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Novel iterative learning controls for linear discrete-time systems based on a performance index over iterations. (English) Zbl 1283.93178

Summary: An optimal iterative learning control (ILC) is proposed to optimize an accumulative quadratic performance index in the iteration domain for the nominal dynamics of linear discrete-time systems. Properties of stability, convergence, robustness, and optimality are investigated and demonstrated. In the case that the system under consideration contains uncertain dynamics, the proposed ILC design can be applied to yield a guaranteed-cost ILC whose solution can be found using the linear matrix inequality (LMI) technique. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed learning controls.

MSC:

93C55 Discrete-time control/observation systems
93C05 Linear systems in control theory
68T05 Learning and adaptive systems in artificial intelligence
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