A Parallel GRU-Transformer Neural Network Approach for Feedforward Compensation in Precision Motion Platform Control

This paper proposes a feedforward compensation strategy based on Parallel GRU-Transformer neural network to address the issues of large tracking errors and insufficient stability of multi degree of freedom precision motion platforms in complex dynamic environments. By accurately predicting the output of the feedback controller at the next moment and using the predicted results as feedforward compensation inputs, the overall performance of the control system is significantly improved. First, a Parallel GRU-Transformer neural network was constructed to compensate for the limitations of LSTM and GRU neural networks in parallel processing due to their recursive structure, enabling the network to accurately predict the control output at the next moment. Then, a control strategy integrating Parallel GRU-Transformer feedforward compensation was designed, and the predicted results were introduced into the feedforward compensation control strategy to compensate for the output of the feedback controller. The effectiveness of the proposed method was verified by comparing its prediction accuracy with the State-of-the-Art neural network model and the original control strategy. The experimental results show that the neural network based on Parallel GRU-Transformer has excellent prediction ability. Introducing its prediction results into the feedforward compensation control strategy significantly reduces tracking error and its Moving Average (MA) and Moving Standard Deviation (MSD), improving tracking performance. Meanwhile, under different reference trajectories and controller conditions, the control strategy exhibits a certain level of robustness, demonstrating notable potential for industrial applications.