Decentralized Adaptive Fuzzy Iterative Learning Control for Repeatable Nonlinear Interconnected Systems

In this paper, we investigate the iterative learning control of interconnected nonlinear systems with repeatable control tasks. For each subsystem, the main structure of the local learning controller is constructed by a fuzzy learning component and a robust learning component. The learning control algorithm employs fuzzy system and sliding-mode like technique to adaptively compensate for the plant nonlinearities and interconnections. The interaction between each subsystem can be a general type of unknown nonlinear functions. Under a bounding condition on the nonlinear interconnections, we show that all the internal signals are bounded during the learning process and the state tracking errors of each subsystem converge asymptotically along the iteration axis to a tunable residual set.