A feedforward control method for turntable servo system based on physics-guided neural network

Abstract

Model-based feedforward control is a critical control method for high-dynamic and high-precision requirements of turntable servo systems. However, the turntable servo system will be affected by the change of working state in actual work, so the traditional physical feedforward control makes it difficult to obtain an accurate model of the turntable servo system. In this article, a feedforward controller based on a physics-guided neural network (PGNN) is proposed to improve the tracking performance of the turntable servo system. Firstly, a PGNN model is constructed according to the characteristics of the turntable, which is divided into a physical model part and a neural network part. Then the above model is applied to the design of the feedforward controller to replace the traditional feedforward controller. In order to improve the extension of the network, a new penalty function mechanism is proposed, which integrates regularization and physical consistency on the basis of the mean square error term. The automatic tuning algorithm of the parameters of the PGNN model is also designed to facilitate its wide application in the field of turntable control. Simulation results show that the neural network model designed in this article can accurately describe the nonlinear characteristics of the turntable servo system, and the fitting effect under different forms of excitation signals is better than the traditional physical model, with strong generalization ability and robustness. In addition, the feedforward control based on the model effectively improves the tracking accuracy of the system, which has important value and significance for the intelligent development of traditional servo control systems.

Keywords

Physics-guided neural network turntable servo system intelligent control

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