Meta-Heuristic Optimization of Pid Controllers for a 5-Dof Robotic Manipulator

Adeleke, Olorunnisola; Dahunsi, Olurotimi

doi:10.22115/cepm.2025.544303.1383

Meta-Heuristic Optimization of Pid Controllers for a 5-Dof Robotic Manipulator

Document Type : Original Article

Authors

Olorunnisola Adeleke ¹

Olurotimi Dahunsi ²

¹ Student, Department of Mechanical Engineering, Federal University of Technology, Akure, Ondo State. Nigeria

² Professor, Department of Mechanical Engineering, Federal University of Technology, Akure, Ondo State. Nigeria

10.22115/cepm.2025.544303.1383

Abstract

Accurate motion control is a critical requirement for robotic manipulators in advanced industrial and research applications. Proportional–Integral–Derivative (PID) controllers remain the preferred choice due to their simplicity and reliability, although their performance is highly dependent on effective gain tuning. Conventional tuning techniques are often inefficient and fail to deliver consistent results across complex robotic systems. This study investigates the application of metaheuristic optimization methods, specifically Genetic Algorithm (GA) and Ant Colony Optimization (ACO), for tuning PID controllers in a five-degree-of-freedom robotic manipulator. The manipulator was modeled in SolidWorks, simulated in Simscape, and integrated with MATLAB-based control. A sinusoidal trajectory was employed as the reference input, and performance was evaluated using Integral Time Absolute Error (ITAE) and overshoot metrics across all joints. The results show that both GA and ACO outperform manual tuning. GA reduced the average overshoot by approximately 51% and ACO by 61% compared with manual tuning, while the GA and ACO algorithms achieved fitness (ITAE) improvements of 0.36% and 0.04%, respectively. GA demonstrated faster convergence (within 10 generations), whereas ACO achieved more stable fitness reduction and superior trajectory tracking, indicating enhanced robustness. These findings suggest that while GA offers computational efficiency, ACO provides improved stability and accuracy, making it a more effective strategy for PID tuning in this system.

Graphical Abstract

Meta-Heuristic Optimization of Pid Controllers for a 5-Dof Robotic Manipulator

Highlights

· Developed and validated a PID-based control framework for a 5-DOF robotic manipulator.

· Implemented Genetic Algorithm (GA) and Ant Colony Optimization (ACO) for PID gain tuning.

· GA achieved faster convergence, whereas ACO delivered lower ITAE and improved robustness.

· Both GA and ACO significantly reduced overshoot and enhanced trajectory tracking accuracy.

· Comparative analysis establishes ACO as superior for stability, while GA excels in efficiency.

Keywords

Industrial robotics

Intelligent control

Motion control

Trajectory tracking

Metaheuristic optimization

Control system optimization

Subjects

Mechanical Engineering

[1] Tinoco V, Silva MF, Santos FN, Morais R, Magalhães SA, Oliveira PM. A review of advanced controller methodologies for robotic manipulators. Int J Dyn Control 2025;13:36. https://doi.org/10.1007/s40435-024-01533-1.

[2] Sariyildiz E, Yu H, Ohnishi K. A Practical Tuning Method for the Robust PID Controller with Velocity Feed-Back. Machines 2015;3:208–22. https://doi.org/10.3390/machines3030208.

[3] Somefun OA, Akingbade K, Dahunsi F. The dilemma of PID tuning. Annu Rev Control 2021;52:65–74. https://doi.org/10.1016/j.arcontrol.2021.05.002.

[4] Joseph SB, Dada EG, Abidemi A, Oyewola DO, Khammas BM. Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems. Heliyon 2022;8:e09399. https://doi.org/10.1016/j.heliyon.2022.e09399.

[5] Bartz‐Beielstein T, Branke J, Mehnen J, Mersmann O. Evolutionary Algorithms. WIREs Data Min Knowl Discov 2014;4:178–95. https://doi.org/10.1002/widm.1124.

[6] Gonzalez TF, editor. Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC; 2007. https://doi.org/10.1201/9781420010749.

[7] Ünal M, Ak A, Topuz V, Erdal H. Optimization of PID controllers using ant colony and genetic algorithms. vol. 449. Springer; 2012.

[8] Sharma R, Rana KPS, Kumar V. Comparative Study of Controller Optimization Techniques for a Robotic Manipulator, 2014, p. 379–93. https://doi.org/10.1007/978-81-322-1771-8_33.

[9] Son VN, Van Cuong P, Minh ND, Nha PH. Optimize the parameters of the PID Controller using Genetic Algorithm for Robot Manipulators. ArXiv Prepr ArXiv250104759 2025.

[10] Dahmane SA, Azzedine A, Megueni A. Ant colony optimization algorithm based on optimal PID parameters for a robotic arm. Int J Control Syst Robot 2020;5.

