-VSI) fed squirrel cage induction motor drive (SCIMD). The

simulated response of a CC-VSI fed cage induction motor drive is presented for different

modes of operation such as starting, speed reversal, load application and removal to

demonstrate the effectiveness of the various mentioned controllers.

Author Keywords: vector control mode, Proportional Integral controller, FuzzyLogiccontroller, Hybrid of Fuzzy Logic and Proportional Integral controller

KeyWords Plus: CONTROL-SYSTEMS

Addresses: Singh B, Indian Inst Technol, Dept Elect Engn, New Delhi 110016, India.Indian Inst Technol, Dept Elect Engn, New Delhi 110016, India.

Publisher: INST ELECTRONICS TELECOMMUNICATION ENGINEERS, NEW DELHI

IDS Number: 637NV ISSN: 0377-2063

---A new methodology for designing a fuzzylogiccontrollerand PI, PD blendingmechanism

Guzelkaya M, Eksin I, Gurleyen F

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

11: (1-2) 85-98 2001Document type: Article Language: English Cited References: 20 Times Cited: 1
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/66&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180519600003&PW=2003&doc=134/66&PR=134/66http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/66&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180519600003&PW=2003&doc=134/66&PR=134/66http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/66&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180519600003&PW=2003&doc=134/66&PR=134/66http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180042100007&PW=2003&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180042100007&PW=2003&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180042100007&PW=2003&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.92005425&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.92005425&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.92005425&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/66&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180519600003&PW=2003&doc=134/66&PR=134/66http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000180042100007&PW=2003&doc=134/69&PR=134/69http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/69&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.92005425&doc=134/69&PR=134/69


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Abstract: In this study, a hybrid and intelligent structure that blends the PI-type and PD-type output portions of a new fuzzylogiccontroller is presented. The new fuzzylogiccontroller consists of two rule-base blocks and a logical switch in between and its rule-base isformed using new meta-rules. The rule-base blocks admit two inputs one of which is newly

devised and called normalized acceleration and the other one is the classical error. The

newly devised input gives a relative value about the fastness or slowness of the systemresponse and provides an easy and straightforward way of forming the rule-base. The new

hybrid controller structure is formed in such a way that it intelligently blends PI and PD

portions of the fuzzylogiccontroller through a new empirical relation or a rule-base. Both

the empirical relation and the rule-base use the same inputs of the new fuzzylogiccontroller.

The robustness and effectiveness of the new fuzzylogiccontroller with/without the blendingmechanism are illustrated through simulations done on a second-order system with varying

parameters. Furthermore, the results of the new hybrid fuzzy PID controller is compared with

the results obtained using both the conventional fuzzy PID controller and another hybrid

method which is an augmented version of fuzzy PI controller with a resetting factor.

Author Keywords: fuzzy logic control, fuzzy rule-base generation, PI-PD-blending

Addresses: Eksin I, Istanbul Tech Univ,Control Syst Div, TR-80626 Istanbul, Turkey.

Publisher: IOS PRESS, AMSTERDAM

IDS Number: 629GM ISSN: 1064-1246

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Fuzzy controller for flexible-link robot arm by reduced-order techniquesLin J, Lewis FL

IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS

149: (3) 177-187 MAY 2002


Abstract: The design and analysis of a large-scale control system should be based on the best

available knowledge instead of the simplest available model when treating uncertainties in the

system. Therefore, a large-scale system is better treated by knowledge-based methods such as

fuzzy logic, neural networks, expert systems, etc. This paper concentrates on fuzzy logic

using the singular perturbation approach for flexible-link robot arm control. To reduce the

spillover effect, we will introduce a singular perturbation approach to derive the slow and fast

subsystems. A composite control design is adopted. Therefore, a two-time scale fuzzylogiccontroller will be applied to the system. The fast-subsystem controller will damp out the

vibration of the flexible structure by an optimal control method. Hence, the slow-subsystem

fuzzy controller dominates the trajectory tracking. We guarantee the stability of the internal

dynamics by adding a boundary-layer correction based on singular perturbations. Various

case studies are given to verify the control algorithm. It appears that the fuzzy control method

is quite useful in terms of reliability and robustness.

KeyWords Plus: SINGULAR PERTURBATION APPROACH, LOGIC CONTROL,

MANIPULATORS, SYSTEMS

Addresses: Lin J, Ching Yun Inst Technol, Dept Mech Engn, 229 Chien Hsin Rd, Jung Li320, Taiwan. Univ Texas, Automat & Robot Res Inst, Arlington, TX USA.
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176541000001&PW=2002&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176541000001&PW=2002&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176541000001&PW=2002&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88638759&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88638759&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88638759&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=DispCitedRef&UT=000176541000001&PW=2002&doc=134/94&PR=134/94http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&Func=Abstract&doc=134/94&PR=134/CIW.cgi?120018_BBCB32CB&Func=Citing&isickref=.r.88638759&doc=134/94&PR=134/94


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IDS Number: 568KM ISSN: 1350-2379

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Comparison of two inference methods for P-type fuzzy logic control throughexperimental investigation using a hydraulic manipulator

Rahbari R, de Silva CW

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

14: (6) 763-784 DEC 2001


Abstract: This paper presents a comparison of the two important inference schemes:individual-rule-based inference and compositional rule of inference as applied to fuzzy

logic control. through experimental investigation. The techniques are implemented on a

hydraulic manipulator of an industrial machine with P-type fuzzy control. The fuzzylogiccontroller is designed for automatic positioning of the cutter blade of an automated fish-

cutting machine. The features of the machine. which uses hydraulic servo control for cutterpositioning. are outlined. The performance of the machine under the two inference schemes is

examined and contrasted. Some practical implementations of the results are indicated. (C)

2002 Elsevier Science Ltd. All rights reserved.

Author Keywords: fuzzy logic control, hydraulic positioning systems, inference method,

individual-rule-based inferences, compositional rule of inference, P-type fuzzy logic control

Addresses: de Silva CW, Univ British Columbia, Dept Mech Engn, 2324 Main Mall,

Vancouver, BC V6T 1Z4, Canada.


IDS Number: 562LB ISSN: 0952-1976

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Design of a PID-like compound fuzzylogiccontroller

Kukolj DD, Kuzmanovic SB, Levi E

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE14: (6) 785-803 DEC 2001

Document type: Article Language: English Cited References: 15 Times Cited: 0Abstract: The paper describes a novel method for the design of a fuzzylogiccontroller(FLC) with near-optimal performance for a variety of operating conditions. The approach is

based on the analysis of the system behaviour in the error state-space. The final control

structure, in a form of a compound FLC, is arrived at in two stages. The first stage

encompasses design and tuning of a PID-like fuzzy controller. The second stage consists of

placing an additional fuzzy controller, of a structure similar to that of the first one, in parallel

with the PID-like fuzzy controller designed in the first stage. The resulting compound

controller is characterised with high performance in the wide range of operating conditions.

and with small number of parameters that can be adjusted using simple optimisation methods.

The controller is developed and tested for a plant comprising a vector controlled induction

motor drive. (C) 2002 Elsevier Science Ltd. All rights reserved.
http://wos.cuni.cz/CIW.cgi?120018_BBCB32CB&

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