Zhang Xiaoyu, Longman Richard W
IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4346-4360. doi: 10.1109/TNNLS.2021.3056680. Epub 2022 Aug 31.
This article studied the stability and convergence of a robust iterative learning control (ILC) design for a class of nonlinear systems with unknown control input delay. First, the iterative integral sliding mode (IISM) design was proposed, which comprised iterative actions. The iterative action made the convergence of the tracking error under the ideal sliding mode. Then, a suitable iterative update law was provided for the IISM-based robust ILC controller. The controller had the capability of both minimizing the steady tracking error and suppressing the unrepeatable disturbance. Using the controller, the closed-loop system stability was analyzed, and the stability conditions were given. Consequently, the sliding mode convergence in the iteration domain was proved by a composite energy function (CEF). In addition, by analyzing the influence of affection on the tracking error, several measures were taken to solve the chattering problem of the sliding mode control. Finally, a one-link robotic manipulator and a vertical three-tank system were used to verify the control design. The application simulations validated the performance of the proposed sliding mode iterative learning control (SMILC) design, which achieved the stability of the nonlinear system and overcame the control input time delay.
本文研究了一类具有未知控制输入延迟的非线性系统的鲁棒迭代学习控制(ILC)设计的稳定性和收敛性。首先,提出了迭代积分滑模(IISM)设计,其包含迭代动作。该迭代动作使得在理想滑模下跟踪误差收敛。然后,为基于IISM的鲁棒ILC控制器提供了合适的迭代更新律。该控制器具有最小化稳态跟踪误差和抑制不可重复干扰的能力。利用该控制器,分析了闭环系统的稳定性,并给出了稳定性条件。因此,通过复合能量函数(CEF)证明了迭代域中的滑模收敛性。此外,通过分析扰动对跟踪误差的影响,采取了几种措施来解决滑模控制的抖振问题。最后,使用单连杆机器人机械手和垂直三水箱系统来验证控制设计。应用仿真验证了所提出的滑模迭代学习控制(SMILC)设计的性能,该设计实现了非线性系统的稳定性并克服了控制输入时滞。