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A survey on online learning and optimization for spark advance control of SI engines

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Abstract

One of the most important factors affecting fuel efficiency and emissions of automotive engines is combustion quality that is usually controlled by managing spark advance (SA) in spark ignition (SI) engines. With increasing sensing capabilities and enhancements in on-board computation capability, online learning and optimization techniques have been the subject of significant research interest. This article surveys the literature of learning and optimization algorithms with applications to combustion quality optimization and control of SI engines. In particular, this paper reviews extremum seeking control algorithms for iterative solution of online optimization problems, stochastic threshold control algorithms for iterative solution of probability control of stochastic knock event, as well as feedforward learning algorithms for iterative solution of operating-point-dependent feedforward adaptation problems. Finally, two experimental case studies including knock probabilistic constrained optimal combustion control and on-board map learning-based combustion control are carried out on an SI gasoline engine.

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References

  1. Emiliano P. Spark ignition feedback control by means of combustion phase indicators on steady and transient operation. J Dyn Syst-T ASME, 2014, 136: 051021

    Article  Google Scholar 

  2. Jones J C P, Spelina J M, Frey J. Likelihood-based control of engine knock. IEEE Trans Contr Syst Technol, 2013, 21: 2169–2180

    Article  Google Scholar 

  3. Kaleli A, Ceviz M A, Erenturk K. Controlling spark timing for consecutive cycles to reduce the cyclic variations of SI engines. Appl Thermal Eng, 2015, 87: 624–632

    Article  Google Scholar 

  4. Corti E, Forte C. Spark advance real-time optimization based on combustion analysis. J Eng Gas Turbines Power, 2011, 133: 092804

    Article  Google Scholar 

  5. Shen T L, Kang M X, Gao J W, et al. Challenges and solutions in automotive powertrain systems. J Control Dec, 2017, 5: 61–93

    Article  Google Scholar 

  6. Janakiraman V M. Machine learning for identification and optimal control of advanced automotive engines. Dissertation for Ph.D. Degree. Ann Arbor: University of Michigan, 2013. 1–32

    Google Scholar 

  7. Popovic D, Jankovic M, Magner S, et al. Extremum seeking methods for optimization of variable cam timing engine operation. IEEE Trans Contr Syst Technol, 2006, 14: 398–407

    Article  Google Scholar 

  8. Mohammadi A, Manzie C, Nešić D. Online optimization of spark advance in alternative fueled engines using extremum seeking control. Control Eng Practice, 2014, 29: 201–211

    Article  Google Scholar 

  9. Malikopoulos A. Real-time, self-learning identification and stochastic optimal control of advanced powertrain systems. Dissertation for Ph.D. Degree. Ann Arbor: University of Michigan, 2008. 1–13

    Google Scholar 

  10. Zhu G M, Haskara I, Winkelman J. Stochastic limit control and its applications to spark limited control using ionization feedback. In: Proceedings of American Control Conference, Portland, 2005. 5027–5034

    Google Scholar 

  11. Shen X, Zhang Y H, Shen T L, et al. Spark advance self-optimization with knock probability threshold for lean-burn operation mode of SI engine. Energy, 2017, 122: 1–10

    Article  Google Scholar 

  12. Blum J R. Multidimensional stochastic approximation methods. Ann Math Statist, 1954, 25: 737–744

    Article  MathSciNet  MATH  Google Scholar 

  13. Kiefer J, Wolfowitz J. Stochastic estimation of the maximum of a regression function. Ann Math Statist, 1952, 23: 462–466

    Article  MathSciNet  MATH  Google Scholar 

  14. Spall J C. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans Automat Contr, 1992, 37: 332–341

    Article  MathSciNet  MATH  Google Scholar 

  15. Manzie C, Krstic M. Extremum seeking with stochastic perturbations. IEEE Trans Automat Contr, 2009, 54: 580–585

    Article  MathSciNet  MATH  Google Scholar 

  16. Kushner H J, Clark D S. Stochastic Approximation Methods for Constrained and Unconstrained Systems. New York: Springer, 1978. 19–58

