西安交通大学教师主页管理系统阮小娥 Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes Home

Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes

Release Time:2025-04-30

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Title of Paper:: Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes

Summary:: 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.