MODEL ORDER REDUCTION TECHNIQUES WITH APPLICATIONS IN ELECTRICAL AND ELECTRONICS ENGINEERING
Main Article Content
Abstract
Model order reduction Model order reduction is a significant method employed to reduce complex control system models keeping needed dynamic behaviour. This research paper discusses the concept and the effectiveness of model order reduction techniques on the basis of available literature and mathematical models. Dominating pole approximation is used to obtain a lower-order system by analysing a high-order transfer function model and reducing it. The findings display that reduced-order models can sustain stability and realistic system response and at the same time greatly minimize the calculation complexity.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
1. Deep, A.K., Raja, G.L. and Meena, G.D., 2025. Robust moments-matching load frequency control strategy for cyber–physical power system amid communication time delay. Applied Energy, 382, p.125251.
2. Mathew, A.T., Feliu-Talegon, D., Alkayas, A.Y., Boyer, F. and Renda, F., 2025. Reduced order modeling of hybrid soft-rigid robots using global, local, and state-dependent strain parameterization. The International Journal of Robotics Research, 44(1), pp.129-154.
3. Sarker, A., Ul Islam, T. and Islam, M.R., 2025. A review on recent trends of bioinspired soft robotics: actuators, control methods, materials selection, sensors, challenges, and future prospects. Advanced Intelligent Systems, 7(3), p.2400414.
4. Kong, X., Cai, B., Yu, Y., Yang, J., Wang, B., Liu, Z., Shao, X. and Yang, C., 2025. Intelligent diagnosis method for early faults of electric-hydraulic control system based on residual analysis. Reliability Engineering & System Safety, 261, p.111142.
5. Zhang, X., Gao, X., Duan, L., Gong, Q., Wang, Y. and Ao, X., 2025. A novel method for state of health estimation of lithium-ion batteries based on fractional-order differential voltage-capacity curve. Applied Energy, 377, p.124404.
6. Madani, S.S., Shabeer, Y., Fowler, M., Panchal, S., Chaoui, H., Mekhilef, S., Dou, S.X. and See, K., 2025. Artificial intelligence and digital twin technologies for intelligent lithium-ion battery management systems: A comprehensive review of state estimation, lifecycle optimization, and cloud-edge integration. Batteries, 11(8), p.298.
7. Godase, V., 2025, April. Advanced Neural Network Models for Optimal Energy Management in Microgrids with Integrated Electric Vehicles. In Proceedings of the International Conference on Trends in Material Science and Inventive Materials (ICTMIM-2025) DVD Part Number: CFP250J1-DVD.
8. Khosravi, N., Dowlatabadi, M. and Sabzevari, K., 2025. A hierarchical deep learning approach to optimizing voltage and frequency control in networked microgrid systems. Applied Energy, 377, p.124313.
9. Tang, Y.Q., Fang, W.Z., Zheng, C.Y. and Tao, W.Q., 2025. Applications of POD-based reduced order model to the rapid prediction of velocity and temperature in data centers. Applied Thermal Engineering, 263, p.125310.
10. Jabari, M. and Rad, A., 2025. Optimization of speed control and reduction of torque ripple in switched reluctance motors using metaheuristic algorithms based PID and FOPID controllers at the edge. Tsinghua Science and Technology, 30(4), pp.1526-1538.
11. Lin, X., Liu, J., Liu, Z., Gao, Y., Peretti, L. and Wu, L., 2025. Model-free current predictive control for PMSMs with ultra-local model employing fixed-time observer and extremum-seeking method. IEEE Transactions on Power Electronics.
12. Łach, Ł. and Svyetlichnyy, D., 2025. Advances in numerical modeling for heat transfer and thermal management: a review of computational approaches and environmental impacts. Energies, 18(5), p.1302.
13. Berberich, J. and Allgöwer, F., 2025. An overview of systems-theoretic guarantees in data-driven model predictive control. Annual Review of Control, Robotics, and Autonomous Systems, 8(1), pp.77-100.
14. Masri, S., Ashqar, H.I. and Elhenawy, M., 2025. Large language models (llms) as traffic control systems at urban intersections: A new paradigm. Vehicles, 7(1), p.11.
15. Ding, F., Liu, Z., Wang, Y., Liu, J., Wei, C., Nguyen, A.T. and Wang, N., 2025. Intelligent event triggered lane keeping security control for autonomous vehicle under DoS attacks. IEEE Transactions on Fuzzy Systems.
16. Duddeti, B.B. and Meena, V., 2025. Approximation of interconnected power system models using enhanced Schur method for balanced truncation and application to controller design. International Journal of Automation and Control, 19(5), pp.511-546.
17. Ravishankar, R. and de Weck, O.L., 2025. Bode Performance Comparison of Model Order Reduction Techniques on a Large Linear Dynamical System: Eigenmode Truncation and Krylov-Subspace Based Moment Matching. In AIAA SCITECH 2025 Forum (p. 2291).
18. Duddeti, B.B. and Sikander, A., 2026. A New Control Design Strategy Based on Dominant Pole Concept and System Reduction. Circuits, Systems, and Signal Processing, pp.1-52.