基于粒子群核极限学习机的涡扇发动机加速过程模型辨识

针对解析法建立涡扇发动机加速过程模型精度和实时性不高的问题,提出了一种基于粒子群核极值学习机(PSO-KELM)的涡扇发动机加速过程模型数据驱动辨识方法,构建涡扇发动机加速过程模型,结合加速过程试车数据,利用PSO-KELM方法对该加速模型进行辨识。试验结果表明:低压转子转速、高压转子转速和低压涡轮出口燃气总温都较好地逼近了试车数据,最大相对误差均值分别为1.013%,0.355%和1.055%,平均计算时间为0.04ms。精度和实时性均优于反向传播神经网络和粒子群支持向量回归方法,可用于发动机状态监控和性能优化控制。

Turbofan Engine Model Identification of Acceleration Process Based on Particle Swarm Optimization Kernel Extreme Learning Machine

In order to solve the difficulty of establishing the model of turbofan engine acceleration process by analytic method, a data-driven method on identification the turbofan engine acceleration process model based on kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) was proposed. Firstly, a turbofan engine acceleration process model was constructed. Then, PSO-KELM was adopted to identify the model using engine acceleration process test data. The results show that identification results of the low-pressure rotor speed, the high-pressure rotor speed and the low-pressure turbine outlet gas temperature are all close to measured data, the mean maximum relative errors are 1.013%, 0.355% and 1.055%, respectively, the mean computing time is 0.04ms. The precision and real-time are better than BP neural network method and PSO-SVR method, which can be used for engine condition monitoring and performance optimization control.