Decentralized Adaptive Iterative Learning Control for Reconfigurable Manipulators
In this paper, a decentralized adaptive iterative learning control algorithm for reconfigurable manipulators is proposed. The dynamics of reconfigurable manipulators is represented as a set of interconnected subsystems. A local learning controller for each subsystem is constructed by a neural network learning component and a robust learning component to adaptively compensate for the unknown dynamic functions and interconnections. Under a bounding condition on the nonlinear interconnections, the iterative learning controller guarantees that all the internal signals are bounded during the learning process and the state tracking errors of each subsystem converge asymptotically to a tunable residual set over a finite time interval. The simulation results are presented to show the effectiveness of the proposed decentralized adaptive iterative learning control scheme.
ZHU Lu DONG Bo ZHAO Bo LI Yuanchun
State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun 130022, P.R.China Department of Control Science and Engineering, Jilin University, Changchun 130022, P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）