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Iterative Learning and Extremum Seeking for Repetitive Time-Varying Mappings. (arXiv:1509.01912v2 [math.OC] UPDATED)
来源于:arXiv
In this paper, we develop an extremum seeking control method integrated with
iterative learning control to track a time-varying optimizer within finite
time. The behavior of the extremum seeking system is analyzed via an
approximating system - the modified Lie bracket system. The modified Lie
bracket system is essentially an online integral-type iterative learning
control law. The paper contributes to two fields, namely, iterative learning
control and extremum seeking. First, an online integral type iterative learning
control with a forgetting factor is proposed. Its convergence is analyzed via
$k$-dependent (iteration- dependent) contraction mapping in a Banach space
equipped with $\lambda$-norm. Second, the iterative learning extremum seeking
system can be regarded as an iterative learning control with "control input
disturbance." The tracking error of its modified Lie bracket system can be
shown uniformly bounded in terms of iterations by selecting a sufficiently
large frequency. Fu 查看全文>>