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An open-closed-loop iterative learning control (ILC) scheme is presented for linear discrete-time multiple-input multiple-output (MIMO) systems with iterative varying duration in this article, where the vector relative degree of MIMO system is considered. To recompense the absent tracking information at former iterations caused by iterative varying duration, the feedback control strategy is adopted in modified tracking error based ILC law. It is proved that the convergent condition depends on the P-type control gain. By selecting the proper feedback control gain, the developed open-closed-loop ILC scheme can expedite the convergent speed. Finally, a numerical example is provided for validation.

In this article, a novel fuzzy systems based on adaptive Iterative Learning Control (ILC) strategy is presented to deal with a class of non-parametric nonlinear discrete-time systems which perform iteration-varying reference trajectory tracking. Using the technique of fuzzy systems to compensate for the non-parametric uncertainty of the discrete-time system dynamics, the proposed adaptive ILC scheme can well track the iteration-varying reference trajectory beyond the initial time points. The convergence of the fuzzy systems based adaptive ILC algorithm is guaranteed by theoretical analysis, and a numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.

For non-strictly repetitive linear time-variant continuous systems, when both the iterative initial conditions and the reference trajectories are iteration-varying within a bound, we present a kind of iterative learning controller with a rectifying action to the non-strictly repetitive tracking. The proposed controller can make the output tracking error beyond the initial time interval driven to a residual set whose size depends on the estimation error of input matrix. Especially, when the input matrix is accurately known, the output tracking error beyond the initial time interval can be driven to zero.