A Deep Residual Recurrent Neural Network Model-Augmented Attention With Physical Characteristics: Application to Turntable Servo System

Abstract

Turntable servo systems are affected by working status changing in actual work, thus, bringing some uncertain factors. Having models that have accurate predictive capabilities can be valuable for control and decision-making purposes. This article proposes a deep residual recurrent neural network (DRRNN) model-augmented attention with physical characteristics, which is used to build an accurate speed prediction model for a turntable servo system. Integrating the known physical features into the design of the residual network model can increase the interpretability of the overall model. Meanwhile, the recurrent neural network-augmented attention model is used to represent the unknown nonlinear characteristics. The addition of the attention mechanism can effectively improve the accuracy of the nonlinear model. Finally, this article proposes an iterative identification method based on the linear and nonlinear model error with alternative compensation. Through alternating error compensation, the linear model’s accuracy can be continuously improved, then the overall and the nonlinear part of the model can be established accurately by retraining the whole model. Experimental results show that the DRRNN model designed in this article can predict the rotational speed in single-step or multistep well. Compared with the traditional mechanism modeling method, the modeling accuracy can be greatly improved.