    Book  Google Scholar 

  17. Chin D C. Comparative study of stochastic algorithms for system optimization based on gradient approximations. IEEE Trans Syst Man Cyber, 1997, 27: 244–249

    Article  Google Scholar 

  18. Spall J C. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. Hoboken: John Wiley & Sons, 2003. 95–204

    Book  MATH  Google Scholar 

  19. Hannah L A. Stochastic optimization. In: International Encyclopedia of the Social & Behavioral Sciences. 2nd ed. London: Elsevier, 2015, 2: 473–481

    Article  Google Scholar 

  20. Khong S Z, Tan Y, Manzie C, et al. Extremum seeking of dynamical systems via gradient descent and stochastic approximation methods. Automatica, 2015, 56: 44–52

    Article  MathSciNet  MATH  Google Scholar 

  21. Tan Y, Moase W H, Manzie C, et al. Extremum seeking from 1922 to 2010. In: Proceedings of the 29th Chinese Control Conference, Beijing, 2010. 14–26

    Google Scholar 

  22. Scotson P G, Wellstead P E. Self-tuning optimization of spark ignition automotive engines. IEEE Control Syst Mag, 1990, 10: 94–101

    Article  Google Scholar 

  23. Nesic D, Mohammadi A, Manzie C. A framework for extremum seeking control of systems with parameter uncertainties. IEEE Trans Automat Contr, 2013, 58: 435–448

    Article  MathSciNet  MATH  Google Scholar 

  24. Manzie C, Moase W, Shekhar R, et al. Extremum seeking methods for online automotive calibration. In: Optimization and Optimal Control in Automotive Systems. Berlin: Springer, 2014. 23–39

    Chapter  Google Scholar 

  25. Hellstrom E, Lee D, Jiang L, et al. On-board calibration of spark timing by extremum seeking for flex-fuel engines. IEEE Trans Contr Syst Technol, 2013, 21: 2273–2279

    Article  Google Scholar 

  26. Corti E, Forte C, Mancini G, et al. Automatic combustion phase calibration with extremum seeking approach. J Eng Gas Turbines Power, 2014, 136: 091402

    Article  Google Scholar 

  27. Tan Y, Nešić D, Mareels I. On the choice of dither in extremum seeking systems: A case study. Automatica, 2008, 44: 1446–1450

    Article  MathSciNet  MATH  Google Scholar 

  28. Ariyur K B, Krstic M. Real-Time Optimization by Extremum-Seeking Control. Hoboken: John Wiley & Sons. 2003. 1–89

    Book  MATH  Google Scholar 

  29. Wang H H, Krstic M. Extremum seeking for limit cycle minimization. IEEE Trans Automat Contr, 2000, 45: 2432–2436

    Article  MathSciNet  MATH  Google Scholar 

  30. Tan Y, Nešić D, Mareels I. On non-local stability properties of extremum seeking control. Automatica, 2006, 42: 889–903

    Article  MathSciNet  MATH  Google Scholar 

  31. Choi J Y, Krstic M, Ariyur K B, et al. Extremum seeking control for discrete-time systems. IEEE Trans Automat Contr, 2002, 47: 318–323

    Article  MathSciNet  MATH  Google Scholar 

  32. Krstić M, Wang H H. Stability of extremum seeking feedback for general nonlinear dynamic systems. Automatica, 2000, 36: 595–601

    Article  MathSciNet  MATH  Google Scholar 

  33. Khong S Z, Nešić D, Tan Y, et al. Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems. Automatica, 2013, 49: 2720–2733

    Article  MathSciNet  MATH  Google Scholar 

  34. Song K, Xie H, Jiang W, et al. On-line Optimization of Direct-Injection-Timing for SI-CAI Hybrid Combustion in a PFI-DI Gasoline Engine. SAE Technical Paper, 2016–01-0757. 2016

