Precise Synchronization Control for Biaxial System via a Cross-Iterative PID Neural Networks Control Algorithm

The crossiterative proportion, integration, and differentiation (PID) Neural Networks control algorithm presented here enhances position synchronization control in machine tools driven by two ball screws. An electromechanical coupling dynamics model reflecting typical system characteristics is established and then, based on dynamic analysis, a coordination control between two motor forces is investigated by separating machine tool translational and rotational dynamics. Based on state feedback, we adopt a crossiterative PID Neural Networks control algorithm using the Lyapunov function to guarantee controller stability to achieve coordination between two motor forces. Computer simulation and experimental results indicate that the algorithm follows reference input well and shows good control performance in reducing synchronization errors. The proposed algorithm also has good control performance on a biaxial synchronous machine system regardless of whether interference effects are large or small.