Aeroengine thrust estimation and embedded verification based on improved temporal convolutional network

Abstract

Thrust estimation is a significant part of aeroengine thrust control systems. The traditional estimation methods are either low in accuracy or large in computation. To further improve the estimation effect, a thrust estimator based on Multi-layer Residual Temporal Convolutional Network (M-RTCN) is proposed. To solve the problem of dead Rectified Linear Unit (ReLU), the proposed method uses the Gaussian Error Linear Unit (GELU) activation function instead of ReLU in residual block. Then the overall architecture of the multi-layer convolutional network is adjusted by using residual connections, so that the network thrust estimation effect and memory consumption are further improved. Moreover, the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence speed. Furthermore, six neural network models are deployed in the embedded controller of the micro-turbojet engine. The Hardware-in-the-Loop (HIL) testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy, memory occupation and running time. Finally, an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.