Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming (SQP) for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control. The designed control system includes four parts, namely a predictive model, rolling optimization, online correction, and feedback correction. Considering the strong nonlinearity of engine, a predictive model is established by Back Propagation (BP) neural network for the entire flight envelope, whose input and output are determined with random forest algorithm and actual situation analysis. Rolling optimization typically uses SQP as the optimization algorithm, but SQP algorithm is easy to trap into local optimization. Therefore, the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP. In addition to the traditional three parts of model predictive control, an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain. Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy, and the proposed control system can achieve good tracking performance with the limited parameters within the safe range.