Neural Network Model Predictive Control with Genetic Algorithm Optimization and Its Application to Turbofan Engine Starting

Turbofan engine starting is one of the most important procedures during the whole process of job, but also very complicated due to its nonlinear dynamic working procedure. Recognizing the weaknesses of predict model and traditional algorithm for rolling optimization to deal with strong nonlinear systems, this paper presents neural network model predictive control method with genetic algorithm optimization, and uses this method to devise an optimal controller for turbofan engine starting. Experiment results show that under the premise of accurate limits, we can obtain the optimal fuel supply rate with enough precision.