Summary
In this paper, the problem of iterative learning control for a class of nonlinear systems is studied. Here, the nonlinear functions satisfy the one‐sided Lipschitz and quadratically inner‐bounded conditions. For such nonlinear systems, open‐loop and closed‐loop D‐type learning algorithms are adopted, respectively, and furthermore, the convergence conditions of the D‐type learning algorithms are established. It is shown that both algorithms can ensure that the system output converges to the desired trajectory on the whole time interval. Finally, the validity of the presented D‐type learning algorithms is verified by a numerical example.
SummaryThis article deals with the problem of iterative learning control algorithm for a class of nonlinear parabolic distributed parameter systems (DPSs) with iteration‐varying desired trajectories. Here, the variation of the desired trajectories in the iteration domain is described by a high‐order internal model. According to the characteristics of the systems, the high‐order internal model‐based P‐type learning algorithm is constructed for such nonlinear DPSs, and furthermore, the corresponding convergence theorem of the presented algorithm is established. It is shown that the output trajectory can converge to the desired trajectory in the sense of (L2,λ)‐norm along the iteration axis within arbitrarily small error. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
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