Application and Comparison of Optimal LQR Control Techniques for Engine Modeling

Shashidhar S. Gokhale*, Yathisha L. **, Sudarshan S. Patil Kulkarni ***
* Ph.D. Scholar, Department of Electronics, Mysore University, Karnataka, India.
** Associate Professor, Department of Electronics and Communication Engineering, ATME College of Engineering, Mysore, Karnataka, India.
*** Professor, Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysore, Karnataka, India.
Periodicity:May - July'2018

Abstract

In the present scenario, the optimization of engine parameters is very much necessary for the smooth operation of automotive engines. This paper presents the three Optimal Linear Quadratic Control approaches for the state space engine model for the optimization of manifold pressure and engine speed state variables. The proposed three optimal controllers are Bryson, Bouderal, and Multistage Linear Quadratic Regulator (LQR) control techniques. The proposed controllers are applied and compared for the engine state space model using MATLAB/Simulink platform.

Keywords

LQR, Manifold Pressure, Engine Speed.

How to Cite this Article?

Gokhale, S.S., Yathisha, L., & Kulkarni, S. S. P. (2018). Application and Comparison of Optimal LQR Control Techniques for Engine Modeling. i-manager’s Journal on Instrumentation and Control Engineering, 6(3), 36-43.

References

[1]. Anderson, B. D. O., & Moore, J. B. (2007). Optimal Control: Linear Quadratic Methods. Courier Corporation.
[2]. Berger, B., Rauscher, F., & Lohmann, B. (2011). Analysing Gaussian processes for stationary black-box combustion engine modelling. IFAC Proceedings (Vol. 44, No. 1, pp. 10633-10640).
[3]. Boudarel, R., Delmas, J., & Guichet, P. (1971). Dynamic Programming and its Application to Optimal Control (Vol. 81). Elsevier.
[4]. Bryson, A. E., Ho Y., & Siouris, G. M. (1975). Applied Optimal Control: Optimization, Estimation and Control. Academic, New York.
[5]. Guardiola, C., Pla, B., Blanco-Rodriguez, D., & Eriksson, L. (2013). A computationally efficient Kalman filter based estimator for updating look-up tables applied to NOx estimation in diesel engines. Control Engineering Practice, 21(11), 1455-1468.
[6]. Jongeneel, J. P. R., Nijmeijer, H., Manzie, C., & Nesic, D. (2009). Input redundant internal combustion engine with linear quadratic Gaussian control and dynamic control allocation. Eindhoven University of Technology, Eindhoven, The Netherlands, Internal Report.
[7]. Lopez, J. D., Espinosa, J. J., & Agudelo, J. R. (2011). LQR control for speed and torque of internal combustion engines. Preprints of the 18th IFAC World Congress (pp. 2230-2235).
[8]. Pandey, R. K. (2010). Analysis and design of multi-stage LQR UPFC. In Power, Control and Embedded Systems (ICPCES), 2010 International Conference on (pp. 1-6). IEEE.
[9]. Panse, P. A. (2005). Dynamic Modeling and Control of Port Fuel Injection Engines (Master Thesis, Indian Institute of Technology Bombay).
[10]. Skogestad, S., & Postlethwaite, I. (2007). Multivariable Feedback Control: Analysis and Design (Vol. 2, pp. 359- 368). New York: Wiley.
[11]. Yang, X., & Marjanovic, O. (2011). LQG control with extended Kalman filter for Power Systems with unknown time-delays. IFAC Proceedings (Vol. 44, No. 1, pp. 3708- 3713).
[12]. Yathisha, L., & Kulkarni, S. P. (2013). Optimum LQR switching approach for the improvement of STATCOM performance. In Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing (pp. 259-266). Springer, New York, NY.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 200 35 35 200 15
Pdf 35 35 200 20
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.