不确定性机器人系统自适应鲁棒迭代学习控制

利用Lyapunov方法, 提出了一种不确定性机器人系统的自适应鲁棒迭代学习控制策略, 整个系统在迭代域里是全局渐近稳定的. 所考虑的机器人系统同时包含了结构和非结构不确定性. 在设计时, 系统的不确定性被分解成可重复性和非重复性两部分, 并考虑了系统的标称模型. 在所提出的控制策略中, 自适应策略用来估算做法确定性的界, 界的修正与迭代学习控制量一样的迭代域得以实现的. 计算机仿真表明本文提出的控制策略是有效的.

Adaptive robust iterative learning control for uncertain robotic systems

The uncertain model of the robotic system was decomposed into repetitive and non-repetitive parts, and the normal model of the system was taken into account. By using Lyapunov method, an adaptive robust iterative learning control scheme was presented for the robotic system with both structured and unstructured uncertainties, and the overall stability of the system in the iteration domain was established. In the scheme the bound parameter estimates and the iterative learning control input were adjusted in the iteration domain. The validity of the scheme is illustrated through a simulation example.