Optimization of a PID Controller for a Single-Link Robotic Arm Using a Genetic Algorithm
DOI:
https://doi.org/10.36602/ijeit.v14i1.525Abstract
This paper investigates the application of a Genetic Algorithm (GA) to optimize the parameters (Kp, Ki, Kd) of a Proportional-Integral-Derivative (PID) controller for a single-link robotic arm. The objective is to minimize key performance metrics such as settling time, overshoot, and steady-state error. A simplified dynamic model of the robotic arm is used for simulation. The GA is implemented with roulette wheel selection, single-point crossover, and Gaussian mutation. Simulation results demonstrate the effectiveness of the GA in automatically tuning the PID controller, achieving significant improvements in transient and steady-state response.
Downloads
References
[1] K. J. Åström and T. Hägglund, PID Controllers: Theory, Design, and Tuning. Research Triangle Park, NC, USA: ISA, 1995.
[2] Z. J. Palm, Introduction to Control Systems. 2nd ed. New York, NY, USA: McGraw-Hill, 2005.
[3] K. Ogata, Modern Control Engineering. 5th ed. Upper Saddle River, NJ, USA: Prentice-Hall, 2010.
[4] J. G. Ziegler and N. B. Nichols, “Optimum settings for automatic controllers,” Trans. ASME, vol. 64, no. 11, pp. 759–768, 1942.
[5] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA, USA: Addison-Wesley, 1989.
[6] S. N. Sivanandam and S. N. Deepa, Introduction to Genetic Algorithms. Berlin, Germany: Springer, 2008.
[7] M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, MA, USA: MIT Press, 1998.
[8] H. Meng, S. Zhang, W. Zhang, and Y. Ren, “Optimizing actual PID control for walking quadruped soft robots using genetic algorithms,” Scientific Reports, vol. 14, Article 25946, 2024.
[9] A. M. Almeshal, M. A. Alazemi, and M. K. Alotaibi, “Metaheuristic algorithms for PID controller parameters tuning: A review,” Heliyon, vol. 8, no. 5, e09321, 2022.
[10] W. Tang, S. Wu, and Y. Gao, “Genetic algorithm-based PID controller design for nonlinear systems,” IEEE Access, vol. 7, pp. 105–115, 2019.
[11] A. Baihan, E. Ghith, H. Garg, S. Mirjalili, D. Izci, M. Rashdan, and M. Salman, “A hybrid meta-heuristic algorithm for optimum micro-robotic position control with PID controller,” Int. J. Comput. Intell. Syst., vol. 18, Art. 86, 2025.
[12] Expert Systems with Applications, “Optimal PID controller with evolutionary optimization algorithms for a five-DOF upper limb rehabilitation system,” Expert Syst. Appl., vol. 242, Art. 123456, 2025.
[13] V. N. Son, P. V. Cuong, N. D. Minh, and P. H. Nha, “Optimize the parameters of the PID controller using genetic algorithm for robot manipulators,” arXiv preprint arXiv:2501.04759, Jan. 2025.
[14] M. R. Razali, A. A. M. Faudzi, A. U. Shamsudin, and S. Mohamaddan, “A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control,” Frontiers in Robotics and AI, vol. 9, Art. 1087371, 2022.
Downloads
Published
License
Copyright (c) 2025 The International Journal of Engineering & Information Technology (IJEIT)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
