Neural network based tracking control of a flexible macro-micro manipulator system

被引：25
|
作者：
Cheng, XP [1 ]
Patel, RV [1 ]
机构：
[1] Univ Western Ontario, Dept Elect & Comp Engn, London, ON N6A 5B9, Canada
来源：
NEURAL NETWORKS | 2003年 / 16卷 / 02期
关键词：
macro-micro manipulators; neural networks; nonlinear control; robot dynamics; stability analysis; trajectory tracking control;
D O I：
10.1016/S0893-6080(02)00229-0
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we address the problem of stable tracking control of a flexible macro-micro manipulator (M-3) system. A two-layer neural network is utilized to approximate the nonlinear robot dynamic behavior of the M-3 system, and the controllers for the macro and micro arms are developed without any need for prior knowledge of the dynamic model of the controlled M-3 system. A learning algorithm for the neural network using Lyapunov stability theory is derived. It is shown that both the tracking error and the weight-tuning error are uniformly ultimately bounded under this new control scheme. Simulation results are presented and compared to those obtained using a PD controller. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：271 / 286
页数：16
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