Nonlinear Decentralized Repetitive Control for Global Asymptotic Tracking of Robot Manipulators
机器人系统非线性分散重复学习轨迹跟踪控制

A new kind of nonlinear decentralized repetitive control (NRC) for robot manipulators is proposed to give faster response and higher tracking precision over the commonly used repetitive control (RC) without increased torque. The proposed NRC is formulated with a class of nonlinear saturated functions with the characteristics of "enlargement of small error and saturated in large error". The global asymptotic stability of the resulting closed-loop system is proved by the Lyapunov's direct method and LaSalle invariance principle. The simulation results on a three degree-of-freedom robot illustrate the effectiveness and improved performance of the proposed controller.