李进龙,王贵勇,王煜华,等.基于高斯–柯西变异海鸥优化算法的柴油机共轨压力控制研究[J].内燃机工程,2022,43(5):16-25.
基于高斯–柯西变异海鸥优化算法的柴油机共轨压力控制研究
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
关键词:柴油机  高压共轨  海鸥优化算法  轨压控制  比例积分微分
Key Words:diesel engine  high-pressure common-rail system  seagull optimization algorithm  rail pressure control  PID
基金项目:国家自然科学基金项目（52066008）；云南省科技厅揭榜制项目（202104BN050007）；云南省科技计划项目（202102AC080004）
作者单位E-mail
李进龙* 昆明理工大学 云南省内燃机重点实验室，昆明 650500 947139984@qq.com 
王贵勇* 昆明理工大学 云南省内燃机重点实验室，昆明 650500 wangguiyong@kust.edu.cn 
王煜华 昆明理工大学 云南省内燃机重点实验室，昆明 650500  
邓冬荣 昆明理工大学 云南省内燃机重点实验室，昆明 650500  
赵友 柳州职业技术学院，柳州 545616  
何述超 昆明云内动力股份有限公司，昆明 650500  
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摘要:为优化柴油机轨压稳定控制和快速响应控制，提出一种将改进的海鸥算法与PID控制器相结合的共轨压力控制方法。根据高压共轨系统的物理结构和工作原理，搭建了高压共轨系统实时仿真模型；针对原始海鸥算法种群分布不均匀、容易陷入局部最优等问题，提出将Tent混沌映射、非线性惯性权重和高斯–柯西混合变异策略融入海鸥算法中，提升了算法的计算精度和收敛速度等方面的性能；基于改进后的海鸥算法完成对模型的轨压PID控制器参数自适应整定。研究结果表明：改进海鸥算法优化后的PID控制的轨压稳态和动态特性都明显优于常规PID控制。在稳定工况下，轨压波动幅值减小了33%以上；在动态过程中，稳定时间缩短了14%以上，超调量减小了71%以上。
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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