论文标题
在工业机器人的不连续摩擦下，生物启发的综合学习控制
Bioinspired composite learning control under discontinuous friction for industrial robots
论文作者
论文摘要
自适应控制可以应用于具有参数不确定性的机器人系统，但是提高其性能通常很困难，尤其是在不连续的摩擦下。受到人类运动学习控制机制的启发，针对具有不连续摩擦的广泛机器人系统提出了一种自适应学习控制方法，其中采用了利用数据记忆来增强参数估计的复合错误学习技术。与经典的反馈误差学习控制相比，所提出的方法可以实现出色的瞬态和稳态跟踪，而无需高增强反馈和持续激发，而持续的兴奋则以额外的计算负担和内存使用费用。基于Denso工业机器人的实验验证了所提出方法的性能提高。
Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.
