机器人系统输出反馈重复学习轨迹跟踪控制

针对高度非线性多关节机器人的轨迹跟踪问题,提出一类输出反馈重复学习控制算法,使得在只有位置信息可测以及模型信息不确定的条件下即能获得良好的控制品质.非线性滤波器的引入解决了现实中速度信号较难获得的问题,重复学习控制策略实现了对周期性参考输入的渐近稳定跟踪.应用Lyapunov直接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表明了所提出的输出反馈重复学习控制的有效性.

Global asymptotic stable repetitive output feedback tracking control ofrobot manipulators

An output feedback repetitive control(ORC) method is developed for the trajectory tracking control of robot
manipulators with model uncertainty. Despite the fact that only link position is available, the proposed controller obtains
favorable performance. A nonlinear filter is utilized in the controller development to remove the requirement of link velocity
measurement. The repetitive control strategy ensures that the link position globally asymptotically tracks the desired periodic
reference signals. The global asymptotic stability of the resulting closed-loop system is proved by using the Lyapunov’s direct
method. Simulation results on a three degree-of-freedom robot show the effectiveness of the proposed scheme.