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Optimization of PID Sliding Surface Using Ant Lion Optimizer

  • Diab Mokeddem
  • Hakim Draidi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)

Abstract

In this paper, a sliding mode control SMC system with a proportional integral derivative PID sliding surface is presented. The main contribution in this work is to determine the optimal values of the PID sliding surface parameters using biologically-inspired algorithm, namely Ant lion optimization (ALO). This technique guarantee a robust sliding mode controller insensitive to uncertainty conditions, nonlinear dynamics, external disturbances and allowing the system to reach maximum switching and minimum chattering. The proposed system stability during reaching phase and sliding phase is mathematically confirmed by Lyapunov theorem. Simulation results of ALO tuning of PID sliding surface proved better tracking performance of the desired trajectory compared to conventional SMC.

Keywords

Optimization Ant lion optimizer ALO Sliding mode controller SMC PID sliding surface Genetic algorithm PSO 

References

  1. 1.
    Bandyopadhyay, B., Deepak, F., Kim, K.: Sliding mode control using novel sliding surfaces. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Utkin, V.: Sliding Modes in Control and Optimization. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  3. 3.
    Young, K., Utkin, V., Ozguner, U.: A control enginees guide to sliding mode control. IEEE Trans. Control Syst. Technol. 7, 328–342 (1999)CrossRefGoogle Scholar
  4. 4.
    Perruquetti, W., Barbot, J.: Sliding Mode Control in Engineering. Marcel Dekker, New York (2002)CrossRefGoogle Scholar
  5. 5.
    Zinober, A.S.I.: Variable Structure and Lyapunov Control. Springer, London (1993)zbMATHGoogle Scholar
  6. 6.
    Derbel, N., Q Zhu, J.G.: Applications of Sliding Mode Control. Springer, Heidelberg (2017)Google Scholar
  7. 7.
    Eker, I.: Sliding mode control with PID sliding surface and experimental application to an electromechanical plant. ISA Trans. 45(1), 109–118 (2006)CrossRefGoogle Scholar
  8. 8.
    Aliakbari, S., Ayati, M., Osman, J.H.S., Sam, Y.M.: Second order sliding mode fault-tolerant control of heat recovery steam generator boiler in combined cycle power plants. Appl. Therm. Eng. 50(1), 1326–1338 (2013)CrossRefGoogle Scholar
  9. 9.
    Amer, A.F., Sallam, E.A., Elawady, W.M.: Adaptive fuzzy sliding mode control using supervisory fuzzy control for 3 DOF planar robot manipulators. Appl. Soft Comput. 11(8), 4943–4953 (2011)CrossRefGoogle Scholar
  10. 10.
    Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)CrossRefGoogle Scholar
  11. 11.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)CrossRefGoogle Scholar
  12. 12.
    Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J Bio-Inspired Comput. 2, 78–84 (2010)CrossRefGoogle Scholar
  13. 13.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, pp. 134–42 (1991)Google Scholar
  14. 14.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)CrossRefGoogle Scholar
  16. 16.
    Xiaofei, Z., Hongbin, M., Nannan, L.: Application of sliding mode controller with proportional integral switching gain in robot. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 478–482 (2017)Google Scholar
  17. 17.
    Cao, Y., Chen, X.B.: An output-tracking-based discrete PID-Sliding mode control for MIMO systems. IEEE/ASME Trans. Mechatron. 19(4), 1183–1194 (2014)CrossRefGoogle Scholar
  18. 18.
    Li, Y., Xu, Q.: Adaptive sliding mode control with perturbation estimation and PID sliding surface for motion tracking of a piezo-driven micromanipulator. IEEE Trans. Control Syst. Technol. 18(4), 798–810 (2010)CrossRefGoogle Scholar
  19. 19.
    LY, I., Wang, Z., Zhu, L.: Adaptive neural network PID sliding mode dynamic control of non holonomic mobile robot. In: Proceedings of 2010 IEEE International Conference on Information and Automation, pp. 753–757 (2010)Google Scholar
  20. 20.
    Kumanan, D., Nagaraj, B.: Tuning of proportional integral derivative controller based on firefly algorithm. Syst. Sci. Control Eng. 1, 5 (2013)Google Scholar
  21. 21.
    Solihin, M.I., Lee, F.T., Moey, K.L.: Tuning of PID controller using particle swarm optimization (PSO). Int. J. Adv. Sci. Eng. Inf. Technol. 1(4), 458–461 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of TechnologyUniversity of Ferhat Abbas Setif-1SetifAlgeria

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