针对存在不确定性和外界干扰的受限机器人系统提出一种自适应迭代学习控制律.不确定性参数被估计在时间域内,同时重复性外界干扰在迭代域内得到补偿.通过引入饱和学习函数,保证了闭环系统所有信号有界.借助Lyapunov复合能量函数法,证明了系统渐进收敛到期望轨迹的同时,能够保证力跟踪误差有界可调.%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.
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