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An Optimized and Improved STF-PID Speed Control of Throttle Controlled HEV

  • Research Article - Systems Engineering
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Abstract

An improved self-tuning fuzzy proportional–integral–derivative (ISTF-PID), fuzzy logic-based PID (FPID) and optimal PID controllers for speed control of nonlinear hybrid electric vehicle (HEV) are proposed in this paper. The performances of HEV with ISTF-PID, FPID and optimal PID controllers are compared with the performance of HEV with existing self-tuning fuzzy PID and conventional PID controllers. The gains of PID, FPID and ISTF-PID controllers are tuned using multiobjective genetic algorithm. The performance specifications such as integral of the absolute error, integral of the square of error, peak overshoot, rise time and settling time are considered as objective function of GA and for performance analysis of HEV with designed controllers. The proposed control techniques are designed to achieve the variable speed, fuel economy, reduced pollution and improved efficiency under uncertain environment.

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References

  1. Wirasingha S.G., Emadi A.: Classification and review of control strategies for plug-in hybrid electric vehicles. IEEE Trans. Veh. Technol. 60(1), 111–122 (2011)

    Article  Google Scholar 

  2. Yadav A.K., Gaur P.: Robust adaptive speed control of uncertain hybrid electric vehicle using electronic throttle control with varying road grade. Nonlinear Dyn. 76(1), 305–321 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Naranjo J.E., Gonzalez C., García R., de Pedro T.: Cooperative throttle and brake fuzzy control for ACC+ Stop & Go maneuvers. IEEE Trans. Veh. Technol. 56(4), 1623–1630 (2007)

    Article  Google Scholar 

  4. Ducusin M., Gargies S., Mi C.: Modeling of a series hybrid electric high-mobility multipurpose wheeled vehicle. IEEE Trans. Veh. Technol. 56(2), 557–565 (2007)

    Article  Google Scholar 

  5. Yadav A.K., Gaur P., Jha S.K., Gupta J.R.P., Mittal A.P.: Optimal speed control of hybrid electric vehicles. J. Power Electron. 11(4), 393–400 (2011)

    Article  Google Scholar 

  6. Liu W.: Introduction to Hybrid Vehicles System Modeling and Control, 2nd edn. Wiley, New York (2013)

    Book  Google Scholar 

  7. Guardiola C., Pla B., Onori S., Rizzoni G.: Insight into the HEV/PHEV optimal control solution based on a new tuning method. Control Eng. Pract. 29(1), 247–256 (2014)

    Article  Google Scholar 

  8. Vasak M., Baotic M., Petrovic I., Peric N.: Hybrid theory-based time-optimal control of an electronic throttle. IEEE Trans. Ind. Electron. 54(3), 1483–1494 (2007)

    Article  MATH  Google Scholar 

  9. Jiao X., Zhang J., Shen T.: An adaptive servo control strategy for automotive electronic throttle and experimental validation. IEEE Trans. Ind. Electron. 61(11), 6275–6284 (2014)

    Article  Google Scholar 

  10. Yadav A.K., Gaur P.: AI-based adaptive control and design of autopilot system for nonlinear UAV. Sadhana 39(4), 765–783 (2014)

    Article  MATH  Google Scholar 

  11. Attia R., Orjuela R., Basset M.: Nonlinear cascade strategy for longitudinal control in automated vehicle guidance. Control Eng. Pract. 29(1), 225–234 (2014)

    Article  Google Scholar 

  12. Meza J.L., Santibanez V., Sotoand R., Llama M.A.: Fuzzy self-tuning PID semiglobal regulator for robot manipulators. IEEE Trans. Ind. Electron. 59(6), 2709–2718 (2012)

    Article  Google Scholar 

  13. Lin C.-M., Linand M.-H., Chen C.-W.: SoPC-based adaptive PID control system design for magnetic levitation system. IEEE Syst. J. 5(2), 278–287 (2011)

    Article  Google Scholar 

  14. Neath M.J., Swain A.K., Madawalaand U.K., Thrimawithana D.J.: An optimal PID controller for a bidirectional inductive power transfer system using multiobjective genetic algorithm. IEEE Trans. Power Electron. 29(3), 1523–1531 (2014)

    Article  Google Scholar 

  15. Ahn K.K., Truong D.Q.: Online tuning fuzzy PID controller using robust extended Kalman filter. J. Process Control 19(6), 1011–1023 (2009)

    Article  Google Scholar 

  16. Truong D.Q., Ahn K.K.: Force control for press machines using an online smart tuning fuzzy PID based on a robust extended Kalman filter. Expert Syst. Appl. 38(5), 5879–5894 (2011)

    Article  Google Scholar 

  17. Nasir U.M., Abidoand M.A., Rahman M.A.: Real-time performance evaluation of a genetic-algorithm-based fuzzy logic controller for IPM motor drives. IEEE Trans. Ind. Appl. 41(1), 246–252 (2005)

