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.