Composite Error Learning Robot Control Using Discontinuous Lyapunov Analysis

• 成果类型: Article
• 署名作者: Pan, Yongping; Guo, Kai; Bobtsov, Alexey; Yang, Chenguang; Yu, Haoyong
• 署名单位: Sun Yat Sen University; Shandong University; ITMO University; South China University of Technology; National University of Singapore
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3326749
• 发表日期: 2024
• 页码: 1705-1712
• 关键词: adaptive control; discontinuous friction; feedback error learning (FEL); parameter convergence; robot control

摘要

A feedback-error learning (FEL) framework, which is characterized by internal dynamics modeling and hybrid feedback-feedforward (HFF) control, provides a computational model for motor learning control in the cerebellum. For FEL-based adaptive robot control, closed-loop stability and parameter convergence involve in stringent conditions, such as high-gain feedback and persistent excitation. This article proposes a composite error learning framework for adaptive robot control under discontinuous friction, where an HFF control structure with forward and inverse models is introduced to mimic the cerebellar motor learning control mechanism, and a composite learning technique with memory regressor extension is employed to capture the robot dynamics. Using discontinuous Lyapunov analysis with Filippov's differential inclusion, we rigorously prove that semiglobal stability of the closed-loop system is ensured without high feedback gains, and exponential parameter convergence (implying accurate robot modeling) is guaranteed by a weakened condition of interval excitation. Experiments on an industrial robot manipulator have demonstrated the effectiveness and superiority of the proposed method.