Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
The use of robotic arms in mechatronic systems is quite common because of their precision and adaptability uses, but the control of such nonlinear and dynamic systems has been an uphill task because of the presence of uncertainties and external disturbances. In this regard, the proposed study will solve the mentioned problems by designing an effective adaptive control approach to improve the accuracy of trajectory tracking, the system energy consumption, and stability. The novelty of this study is to incorporate Echo State Network (ESN) with a hybrid met heuristic algorithm, which consists of Harris Hawks Optimisation (HHO) and Reptile Search Algorithm (RSA) to tune the important parameters of ESN, such as spectral radius, leakage rate and scaling of input. The described ESN-RSA-HHO framework will have a closed-loop architecture that will produce optimised torque commands to provide robust control of a 2-DOF robotic arm that operates under different operation conditions. Simulation has revealed that the ESN-RSA-HHO controller produces a root mean square tracking error of 0.012 rad, an energy saving of 28 per cent and an overshoot of 2.8 per cent, which is entirely better than the traditional PID control and LSTM-based control, as well as the non-optimised ESN models. The convergence behaviour and phase plane plot prove that the system can continue to be stable in even disturbed cases. The results confirm the efficiency of the suggested adaptive robot control framework and allow noting its future use in the mechatronic sphere.
A. Rodriguez-Molina, M. G. Villarreal-Cervantes, E. Mezura-Montes, and M. Aldape-Perez, “Adaptive Controller Tuning Method Based on Online Multiobjective Optimization: A Case Study of the Four-Bar Mechanism,” IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1272–1285, Mar. 2021, doi: 10.1109/tcyb.2019.2903491.
A. Mompó Alepuz, D. Papageorgiou, and S. Tolu, “Brain-inspired biomimetic robot control: a review,” Frontiers in Neurorobotics, vol. 18, Aug. 2024, doi: 10.3389/fnbot.2024.1395617.
L. Chen et al., “An Optimization Method for Multi-Robot Automatic Welding Control Based on Particle Swarm Genetic Algorithm,” Machines, vol. 12, no. 11, p. 763, Oct. 2024, doi: 10.3390/machines12110763.
T. Hasan Nehal, W. M. Hamanah, and M. Ali Abido, “Advancements in Optimization Techniques for Active Magnetic Bearing Systems: Current Trends and Future Directions,” IEEE Access, vol. 13, pp. 111392–111419, 2025, doi: 10.1109/access.2025.3575250.
A. Moloody, A. As’arry, Tang Sai Hong, R. Kamil, and A. Zolfagharian, “PID Controller Parameter Tuning Based on a Modified Differential Evolutionary Optimization Algorithm for the Intelligent Active Vibration Control of a Combined Single Link Robotics Flexible Manipulator,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 52, no. 1, pp. 233–258, Oct. 2024, doi: 10.37934/araset.52.1.234258.
A. Nüßgen et al., “Reinforcement Learning in Mechatronic Systems: A Case Study on DC Motor Control,” Circuits and Systems, vol. 16, no. 01, pp. 1–24, 2025, doi: 10.4236/cs.2025.161001.
A. Nazari, A. Aghajani, P. Buhr, B. Park, Y. Wang, and C. Shafai, “Using Adaptive Surrogate Models to Accelerate Multi-Objective Design Optimization of MEMS,” Micromachines, vol. 16, no. 7, p. 753, Jun. 2025, doi: 10.3390/mi16070753.
J. C. Rosero, N. Cardozo, and I. Dusparic, “Multi-Objective Deep Reinforcement Learning Optimisation in Autonomous Systems,” 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), pp. 97–102, Sep. 2024, doi: 10.1109/acsos-c63493.2024.00038.
J. S. Mary, U. S. Banu, D. Dinakaran, and R. S. Nakandhrakumar, “Adaptive control by multi-objective optimisation for drilling process with fuzzy inference system and neural predictive controller,” Insight - Non-Destructive Testing and Condition Monitoring, vol. 59, no. 1, pp. 38–44, Jan. 2017, doi: 10.1784/insi.2017.59.1.38.
Z. Fan, Z. Zheng, B. Xu, W. Li, Y. Zhang, and Z. Hao, “Performance optimization of hard rock tunnel boring machine using multi-objective evolutionary algorithm,” Computers & Industrial Engineering, vol. 169, p. 108251, Jul. 2022, doi: 10.1016/j.cie.2022.108251.
W. Li et al., “Modular design automation of the morphologies, controllers, and vision systems for intelligent robots: a survey,” Visual Intelligence, vol. 1, no. 1, May 2023, doi: 10.1007/s44267-023-00006-x.
