基于迭代学习算法的机械臂系统轨迹跟踪控制

针对平面二自由度机械臂这一非线性系统，设计了带初态学习的指数变增益D型迭代学习律，并给出收敛性证明.仿真结果表明，迭代学习控制对于诸如二自由度机械臂系统这类具有重复运动性质的被控对象具有很好的控制效果.设计带初态学习的指数变增益D型学习律，系统不仅在存在初态偏移的情况下实现了机械臂期望轨迹的完全跟踪，还加快了收敛速度，增强了迭代学习控制的鲁棒性.

Trajectory Tracking Control of Robotic Manipulator System Based on Iterative Learning Algorithm

An improved D type learning law with time varying exponential gain and initial state learning was proposed and the certification of convergence for this learning law was given for the planar 2 DOF manipulator which is an non linear system. Simulation results showed that the iterative learning control was very effective for the planar 2 DOF manipulator system which owned property of repetitiveness. D type learning law with time varying exponential gain and initial state learning was obtained which could eliminate the impact of the initial state migration on tracking performance and improve the convergence rates of the algorithm. What’s more， the robustness of iterative learning control was also improved.