Shen Mouquan, Wu Xingzheng, Park Ju H, Yi Yang, Sun Yonghui
IEEE Trans Neural Netw Learn Syst. 2023 Sep;34(9):6670-6676. doi: 10.1109/TNNLS.2021.3135504. Epub 2023 Sep 1.
This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.
本简报关注在状态对齐条件下具有变化试验长度的约束多输入多输出（MIMO）非线性系统的迭代学习控制（ILC）。通过调整参考轨迹使其在空间上闭合来构建修改后的参考轨迹，以满足对齐条件。借助障碍复合能量函数（BCEF）方法，构建了一种自适应ILC方案，以保证所得闭环系统的有界收敛。给出了示例以验证所提出迭代方案的有效性。
