Decentralized adaptive iterative learning control for nonaffine nonlinear interconnected systems

In this paper, we present an iterative learning controller for interconnected nonlinear nonaffine systems with repeatable control tasks. A local learning controller for each subsystem is constructed by a fuzzy neural learning component and a robust learning component to adaptively compensate for the nonaffine nonlinearities and interconnections. The fuzzy neural learning component is designed based on an interval type-2 output recurrent fuzzy neural network. The interaction between each subsystem can be a general type of unknown nonlinear functions. 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 along the iteration axis to a tunable residual set.