A New Adaptive Fuzzy Iterative Learning Control for Nonlinear Systems with Repeatable Control Tasks

For the control of uncertain repeatable nonlinear systems with varying initial resetting state errors and non- repeatable input disturbance, a new adaptive fuzzy iterative learning controller is proposed in this paper. The main structure of this learning controller is constructed by a fuzzy learning component and a robust learning component. A new adap- tive algorithm combining time-domain and iteration-domain adaptation is derived to search for suitable values of control parameters and then guarantee the closed-loop stability and error convergence. This adaptive algorithm is designed without using projection or deadzone mechanism. With a suitable choice of the weighting gains, the memory size for the storage of parameter profiles can be greatly reduced. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. Moreover, the norm of tracking state error vector will asymptotically converge to a tunable residual set as iteration number tends to infinity.