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JRM Vol.16 No.1 pp. 54-60
doi: 10.20965/jrm.2004.p0054
(2004)

Paper:

Expert Skill-Based Gain Tuning in Discrete-Time Adaptive Control for Robots

Haruhisa Kawasaki, and Geng Li

Faculty of Engineering, Gifu University, 1-1 Yanagito, Gifu 501-1193, Japan

Received:
October 10, 2003
Accepted:
January 15, 2004
Published:
February 20, 2004
Keywords:
robot, adaptive control, discrete time system, gain tuning, sampling period, Lyapunov function, stability
Abstract

This paper presents a gain tuning method based on the sampling period in discrete-time adaptive control for robots. Gain matrices of model-based adaptive control in a continuous-time system are allowed a high gain positive definite. The maximum of the gains depends on the sampling period, however, and gain tuning is very time-consuming. It is thus desirable to give a gain tuning rule in discrete-time adaptive control. The proposed gain tuning consists of two steps. The first is gain tuning at the basic sampling period by a skilful specialist by trial and error. The second step, executed if the sampling period changes, is a new gain calculation based on a new sampling period. The simulation and experiments with 1-dof and 3-dof robots demonstrate that the robot controller is stable at the large variance of sampling period changes and more accurate than a fixed gain controller.

Cite this article as:
Haruhisa Kawasaki and Geng Li, “Expert Skill-Based Gain Tuning in Discrete-Time Adaptive Control for Robots,” J. Robot. Mechatron., Vol.16, No.1, pp. 54-60, 2004.
Data files:
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