Adaptive iterative learning control of robot manipulators in the presence of environmental constraint
存在环境约束的机器人自适应迭代学习控制

A novel adaptive iterative learning algorithm is proposed for a class of constraint robotic manipulators with uncertainties and external disturbances. The uncertain parameters are estimated in the time domain whereas the repetitive disturbances is compensated in the iteration domain. With the adoption of saturated learning, all the signals in the closed loop are guaranteed to be bounded. By constructing a Lyapunov-Krasovskii-like composite energy function, the states of the closed system is proved to be asymptotically convergent to the desired trajectory while ensuring the constrained force remains bounded. Simulation results show the effectiveness of the proposed algorithm.