Bao-Bin Liu and Wei Zhou
Second-order internal model, adaptive iterative learning control, non-repetitiveness, least-squares algorithm, random initial condition
In this paper, the adaptive iterative learning control (AILC) scheme is proposed for a class of nonlinear discrete-time systems with parametric uncertainties subject to second-order internal model. Using the second-order internal model, it is shown that the time-iteration–varying uncertainties can be divided into two parts: the known iteration-varying part and unknown time-varying part. Least-squares algorithm–based AILC method is proposed to deal with non-repetitive parameters. Through rigorous proof, we show that the proposed AILC method can deal with random initial condition and iteration-varying reference trajectory, in the sequel achieving asymptotical convergence in the iteration domain. The effectiveness of proposed algorithm is verified by simulation example.
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