Meng Deyuan, Zhang Jingyao
IEEE Trans Neural Netw Learn Syst. 2021 Sep;32(9):3867-3879. doi: 10.1109/TNNLS.2020.3016057. Epub 2021 Aug 31.
This article is concerned with the robust convergence analysis of iterative learning control (ILC) against nonrepetitive uncertainties, where the contradiction between convergence conditions for the output tracking error and the input signal (or error) is addressed. A system equivalence transformation (SET) is proposed for robust ILC such that given any desired reference trajectories, the output tracking problems for general nonsquare multi-input, multi-output (MIMO) systems can be equivalently transformed into those for the specific class of square MIMO systems with the same input and output numbers. As a benefit of SET, a unified condition is only needed to guarantee both the uniform boundedness of all system signals and the robust convergence of the output tracking error, which avoids causing the condition contradiction problem in implementing the double-dynamics analysis approach to ILC. Simulation examples are included to demonstrate the validity of our established robust ILC results.
本文关注迭代学习控制（ILC）针对非重复不确定性的鲁棒收敛性分析，其中解决了输出跟踪误差的收敛条件与输入信号（或误差）之间的矛盾。针对鲁棒ILC提出了一种系统等价变换（SET），使得对于任意期望的参考轨迹，一般非方阵多输入多输出（MIMO）系统的输出跟踪问题可以等价地变换为具有相同输入和输出数量的特定方阵MIMO系统的输出跟踪问题。作为SET的一个优点，仅需要一个统一的条件来保证所有系统信号的一致有界性和输出跟踪误差的鲁棒收敛性，这避免了在对ILC实施双动力学分析方法时出现条件矛盾问题。文中包含仿真示例以证明我们所建立的鲁棒ILC结果的有效性。
