航空活塞式发动机瞬态空燃比控制仿真研究

胡春明; 毕延飞; 王齐英; 仲伟军

doi:10.13224/j.cnki.jasp.2018.05.026

航空活塞式发动机瞬态空燃比控制仿真研究

doi: 10.13224/j.cnki.jasp.2018.05.026

1.
天津大学天津内燃机研究所,天津 300072；天津大学内燃机燃烧学国家重点实验室,天津 300072
2.
天津大学天津内燃机研究所,天津 300072

基金项目: 国家自然科学基金（51476112）

计量
- 文章访问数: 703
- HTML浏览量: 3
- PDF量: 475
- 被引次数: 0
出版历程
- 收稿日期: 2016-09-02
- 刊出日期: 2018-05-28

Simulation research on transient air-fuel ratio control of aero piston engine

摘要

摘要: 针对航空活塞式直喷发动机瞬态空燃比难以精确控制、动态超调大等问题,采用基于改进的粒子群优化算法和Elman神经网络(VPSO-Elman网络)的模型预测控制算法对发动机过渡工况空燃比进行控制。在实验数据的基础上,利用发动机建模软件AMESim建立发动机模型,在MATLAB/Simulink中建立VPSO-Elman空燃比预测模型控制系统,通过联合仿真检验控制系统的性能。结果表明:瞬态工况下,相比于比例-积分-微分（PID）控制,VPSO-Elman网络模型预测控制下的空燃比超调量可以减小约20%,回调时间缩短约75%；针对不同的节气门开度变化速率,VPSO-Elman控制器同样具有良好的控制效果。
- 航空活塞式发动机 /
- 过渡工况 /
- 空燃比控制 /
- 模型预测控制 /
- 神经网络
Abstract: In order to solve the problems like the large dynamic overshoot in the control of transient air-fuel ratio of aero piston engine, the model predictive control strategy based on improved particle swarm optimization algorithm and Elman(VPSO-Elman) neural network was proposed. The engine model used in the simulation was established based on a real engine in AMESim. The model predictive control system was established in MATLAB/Simulink. Coupling simulation was carried out to test the effect of the model prediction control. The simulation result shows that, compared with proportional integral derivative (PID) control, using VPSO-Elman network model prediction control the air-fuel overshoot can be reduced by 20%, callback time could be reduced by about 75%; for different throttle opening rate, VPSO-Elman controller also has a good control effect.
- aero piston engine /
- transition condition /
- air-fuel ratio control /
- model predictive control /
- neural network

HTML

参考文献(18)

[1]	王兴海,马震,郑勇.无人机用小型航空活塞发动机的发展［C］∥中国无人机大会论文集.北京:航空工业出版社,2006:523-527.
[2]	尹泽勇,李上福,李概奇.无人机动力装置的现状与发展［J］.航空发动机,2007,33(1):10-15.YIN Zeyong,LI Shangfu,LI Gaiqi.Current state and development of the unmanned aerial vehicle power plants［J］.Aeroengine,2007,33(1):10-15.(in Chinese)
[3]	胡春明,谷俊,周浩.活塞式航空直喷发动机的燃烧特性［J］.航空动力学报,2015,30(10):2368-2375.HU Chunming,GU Jun,ZHOU Hao.Combustion characteristics for direct injection piston aviation engine［J］.Journal of Aerospace Power,2015,30(10):2368-2375.(in Chinese)
[4]	李玉珍,高丕周,马震.电控汽油喷射改善小型无人机发动机性能的研究［J］.航空学报,2004,25(3):233-236.LI Yuzhen,GAO Peizhou,MA Zhen.Research on the improvement of performance of mini-UAV’s engine by electronically controlled gasoline injection［J］.Acta Aeronautica et Astronautica Sinica,2004,25(3):233-236.(in Chinese)
[5]	王振宇,丁水汀,杜发荣.航空重油发动机燃油供给系统动力学模型分析［J］.航空动力学报,2012,27(4):846-853.WANG Zhenyu,DING Shuiting,DU Farong.Analysis of dynamic modeling of aviation heavy oil piston engine fuel delivery system［J］.Journal of Aerospace Power,2012,27(4):846-853.(in Chinese)
[6]	周彰毅.新型航空活塞发动机电控系统关键技术设计及验证［D］.南京:南京航空航天大学,2012.ZHOU Zhangyi.Design and verification of key techniques for electronic control system on new aero piston engine［D］.Nanjing:Nanjing University of Aeronautics and Astronautics,2012.(in Chinese)
[7]	李薛.汽油机瞬态工况空燃比控制策略仿真研究［D］.长沙:长沙理工大学,2010.LI Xue.Study on simulation of transient air-fuel ratio control strategy for gasoline engine［D］.Changsha:Changsha University of Science and Technology,2010.(in Chinese)
[8]	范亚南.基于数据驱动的发动机空燃比预测控制［D］.长春:吉林大学,2014.FAN Yanan.Data-driven based engine air-fuel ratio predictive control［D］.Changchun:Jilin University,2014.(in Chinese)
[9]	张青贵.人工神经网络导论［M］.北京:中国水利水电出版社,2004:11-12.
[10]	韩旭明.Elman神经网络的应用研究［D］.天津:天津大学,2006.HAN Xuming.A study on application of Elman neutral network［D］.Tianjin:Tianjin University,2014.(in Chinese)
[11]	WEN Jinwei,ZHAO Jiali,LUO Siwei,et al.The improvements of BP neural network learning algorithm［C］∥Proceedings of 5th International Conference on Signal Processing.Beijing:IEEE,2000:1647-1649.
[12]	许少华,何新贵.一种基于混沌遗传与粒子群混合优化的过程神经网络训练算法［J］.控制与决策,2013,28(9):1393-1398.XU Shaohua,HE Xingui.A training algorithm of process neural networks based on CGA combined with PSO［J］.Control and Decision,2013,28(9):1393-1398.(in Chinese)
[13]	HIGASHI N,IBA H.Particle swarm optimization with Gaussian mutation［C］∥Swarm Intelligence Symposium.Piscataway,NJ，US:IEEE Press,2003:72-79.
[14]	SCOKAERT P O M,RAWLINGS J B,MEADOWS E S.Discrete-time stability with perturbations:application to model predictive control［J］.Automatica,1997,33(3):463-470.
[15]	黄立波.非线性系统的神经网络模型预测控制［D］.哈尔滨:哈尔滨理工大学,2007.HUANG Libo.The predictive control with neural networks based on the non-linear system［D］.Harbin:Harbin University of Science and Technology,2007.(in Chinese)
[16]	佟哲琼.基于神经网络的非线性预测控制的研究［D］.大连:大连理工大学,2006.TONG Zheqiong.Study of predictive control based no neural network in nonlinear system［D］.Dalian:Dalian University of Technology,2006.(in Chinese)
[17]	邓元望,贾国海.汽油机燃油控制系统联合仿真［J］.中南大学学报(自然科学版),2011,42(2):384-390.DENG Yuanwang,JIA Guohai.Co-simulation of injection control system of gasoline engine［J］.Journal of Central South University (Science and Technology),2011,42(2):384-390.(in Chinese)
[18]	夏飞,黄金泉,周文祥.基于MATLAB/SIMULINK的航空发动机建模与仿真研究［J］.航空动力学报,2007,22(12):2134-2138.XIA Fei,HUANG Jinquan,ZHOU Wenxiang.Modeling of and simulation research on turbofan engine based on MATLAB/SIMULINK［J］.Journal of Aerospace Power,2007,22(12):2134-2138.(in Chinese)