Concurrent Learning-Based Adaptive Control of an Uncertain Robot Manipulator With Guaranteed Safety and Performance
研究了不确定n连杆机器人操作臂的跟踪问题，提出扭矩滤波增强的并发学习方法在线辨识未知系统，无需关节加速度，并基于估计模型设计障碍李雅普诺夫函数自适应控制律，保证系统输出在安全集内、跟踪误差在性能集内。
This article investigates the tracking problem of an uncertain <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-link robot manipulator with guaranteed safety and performance. To tackle parametric uncertainties, the torque filtering-augmented concurrent learning (CL) method is introduced for online identification of the unknown system without requirements of joints acceleration. By using CL, the parameter convergence is guaranteed by exploiting the current and historical data simultaneously. This technique enjoys practicability compared with common methods that need to incorporate external noises to satisfy the persistence of excitation condition for the parameter convergence. Based on the estimated model, we design a barrier Lyapunov function (BLF)-based adaptive control law by the backstepping technique and Lyapunov analysis. By ensuring the boundness of the BLF, the system output and the tracking error are proved to lie in the safety set and performance set, respectively. Numerical simulation results and experiment tests validate the proposed strategy.