关键词: 航空、航天推进系统;航空发动机;状态变量模型;建模;粒子群算法

Abstract: For overcoming some insufficiencies of establishing the state variable model of aero-engine, the state variable model of aero-engine was proposed based on improved Particle Swarm Optimization (PSO). Firstly, the improvements of PSO were proposed. Each particle could adjust inertia coefficient dynamically based on own fitness value to balance search performance. The best position of the colony was updated in each generation real time. The random search was carried out near by the best individual to avoid plunging into local optima. Secondly, the improved algorithm was used to establish the aero-engine state variable model. The fitness function of PSO was established according to the principle that the aero-engine responses of the linear model should be in accordance with that of the nonlinear model at the same steady working point. The simulation results show that the established state variable model has the same responses as the nonlinear model for both the steady process and the dynamic process, and has high accuracy. And the established process for the model is simple and convenient.

Key words: Aerospace propulsion system; Aero-engine; State variable model; Modeling; Particle swarm optimization