[11] Cao Y, Zhou Q, Yuan W, Ye Q, Popa D, Zhang Y. Human-robot collaborative assembly and welding: A review and analysis of the state of the art. J Manuf Process 2024;131:1388–403. https://doi.org/10.1016/j.jmapro.2024.09.044.

[12] Thomas G, Akporhuarho J. The Basics and Significance of Industrial Robots in Manufacturing: A Review. Niger J Eng Sci Res 2021;4:38–54.

[13] Adebayo RA, Obiuto NC, Festus-Ikhuoria IC, Olajiga OK. Robotics in manufacturing: A review of advances in automation and workforce implications. Int J Adv Multidiscip Res Stud 2024;4:632–8.

[14] Ansari ZJ, Aher A, Thitame SN. Advancements in Robotics and AI Transforming Surgery and Rehabilitation. J Pharm Bioallied Sci 2025;17:S46–8. https://doi.org/10.4103/jpbs.jpbs_1937_24.

[15] Banyai AD, Brișan C. Robotics in Physical Rehabilitation: Systematic Review. Healthcare 2024;12:1720. https://doi.org/10.3390/healthcare12171720.

[16] Yang Q, Du X, Wang Z, Meng Z, Ma Z, Zhang Q. A review of core agricultural robot technologies for crop productions. Comput Electron Agric 2023;206:107701. https://doi.org/10.1016/j.compag.2023.107701.

[17] Mills JK. Stability and control aspects of flexible link robot manipulators during constrained motion tasks. J Robot Syst 1992;9:933–53. https://doi.org/10.1002/rob.4620090705.

[18] Xu S, Wu Z, Shen T. High-Precision Control of Industrial Robot Manipulator Based on Extended Flexible Joint Model. Actuators 2023;12:357. https://doi.org/10.3390/act12090357.

[19] Lyu G, Wang P, Li G, Lu F, Dai S. Precise torques and sliding mode compensation for trajectory tracking of manipulator with uncertainty. Int J Adv Robot Syst 2022;19:172988062211212. https://doi.org/10.1177/17298806221121212.

[20] Azeez MI, Atia KR. Modeling of PID controlled 3DOF robotic manipulator using Lyapunov function for enhancing trajectory tracking and robustness exploiting Golden Jackal algorithm. ISA Trans 2024;145:190–204. https://doi.org/10.1016/j.isatra.2023.11.033.

[21] Rakhmatillaev J, Bucinskas V, Kabulov N. An integrative review of control strategies in robotics. Robot Syst Appl 2025. https://doi.org/10.21595/rsa.2025.25014.

[22] Pezeshki S, Badalkhani S, Javadi A. Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator. Int J Adv Robot Syst 2012;9. https://doi.org/10.5772/51280.

[23] Wei B. Adaptive Control Design and Stability Analysis of Robotic Manipulators. Actuators 2018;7:89. https://doi.org/10.3390/act7040089.

[24] Dawson D, Quf† Z, Duffie J. Robust tracking control for robot manipulators: theory, simulation, and implementation. Robotica 1993;11:201–8. https://doi.org/10.1017/S0263574700016064.

[25] Shoja Majidabad S, Toosian Shandiz H. Discrete‐time based sliding‐mode control of robot manipulators. Int J Intell Comput Cybern 2012;5:340–58. https://doi.org/10.1108/17563781211255880.

[26] Durmuş B, Temurtaş H, Yumuşak N, Temurtaş F. A study on industrial robotic manipulator model using model based predictive controls. J Intell Manuf 2009;20:233–41. https://doi.org/10.1007/s10845-008-0221-2.

[27] Dai L, Yu Y, Zhai D-H, Huang T, Xia Y. Robust Model Predictive Tracking Control for Robot Manipulators With Disturbances. IEEE Trans Ind Electron 2021;68:4288–97. https://doi.org/10.1109/TIE.2020.2984986.

[28] Kern J, Marrero D, Urrea C. Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking. Processes 2023;11:3267. https://doi.org/10.3390/pr11123267.

[29] Liu Y, Chen B, Ma L, Yang S, Li R, Guo Y. A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network. Proc Inst Mech Eng Part I J Syst Control Eng 2023;237:1032–44. https://doi.org/10.1177/09596518221144796.

[30] Sheng L, Li W. Optimization Design by Genetic Algorithm Controller for Trajectory Control of a 3-RRR Parallel Robot. Algorithms 2018;11:7. https://doi.org/10.3390/a11010007.

[31] Baghli FZ, Lakhal Y, Kadi YA El. The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System. J Robot Control 2023;4:289–98. https://doi.org/10.18196/jrc.v4i3.17709.

[32] Patidar AK, Sinha SS, Mukherjee S. Dimensional Synthesis of Delta Manipulator Using Genetic Algorithm-Based Multi-objective Optimization, 2021, p. 609–24. https://doi.org/10.1007/978-981-15-4477-4_44.

