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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.
Aiming at the trajectory tracking problem of highly nonlinear multi-joint robot, a class of output feedback iterative learning control algorithm is proposed, which can obtain good control quality under the condition that only the position information can be measured and the model information is indefinite. Introduced to solve the problem that the real speed signal is difficult to obtain, and repeated learning control strategy to achieve the asymptotic stability tracking of periodic reference input.The global asymptotic stability of the closed-loop system is proved by Lyapunov direct stability theory. The simulation results of the robot system show the validity of the proposed iterative learning control of output feedback.