Abstract
For the strong nonlinear and variable parameters properties of high bypass ratio turbofan engine,an adaptive PID control method based on optimized extreme learning machine( ELM) was proposed to predict the engine's parameters. To improve the prediction accuracy and the real-time property of ELM,an improved Firefly algorithm( IFA) for multi-peak optimization was adopted to optimize the network parameters of the ELM,and formed an optimized ELM training method IFA-ELM. Under the premise of ensuring prediction accuracy,the algorithm effectively simplified the network scale and improved its generalization capability.The engine fan speed prediction model was built by this algorithm,and gradient descent method was adopted to adjust the PID parameters online based on the model to improve the dynamic performance of engine. Digital simulation results show that compared with conventional PID control,IFA-ELM based adaptive PID method shortens the settling time by 0. 2 ~ 1. 4 s,and reduces the overshoot by 0. 2% ~ 1. 5%,which demonstrates the effectiveness of the proposed control method.
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