Intelligent Control for Mobile Robot Trajectory Tracking
Nabil NADAH, Mohamed KMICH, Mhamed SAYYOURI
Abstract. This paper proposes a PID controller optimized using the Puma Optimizer Algorithm (POA) for accurate tracking of a wheeled mobile robot trajectory and improved system stability. An in-depth comparative study was also conducted with PID controllers optimized by other metaheuristic algorithms, including the Sine Cosine Algorithm (SCA) and the Arithmetic Optimization Algorithm (AOA). Our simulation results demonstrate that our proposed controller POA-PID-ITAE performs very effectively with a settling time Ts = 13s, a maximum overshoot of Mp = 0.0493%, and an Integral of Time multiplied by Absolute Error (ITAE) value of 96.72. These metrics demonstrate the effectiveness of our method in ensuring a stable and effective tuning mechanism for PID-based trajectory tracking of mobile robots within the adopted kinematic modeling assumptions.
Keywords
PID Controller, Puma Optimizer Algorithm, Mobile Robot, Metaheuristic Algorithms, Trajectory Tracking
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Nabil NADAH, Mohamed KMICH, Mhamed SAYYOURI, Intelligent Control for Mobile Robot Trajectory Tracking, Materials Research Proceedings, Vol. 64, pp 527-534, 2026
DOI: https://doi.org/10.21741/9781644904091-66
The article was published as article 66 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] A. Dzedzickis, J. Subačiūtė-Žemaitienė, E. Šutinys, U. Samukaitė-Bubnienė, and V. Bučinskas, ‘Advanced applications of industrial robotics: New trends and possibilities’, Applied Sciences, vol. 12, no. 1, p. 135, 2021.
[2] M. Javaid, A. Haleem, R. Pratap Singh, S. Rab, R. Suman, and L. Kumar, ‘Utilization of robotics for healthcare: a scoping review’, Journal of Industrial Integration and Management, vol. 10, no. 01, pp. 43–65, 2025.
[3] L. F. Oliveira, A. P. Moreira, and M. F. Silva, ‘Advances in agriculture robotics: A state-of-the-art review and challenges ahead’, Robotics, vol. 10, no. 2, p. 52, 2021.
[4] J. Zhao et al., ‘Autonomous driving system: A comprehensive survey’, Expert Systems with Applications, vol. 242, p. 122836, 2024.
[5] H. Taheri and C. X. Zhao, ‘Omnidirectional mobile robots, mechanisms and navigation approaches’, Mechanism and Machine Theory, vol. 153, p. 103958, 2020.
[6] E. V. Larkin, M. A. Antonov, and A. N. Privalov, ‘The tricycle mobile robot movement simulation’, presented at the MATEC Web of Conferences, EDP Sciences, 2018, p. 06001.
[7] K. Shojaei, A. M. Shahri, A. Tarakameh, and B. Tabibian, ‘Adaptive trajectory tracking control of a differential drive wheeled mobile robot’, Robotica, vol. 29, no. 3, pp. 391–402, 2011.
[8] J. Riera, S. Cachiguango, M. Pedraza, G. M. Andaluz, and P. Leica, ‘Sliding Mode Control for Trajectory Tracking of a TurtleBot3 Mobile Robot in Obstacle Environments’, Engineering Proceedings, vol. 77, no. 1, p. 7, 2024.
[9] Z. Wang, ‘Application of the PID Algorithm in Robot’, ITM Web Conf., vol. 73, p. 01025, 2025. https://doi.org/10.1051/itmconf/20257301025.
[10] A. Waga, S. Benhlima, A. Bekri, J. Abdouni, and F. Z. Saber, ‘A survey on autonomous navigation for mobile robots: From traditional techniques to deep learning and large language models’, Journal of King Saud University Computer and Information Sciences, vol. 37, no. 7, p. 198, 2025.
[11] F. Isdaryani, F. Feriyonika, and R. Ferdiansyah, ‘Comparison of Ziegler-Nichols and Cohen Coon tuning method for magnetic levitation control system’, J. Phys.: Conf. Ser., vol. 1450, no. 1, p. 012033, Feb. 2020. https://doi.org/10.1088/1742-6596/1450/1/012033.
[12] Z. Yang et al., ‘Research on Trajectory Tracking Control Method for Crawler Robot Based on Improved PSO Sliding Mode Disturbance Rejection Control’, Sensors, vol. 25, no. 7, p. 2113, 2025.
[13] M. Kmich et al., ‘Chaotic Puma Optimizer Algorithm for controlling wheeled mobile robots’, Engineering Science and Technology, an International Journal, vol. 63, p. 101982, Mar. 2025. https://doi.org/10.1016/j.jestch.2025.101982.
[14] H. Tahiri, I. Mchichou, M. Ouabdou, and M. Sayyouri, ‘Advanced Control Strategies for Mobile Robots Using Artificial Intelligence’, presented at the 2025 7th Global Power, Energy and Communication Conference (GPECOM), IEEE, 2025, pp. 1130–1135.
[15] B. Abdollahzadeh et al., ‘Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning’, Cluster Computing, vol. 27, no. 4, pp. 5235–5283, 2024.
