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Data Driven Learning Based Robust Control for Nonlinear Electromechanical Systems: A Case Study on SRV02

Authors

  • Chandar Kumar

    Department of Electrical Engineering, Faculty of Engineering and Applied Science, DHA Suffa University, Karachi – Pakistan
    Author

  • Dur Muhammad Soomro

    Department of Electrical Engineering, Faculty of Engineering and Applied Science, DHA Suffa University, Karachi – Pakistan
    Author
    https://orcid.org/0000-0001-5436-8482

  • Najeeb Ur Rehman Malik

    Faculty of Computing Information and Technology, DHA Suffa University, Karachi – Pakistan
    Author

DOI:

https://doi.org/10.22581/muet1982.0491

Keywords:

Neural network-based ILSMC , SRV02 motor, Iterative learning control, Sliding mode control, robust tracking, electromechanical systems

Abstract

In this paper, a data-driven neural network-based iterative learning sliding mode control (NN-ILSMC) scheme is proposed for precise position and velocity control of the SRV02 motor-driven system. Controlling electromechanical systems like the SRV02, which exhibit second-order nonlinear behavior under significant parametric uncertainties, remains challenging in industrial applications, particularly when model inaccuracies degrade control performance. Traditional model-based ILSMC requires accurate system identification, which limits its effectiveness under high levels of uncertainty. This paper develops and validates a novel NN-ILSMC strategy that leverages neural network approximation to enhance learning from tracking errors, eliminating the need for explicit mathematical models. The proposed control approach is tested against model-based ILSMC using a Quanser SRV02 DC motor system subject to 30% parametric uncertainty, Coulomb friction, and time-varying external disturbances. Monte Carlo simulations with 20 trials and 50 learning iterations are conducted to assess performance using position and velocity RMSE metrics. Results demonstrate that the proposed NN-ILSMC achieves an 86.3% reduction in position RMSE and an 87.6% reduction in velocity RMSE compared to the model-based ILSMC. The NN-based data-driven approach provides superior accuracy, robustness, and practical scalability, making it highly suitable for real-time industrial automation systems where dynamic uncertainties and modeling challenges are prevalent.

 

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Published

2025-10-28

Issue

Vol. 44 No. 4 (2025): October Issue

Section

Articles

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Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Data Driven Learning Based Robust Control for Nonlinear Electromechanical Systems: A Case Study on SRV02. (2025). Mehran University Research Journal of Engineering and Technology, 44(4), 186-197. https://doi.org/10.22581/muet1982.0491

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