时变切换信号驱动的线性连续切换系统的迭代学习控制收敛性分析

Abstract

Abstract: This paper addresses the convergence performance of first-order and higher-order PD-type iterative learning control strategies for a class of linear continuous-time switched systems. The manipulated systems are elaborated by arbitrary time-driven switching signals and can repetitively operate over a finite time interval. By employing the generalized Young inequality of convolution integral theoretical analysis is launched in the sense of Lebesgue-p norm. Simultaneously, sufficient convergence conditions of the algorithms are derived and the effect of the state matrices on the learning performance is quantized. To illustrate the validity and effectiveness of the theoretical results, numerical simulations are conducted.

Key words: Iterative learning control, Switched systems, Switching rules, Lebesgue-p norm, Generalized Young inequality, Convergence

CLC Number:

O231.1

Yang Xuan, Ruan Xiaoe, Wang Peng. Convergence Analysis of Iterative Learning Control for Linear Continuous-Time Switched Systems with Arbitrary Time-Driven Switching Rules[J].Acta mathematica scientia,Series A, 2018, 38(3): 599-612.

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Convergence Analysis of Iterative Learning Control for Linear Continuous-Time Switched Systems with Arbitrary Time-Driven Switching Rules

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