可重构机械臂模糊神经补偿控制

Neurofuzzy compensation control for reconfigurable manipulator

由于可重构机械臂的动力学系统中存在大量的不确定性,导致PID等传统的控制器无法实现精确的位置控制。作者在基于精确模型PD控制的基础上,提出了模糊神经控制算法辨识补偿结构、非结构不确定性。通过模糊神经控制器融合了模糊逻辑和神经网络的各自优势,实现了可重构机械臂轨迹跟踪的有效的补偿控制。基于牛顿-欧拉的几何方法,推导了n连杆可重构机械臂的动力学方程,该方法相对于其他形式的动力学方程,计算量小、通用性强。最后以RRP(Revolute-Revolute-Prismatic)三连杆机械臂为例研究设计了可重构机械臂的控制器,并且通过仿真验证了算法对轨迹跟踪的有效性。

There are many uncertainties in real dynamic system of reconfigurable manipulator that make PID type or traditional control methods unable to realize accurate position control. Thus a neurofuzzy control scheme based on PD control of accurate model was developed to identify and compensate structured and unstructured uncertainties for reconfigurable manipulator. This neurofuzzy compensator infusing fuzzy and neural networks proved to be an effective compensation control strategy for the manipulator trajectory tracking. The dynamics was derived through geometric form Newton-Euler algorithm,and it is characterized by less computation complexity and universal on n-link dynamics equations than other modeling methods for reconfigurable manipulator system. A controller for a Revolute-Revolute-Prismatic（RRP） reconfigurable manipulator was designed and its effectiveness on tracking performance is validated by simulations.