Design and simulation of a 2DOF PID controller based on particle swarm optimization algorithms for a thermal phase of hybrid vehicle

  • Authors

    • Mohamed Hedi Hmidi ENICarthage
    • Ines Ben Salem ENICarthage
    • Lilia El Amraoui
    2018-04-11
    https://doi.org/10.14419/ijet.v7i2.8428
  • Hybrid Vehicle, Modeling, PID Two Degree of Freedom Control, Driving Cycle, Particle Swarm Optimization, PSO with Constriction Factor.

  • Abstract

    This paper deals with the systematic design of a PID regulators with two degree of freedom 2DOF for a Hybrid vehicle driving cycle based on different variants of the Particle Swarm Optimization (PSO) algorithm. The PID 2DOF problem for the stabilization of the velocity dynamics of the hybrid vehicle are formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. Both PSO algorithm with variable inertia weight (PSO-In), PSO with Constriction factor (PSO-Co), PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim to further improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. All optimized 2DOF PID controllers are then simulated within a Matlab Simulink. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based 2 DOF controllers for the hybrid Vehicle velocity stabilization.

     

     

  • References

    1. [1] F. Mensing, R. Trigui, E. Bideaux (2011). «Vehicle Trajectory Optimization for Application in Eco-Driving», IEEE Vehicle Power and Propulsion Conference, September 2011, Chicago, Illinois. https://doi.org/10.1109/VPPC.2011.6042993.

      [2] A. Sciarretta, M. Back and L. Guzzella .Optimal Control of Parallel Hybrid Electric Vehicles, IEEE Transactions on Control Systems Technology, Vol. 12, no. 3 , May 2004. https://doi.org/10.1109/TCST.2004.824312.

      [3] M.G. Hinton, L. Forrest, D.P. Duclos, T.H. Davey, R.R. Sheahan, and K.B. Swan. Survey of driver aided devices for improved fuel economy. TechnicalReport DOT-TSC-OST-76-45, Department of Transportation, 1976.

      [4] ACEA (European Automobile Manufacturers’ Association). Reducint CO2 emission: Working together to achieve better results, 2013.

      [5] R. Trigui, B. Jeanneret, F. Badin, “Systemic modeling of hybrid vehicles in order to predict dynamic performance and energy consumption. Building the VEHLIB library of modelsâ€, (text in French with abridged version in English), RTS, November 2003.

      [6] T. D. Gillespie. Fundamentals of Vehicle Dynamics. Society of Automotive Engineers (SAE), 1992. https://doi.org/10.4271/R-114.

      [7] J. A. Romero, AA. Lozano-Guzman , E. Betanzo-Quezada, G.M. Arroyo-Conteras , Cargo securement methods and vehicle braking performance , International Journal of Vehicle Performance , Vol. 4 ,pp. 353-373 , 2016. https://doi.org/10.1504/IJVP.2016.079919.

      [8] E. Tekin, S. Ertugrul, Multi-input multi-output intelligent modeling techniques ans application to human driver, International Journal of Vehicle Performance, Vol. 4, pp. 390-417, 2016. https://doi.org/10.1504/IJVP.2016.079921.

      [9] J. G. Ziegler and N.B. Nichols, Optimum setting for automatic controllers, Trans ASME , Vol 64, pp. 759-768, 1942.

      [10] R. Gorez, New desing relations for 2-DOF PID-like control systems. Automatica, Vol. 39, pp. 901–908. https://doi.org/10.1016/S0005-1098(03)00029-3.

      [11] k. J. Åström and T. Hägglund, Automatic tuning of simple regulators with specifications on phase and amplitude margins, Automatica, vol. 20, pp. 645–651, 1984. https://doi.org/10.1016/0005-1098(84)90014-1.

      [12] Alvaro R. Vilanova , Conversion formulate and performance capabilities of two-degree of freedom PID Control algorithm , 17th IEEE International Conference on Emerging Technologies ans factory Automation ( ETFA), pp. 17-21, Karakow, Poland , sept 2012.

      [13] M.A. Johnson, PID Control New Identification and design methods , chap. PID Control Technology , Springer verlag London Ltd., pp. 1-46 U.K. 2005

      [14] M. Kumar, P. Vandana , V. Patel , Two Degree of Freedom PID Controller For speed control of DC Motor, American International Journal of Research in Science, Technology, Engineering & Mathematics, Vol. 39, pp. 94-97 , 2015.

      [15] M. Araki, Two degree of freedom PID Controllers their functions and optimal tuning, IFAC Digital Control: Past, Present and Futur of PID Control, pp. 5-7. Terrassa, Spain 2000.

      [16] M. Araki , H. Taguchi, Two- degree of freedom PID Controllers, International Journal of Control, Automation, and Systems , Vol. 1, No. 4, December 2003.

      [17] Z-Y. Zhao, M. Tomizuka and S. Isaka. Fuzzy Gain Scheduling of PID Controllers, IEEE Transactions on Systems, Man, and Cybernetics, 1993, vol. 23, no. 5, pp. 1392-1398. https://doi.org/10.1109/21.260670.

      [18] Z. Xinchao. A perturbed particle swarm algorithm for numerical optimization, Applied Soft Computing, 2010, vol. 10, pp. 119-124. https://doi.org/10.1016/j.asoc.2009.06.010.

      [19] R. C. Eberhart, Y. Shi. Particle Swarm Optimization: Developments, Applications and Resources, Proceedings of the IEEE Congress on Evolutionary Computation, Seoul, Korea, 2001, pp. 81-86. https://doi.org/10.1109/CEC.2001.934374.

      [20] R. C. Eberhart, J. Kennedy. A New Optimizer Using Particle Swarm Theory, Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, 1995, pp. 39-43. https://doi.org/10.1109/MHS.1995.494215.

      [21] M. L. Derouiche, S. Bouallègue, J. Haggège and G. Sandou. LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning, Proceedings of the 4th IFAC International Conference on Intelligent Control and Automation Sciences (IFAC ICONS’ 2016), Reims, France, 2016, pp. 359-364. https://doi.org/10.1016/j.ifacol.2016.07.138.

      [22] M. Clerc and J. Kennedy. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space, IEEE Transactions on Evolutionary Computation, 2002, vol. 6, no. 1, pp. 58-73, 2002.

  • Downloads

  • How to Cite

    Hmidi, M. H., Ben Salem, I., & El Amraoui, L. (2018). Design and simulation of a 2DOF PID controller based on particle swarm optimization algorithms for a thermal phase of hybrid vehicle. International Journal of Engineering & Technology, 7(4), 4644-4650. https://doi.org/10.14419/ijet.v7i2.8428

    Received date: 2017-10-01

    Accepted date: 2018-01-24

    Published date: 2018-04-11