Repetitive Tracking Control of Nonlinear Systems Using Reinforcement Fuzzy-Neural Adaptive Iterative Learning Controller

This paper proposes a new fuzzy neural network based reinforcement adaptive iterative learning controller for a class of nonlinear systems. Different from some existing reinforcement learning schemes, the reinforcement adaptive iterative learning controller has the advantages of rigorous proofs without using an approximation of the plant Jacobian. The critic is appended into the reinforcement adaptive iterative learning controller to generate the simple discrete reinforcement signal, which provides a satisfaction about the tracking performance. In addition, the reinforcement signal can be further applied in the weight adaptation rules. Iterative learning components of the reinforcement adaptive iterative learning controller are designed to compensate for the uncertainties of plant nonlinearities. The overall adaptive scheme guarantees all adjustable parameters and the internal signals remain bounded for all iterations. Moreover, the norm of tracking error vector at each time instant will asymptotically converge to a tunable residual set as iteration goes to infinity even the initial state error exists. Finally, a simulation result is given to demonstrate the learning performance of the fuzzy neural network based reinforcement adaptive iterative learning controller.