A Multi-Axis Synchronization Control Approach Based on Adjacent Cross-Coupling Strategy

Abstract

The high precision synchronized multi-axis control has become one of the key issues in modern manufacturing industry. As synchronized multi-axis control systems are nonlinear, time-variable and easily affected by disturbances, it is difficult to determine reasonable coupling control law and large amount of on-line calculation just through the existing synchronous control strategies for multi-axis system. In this paper the development status of multi-axis control synchronization control strategy is analyzed and the synchronization control algorithm is proposed based on the adjacent coupling error. The parameters of cross-coupled control are set on the basis of BP neural network control theory, which can not only reduce the tracking error, but also eliminate the synchronization error between adjacent axes. The synchronization performance of this approach is good with simple configuration. With this approach, the synchronization performance is good with simple configuration. Simulation results of the multi-axis synchronous system show that this method can effectively obtain the synchronization with a quick convergence. In the end, the multi-axis cross-coupled control approach based on BP neural networks is applied to a three-axis synchronization control system and its effectiveness is discussed.