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Titolo:
Real-time fine motion control of robot manipulators with unknown dynamics
Autore:
Yang, SX; Meng, M;
Indirizzi:
Univ Guelph, Sch Engn, ARIS Lab, Guelph, ON N1G 2W1, Canada Univ Guelph Guelph ON Canada N1G 2W1 ARIS Lab, Guelph, ON N1G 2W1, Canada Univ Alberta, Dept Elect Engn & Comp Sci, ART Lab, Edmonton, AB T6G 2G7, Canada Univ Alberta Edmonton AB Canada T6G 2G7 Lab, Edmonton, AB T6G 2G7, Canada
Titolo Testata:
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS
fascicolo: 3, volume: 8, anno: 2001,
pagine: 339 - 358
SICI:
1201-3390(200109)8:3<339:RFMCOR>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
GUARANTEED TRACKING PERFORMANCE; ADAPTIVE-CONTROL;
Keywords:
fine motion control; neural networks; dynamics uncertainty; robot regressor dynamics; real-time control; Lyapunov stability;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
26
Recensione:
Indirizzi per estratti:
Indirizzo: Yang, SX Univ Guelph, Sch Engn, ARIS Lab, Guelph, ON N1G 2W1, Canada Univ Guelph Guelph ON Canada N1G 2W1 Guelph, ON N1G 2W1, Canada
Citazione:
S.X. Yang e M. Meng, "Real-time fine motion control of robot manipulators with unknown dynamics", DYN CONT B, 8(3), 2001, pp. 339-358

Abstract

A novel neural network based approach is proposed for real-time fine motion control of robot manipulators without any knowledge of the robot dynamicsand subject to significant dynamics uncertainties. The controller structure consists of a simple feedforward neural network and a PD feedback loop, which inherits advantages from both the neural network based controllers andthe traditional PD-type controllers. By taking advantage of the robot regressor dynamics, the neural network assumes a single-layer structure, and the learning algorithm is computationally efficient. The real-time fine motion control of robot manipulators is achieved through the on-line learning ofthe neural network without any off-line training procedures. The PD control loop guarantees the global stability during the learning period of the neural network. In addition, the proposed controller does not require any knowledge of the robot dynamics and is capable of quickly compensating sudden changes in the robot dynamics. The global system stability and convergence are proved using a Lyapunov stability theory. The proposed controller is applied to track an elliptic trajectory and to compensate a sudden change in the robot dynamics in real-time. The effectiveness and the efficiency of the proposed controller are demonstrated through simulation and comparison studies.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 06/04/20 alle ore 01:56:49