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Design and Analysis of Adaptive Iterative Learning Control for Iteration-varying Nonlinear Systems

机译:迭代非线性系统的自适应迭代学习控制设计与分析

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Design of iterative learning controller for continuous-time nonlinear systems with iteration-varying uncertainties is studied in this paper. The iteration-varying uncertainties include initial resetting tracking error, iteration-varying external disturbance, iteration-varying desired trajectory and iteration-varying system parameters. The iteration-varying uncertainties are not required to take any special structure and the uncertain bounds are not necessarily small. All the iteration-varying uncertainties are compensated by an adaptive iterative learning controller with a projection-type adaptive law. We show that the system output can converge to the desired one as close as possible after suitable numbers of learning trials. Compared with the existing papers studying the similar problems, this approach can be used to solve the iterative learning control issue with more general class of nonlinear uncertain systems and achieve better learning performance.
机译:研究了具有迭代不确定性的连续时间非线性系统的迭代学习控制器的设计。迭代变化的不确定性包括初始重置跟踪误差,迭代变化的外部干扰,迭代变化的期望轨迹和迭代变化的系统参数。不需要改变迭代的不确定性就可以采用任何特殊的结构,并且不确定范围不一定很小。所有具有迭代变化的不确定性均由具有投影型自适应律的自适应迭代学习控制器进行补偿。我们表明,经过适当数量的学习试验,系统输出可以收敛到尽可能接近的期望值。与现有研究相似问题的论文相比,该方法可用于解决非线性不确定系统更通用的迭代学习控制问题,并获得更好的学习性能。

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