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Iterative learning control for manipulator trajectory tracking without any control singularity

Published online by Cambridge University Press:  09 April 2002

Ping Jiang ,
Peng-Yung Woo  and
Rolf Unbehauen
Ping Jiang
Affiliation:
Dept. of Information and Control Engineering, Tongji University, Shanghai 200092 (P.R. China)
Peng-Yung Woo
Affiliation:
Dept. of Electrical Engineering, Northern Illinois University, Dekalb, IL 60115 (USA)
Rolf Unbehauen
Affiliation:
Lehrstuhl für Allgemeine und Theoretische Elektrotechnik, Universität Erlangen-Nürnberg, Cauerstraße 7, D-91058, Erlangen (Germany)
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Abstract

In this paper, we investigate trajectory tracking in a multi-input nonlinear system, where there is little knowledge of the system parameters and the form of the nonlinear function. An identification-based iterative learning control (ILC) scheme to repetitively estimate the linearity in a neighborhood of a desired trajectory is presented. Based on this estimation, the original nonlinear system can track the desired trajectory perfectly by the aid of a regional training scheme. Just like in adaptive control, a singularity exists in ILC when the input coupling matrix is estimated. Singularity avoidance is discussed. A new parameter modification procedure for ILC is presented such that the determinant of the estimate of the input coupling matrix is uniformly bounded from below. Compared with the scheme used for adaptive control of a MIMO system, the proposed scheme reduces the computation load greatly. It is used in a robotic visual system for manipulator trajectory tracking without any information about the camera-robot relationship. The estimated image Jacobian is updated repetitively and then its inverse is used to calculate the manipulator velocity without any singularity.

Keywords

Iterative learning controlVisual servoAdaptive controlSingularity avoidance
Type
Research Article
Information
Robotica , Volume 20 , Issue 2 , March 2002 , pp. 149 - 158
Copyright
© 2002 Cambridge University Press

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