Learning Synchronous Control Based on Intelligent Methods for Ultra-Precision Stage Systems

With the increasing demands on equipment accuracy, synchronous performance becomes more and more significant for ultra-precision systems. In order to achieve nanometer-level accuracy and millisecond synchronization in practice, a learning synchronous control (LSC) strategy has been proposed in this research. Specifically, in our research, the LSC strategy includes Genetic Algorithm (GA) tuning method and Back Propagation (BP) neural network synchronous controller. Herein, GA tuning method has been designed to optimize the parameters of controllers for each axis offline. In order to enhance real-time information exchange between axes, a BP synchronous controller has been designed to enhance the coupling between axes and compensate for dynamic disturbances. Then the convergence criteria and stability analysis of the method have been presented. The proposed LSC was simulated and experimented on an ultra-precision stage system, and the HIL experimental results demonstrate that the proposed LSC can simultaneously possess nanometer-level tracking/synchronous accuracy and millisecond synchronization. Furthermore, compared to the conventional proportion integration differentiation (PID) method, the proposed LSC has higher synchronization performance with MA of 0.64 nm, MSD of 3.98 nm, and a settling time of 10 ms. The LSC strategy provides an effective control technology with good potential in industrial applications.