基于阻抗控制的动态装配过程仿真研究

机器人化装配是一个复杂的动力学过程,在高速装配时不可避免地会对工件造成 损伤.为了寻求解决该问题的有效方法,根据采用阻抗控制方法推导出的装配过程的动力 学方程,提出了一种采用径向基函数网络(RBFN)来学习装配过程动力学的渐进学习机制和 通过梯度下降法调整阻抗参数的强化学习算法.数值仿真结果证明了该方法的有效性和渐 进学习的优越性.

A SIMULATION INVESTIGATION ON DYNAMIC ASSEMBLY PROCESS BASED ON IMPEDANCE CONTROL

Robotic assembly is a complicated dynamic process. High speed assembly may incur serious damage to robotic systems and workpieces.In order to find out an effective method to resolve the problem, this paper presents a progressive learning strategy of RBFN to learn the dynamics of the assembly process. We then implement an intensified learning algorithm using gradient search method to adjust impedance components. All of the strategies are based on the dynamic equations that are derived on the basis of the concept of impedance control. Computational simulation results are given to demonstrate the effectiveness and advantage of the approach.