Shen Dong, Xu Jian-Xin
IEEE Trans Neural Netw Learn Syst. 2019 Apr;30(4):1119-1132. doi: 10.1109/TNNLS.2018.2861216. Epub 2018 Aug 21.
This paper proposes adaptive iterative learning control (ILC) schemes for continuous-time parametric nonlinear systems with iteration lengths that randomly vary. As opposed to the existing ILC works that feature nonuniform trial lengths, this paper is applicable to nonlinear systems that do not satisfy the globally Lipschitz continuous condition. In addition, this paper introduces a novel composite energy function based on newly defined virtual tracking error information for proving the asymptotical convergence. Both an original update algorithm and a projection-based update algorithm for estimating the unknown parameters are proposed. Extensions to cases with unknown input gains, iteration-varying tracking references, nonparametric uncertainty, high-order nonlinear systems, and multi-input-multi-output systems are all elaborated upon. Illustrative simulations are provided to verify the theoretical results.
本文针对迭代长度随机变化的连续时间参数非线性系统，提出了自适应迭代学习控制（ILC）方案。与现有具有非均匀试验长度的ILC研究不同，本文适用于不满足全局Lipschitz连续条件的非线性系统。此外，本文基于新定义的虚拟跟踪误差信息引入了一种新颖的复合能量函数，用于证明渐近收敛性。提出了用于估计未知参数的原始更新算法和基于投影的更新算法。详细阐述了对具有未知输入增益、迭代变化的跟踪参考、非参数不确定性、高阶非线性系统和多输入多输出系统情况的扩展。提供了说明性仿真以验证理论结果。
