Experimental Comparisons between LQR and MPC for Spark-Ignition Engine Control Problem
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- 英文论文题名：Experimental Comparisons between LQR and MPC for Spark-Ignition Engine Control Problem
- 论文作者：Mingxin Kang ; Tielong Shen
- 英文论文作者：Mingxin Kang ; Tielong Shen ; Department of Applied Science and Engineering ; Sophia University
- 年：2017
- 作者机构：Department of Applied Science and Engineering,Sophia University;
- 英文论文关键词：Optimal Control ; Automotive Engines ; LQR ; MPC ; torque control
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：U464
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：1077k
- 原文格式：D
- 会议级别：全国
摘要
This paper proposes an experimental comparison between two optimal controllers for automotive spark ignition engines, including an gain-scheduled linear quadratic regulation(LQR) controller and model predictive controller(MPC). The aim of the study is to highlight the control effects between the LQR and MPC scheme on the specific engine control problem,and provide a reference for the future controller design. The control problem is formulated to ensure the fast torque tracking performance, and meanwhile to improve the thermal efficiency by reducing the pumping loss. The nitrogen oxide emission is chosen as constraint. The experiment were implemented on the full-scale gasoline engine, and the experiment results demonstrate the performance of the proposed optimal control designs.
This paper proposes an experimental comparison between two optimal controllers for automotive spark ignition engines, including an gain-scheduled linear quadratic regulation(LQR) controller and model predictive controller(MPC). The aim of the study is to highlight the control effects between the LQR and MPC scheme on the specific engine control problem,and provide a reference for the future controller design. The control problem is formulated to ensure the fast torque tracking performance, and meanwhile to improve the thermal efficiency by reducing the pumping loss. The nitrogen oxide emission is chosen as constraint. The experiment were implemented on the full-scale gasoline engine, and the experiment results demonstrate the performance of the proposed optimal control designs.
This paper proposes an experimental comparison between two optimal controllers for automotive spark ignition engines, including an gain-scheduled linear quadratic regulation(LQR) controller and model predictive controller(MPC). The aim of the study is to highlight the control effects between the LQR and MPC scheme on the specific engine control problem,and provide a reference for the future controller design. The control problem is formulated to ensure the fast torque tracking performance, and meanwhile to improve the thermal efficiency by reducing the pumping loss. The nitrogen oxide emission is chosen as constraint. The experiment were implemented on the full-scale gasoline engine, and the experiment results demonstrate the performance of the proposed optimal control designs.
引文
[1]R.E.Kalman,When is a linear control system optimal?,J.Fluids Eng.,ASME,Ser.D,86(1),1960,pp.51-60.
[2]M.Athans.The role and use of the stochastic linear quadratic Gaussian problem in control system design.IEEE Transactions on Automatic Control,Vol.16(2),1971,pp.529-552.
[3]Maciejowski,Jan Marian.Predictive control:with constraints.Pearson education,2002.
[4]Wang,Liuping.Model predictive control system design and implementation using MATLAB.Springer Science&Business Media,2009.
[5]Lpez,Jos David,Jairo Jos Espinosa,and John Ramiro Agudelo.”LQR control for speed and torque of internal combustion engines.”IFAC Proceedings on world congress,Volumes 44.1(2011):2230-2235.
[6]Kemper,A.,Wbbekind,M.,Bskens,C.,&Schollmeyer,M.(2013).LQR control for a Diesel engine’s air system.PAMM,13(1),473-474.
[7]Y.Chamaillard,P.Higelin,A.Charlet.A simple method for robust control design,application on a non-linear and delayed system:engine torque control,Control engineering practice,Vol.12,2004,pp.417-429.
[8]Kang,Mingxin,and Tielong Shen.”Receding horizon online optimization for torque control of gasoline engines.”ISA transactions 65(2016):371-383.
[9]F.Xu,H.Chen,X.Gong,Y.F.Hu,Engine idle speed control using nonlinear model predictive control,7th IFAC Symposium on advances in Automotive control,National Olympics Memorial Youth Center,Tokyo,Japan,Sep.4-7,2013.
