【摘要】 针对含非线性死区的双电机伺服系统跟踪与同步误差收敛速度慢的问题，设计了一款基于径向基函数神经网络(RBFNN)的固定时间滑模同步与跟踪控制方法。首先，使用径向基函数神经网络(RBFNN)对非线性死区进行估计，并将估计值嵌入控制器中进行补偿；然后，在构造固定时间稳定系统的基础上，提出一种固定时间滑模面曲面跟踪与同步控制器，从而加快跟踪与同步误差收敛速度，在固定时间内同时完成负载跟踪与电机同步，并且该滑模面的趋近时间与系统状态初值无关；最后，通过仿真验证了所提控制算法的有效性。更多还原

【Abstract】 A fixed-time sliding mode synchronization and tracking control based on radial basis function neural network(RBFNN) is proposed to solve the problem of slow convergence of tracking and synchronization errors in dual motor servo systems with nonlinear dead zones. Firstly, radial basis function neural network(RBFNN) is used to estimate the nonlinear dead zone, and finally embedded in the controller for compensation. Then, based on the fixed time stable system, a fixed time sliding mode surface tracking and synchronization controller is proposed to accelerate the convergence of tracking and synchronization errors, complete the load tracking and motor synchronization in a fixed time, and the approaching time of the sliding mode surface has nothing to do with the initial value of the system state. Finally, the effectiveness of the proposed control algorithm was verified through simulation.更多还原