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In this study, the problem of adaptive iterative learning control (AILC) is considered for a class of multiple-input-multiple-output discrete-time nonlinear systems where the initial condition and reference trajectory could be randomly varying in the iteration domain. It is assumed that the considered systems are subjected to time-iteration-varying unknown parameters. The iteration-varying parameters are generated by a known high-order internal model (HOIM) that is formulated as a polynomial between two consecutive iterations. By incorporating the HOIM into the controller design, the learning convergence of ILC is guaranteed through rigorous analysis under Lyapunov theory. The illustrative example is presented to demonstrate the effectiveness of AILC method.

Summary In this paper, a high-order internal model (HOIM)-based iterative learning control (ILC) scheme is proposed for discrete-time nonlinear systems to tackle the tracking problem under iteration-varying desired trajectories. By incorporating the HOIM that is utilized to describe the variation of desired trajectories in the iteration domain into the ILC design, it is shown that the system output can converge to the desired trajectory along the iteration axis within arbitrarily small error. Furthermore, the learning property in the presence of state disturbances and output noise is discussed under HOIM-based ILC with an integrator in the iteration axis. Two simulation examples are given to demonstrate the effectiveness of the proposed control method. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a direct learning control scheme is proposed for a class of discrete-time systems tracking magnitude-varying reference trajectories. The reference trajectories vary in magnitude with known ratios. A direct learning control algorithm is designed for systems with adequate pre-stored control information profiles, especially for systems working nonrepetitively in practice. By using pre-stored control input data, the new learning control input is developed directly. The simulation results of the permanent magnet linear motor show that the proposed direct learning control approach achieves perfect tracking.