吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 726-734.doi: 10.13229/j.cnki.jdxbgxb201403024

• 论文 • 上一篇    下一篇

SI发动机空燃比联合非线性模型预测控制

石屹然1,田彦涛1,张立2,单泽彪1,吴昊1   

  1. 1.吉林大学 通信工程学院,长春 130012;
    2.吉林大学 珠海学院, 广东 珠海 519041
  • 收稿日期:2013-12-23 出版日期:2014-03-01 发布日期:2014-03-01
  • 通讯作者: 张立(1966),男,教授,博士.研究方向:信号处理及机械故障诊断.E-mail:lzhzip@126.com E-mail:35105767@qq.com
  • 作者简介:石屹然(1984),男,博士研究生.研究方向:控制理论与控制工程.E-mail:35105767@qq.com
  • 基金资助:
    国家电动车重大科技专项项目(JS-102);国家自然科学基金重点项目(51075175).

Joint non-linear model predict control for air-fuel ratio of SI engine

SHI Yi-ran1,TIAN Yan-tao1,ZHANG Li2,SHAN Ze-biao1,WU Hao1   

  1. 1.College of Communication Engineering, Jilin University, Changchun 130012,China;
    2.Zhuhai College of Jilin University, Zhuhai 519041,China
  • Received:2013-12-23 Online:2014-03-01 Published:2014-03-01

摘要: 针对火花塞点火(SI)发动机空燃比(AFR)控制系统提出了一种变采样间隔的Modified Volterra模型,并以此为基础,提出了一种基于RBFNN和Modified Volterra模型的SI发动机AFR的联合NMPC 控制方法。该方法既具有RBFNN模型计算量小、预测精度高的特点,又具有可直接计算NMPC最优控制序列的优势,明显地提高了SI发动机AFR的控制精度,大大地减少了常规迭代寻优算法的计算时间。在dSPACE实时仿真试验平台上对平均值发动机模型进行仿真试验,结果表明:本文所提出的NMPC控制方法对SI发动机AFR的控制效果明显优于单独基于Modified Volterra和RBFNN模型的NMPC控制方法。

关键词: 自动控制技术, Volterra模型, 空燃比, RBFNN模型

Abstract: A variable sampling period Modified Volterra model is proposed for the Air-Fuel Ratio (AFR) control system in Spark Ignition (SI) engine. On this basis, a joint Nonlinear Model Predictive Control (NMPC) method is developed based on the Radial Basis Function Neural Network (RBFNN) model combining with the modified Volterra model. The advantages of this method are small amount of calculation and high prediction accuracy; also the optimal control sequence can be directly calculated. Thus it significantly improves the AFR control performance of SI engine, and greatly reduces the computing time compared with the conventional iterative optimization algorithm. Real-time simulations based on the mean value engine model are conducted on the dSPASCE simulation platform. Results show that the control performance of the proposed method is significantly better than the RBFNN model or the Modified Volterra model based NMPC method.

Key words: automatic control technology, Volterra model, air-fuel ratio(AFR), RBFNN model

中图分类号: 

  • TP273
[1] Manzie C, Palaniswami M, Watson H. Model predictive control of a fuel injection system with a radial basis function network observer[C]∥Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy,2000:359-364.
[2] Manzie C, Palaniswami M, Watson H. Gaussian networks for fuel injection control[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2001, 215(10):1053-1068.
[3] Choi S B, Hedrick J K. An observer-based controller design method for improving air/fuel characteristics of spark ignition engines[J]. IEEE Transactions on Control Systems Technology, 1998, 6(3): 325-334.
[4] Hsieh M F, Canova M, Wang J. Model predictive control approach for AFR control during lean NOx trap regenerations[J]. SAE International Journal of Fuels and Lubricants, 2009, 2(1): 149-157.
[5] Wojnar S, Honek M, Rohal'-llkiv B. Nonlinear air-fuel ratio predictive control of spark ignited engines[C]∥2013 International Conference on Process Control, Strbske Pleson,Slovakia,2013:225-230.
[6] Wang S, Yu D L, Gomm J B, et al, Adaptive neural network model based predictive control for air-fuel ratio of SI engines[J]. Engineering Applications of Artificial Intelligence,2006,19(2): 189-200.
[7] Wang S W,Yu D L. Adaptive air-fuel ratio control with MLP network[J]. International Journal of Automation and Computing, 2005, 2(2): 125-133.
[8] Han H G, Wu X L, Qiao J F. Real-time model predictive control using a self-organizing neural network[J]. Neural Networks and Learning Systems, 2013, 24(9):1425-1436.
[9] Maner B R, Doyle III F J, Ogunnaike B A, et al. Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models[J]. Automatica, 1996, 32(9): 1285-1301.
[10] Gruber J K, Guzmán J L, Rodríguez F, et al. Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation[J]. Control Engineering Practice, 2011, 19(4): 354-366.
[11] Boland M D, Zoubir A M. Identification of time-varying non-linear systems with application to knock detection in combustion engines[C]∥IEEE Region 10 Annual Conference on Speech and Image Technologies for Computing and Telecommunications, Brisbane, Qld, Australia,1997:799-802.
[12] Gruber J K, Bordons C, Oliva A. Nonlinear MPC for the airflow in a PEM fuel cell using a Volterra series model[J]. Control Engineering Practice, 2012, 20(2): 205-217.
[13] 陈虹. 模型预测控制[M]. 北京: 科学出版社,2013.
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