Repetitive Learning Control for Time-varying Robotic Systems： A Hybrid Learning Scheme

重复学习控制为不明确的变化时间的机器的系统追踪的 finite-time-trajectory 被介绍。在时间函数以一个反复的学习方法被学习的地方，一个混合学习计划被给在系统动力学应付经常、变化时间的 unknowns，没有泰勒表示的帮助，当常规微分学习方法为估计经常的被建议时。介绍重复学习控制为在每个周期的开始的起始的重新定位避免要求，是不同的，并且变化时间的 unknowns 不是必要的周期。随混合学习的采纳，靠近环的系统的州的变量的固定被保证，追踪的错误被保证作为重复增加收敛到零，这被显示出。建议计划的有效性通过数字模拟被表明。

Repetitive learning control is presented for finitetime-trajectory tracking of uncertain time-varying robotic systems. A hybrid learning scheme is given to cope with the constant and time-varying unknowns in system dynamics, where the time functions are learned in an iterative learning way, without the aid of Taylor expression, while the conventional differential learning method is suggested for estimating the constant ones. It is distinct that the presented repetitive learning control avoids the requirement for initial repositioning at the beginning of each cycle, and the time-varying unknowns are not necessary to be periodic. It is shown that with the adoption of hybrid learning, the boundedness of state variables of the closed-loop system is guaranteed and the tracking error is ensured to converge to zero as iteration increases. The effectiveness of the proposed scheme is demonstrated through numerical simulation.