[10]Del Re,Luigi,et al.,eds.Automotive model predictive control:models,methods and applications.Vol.402.Springer,2010.
[11]Heywood J B.Internal combustion engine fundamentals.New York:Mcgraw-hill,1988.
[12]Eriksson,Lars,and Lars Nielsen.Modeling and control of engines and drivelines.John Wiley&Sons,chapter 4-5,2014.
[13]Guzzella L,Onder C.Introduction to modeling and control of internal combustion engine systems.Springer Science&Business Media,2009.
[14]T.Shen,J.Zhang,X.Jiao,M.Kang,J.Kako,&A.Ohata.Transient control of gasoline engines.CRC Press,2015.
[15]Ming Kang,and Tielong Shen.Modeling and Optimal Control for Torque Tracking of Spark-ignition Engines with Low Pumping Loss,35th Chinese Control Conference,Chengdu,July 27-29,2016.
[16]Ferreau H J,Kirches C,Potschka A,et al.qp OASES:A parametric active-set algorithm for quadratic programming[J].Mathematical Programming Computation,2014,6(4):327-363.
[17]Ferreau H J,Bock H G,Diehl M.An online active set strategy to overcome the limitations of explicit MPC[J].International Journal of Robust and Nonlinear Control,2008,18(8):816-830.
[18]qp OASES webpage:http://www.qp OASES.org
[2]M.Athans.The role and use of the stochastic linear quadratic Gaussian problem in control system design.IEEE Transactions on Automatic Control,Vol.16(2),1971,pp.529-552.
[3]Maciejowski,Jan Marian.Predictive control:with constraints.Pearson education,2002.
[4]Wang,Liuping.Model predictive control system design and implementation using MATLAB.Springer Science&Business Media,2009.
[5]Lpez,Jos David,Jairo Jos Espinosa,and John Ramiro Agudelo.”LQR control for speed and torque of internal combustion engines.”IFAC Proceedings on world congress,Volumes 44.1(2011):2230-2235.
[6]Kemper,A.,Wbbekind,M.,Bskens,C.,&Schollmeyer,M.(2013).LQR control for a Diesel engine’s air system.PAMM,13(1),473-474.
[7]Y.Chamaillard,P.Higelin,A.Charlet.A simple method for robust control design,application on a non-linear and delayed system:engine torque control,Control engineering practice,Vol.12,2004,pp.417-429.
[8]Kang,Mingxin,and Tielong Shen.”Receding horizon online optimization for torque control of gasoline engines.”ISA transactions 65(2016):371-383.
[9]F.Xu,H.Chen,X.Gong,Y.F.Hu,Engine idle speed control using nonlinear model predictive control,7th IFAC Symposium on advances in Automotive control,National Olympics Memorial Youth Center,Tokyo,Japan,Sep.4-7,2013.
[10]Del Re,Luigi,et al.,eds.Automotive model predictive control:models,methods and applications.Vol.402.Springer,2010.
[11]Heywood J B.Internal combustion engine fundamentals.New York:Mcgraw-hill,1988.
[12]Eriksson,Lars,and Lars Nielsen.Modeling and control of engines and drivelines.John Wiley&Sons,chapter 4-5,2014.
[13]Guzzella L,Onder C.Introduction to modeling and control of internal combustion engine systems.Springer Science&Business Media,2009.
[14]T.Shen,J.Zhang,X.Jiao,M.Kang,J.Kako,&A.Ohata.Transient control of gasoline engines.CRC Press,2015.
[15]Ming Kang,and Tielong Shen.Modeling and Optimal Control for Torque Tracking of Spark-ignition Engines with Low Pumping Loss,35th Chinese Control Conference,Chengdu,July 27-29,2016.
[16]Ferreau H J,Kirches C,Potschka A,et al.qp OASES:A parametric active-set algorithm for quadratic programming[J].Mathematical Programming Computation,2014,6(4):327-363.
[17]Ferreau H J,Bock H G,Diehl M.An online active set strategy to overcome the limitations of explicit MPC[J].International Journal of Robust and Nonlinear Control,2008,18(8):816-830.
[18]qp OASES webpage:http://www.qp OASES.org