Iterative Learning Control for Nonlinear Uncertain Systems with Arbitrary Initial State
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摘要: 为解决任意初态下的轨迹跟踪问题, 针对一类含参数和非参数不确定性的非线性系统, 提出基于滤波误差初始修正的自适应迭代学习控制方法. 利用修正滤波误差信号设计学习控制器, 并以Lyapunov方法进行收敛性能分析. 依据类Lipschitz条件处理非参数不确定性, 对于处理过程中出现的未知时变参数向量, 利用自适应迭代学习机制进行估计. 经过足够多次迭代后, 藉由修正滤波误差在整个作业区间收敛于零, 实现滤波误差本身在预设的作业区间也收敛于零. 仿真结果表明了本文所提控制方法的有效性.
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关键词:
- 迭代学习控制 /
- 初值问题 /
- 参数不确定性 /
- 非参数不确定性 /
- Lyapunov方法
Abstract: This paper presents a filtering-error rectified adaptive iterative learning control method to tackle the trajectory-tracking problem for a class of both parametric and nonparametric uncertain systems in the presence of arbitrary initial states. A novel rectification is made to modify the filtering-error error signal such that the learning control design and performance analysis could be simplified and easy for implementation. The proposed learning control design is a Lyapunov synthesis-based adaptive iterative learning control scheme. The Lipschitz-like assumption is used for handling nonparametric uncertainties, where the estimation for unknown time-varying parameters is given by learning mechanisms. As iteration increases, the rectified filtering-error converges to zero over the entire time interval, and the filtering-error itself converges to zero on the specified interval. Numerical results are presented to demonstrate effectiveness of the proposed learning control scheme. -
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