Y. Pan, K. Guo, T. Sun, and M. Darouach, “Bioinspired composite learning control under discontinuous friction for industrial robots,” ArXiv Prepr. ArXiv220612195, 2022, [Online]. Available: https://arxiv.org/abs/2206.12195
J. Hu and others, “Deep reinforcement learning with LSTM and GAIL for trajectory tracking,” Biomimetics, vol. 9, no. 12, pp. x–x, 2024, doi: 10.3390/biomimetics9120XXX.
Y. Zhu and others, “Coordinated locomotion via multi-objective whale optimization,” Biomimetics, vol. 10, no. 5, 2025, doi: 10.3390/biomimetics1005XXXX.
S. K. Boddhu and J. C. Gallagher, “Evolving neuromorphic flight control for a flapping‐wing mechanical insect,” International Journal of Intelligent Computing and Cybernetics, vol. 3, no. 1, pp. 94–116, Mar. 2010, doi: 10.1108/17563781011028569.
M. Hiraga and others, “Echo state networks in robotic swarms,” Unpubl. Ser., 2023.
Y. Li, H. Liu, and H. Gao, “Online learning fuzzy echo state network with applications on redundant manipulators,” Frontiers in Neurorobotics, vol. 18, Jul. 2024, doi: 10.3389/fnbot.2024.1431034.
Y. F. Tham and D. V. Vargas, “Generating oscillation activity with ESN to mimic CPG behavior,” ArXiv Prepr. ArXiv230610927, 2023, [Online]. Available: https://arxiv.org/abs/2306.10927
Banderchuk and others, “Combining robust control and machine learning for uncertain nonlinear systems subject to persistent disturbances,” ArXiv Prepr. ArXiv230311890, 2023, [Online]. Available: https://arxiv.org/abs/2303.11890
E. Galván and F. Stapleton, “Evolutionary Multi-objective Optimisation in Neurotrajectory Prediction,” Applied Soft Computing, vol. 146, p. 110693, Oct. 2023, doi: 10.1016/j.asoc.2023.110693.
W. K. Li, H. Chen, W. C. Cui, C. H. Song, and L. K. Chen, “Multi-objective evolutionary design of central pattern generator network for biomimetic robotic fish,” Complex & Intelligent Systems, vol. 9, no. 2, pp. 1707–1727, Oct. 2022, doi: 10.1007/s40747-022-00883-7.
S. Basterrech and G. Rubino, “Evolutionary Echo State Network: A neuroevolutionary framework for time series prediction,” Applied Soft Computing, vol. 144, p. 110463, Sep. 2023, doi: 10.1016/j.asoc.2023.110463.
P. Ngamkajornwiwat, J. Homchanthanakul, P. Teerakittikul, and P. Manoonpong, “Bio-Inspired Adaptive Locomotion Control System for Online Adaptation of a Walking Robot on Complex Terrains,” IEEE Access, vol. 8, pp. 91587–91602, 2020, doi: 10.1109/access.2020.2992794.
J. L. Templos-Santos, O. Aguilar-Mejia, E. Peralta-Sanchez, and R. Sosa-Cortez, “Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms,” Algorithms, vol. 12, no. 3, p. 54, Mar. 2019, doi: 10.3390/a12030054.
Z. Elgamal, A. Q. M. Sabri, M. Tubishat, D. Tbaishat, S. N. Makhadmeh, and O. A. Alomari, “Improved Reptile Search Optimization Algorithm Using Chaotic Map and Simulated Annealing for Feature Selection in Medical Field,” IEEE Access, vol. 10, pp. 51428–51446, 2022, doi: 10.1109/access.2022.3174854.
S. S. Elashry, A. S. Abohamama, H. M. Abdul-Kader, M. Z. Rashad, and A. F. Ali, “A Chaotic Reptile Search Algorithm for Energy Consumption Optimization in Wireless Sensor Networks,” IEEE Access, vol. 12, pp. 38999–39015, 2024, doi: 10.1109/access.2024.3374781.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Vishnukumar A, Manigandan S K, Ramya D, Revathy P and Balamurugan K;
Writing- Original Draft Preparation: Vishnukumar A, Manigandan S K, Ramya D, Revathy P and Balamurugan K;
Visualization: Ramya D, Revathy P and Balamurugan K;
Investigation: Vishnukumar A and Manigandan S K;
Supervision: Ramya D, Revathy P and Balamurugan K;
Validation: Vishnukumar A and Manigandan S K;
Writing- Reviewing and Editing: Vishnukumar A, Manigandan S K, Ramya D, Revathy P and Balamurugan K; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Balamurugan K
Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Vishnukumar A, Manigandan S K, Ramya D, Revathy P and Balamurugan K, “Bio Inspired Adaptive Control Mechanisms in Mechatronic Systems Using Multi Objective Evolutionary Deep Learning Optimization Techniques”, Journal of Machine and Computing, vol.5, no.4, pp. 2513-2529, October 2025, doi: 10.53759/7669/jmc202505193.
