Advertisement

Optimal Design of PID Controller for 2-DOF Drawing Robot Using Bat-Inspired Algorithm

  • Ahmad Taher AzarEmail author
  • Hossam Hassan Ammar
  • Mayra Yucely Beb
  • Santiago Ramos Garces
  • Abdoulaye Boubakari
Conference paper
  • 232 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1058)

Abstract

Tuning process which is used to find the optimum values of the proportional integral derivative (PID) parameters, can be performed automatically using meta-heuristics algorithms such as BA (Bat Algorithm), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony). This paper presented a theoretical and practical implementation of a drawing robot using BA to tune the PID controller governing the robotic arm which is a non linear system difficult to be controlled using classical control. In line with the above and in order to achieve this aim and meet high performance feedback and robust dynamic stability of the system, the PID controller is designed considering the realistic constraints. For faster tuning of the controller parameters, ten individuals and five iterations have been selected. BA, ABC and PSO have been compared and it’s noticed that BA is the best choice to achieve good performance control. In the proposed design, MATLAB was used for trajectory reckoning. Afterwards, the value of coordinate position of the shape to be drawn is translated into a joint angle by applying the inverse kinematics to control the two DC motors through the ATMEGA 2560 microcontroller. The suggested technique reveals via simulations and hardware implementation the high efficiency of the applied algorithm. The PID controller approach presents an impressive stability and robustness results. The achieved results demonstrated that high performance can be obtained by tuning a PID controller using nature inspired based algorithms.

