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    • Research on Diesel Engine Common Rail Pressure Control Based on Gaussian–Cauchy Mutation Seagull Optimization Algorithm
    DOI：10.13949/j.cnki.nrjgc.2022.05.003
    Key Words:diesel engine  high-pressure common-rail system  seagull optimization algorithm  rail pressure control  PID
    Author NameAffiliationE-mail
    LI Jinlong* Yunnan Key Laboratory of Internal Combustion Engine， Kunming University of Science and Technology，Kunming 650500， China 947139984@qq.com 
    WANG Guiyong* Yunnan Key Laboratory of Internal Combustion Engine， Kunming University of Science and Technology，Kunming 650500， China wangguiyong@kust.edu.cn 
    WANG Yuhua Yunnan Key Laboratory of Internal Combustion Engine， Kunming University of Science and Technology，Kunming 650500， China  
    DENG Dongrong Yunnan Key Laboratory of Internal Combustion Engine， Kunming University of Science and Technology，Kunming 650500， China  
    ZHAO You Liuzhou Vocational and Technical College， Liuzhou 545616， China  
    HE Shuchao Kunming Yunnei Power Co.， Ltd.， Kunming 650500， China  
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    Abstract:In order to optimize the diesel engine rail pressure stability control and fast response control， a common-rail pressure control method combining the improved seagull optimization algorithm and PID controller was proposed. A real-time simulation model of the high-pressure common-rail system was built according to the physical structure and working principle of the high-pressure common-rail system. In view of the problems of uneven population distribution and easily fall into the local optimum of the original seagull optimization algorithm， Tent chaos map， nonlinear inertia weight and Gaussian–Cauchy hybrid mutation strategy were integrated into the seagull optimization algorithm， which improves the algorithm’s calculation accuracy and convergence speed. Based on the improved seagull optimization algorithm， the parameters of the rail pressure PID controller of the model were adaptively tuned. The research results show that the rail pressure steady-state and dynamic characteristics of the PID control optimized by the improved seagull optimization algorithm are better than those of the conventional PID control. In the stable condition， the rail pressure fluctuation amplitude was reduced by more than 33%. In the dynamic process， the stabilization time was shortened by more than 14%， and the overshoot was reduced by more than 71%.
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