Finite Element-Informed Tunable Control for Robust Vibration Mitigation in Flexible 3D of Robotic Arm
Authors
Abia State Polytechnic, Aba, Nigeria (Nigeria)
Department of Telecommunication Engineering, Nnamdi Azikiwe University Awka (Nigeria)
Department of Computer Science, Faculty of Applied and Natural Science, Enugu State, University of Science and Technology (ESUT) Agbani (Nigeria)
Article Information
DOI: 10.51584/IJRIAS.2026.11080112
Subject Category: Computer Science
Volume/Issue: 11/8 | Page No: 1496-1510
Publication Timeline
Submitted: 2026-08-26
Accepted: 2026-08-31
Published: 2026-09-14
Abstract
This study presents the dynamic modeling and control of a clamped-free beam system using Proportional–Integral–Derivative (PID) control strategies, with a focus on comparing the performance of a Standard PID controller and an optimally Tuned PID controller. Flexible beam structures are widely used in robotic and precision engineering applications, where vibration suppression and accurate positioning are critical challenges due to inherent structural dynamics. A simulation-based approach was adopted to evaluate the effectiveness of both controllers using key performance metrics, including displacement response, velocity response, settling time, overshoot, Integral of Absolute Error (IAE), Steady-State Error (SSE), and Root Mean Square (RMS) control effort. The results demonstrate that the Standard PID controller exhibits significant oscillations, slower convergence, and higher accumulated tracking error due to insufficient damping of flexible modes. In contrast, the Tuned PID controller achieves superior performance by significantly reducing oscillations, minimizing steady-state error, and improving transient response characteristics. Quantitative analysis shows that the Tuned PID consistently yields lower IAE and SSE values across all joints, indicating enhanced tracking accuracy and improved overall system efficiency. However, this improved performance comes at the cost of increased control effort, highlighting a fundamental trade-off between control accuracy and actuator energy consumption. The findings confirm that proper tuning of PID parameters is essential for effective control of flexible systems. The study provides valuable insights into the design of efficient control strategies for vibration suppression in robotic manipulators and similar engineering applications.
Keywords
Clamped-Free Beam, PID Controller, Tuned PID, Vibration Suppression, Steady-State Error (SSE), Control Effort, Dynamic Modeling
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References
1. Ashagrie A, Salau AO, Weldcherkos T. Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller. Cogent Eng. 2021;8(1):1950105. [Google Scholar] [Crossref]
2. 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. [Google Scholar] [Crossref]
3. Barhaghtalab MH, Sepestanaki MA, Mobayen S, Jalilvand A, Fekih A, Meigoli V. Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators. Appl Soft Comput. 2023;149: 110970. [Google Scholar] [Crossref]
4. Chávez-Vázquez S, Lavín-Delgado JE, Gómez-Aguilar JF, Razo-Hernández JR, Etemad S, Rezapour S. Trajectory tracking of Stanford robot manipulator by fractional-order sliding mode control. Appl Math Model. 2023;120:436–62. [Google Scholar] [Crossref]
5. Harbor M.C, Eneh I.I. Ebere U.C. (2021). Precision control of autonomous vehicle under slip using ANN. International Journal of Research and Innovation in Applied Science (IJRIAS). Vol 6; Issue 9. https://rsisinternational.org/journals/ijrias/DigitalLibrary/volume-6-issue-9/62-68.pdf [Google Scholar] [Crossref]
6. Harbor M.C, Eneh I.I., Ebere U.C. (2021). Nonlinear dynamic control of autonomous vehicle under slip using improved back-propagation algorithm. International Journal of Research and Innovation in Applied Science (IJRIAS); Vol. 6; Issue 9; https://rsisinternational.org/journals/ijrias/DigitalLibrary/volume-6-issue-9/62-68.pdf [Google Scholar] [Crossref]
7. Harbor M.C, Eneh I.I., Ebere U.C. (2021). Nonlinear dynamic control of autonomous vehicle under slip using improved back-propagation algorithm. International Journal of Research and Innovation in Applied Science (IJRIAS); Vol. 6; Issue 9; https://rsisinternational.org/journals/ijrias/DigitalLibrary/volume-6-issue-9/62-68.pdf [Google Scholar] [Crossref]
8. Hazem ZB, Guler N, El Fezzani W. Study of inverse kinematics solution for a 5-axis mitsubishi rv-2aj robotic arm using deep reinforcement learning. In: Guler N, editor. Business sustainability with Artificial Intelligence (AI): challenges and opportunities, vol. 2. Cham: Springer Nature Switzerland; 2024. p. 381–93. [Google Scholar] [Crossref]
9. Krakhmalev O, Krakhmalev N, Gataullin S, Makarenko I, Nikitin P, Serdechnyy D, Liang K, Korchagin S. Mathematics model for 6-DOF joints manipulation robots. Mathematics. 2021;9(21):2828. [Google Scholar] [Crossref]
10. Mousa M, Al-Mahasneh A, Baniyounis M, Biesenbach R, Falkenhain J. Development of ANFIS controller for trajectory tracking control using ROBO2L MATLAB toolbox for KUKA industrial Robot via RSI. In: 2024 21st international multi-conference on systems, signals & devices (SSD). IEEE; 2024. p. 110–117. [Google Scholar] [Crossref]
11. Mukhtar M, Khudher D, Kalganova T. A control structure for ambidextrous robot arm based on Multiple Adaptive Neuro-Fuzzy Inference System. IET Control Theory Appl. 2021;15(11):1518–32. [Google Scholar] [Crossref]
12. Sahu VSDM, Samal P, Panigrahi CK. Modelling, and control techniques of robotic manipulators: A review. Mater Today Proc. 2022;56:2758–66. [Google Scholar] [Crossref]
13. Sochima V.E. Asogwa T.C., Lois O.N. Onuigbo C.M., Frank E.O., Ozor G.O., Ebere U.C. (2025)”; Comparing multi-control algorithms for complex nonlinear system: An embedded programmable logic control applications; DOI: http://doi.org/10.11591/ijpeds.v16.i1.pp212-224 [Google Scholar] [Crossref]
14. Sochima V.E. Asogwa T.C., Lois O.N. Onuigbo C.M., Frank E.O., Ozor G.O., Ebere U.C. (2025)”; Comparing multi-control algorithms for complex nonlinear system: An embedded programmable logic control applications; DOI: http://doi.org/10.11591/ijpeds.v16.i1.pp212-224 [Google Scholar] [Crossref]
15. Xu K, Wang Z. The design of a neural network-based adaptive control method for robotic arm trajectory tracking. Neural Comput Appl. 2023;35(12):8785–95. [Google Scholar] [Crossref]
16. Ye H, Wang D, Wu J, Yue Y, Zhou Y. Forward and inverse kinematics of a 5-DOF hybrid robot for composite material machining. Robot Comput Integr Manuf. 2020;65: 101961. [Google Scholar] [Crossref]
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