【摘要】 针对一类由任意时变切换信号驱动并在某个时间区间可重复运行的切换系统,该文研究一阶和高阶PD-型迭代学习控算法.利用卷积积分的广义Young不等式,在Lebesgue-p范数意义下分析跟踪误差性态,得出算法收敛的充分条件,并量化了状态矩阵对学习效果的影响,数值仿真验证了理论结果的可行性和有效性.更多还原

【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.更多还原