Control Theory and Technology

, Volume 15, Issue 2, pp 150–157 | Cite as

Comparison of generalized engine control and MPC based on maximum principle

  • Akira Ohata


Automotive engine control has been continuously improved due to the strong demands from the society and the market since introducing electronic controls but not always following control theories. Therefore, it is not easy for researchers from academia and even engineers from the automotive industry to grasp the whole aspect of engine control. To encounter the issue, important features of engine control are extracted and generalized from the standpoint of control engineering. Comparisons of the control and model predictive control (MPC) showed an outstanding performance of the control generalized from engine controls and how to apply MPC in the framework.


Automotive engine history of engine control control design input constraints time delay model predictive control 


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  1. [1]
    S. D. Cairano, D. Yanakiev, A. Bemporad, et al. Model predictive powertrain control: an application to idle speed regulation. Automotive Model Predictive Control. Berlin Heidelberg: Springer, 2010: 183–194.CrossRefGoogle Scholar
  2. [2]
    A. Kamik, D. Pachner, A. M. Fuxman, et al. Model Predictive Control for Engine Powertrain Thermal Management Applications. SAE Technical paper 2015-01-0336. Detroit: SAE International,2015.Google Scholar
  3. [3]
    L. He, T. Shen, L. Yu, et al. A model-predictive control-based torque demand control approach for parallel hybrid powertrains. IEEE Transactions on Vehicular Technology, 2013, 62(3): 1041–1052.CrossRefGoogle Scholar
  4. [4]
    A. Lagerberg, B. Egardt. Model predictive control of automotive powertrains with backlash. IFAC Proceedings Volumes, 2005, 38(1): 1–6.CrossRefGoogle Scholar
  5. [5]
    D. E. Rivera, M. Morari, S. Skogestad. Internal model control: PID controller design. Industrial and Engineering Chemistry Process Design and Development, 1986, 25(1): 252–265.CrossRefGoogle Scholar
  6. [6]
    T. N. Hein, O. Kaneko, S. Yamamoto. Fictitious reference iterative tuning of internal model controllers for non-minimum phase systems: A Laguerre expansion approach. SICE Journal of Control, and System Integration, 2013, 6(1): 38–44.CrossRefGoogle Scholar
  7. [7]
    K. Ohnishi, M. Shibata, T. Murakami. Motion control for advanced mechatronics. IEEE/ASME Transactions on Mechatronics, 1996, 1(1): 56–67.CrossRefGoogle Scholar
  8. [8]
    A. Ohata. Automotive engine control with rational function satisfying inequality constraint. IFAC-PapersOnLine, 2016, 49(11): 673–678.CrossRefGoogle Scholar
  9. [9]
    T. Ohtsuka, H. A. Fujii. Real-time optimization algorithm for nonlinear receding-horizon control. Automatica, 1997, 33(6): 1147–1154.MathSciNetCrossRefzbMATHGoogle Scholar
  10. [10]
    E. J. Davison, H. W. Smith. Pole assignment in linear timeinvariant multivariable systems with constant disturbances. Automatica, 1971, 7(4): 489–498.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.TECHNOVA Inc.The Imperial Hotel TowerTokyoJapan

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