一类不确定系统的自适应滑模迭代学习控制

本文针对一类在有限时间内执行重复任务的不确定非线性系统状态跟踪问题,提出一种自适应滑模迭代学习控制方法,在存在初始偏移的情况下也能实现对参考轨迹的完全收敛.本文通过设计全饱和自适应迭代学习更新律,估计参数和非参数不确定性以及未知期望控制输入,并将估计值限制在指定界内,避免估计值的正向累加.文章设计的自适应滑模迭代学习控制方法对系统模型的信息需求少,在对系统非参数不确定性的上界估计时不需要Lipschitz界函数已知.本文给出严格的理论分析,证明闭环系统所有信号的一致有界性以及跟踪误差的一致收敛性,并通过仿真验证所提控制方法的有效性.

Adaptive sliding-mode iterative learning control for a class of uncertain systems

In this paper, an adaptive sliding-mode iterative learning control method is proposed for the state tracking problem of a class of uncertain nonlinear systems that perform repetitive tasks in finite time, and the complete convergence to the reference trajectory can be achieved in the presence of arbitrary initial shifts. The fully saturated adaptive iterative learning laws are designed to estimate the parametric and nonparametric uncertainties and the desired control input, and the estimated values are constrained within the specified bounds to avoid the positive accumulation of the estimated values. The designed control method requires less system model information, and does not need the Lipschitz bound function to be known when estimating the upper bound of the system nonparametric uncertainties. Rigorous theoretical analysis is provided to ensure the uniform boundedness of all signals and the uniform convergence of tracking error in the closed-loop system. The simulation results verify the effectiveness of the proposed control method.