[33] Alvarez-Ramirez J, Cervantes I, Kelly R. PID regulation of robot manipulators: stability and performance. Syst Control Lett 2000;41:73–83. https://doi.org/10.1016/S0167-6911(00)00038-4.

[34] Schwenzer M, Ay M, Bergs T, Abel D. Review on model predictive control: an engineering perspective. Int J Adv Manuf Technol 2021;117:1327–49. https://doi.org/10.1007/s00170-021-07682-3.

[35] Ziegler JG, Nichols NB. Optimum Settings for Automatic Controllers. J Dyn Syst Meas Control 1993;115:220–2. https://doi.org/10.1115/1.2899060.

[36] Cohen GH, Coon GA. Theoretical Consideration of Retarded Control. J Fluids Eng 1953;75:827–34. https://doi.org/10.1115/1.4015451.

[37] Skogestad S. Simple analytic rules for model reduction and PID controller tuning. J Process Control 2003;13:291–309. https://doi.org/10.1016/S0959-1524(02)00062-8.

[38] Åström KJ, Hägglund T. The future of PID control. Control Eng Pract 2001;9:1163–75. https://doi.org/10.1016/S0967-0661(01)00062-4.

[39] Unbehauen H. Control systems, robotics and automation-volume III: system analysis and control: classical approaches-III. EOLSS Publications; 2009.

[40] Visual Servoing, 2010, p. 407–67. https://doi.org/10.1007/978-1-84628-642-1_10.

[41] Mezura-Montes E, Coello CAC. An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 2008;37:443–73. https://doi.org/10.1080/03081070701303470.

[42] D MR, B K, P SD, C G, S NK, K K. PID Controller Optimization for a Three-Link Robot Manipulator Using RGA. 2024 Int. Conf. Integr. Emerg. Technol. Digit. World, IEEE; 2024, p. 1–5. https://doi.org/10.1109/ICIETDW61607.2024.10940004.

[43] Chandrababu Naik B, Ravi Kumar C V., Jweeg MJ, Tolephih MH, Hari Prasad M, Mohammed MN, et al. Designing a PID Controller Using Ant Colony Optimization and Implementing It with FPGA, 2025, p. 965–76. https://doi.org/10.1007/978-3-031-84628-1_82.

[44] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS’95. Proc. Sixth Int. Symp. Micro Mach. Hum. Sci., IEEE; n.d., p. 39–43. https://doi.org/10.1109/MHS.1995.494215.

[45] Goud H, Chandra Sharma P, Nisar K, Reazul Haque M, Asri Ag. Ibrahim A, Singh Yadav N, et al. Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System. Comput Mater Contin 2022;72:3481–92. https://doi.org/10.32604/cmc.2022.019764.

[46] Tanveer A, Ahmad SM. Genetic-Algorithm-Based Proportional Integral Controller (GAPI) for ROV Steering Control. INTERACT 2023, Basel Switzerland: MDPI; 2023, p. 4. https://doi.org/10.3390/engproc2023032004.

[47] Souza DA, Batista JG, dos Reis LLN, Júnior ABS. PID controller with novel PSO applied to a joint of a robotic manipulator. J Brazilian Soc Mech Sci Eng 2021;43:377. https://doi.org/10.1007/s40430-021-03092-4.

[48] Delavari H, Ghaderi R, Ranjbar NA, HosseinNia SH, Momani S. Adaptive fractional PID controller for robot manipulator. ArXiv Prepr ArXiv12062027 2012.

[49] Duan H, Wang D, Yu X. Novel approach to nonlinear PID parameter optimization using ant colony optimization algorithm. J Bionic Eng 2006;3:73–8. https://doi.org/10.1016/S1672-6529(06)60010-3.

[50] Herlambang T, Rahmalia D, Yulianto T. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for optimizing PID parameters on Autonomous Underwater Vehicle (AUV) control system. J Phys Conf Ser 2019;1211:012039. https://doi.org/10.1088/1742-6596/1211/1/012039.

[51] Kang Y, Li Z, Wang T. Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot. Math Probl Eng 2022;2022:1–10. https://doi.org/10.1155/2022/9453219.

[52] Suwoyo H, Tian Y, Adriansyah A, Ibnu Hajar MH, Zhou T. The ACA-based PID Controller for Enhancing a Wheeled-Mobile Robot. J FORTEI-JEERI 2020;1:19–23. https://doi.org/10.46962/forteijeeri.v1i2.15.

[53] Singh R, Bhushan B. Improved ant colony optimization for achieving self-balancing and position control for balancer systems. J Ambient Intell Humaniz Comput 2021;12:8339–56. https://doi.org/10.1007/s12652-020-02566-y.

[54] Astrom KJ, Hagglund T. PID Controllers: Theory, Design and Tuning. Research. Triangle Park NC Instrum Soc Amer 1995:241–3.