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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 .
Robot assembly is a complicated dynamic process which inevitably damages the workpiece during high-speed assembly.In order to find an effective method to solve this problem, based on the dynamic equations of the assembly process deduced by impedance control method, A progressive learning mechanism that learns the dynamics of the assembly process using radial basis function networks (RBFN) and an enhanced learning algorithm that adjusts the impedance parameters using the gradient descent method.The numerical simulation results demonstrate the effectiveness of the method and the advantages of progressive learning .