    Article  Google Scholar 

  18. Cheng M., Sun Q., Zhou E.: New self-tuning fuzzy PI control of a novel doubly salient permanent-magnet motor drive. IEEE Trans. Ind. Electron. 53(3), 814–821 (2006)

    Article  Google Scholar 

  19. Yu D.-L., Chang T.K., Yu D.-W.: Fault tolerant control of multivariable processes using auto-tuning PID controller. IEEE Trans. Syst. Man Cybern. B Cybern. 35(1), 32–43 (2005)

    Article  Google Scholar 

  20. Ziegler J.G., Nichols N.B.: Optimum settings for automatic controllers. J. Dyn. Syst. Meas. Control 64(8), 759–768 (1942)

    Google Scholar 

  21. Mudi R.K., Pal N.R.: A robust self-tuning scheme for PI and PD type fuzzy controllers. IEEE Trans. Fuzzy Syst. 7(1), 2–16 (1999)

    Article  Google Scholar 

  22. Chopra S., Mitra R., Kumar V.: Auto tuning of fuzzy PI type controller using fuzzy logic. Int. J. Comput. Cogn. 6(1), 12–18 (2008)

    Google Scholar 

  23. Woo Z.-W., Chung H.-Y., Lin J.-J.: A PID type fuzzy controller with self-tuning scaling factors. Fuzzy Sets Syst. 115(2), 321–326 (2000)

    Article  MATH  Google Scholar 

  24. Xu J.-X., Hang C.-C., Liu C.: Parallel structure and tuning of a fuzzy PID controller. Automatica 36(5), 673–684 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  25. Liu F.-C., Liang L.-H., Gao J.-J.: Fuzzy PID control of space manipulator for both ground alignment and space applications. Int. J. Autom. Comput. 11(4), 353–360 (2014)

    Article  Google Scholar 

  26. Huang, T.-T.; Chung, H.-Y.; Lin J.-J.: A fuzzy PID controller being like parameter varying PID. In: Proceedings of IEEE International Fuzzy Systems Conference. Seoul, Korea (1999)

  27. Yadav, A.K.; Gaur, P.; Mittal, A.P.; Anzar, M.: Comparative analysis of various control techniques for inverted pendulum. In: Proceedings of IICPE-2010 (2011)

  28. Li W.: Design of a hybrid fuzzy logic proportional plus conventional integral–derivative controller. IEEE Trans. Fuzzy Syst. 6(4), 449–463 (1998)

    Article  Google Scholar 

  29. Raviraj V.S.C., Sen P.C.: Comparative study of proportional–integral, sliding mode and fuzzy logic controllers for power converters. IEEE Trans. Ind. Appl. 33(2), 518–524 (1997)

    Article  Google Scholar 

  30. Yadav A.K., Gaur P.: Comparative analysis of modern control and AI-based control for maintaining constant ambient temperature. World Rev. Sci. Technol. Sustain. Dev. 10(1/2/3), 56–77 (2013)

    Article  Google Scholar 

  31. Quang D., Kyoung T., Ahn K.: Force control for hydraulic load simulator using self-tuning grey predictor fuzzy PID. Mechatronics 19(2), 233–246 (2009)

    Article  Google Scholar 

  32. Xu J.-X., Pok Y.-M., Liu C., Hang C.-C.: Tuning and analysis of a fuzzy PI controller based on gain and phase margins. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 28(5), 685–691 (1998)

    Article  Google Scholar 

  33. Mohan B.M., Sinha A.: Analytical structure and stability analysis of a fuzzy PID controller. Appl. Soft Comput. 8(1), 749–758 (2008)

    Article  Google Scholar 

  34. Karasakal O., Guzelkaya M., Eksin I., Yesil E.: An error-based on-line rule weight adjustment method for fuzzy PID controllers. Expert Syst. Appl. 38(8), 10124–10132 (2011)

    Article  Google Scholar 

  35. Karasakal O., Guzelkaya M., Eksin I., Yesil E., Kumbasar T.: Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration. Eng. Appl. Artif. Intell. 26(1), 184–197 (2013)

    Article  Google Scholar 

  36. Visioli A.: Fuzzy logic based set-point weight tuning of PID controllers. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 29(6), 587–592 (1999)

    Article  Google Scholar 

  37. Tang K.S., Man K.F., Chen G., Kwong S.: An optimal fuzzy PID controller. IEEE Trans. Ind. Electron. 48(4), 757–765 (2001)

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of ICE, NSIT, Dwarka, New Delhi, India

    Anil Kumar Yadav & Prerna Gaur

  2. Department of Electronics, Banasthali University, Tonk, Rajasthan, India

    Anil Kumar Yadav

Authors
  1. Anil Kumar Yadav
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  2. Prerna Gaur
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Correspondence to Anil Kumar Yadav.

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Yadav, A.K., Gaur, P. An Optimized and Improved STF-PID Speed Control of Throttle Controlled HEV. Arab J Sci Eng 41, 3749–3760 (2016). https://doi.org/10.1007/s13369-016-2131-5

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  • DOI: https://doi.org/10.1007/s13369-016-2131-5

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