摘要
针对一阶非正则离散时间非线性系统追踪迭代域变化的参考轨迹问题,考虑系统中存在时变状态扰动和时变量测噪声的情况,提出了一种鲁棒迭代学习控制算法。迭代域变化的参考轨迹由高阶内模产生。该算法利用内模原理,先嵌入参考轨迹特征,然后针对时变扰动,用积分器剔除其影响。利用?范数,从理论上严格证明了系统跟踪误差的迭代域收敛性。对机械手模型的仿真结果表明了基于高阶内模的鲁棒迭代学习算法的有效性。
Considering time-varying state disturbances and measurement noise, a robust iterative learning control （ILC） algorithm is proposed to track iteration-varying trajectories for a class of discrete-time nonlinear systems with the relative degree of one. Desired tracking trajectories are generated by high-order internal model （HOIM）. Using internal model principle （IMP）, incorporating HOlM into the ILC method, then eliminating disturbances influence through the integrator, the robustness and learning convergence in the iteration axis can be guaranteed for the system. By using λ-norm, the convergence of the iterative region of the system output error is strictly proved. The simulation with robot manipulators indicates the efficacy of the proposed robust HOlM-based ILC approach.
出处
《控制工程》
CSCD
北大核心
2016年第2期259-264,共6页
Control Engineering of China
基金
江苏省青蓝工程资助项目(2014-2017)
关键词
高阶内模
迭代学习控制
离散时间系统
迭代变化
积分器
High-order intemal model
iterative learning control
discrete-time system
iteration-varying
integrator
