机械臂自调整因子模糊PD迭代学习控制研究

Research on Fuzzy PD Iterative Learning Control of Manipulator Self-adjusting Factor

摘要:

大部分模糊控制器不具有适应控制对象变化的能力，基于此设计一种自调整因子模糊控制器，并针对机械臂长时间重复操作导致运动精确度下降这一类问题，结合迭代学习控制方法，提出一种自调整因子模糊PD迭代学习控制方法。以双关节机械臂为研究对象，利用Fuzzy工具箱编写模糊控制规则，通过系统产生的误差以及误差的变化率作为模糊控制器的输入量调整模糊系统中的量化因子和比例因子，实现模糊规则的更新和对迭代学习控制中的PD参数的实时调整，系统的自适应性得到提高，并在SIMULINK中进行机械臂的运动控制实验，仿真结果表明，所提控制方法最终产生的误差可以精确到0.0001rad，同时在进行第2次迭代时关节角度和角速度误差收敛基本趋于零，整体的控制效果较好。

Abstract:

Most fuzzy controllers do not have the ability to adapt to the change of the control object. Based on this, a self-adjusting factor fuzzy controller is designed. Aiming at the problem that the motion accuracy of the manipulator decreases due to long-time repeated operation, combined with the iterative learning control method, a self-adjusting factor fuzzy PD iterative learning control method is proposed. Taking the double joint manipulator as the research object, the fuzzy control rules are written by using the fuzzy toolbox. The quantitative factor and scale factor in the fuzzy system are adjusted by using the system error and the error change rate as the input of the fuzzy controller. The updating of fuzzy rules and the real-time adjustment of PD parameters in the iterative learning control are realized. The adaptability of the system gets improved. The simulation results show that the error generated by the proposed control method can be accurate to 0.0001rad, and the convergence of joint angle and angular velocity error tends to zero in the second iteration, so the overall control effect is better.