基于终点时刻末端误差的柔性臂迭代学习控制

针对柔性臂重复运行的情况,在仅能测量运行终点时刻末端位置的条件下,提出一种新的结合计算力矩法的迭代学习控制（ILC）方法.该方法利用柔性臂的简化动力学模型,给出各关节控制力矩的参数化表示；并依据终点时刻柔性臂末端位置的误差,通过迭代学习算法调整控制力矩的参数,实现精确到达预期末端位置的目标.算法利用ILC不依赖模型的特点,弥补计算力矩法需要精确模型的缺陷；参数的迭代学习主要起到消除模型误差和各种干扰的作用,增强算法的鲁棒性.通过理论分析给出所提算法的收敛条件.最后在柔性臂系统上进行仿真及实际试验.结果表明,所提出的ILC算法能够克服连杆柔性对柔性臂末端误差的影响,显示良好的控制效果.

Iterative learning control based on terminal endpoint tracking error of flexible manipulator

A new terminal iterative learning control(ILC) approach combined with computed torque method was presented for the flexible manipulator whose motion task was repeatable,in the case of only the elastic link’s endpoint pose was measurable.Firstly,the parameterized representation of each joint control torque was derived based on the simplified flexible manipulator dynamics model.Thereafter,in order to drive the flexible manipulator’s tip to arrive the desired position,the control torque parameters were adjusted by iterative learning,according to the terminal endpoint tracking error.This approach took advantage of the merit that ILC was independent on model,overcame the drawback of computed torque approach which heavily relied on model’s accuracy.The iterative learning of the parameters was mainly used to reduce the effect of the model error and the disturbances,at the same time,to enhance the controller’s robustness.The convergence condition of the approach was obtained by theoretical analysis.Simulation and experiment for a real flexible manipulator were presented.Results show that the proposed ILC scheme can overcome the impact of the endpoint error caused by link flexibility and has good control effect.