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Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise

机译：随机测量噪声的切换离散时间系统的自适应迭代学习控制

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摘要

This paper investigates an adaptive iterative learning control (AILC) scheme for a class of switched discrete-time linear systems with stochastic measurement noise. For the case when the subsystems dynamics are unknown and the switching rule is arbitrarily fixed, the iteration-wise input-output data-based system lower triangular matrix estimation is derived by means of minimizing an objective function with a gradient-type technique. Then, the AILC is constructed in an interactive form with system matrix estimation for the switched linear systems to track the desired trajectory. Based on the derivation of the boundedness of the estimation error of system matrix, by virtue of norm theory and statistics technique, the tracking error and the covariance matrix of the tracking error are derived to be bounded, respectively. Finally, the AILC concept is extended to nonlinear systems by utilizing linearization techniques. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.
机译：本文研究了具有随机测量噪声的一类交换离散时间线性系统的自适应迭代学习控制（AILC）方案。对于子系统动态未知并且切换规则任意固定时，通过以梯度类型技术最小化目标函数来导出基于迭代的输入输出数据的迭代基于基于矩阵估计。然后，AILC以交互式形式构造，具有用于开关的线性系统的系统矩阵估计，以跟踪所需的轨迹。基于系统矩阵估计误差的偏移的导出，借助于规范理论和统计技术，派发误差和转换误差和协方差矩阵被导出为界限。最后，通过利用线性化技术，AILC概念扩展到非线性系统。仿真结果说明了所提出的AILC方案的有效性和有效性。

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