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为了克服现有航空发动机状态变量建模过程中的不足,采用了一种改进粒子群算法建立航空发动机状态变量模型。首先改进了粒子群算法,提出一种每个粒子根据自身适应值动态调整其惯性系数方法来平衡搜索性能;对群体最优位置进行实时的代内更新以提高搜索速度;为避免陷入局部最优,在最优个体附近进行随机搜索。其次利用该算法建立航空发动机状态变量模型,根据航空发动机在稳态点处的线性化模型应与在该同一稳态工作点处的非线性模型响应一致的原则构造适应值函数,仿真结果表明所建立的状态变量模型不论是稳态过程还是动态过程都与非线性模型响应基本一致,建模精度较高,建立过程简便。
In order to overcome the deficiencies in the modeling of state variables of existing aeroengine, an improved particle swarm optimization algorithm was used to establish aeroengine state variable model. Firstly, the particle swarm optimization algorithm is improved and a new method is proposed, in which each particle dynamically adjusts its inertia coefficient according to its own adaptive value to balance the performance of search. In order to improve the search speed, , In the vicinity of the best individuals for random search. Secondly, the algorithm is used to establish the model of aeroengine state variables. The fitness function is constructed according to the principle that the linear model of the aero-engine at the steady-state point should be consistent with the nonlinear model at the same steady-state operating point. The established state variable model is consistent with the non-linear model response in both steady-state process and dynamic process, the modeling accuracy is high and the establishment process is simple.