In the paper, the event-based switching controller (ESC) is utilized to achieve better tracking performance compared with the neural network controller in circumstance of parameter uncertainty and unknown disturbance for the parallel manipulator. The ESC optimizes the choice of the neural weights by combining the prior knowledge of the system dynamics and the estimation of the system parameters. To implement the controller, a general method of computing the system regression matrix for the PM is proposed and the stability proof is given in circumstance of unbounded disturbance. The ESC is tested by the simulated Delta manipulator and the experimental 5R testbed. The results show the effectiveness of the proposed controller.

在并联机器人的参数不确定和未知干扰的情况下，与神经网络控制器相比，基于事件的切换控制器（ESC）具有更好的跟踪性能。ESC通过结合系统动力学的先验知识和系统参数的估计来优化神经权重的选择。为了实现该控制器，提出了一种通用的永磁同步电机系统回归矩阵的计算方法，并给出了无界干扰情况下的稳定性证明。ESC通过模拟的Delta机械手和实验性的5R测试平台进行测试。结果表明了所提出控制器的有效性。