JRM Vol.3 No.6 pp. 491-496
doi: 10.20965/jrm.1991.p0491


Collison Control of the Robot Manipulator by a Learning Control Using the Weighted Least-Squares Method

Hiroshi Wada*, Toshio Fukuda**, Hideo Matsuura**, Fumihito Arai**, Keigo Watanabe*** and Yasumasa Shoji****

*Toyoda Automatic Loom Works, Ltd., 2-1 Toyoda-machi, Kariya, Aichi 448, Lapan

**Faculty of Engineering, Nagoya University, 1 Furo-cho, Chigusa-ku, Nagoya 464-01, Japan

***Faculty of Science and Engineering University, 1 Honjo-machi, Saga 840, Japan

****Toyo Engineering Corp., 2-8-1 Akanehama, Narashino, Chiba 275, Japan

July 4, 1990
October 31, 1991
December 20, 1991
Robotics, Mechatronics, Collision, Force control, Learning control, Weighted least-squares method

Collision phenomena are very fast and nonlinear, thus, it is difficult to control a manipulator by collision phenomena. Therefore, in the past, manipulators moved slowly in order to avoid collision. However, the need for high-speed operation has been increasing, making it is indispensable to control manipulators by collision phenomena. With such fast phenomena, it is effective to use learning control in a forward manner. In this paper, we have proposed a learning control method to optimize the weighted least-squares criterion of learning errors. This method is applied in order to obtain a unique control gain by the Riccati equation which has a state dimension equal to the sampling number. It is shown that the convergence of learning error can be readily assured because the present learning rule consists of a steadystate Kalman filter. Based on this learning control method, experimental results of force control with a collision phenomena are reported.

Cite this article as:
H. Wada, T. Fukuda, H. Matsuura, F. Arai, K. Watanabe, and Y. Shoji, “Collison Control of the Robot Manipulator by a Learning Control Using the Weighted Least-Squares Method,” J. Robot. Mechatron., Vol.3, No.6, pp. 491-496, 1991.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Apr. 19, 2019