机器人系统终端滑模重复学习轨迹跟踪控制

针对非线性机器人系统的轨迹跟踪问题, 提出一种终端滑模重复学习混合控制方案. 该方案综合了重复学习控制和终端滑模技术的特性, 能够有效跟踪周期性参考信号, 抑制周期性和非周期性动态的干扰, 具有较强的鲁棒性和良好的轨迹跟踪性能, 且算法的实现不需要完全已知系统模型信息. 应用Lyapunov 稳定性理论证明了闭环系统的全局渐近稳定性. 三自由度机器人系统数值仿真结果验证了所提出的终端滑模重复学习控制的有效性.

Terminal sliding mode repetitive learning tracking control of robotic manipulators

In order to make a robot precisely track desired periodic trajectories, a terminal sliding mode based repetitive learning control method is proposed, which incorporates characteristics of terminal sliding mode control into repetitive learning control. The hybrid control schemes utilize learning-based feedforward terms to compensate for periodic dynamics and terminal sliding mode-based feedback terms to compensate for nonperiodic dynamics. Advantages of the proposed control include the absence of model parameter in the control law formulation and improved robustness and tracking performance compared with the conventional approaches. The Lyapunov’s direct method is employed to prove global asymptotic tracking. Simulation results on a three degree-of-freedom(DOF) robot illustrate the effectiveness of the proposed scheme.