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Titolo:
NEURAL-NETWORK CONTROL FOR A CLOSED-LOOP SYSTEM USING FEEDBACK-ERROR-LEARNING
Autore:
GOMI H; KAWATO M;
Indirizzi:
ATR,HUMAN INFORMAT PROC RES LABS,2-2 HIKARIDAI,SEIKA CHO KYOTO 61902 JAPAN HOKKAIDO UNIV SAPPORO HOKKAIDO 060 JAPAN
Titolo Testata:
Neural networks
fascicolo: 7, volume: 6, anno: 1993,
pagine: 933 - 946
SICI:
0893-6080(1993)6:7<933:NCFACS>2.0.ZU;2-Z
Fonte:
ISI
Lingua:
ENG
Soggetto:
MODEL; MANIPULATOR; CEREBELLUM; MOVEMENT;
Keywords:
NEURAL NETWORK CONTROL; ADAPTIVE CONTROL; FEEDBACK-CONTROL LEARNING; REFERENCE MODEL; IMPEDANCE CONTROL; FEEDBACK-ERROR-LEARNING; CEREBELLUM MOTOR CONTROL LEARNING;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
CompuMath Citation Index
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
42
Recensione:
Indirizzi per estratti:
Citazione:
H. Gomi e M. Kawato, "NEURAL-NETWORK CONTROL FOR A CLOSED-LOOP SYSTEM USING FEEDBACK-ERROR-LEARNING", Neural networks, 6(7), 1993, pp. 933-946

Abstract

This paper presents new learning schemes using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. Feedback-error-learning was proposed as a learning method for forming a feedforward controller that uses the output of a feedback controller as the error for training a neural network model. Using new schemes for nonlinear feedback control, the actual responses after learning correspond to the desired responses which are defined by an inverse reference model implemented as a conventional feedback controller Inthis respect, these methods are similar to Model Reference Adaptive Control (MRAC) applied to linear or linearized systems. It is shown that learning impedance control is derived when one proposed scheme is used in Cartesian space. We show the results of applying these learning schemes to an inverted pendulum and a 2-link manipulator We also discuss the convergence properties of the neural network models employed inthese learning schemes by applying the Lyapunov method to the averaged equations associated with the stochastic differential equations which describe the system dynamics.

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Documento generato il 05/04/20 alle ore 02:40:29