    Google Scholar 

  35. Larsson S, Andersson I. Self-optimising control of an SI-engine using a torque sensor. Control Eng Practice, 2008, 16: 505–514

    Article  Google Scholar 

  36. Carnevale D, Astolfi A, Centioli C, et al. A new extremum seeking technique and its application to maximize RF heating on FTU. Fusion Eng Des, 2009, 84: 554–558

    Article  Google Scholar 

  37. Zhang Y H, Shen T L. Stochastic approximation for combustion phase optimization of SI gasoline engines. In: Proceedings of European Control Conference (ECC), Aalborg, 2016. 1265–1270

    Google Scholar 

  38. Gao J W, Wu Y H, Shen T L. On-line statistical combustion phase optimization and control of SI gasoline engines. Appl Thermal Eng, 2017, 112: 1396–1407

    Article  Google Scholar 

  39. Zhang Y H, Shen T L. Cylinder pressure based combustion phase optimization and control in spark-ignited engines. Control Theor Technol, 2017, 15: 83–91

    Article  Google Scholar 

  40. Donald E K. The Art of Computer Programming: Seminumerical Algorithms. 3rd ed. Boston: Addison-Wesley, 1998. 232

    Google Scholar 

  41. Myung I J. Tutorial on maximum likelihood estimation. J Math Psychology, 2003, 47: 90–100

    Article  MathSciNet  MATH  Google Scholar 

  42. Allmaras M, Bangerth W, Linhart J M, et al. Estimating parameters in physical models through bayesian inversion: a complete example. SIAM Rev, 2013, 55: 149–167

    Article  MathSciNet  MATH  Google Scholar 

  43. Aguilar O, Allmaras M, Bangerth W, et al. Statistics of parameter estimates: a concrete example. SIAM Rev, 2015, 57: 131–149

    Article  MathSciNet  MATH  Google Scholar 

  44. Shen X, Shen T L. Real-time statistical learning-based stochastic knock limit control for spark-ignition engines. Appl Thermal Eng, 2017, 127: 1518–1529

    Article  Google Scholar 

  45. Khajorntraidet C, Ito K. Simple adaptive air-fuel ratio control of a port injection SI engine with a cylinder pressure sensor. Control Theor Technol, 2015, 13: 141–150

    Article  MathSciNet  Google Scholar 

  46. Jones J C P, Muske K R. Identification and adaptation of linear look-up table parameters using an efficient recursive least-squares technique. ISA Trans, 2009, 48: 476–483

    Article  Google Scholar 

  47. Höckerdal E, Frisk E, Eriksson L. EKF-based adaptation of look-up tables with an air mass-flow sensor application. Control Eng Practice, 2011, 19: 442–453

    Article  Google Scholar 

  48. Guardiola C, Pla B, Blanco-Rodriguez D, et al. A learning algorithm concept for updating look-up tables for automotive applications. Math Comput Model, 2013, 57: 1979–1989

    Article  MathSciNet  MATH  Google Scholar 

  49. Gao J W, Zhang Y H, Shen T L. An on-board calibration scheme for map-based combustion phase control of sparkignition engines. IEEE/ASME Trans Mechatron, 2017, 22: 1485–1496

    Article  Google Scholar 

  50. Pipitone E. A comparison between combustion phase indicators for optimal spark timing. J Eng Gas Turbines Power, 2008, 130: 052808

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Toyota Motor Corporation, Japan, for the financial and technical supports in this research.

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Authors and Affiliations

  1. Department of Engineering and Applied Sciences, Sophia University, Tokyo, 102-8554, Japan

    Yahui Zhang, Xun Shen & Tielong Shen

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  1. Yahui Zhang
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  2. Xun Shen
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  3. Tielong Shen
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Correspondence to Xun Shen.

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Zhang, Y., Shen, X. & Shen, T. A survey on online learning and optimization for spark advance control of SI engines. Sci. China Inf. Sci. 61, 70201 (2018). https://doi.org/10.1007/s11432-017-9377-7

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