Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes
发表刊物：International Journal of Systems Science
摘要：In the procedure of steady-state hierarchical optimization for large-scale industrial processes, it is often necessary that the control system responds to a sequence of step function-type control decisions with distinct magnitudes. In this paper, a set of iterative learning controllers are decentrally embedded into the procedure of the steady-state optimization so as to generate upgraded sequential control signals and thus improve the transient performance of the discrete-time large-scale systems. The convergence of the updating law is derived while the intervention from the distinction of the scales is analyzed. Further, an optimal iterative learning control scheme is also deduced by means of a functional derivation. The effectiveness of the proposed scheme and the optimal rule is verified by simulation.
合写作者：Xiaoe Ruan, Zeungnam Bien, K.-H.Park
卷号：vol.36, no.4
页面范围：221-233
是否译文：否
发表时间：2005-05-15