基于终态滑模的机械臂自适应迭代学习控制

Adaptive Iterative Learning Control for Robot Manipulators Based on Terminal Sliding Mode

摘要:

针对带未知参数且执行重复任务的机械臂,提出一种自适应迭代学习控制算法。为了克服因重置精度低带来的重置误差,引入了终态滑模和初始状态修正吸引子,实现了跟踪误差在有限时间收敛于0,并通过迭代轴上的自适应算法来调节控制器参数。理论证明了跟踪误差的收敛性和系统中所有信号的有界性,仿真结果验证了算法的有效性。

Abstract:

In this paper, an adaptive iterative learning control was presented for robot manipulators with unknown parameters, performing repetitive tasks. In order to overcome the initial resetting errors, terminal sliding mode and initial rectified attractors were introduced, which realized finite time convergence. The adaptive algorithm was derived along the iteration axis to search for suitable parameter values. The technical analysis showed convergence of the tracking error and the bounded-ness of the internal signals. Finally, simulation results were provided to illustrate the effectiveness of the proposed controller.