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.
neural network model predictive control genetic algorithm turbofan engine starting
YU Bo ZHU Jihong
Department of Computer Science and Technology Tsinghua University Beijing,China
国际会议
南京
英文
601-604
2010-08-26(万方平台首次上网日期,不代表论文的发表时间)