陈韬,董奥,谢辉,等.基于模型的直喷汽油机轨压无标定控制[J].内燃机工程,2020,41(5):83-92.
基于模型的直喷汽油机轨压无标定控制
Model Based Non-calibration Control Algorithm for GDI Engine
DOI：10.13949/j.cnki.nrjgc.2020.05.012
关键词:共轨系统  精确建模  模型参数自学习  自抗扰控制  无标定控制
Key Words:common rail system  accurate modeling  model parameters self-learning  active disturbance rejection control  non-calibration control
基金项目:国家重点研发计划（2016YFB0101402）
作者单位E-mail
陈韬 天津大学 内燃机燃烧学国家重点实验室  
董奥 天津大学 内燃机燃烧学国家重点实验室  
谢辉* 天津大学 内燃机燃烧学国家重点实验室 xiehui@tju.edu.cn 
钱振环 天津大学 内燃机燃烧学国家重点实验室  
陈大昕 天津大学 内燃机燃烧学国家重点实验室  
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摘要:为实现汽油机燃油喷射系统全生命周期与复杂工况的精确控制，提出了一种基于模型的无标定控制算法（model based non-calibration control，NC-MBC）。建立共轨系统精确机理模型，设计2600个实验工况点拟合并验证模型。针对模型中未知物理参数与结构参数，设计自学习算法实时优化。结合精确前馈模型与自抗扰反馈算法设计控制器，实现共轨系统无标定化控制并拥有自抗扰能力。NC-MBC通过Simulink仿真平台验证，结果显示：自学习算法能够保证模型参数快速收敛，参数自学习修正后的NC-MBC稳态轨压绝对值误差积分相对比例为分积分（Proportional integral differential，PID）控制降低40%以上，瞬态响应时间与超调量均降低45%以上。NC-MBC在极大提高控制精度与鲁棒性的同时运算复杂度低，适合嵌入式控制。
Abstract:In order to precisely control the GDI engine fuel injection system during it"s whole life and complex working conditions, a model-based non-calibrated control algorithm (NC-MBC) is proposed. Established a precise mechanism model for high pressure common rail system (HPCRS), then used 2600 experimental conditions to fit and validate the model. The self-learning algorithm is designed to optimize the unknown physical and structural parameters in the model online. The controller is designed by combining the precise feedforward model and the active disturbance rejection control algorithm, this realized the non-calibration control of the HPCRS and has disturbance rejection capacity. The NC-MBC is verified by Simulink simulation platform. The results show that the model parameters can converge quickly by using self-learning algorithm. During steady condition, the parameter converged NC-MBC is declined over 40% than PID in integrated absolute error, and is reduced by more than 45% in response time and overshoot during transient condition. NC-MBC has a low computational complexity and greatly improves the control precision and robustness, which is suitable for embedded control.
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