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Monotonically convergent iterative learning control for linear discrete-time systems. (English) Zbl 1086.93066
Summary: In iterative learning control schemes for linear discrete time systems, conditions to guarantee the monotonic convergence of the tracking error norms are derived. By using the Markov parameters, it is shown in the time-domain that there exists a non-increasing function such that when the properly chosen constant learning gain is multiplied by this function, the convergence of the tracking error norms is monotonic, without resort to high-gain feedback.

93E35 Stochastic learning and adaptive control
93B40 Computational methods in systems theory (MSC2010)
93E12 Identification in stochastic control theory
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