Optimization of the PID coefficients for the line-follower mobile robot controller employing genetic algorithm

O b j e c t i v e s. To develop a control system for the movement of a mobile robot along a color-contrast line, as well as to find the values of the coefficients of a proportional-integral-differentiating (PID) controller that allows the robot to move along the line at a given speed.

M e t ho d s. To adjust the values of the coefficients of the PID controller, methods of enumeration, automatic tuning and a genetic algorithm are used.

Re s u l t s. A software package for tuning the PID controller of the educational mobile robot RoboCake, designed to move along a closed color-contrast line at a given speed, has been developed. The software package consists of a simulation model of the specified robot in the Simulink environment, several virtual traces-polygons and a specialized solver based on the developed genetic algorithm. With the help of the proposed fitness function, a mobile robot control system that satisfies the stated conditions is implemented. Based on the conducted model experiments, the desired values of the parameters of the PID controller are obtained.

Co n c l u s i o n. A comparison of the effectiveness of various methods of tuning the PID controller is carried out. The developed software package is designed to solve the practical problem of moving a mobile robot along a color-contrast line at a speed of 1 m/s. The results obtained can be used to study methods of evolutionary tuning of stabilization systems for transport robots, ensuring their movement without overshoot.

1. Filippov A. V., Kosolapov M. A., Maslov I. A. Automated tuning of the PID controller for the control object of the servo system using the MATLAB Simulink software package. Nauka, tehnika i obrazovanie [Science, Technology and Education], 2015, no. 12(18), pp. 53-59 (In Russ.).

2. Aström K. J., Hägglund T. The future of PID control. Control Engineering Practice, 2001, vol. 9, no. 11, pp. 1163-1175. https://doi.org/10.1016/S0967-0661(01)00062-4

3. Ivaschenko N. N. Avtomaticheskoe regulirovanie: teoriya i elementi system. Automatic Regulation: Theory and Elements of Systems. Moscow, Mashinostroenie, 1978, 737 p. (In Russ.).

4. Martins F. G. Tuning PID controllers using the ITAE criterion. International Journal of Engineering Education, 2005, vol. 21, no. 5, pp. 867-873.

5. Kose O., Karas I. R. PID controlled line follower robot design on indoor 3D networks. IAES International Conference on Electrical Engineering, Computer Science and Informatics (EECSI-2014). Yogyakarta, Indonesia, 2014, pp. 20-21.

6. Varlamov I., Zubov P. PID-EXPERT - automation automation’s. Control Engineering, 2017, no. 2(68), pp. 98-101 (In Russ.).

7. Vladu E. E., Dragomir T. L. Controller tuning using genetic algorithms. 1st Romanian-Hungarian Joint Symposium on Applied Computational Intelligence (SACI 2004). Springer, 2004. Available at: https://www.researchgate.net/publication/228954545_Controller_Tuning_Using_Genetic_Algorithms (accessed 12.05.2021).

8. Ünal M., Ak A., Topuz V., Erdal H. Genetic algorithm. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Berlin, Springer, 2013, 449 p. https://doi.org/10.1007/978-3-642-32900-5_3

9. Simon D. Evolutionary Optimization Algorithms. John Wiley & Sons, 2013, 784 r.

10. Koshev A. N., Salmin V. V., Generalova A. A., Bichkov D. S. The development of genetic algorithm with adaptive mutations to determine the global extremum function of n-variables. Internet-jurnal “Naukavedenie” [Online Magazine "Science"], 2016, vol. 8, no. 6, pp. 1-13. Available at: http://naukovedenie.ru/PDF/32TVN616.pdf (accessed 12.05.2021).

11. Podlazov A. V. Genetic algorithms based on examples of solving cutting problems. Problemi upravleniya [Management Problems], 2008, vol. 2, pp. 57-63 (In Russ.).

12. Koo Y. C., Bakar E. A. Motor speed controller for differential wheeled mobile robot. ARPN Journal of Engineering and Applied Sciences, 2015, vol. 10, no. 22, pp. 10698-10702.

13. PID control. In W. S. Levine (ed.). The Control Handbook. Piscataway, New Jersey, IEEE Press, 1996, pp. 198-209.

14. O’Dwyer A. Handbook of PI and PID Controller Tuning Rules. London, Imperial College Press, 2003, 564 p.

15. Mirzal A., Yoshii Sh., Furukawa M. PID parameters optimization by using genetic algorithm. ISTECS Journal, 2006, vol. 8, no. 11, p. 34-43.