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In this paper, a fuzzy system-based adaptive iterative learning controller is proposed for a class of non-Lipschitz nonlinear plants which can repeat a given task over a finite time interval. The variable initial resetting state errors at the beginning of each trial is considered. To overcome the initial errors, a time-varying boundary layer is introduced to design an error function. Based on the error function, the main structure of this controller is constructed by a fuzzy iterative learning component and a feedback stabilization component. The fuzzy system is used as an approximator to compensate for the plant unknown nonlinearity. Since the optimal parameters for a good fuzzy approximation are in general unavailable, the adaptive algorithms are derived along the iteration axis to search for suitable parameter values and then guarantee the closed-loop stability and learning convergence. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. There even exist initial state errors, the norm of tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity and the learning speed can be easily improved if the learning gain is large.
Chien et al. (Fri,) studied this question.