Keywords

PID controller Bat Algorithm Drawing robot Inverse kinematics Metaheuristics 

References

  1. 1.
    Abdelmalek, S., Azar, A.T., Dib, D.: A novel actuator fault-tolerant control strategy of DFIG-based wind turbines using takagi-sugeno multiple models. Int. J. Control Autom. Syst. 16(3), 1415–1424 (2018)CrossRefGoogle Scholar
  2. 2.
    Ammar, H.H., Azar, A.T.: Robust path tracking of mobile robot using fractional order PID controller. In: Hassanien, A.E., Azar, A.T., Gaber, T., Bhatnagar, R., Tolba, F.M. (eds.) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019), pp. 370–381. Springer, Cham (2020)Google Scholar
  3. 3.
    Ammar, H.H., Azar, A.T., Tembi, T.D., Tony, K., Sosa, A.: Design and implementation of fuzzy pid controller into multi agent smart library system prototype. In: Hassanien, A.E., Tolba, M.F., Elhoseny, M., Mostafa, M. (eds.) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), pp. 127–137. Springer, Cham (2018)CrossRefGoogle Scholar
  4. 4.
    Azar, A.T., Serrano, F.E.: Adaptive decentralised sliding mode controller and observer for asynchronous nonlinear large-scale systems with backlash. Int. J. Model. Ident. Control 30(1), 61–71 (2018)CrossRefGoogle Scholar
  5. 5.
    Azar, A.T., Serrano, F.E.: Fractional order two degree of freedom PID controller for a robotic manipulator with a fuzzy type-2 compensator. In: Hassanien, A.E., Tolba, M.F., Shaalan, K., Azar, A.T. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, pp. 77–88. Springer, Cham (2019)Google Scholar
  6. 6.
    Azar, A.T., Ammar, H.H., Barakat, M.H., Saleh, M.A., Abdelwahed, M.A.: Self-balancing robot modeling and control using two degree of freedom PID controller. In: Hassanien, A.E., Tolba, M.F., Shaalan, K., Azar, A.T. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, pp. 64–76. Springer, Cham (2019)Google Scholar
  7. 7.
    Azar, A.T., Hassan, H., Razali, M.S.A.B., de Brito Silva, G., Ali, H.R.: Two-degree of freedom proportional integral derivative (2-DOF PID) controller for robotic infusion stand. In: Hassanien, A.E., Tolba, M.F., Shaalan, K., Azar, A.T. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, pp. 13–25. Springer, Cham (2019)Google Scholar
  8. 8.
    Azar, A.T., Ammar, H.H., de Brito Silva, G., Razali, M.S.A.B.: Optimal proportional integral derivative (PID) controller design for smart irrigation mobile robot with soil moisture sensor. In: Hassanien, A.E., Azar, A.T., Gaber, T., Bhatnagar, R., Tolba, F.M. (eds.) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019), pp. 349–359. Springer, Cham (2020)Google Scholar
  9. 9.
    Chopard, B., Tomassini, M.: An Introduction to Metaheuristics for Optimization. Natural Computing Series. Springer, Heidelberg (2018)CrossRefGoogle Scholar
  10. 10.
    Fekik, A., Denoun, H., Azar, A.T., Hamida, M.L., Zaouia, M., Benyahia, N.: Comparative study of two level and three level PWM-rectifier with voltage oriented control. In: Hassanien, A.E., Tolba, M.F., Shaalan, K., Azar, A.T. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, pp. 40–51. Springer, Cham (2019)Google Scholar
  11. 11.
    Fister, D., Fister, I., Fister, I., Afari, R.: Parameter tuning of PID controller with reactive nature-inspired algorithms. Robot. Auton. Syst. 84, 64–75 (2016)CrossRefGoogle Scholar
  12. 12.
    Gai, W., Qu, C., Liu, J., Zhang, J.: A novel hybrid meta-heuristic algorithm for optimization problems. Syst. Sci. Control Eng. 6(3), 64–73 (2018)CrossRefGoogle Scholar
  13. 13.
    Gorripotu, T.S., Samalla, H., Jagan Mohana Rao, C., Azar, A.T., Pelusi, D.: TLBO algorithm optimized fractional-order PID controller for AGC of interconnected power system. In: Nayak, J., Abraham, A., Krishna, B.M., Chandra Sekhar, G.T., Das, A.K. (eds.) Soft Computing in Data Analytics, pp. 847–855. Springer, Singapore (2019)Google Scholar
  14. 14.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  15. 15.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lau, M.C., Baltes, J., Anderson, J., Durocher, S.: A portrait drawing robot using a geometric graph approach: Furthest neighbour theta-graphs. In: 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 75–79 (2012)Google Scholar
  17. 17.
    Megalingam, R.K., Raagul, S., Dileep, S., Sathi, S.R., Pula, B.T., Vishnu, S., Sasikumar, V., Gupta, U.S.C.: Design implementation and analysis of a low cost drawing bot for educational purpose. Int. J. Pure Appl. Math. 118(6), 213–230 (2018)Google Scholar
  18. 18.
    Meghni, B., Dib, D., Azar, A.T.: A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage. Neural Comput. Appl. 28(6), 1417–1434 (2017)Google Scholar
  19. 19.
    Meghni, B., Dib, D., Azar, A.T., Saadoun, A.: Effective supervisory controller to extend optimal energy management in hybrid wind turbine under energy and reliability constraints. Int. J. Dyn. Control 6(1), 369–383 (2018)Google Scholar
  20. 20.
    Mekki, H., Boukhetala, D., Azar, A.T.: Sliding modes for fault tolerant control. In: Azar, A.T., Zhu, Q. (eds.) Advances and Applications in Sliding Mode Control systems, pp. 407–433. Springer, Cham (2015)CrossRefGoogle Scholar
  21. 21.
    Munna, M.S., Tarafder, B.K., Robbani, M.G., Mallick, T.C.: Design and implementation of a drawbot using matlab and arduino mega. In: 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 769–773 (2017).  https://doi.org/10.1109/ECACE.2017.7913006
  22. 22.
    Sabir, M.M., Ali, T.: Optimal pid controller design through swarm intelligence algorithms for sun tracking system. Appl. Math. Comput. 274, 690–699 (2016)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Smida, M.B., Sakly, A., Vaidyanathan, S., Azar, A.T.: Control-based maximum power point tracking for a grid-connected hybrid renewable energy system optimized by particle swarm optimization. In: Azar, A.T., Vaidyanathan, S. (ed.) Advances in System Dynamics and Control, IGI Global, pp. 58–89 (2018)Google Scholar
  24. 24.
    Soliman, M., Azar, A.T., Saleh, M.A., Ammar, H.H.: Path planning control for 3-omni fighting robot using PID and fuzzy logic controller. In: Hassanien, A.E., Azar, A.T., Gaber, T., Bhatnagar, R., Tolba, F.M. (eds.) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019), pp. 442–452. Springer, Cham (2020)Google Scholar
  25. 25.
    Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994).  https://doi.org/10.1162/evco.1994.2.3.221CrossRefGoogle Scholar
  26. 26.
    Yang, X.S.: A New Metaheuristic Bat-Inspired Algorithm, pp. 65–74. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  27. 27.
    Ziegler, J., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64(8), 759–768 (1942)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ahmad Taher Azar
    • 1
    • 2
    Email author
  • Hossam Hassan Ammar
    • 3
  • Mayra Yucely Beb
    • 3
  • Santiago Ramos Garces
    • 3
  • Abdoulaye Boubakari
    • 3
  1. 1.College of EngineeringPrince Sultan UniversityRiyadhKingdom of Saudi Arabia
  2. 2.Faculty of Computers and Artificial IntelligenceBenha UniversityBenhaEgypt
  3. 3.School of Engineering and Applied SciencesNile University6th of October City, GizaEgypt

Personalised recommendations