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Robust intelligent control design for marine diesel engine

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Abstract

This work deals with the nonlinear control of a marine diesel engine by use of a robust intelligent control strategy based on cerebellar model articulation controller (CMAC). A mathematical model of diesel engine propulsion system is presented. In order to increase the accuracy of dynamical speed, the mathematical model of engagement process based on the law of energy conservation is proposed. Then, a robust cerebellar model articulation controller is proposed for uncertain nonlinear systems. The concept of active disturbance rejection control (ADRC) is adopted so that the proposed controller has more robustness against uncertainties. Finally, the proposed controller is applied to engine speed control system. Both the model of the diesel engine propulsion system and of the control law are validated by a virtual detailed simulation environment. The prediction capability of the model and the control efficiency are clearly shown.

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References

  1. Shiraishi H, Ipri S L, Cho D I D. CMAC neural network controller for fuel-injection systems [J]. Transactions on Control Systems Technology, 1995, 3(1): 32–38.

    Article  Google Scholar 

  2. Meng Wei, Guo Chen. Research on speed intelligent control based on neural networks for large marine main diesel engine [C]//Proceedings of the 8th World Congress on Intelligent Control and Automation. Piscataway, NJ: IEEE Press, 2010: 4667–4670.

    Google Scholar 

  3. Huang Man-lei, Wang Chang-hong. Research on double-pulse H-infinity speed governor for diesel engine of ship power station [J]. Control Theory & Applications, 2007, 24(2): 283–288 (in Chinese).

    Google Scholar 

  4. Kashima T, Takata J. An optimal control of marine propulsion system considering ship dynamics [C]//Proceedings of the 2002 International Conference on Control Applications. Piscataway, NJ: IEEE Press, 2002: 1058–1063.

    Chapter  Google Scholar 

  5. Zhang Gui-chen, Ren Guang. Research on ship diesel engine speed regulator using on-line learning and self-tuning error model [J]. Transactions of CSICE, 2009, 27(3): 259–264 (in Chinese).

    Google Scholar 

  6. Chiu C H, Lin Y W, Lin C H. Real-time control of a wheeled inverted pendulum based on an intelligent model free controller [J]. Mechatronics, 2011, 21(3): 523–533.

    Article  Google Scholar 

  7. Lin C M, Peng Y F. Missile guidance law design using adaptive cerebellar model articulation controller [J]. IEEE Transactions on Neural Networks, 2005, 16(3): 636–644.

    Article  Google Scholar 

  8. Zhang Lin-gen, Zhao Qiao-sheng, He Chun-rong, et al. Simulation of submarine maneuvers using CMAC neural networks [J]. Journal of Ship Mechanics, 2009, 13(2): 226–233 (in Chinese).

    Google Scholar 

  9. Wu J Y. MIMO CMAC neural network classifier for solving classification problems [J]. Applied Soft Computing, 2011, 11(2): 2326–2333.

    Article  Google Scholar 

  10. Lin C M, Peng Y F, Hsu C F. Robust cerebellar model articulation controller design for unknown nonlinear systems [J]. IEEE Transactions on Circuits and Systems. II. Express Briefs, 2004, 51(7): 354–358.

    Article  Google Scholar 

  11. Yeh M F, Chang K C. A self-organizing CMAC network with gray credit assignment [J]. IEEE Transactions on System, Man, and Cybernetics: Part B, 2005, 36(3): 623–635.

    Article  MathSciNet  Google Scholar 

  12. Lin C M, Chen T Y. Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems [J]. IEEE Transactions on Neural Networks, 2009, 20(9): 1377–1384.

    Article  Google Scholar 

  13. Su S F, Tao T, Huang T H. Credit assigned CMAC and its application to online learning robust controllers [J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B, 2003, 33(2): 202–213.

    Article  Google Scholar 

  14. Zhang Lei, Cao Qi-xin, Zhang Chun-yu, et al. A credit-assignment CMAC algorithm and analysis on its convergence [J]. Journal of Shanghai Jiaotong University, 2005, 39(3): 377–385 (in Chinese).

    Google Scholar 

  15. Lu H C, Chuang C Y. Robust parametric CMAC with self-generating design for uncertain nonlinear systems [J]. Neurocomputing, 2011, 74(4): 549–562.

    Article  Google Scholar 

  16. Han Jing-qing. From PID to active disturbance rejection control [J]. IEEE Transactions on Industrial Electronics, 2009, 56(3): 900–906.

    Article  Google Scholar 

  17. Gao Zhi-qiang. Active disturbance rejection control: A paradigm shift in feedback control system design [C]//Proceedings of the 2006 American Control Conference. Piscataway, NJ: IEEE Press, 2006: 2399–2405.

    Google Scholar 

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Correspondence to Hai-de Hua  (华海德).

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Foundation item: the National Natural Science Foundation of China (No. 51179102) and the China Postdoctoral Science Foundation (No. 20110490716)

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Hua, Hd., Ma, N., Ma, J. et al. Robust intelligent control design for marine diesel engine. J. Shanghai Jiaotong Univ. (Sci.) 18, 660–666 (2013). https://doi.org/10.1007/s12204-013-1448-4

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  • DOI: https://doi.org/10.1007/s12204-013-1448-4

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