Adaptive iterative learning control for robot manipulators: Experimental results |
| |
| Affiliation: | 1. Department of Electrical Engineering, Lakehead University, Thunder Bay, ont., Canada, P7B 5E1;2. Department of Systems and Computer Engineering, Carleton University, Ottawa, ont., Canada, K1S 5B6;1. School of Economic Information and Engineering, Southwestern University of Finance and Economics, Chengdu, China;2. School of Electrical and Computer Engineering, RMIT University, Melbourne VIC 3001, Australia;3. Department of Mathematics, Southeast University, Nanjing, China;1. School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266042, PR China;2. Advanced Control Systems Lab, School of Electronics & Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China;3. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6 |
| |
| Abstract: | In this paper, two adaptive iterative learning control schemes, proposed by A. Tayebi 2004, Automatica, 40(7), 1195–1203], are tested experimentally on a five-degrees-of-freedom (5-DOF) robot manipulator CATALYST5. The control strategy consists of using a classical PD feedback structure plus an additional iteratively updated term designed to cope with the unknown parameters and disturbances. The control implementation is very simple in the sense that the knowledge of the robot parameters is not needed, and the only requirement on the PD and learning gains is the positive definiteness condition. Furthermore, in contrast with classical ILC schemes where the number of iterative variables is generally equal to the number of control inputs, the adaptive control schemes tested in this paper involve just one or two iterative variables. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
