A Novel Model Reduction Approach for Linear Time-Invariant Systems via Whale Optimization Algorithm
- DOI
- 10.2991/978-94-6463-074-9_19How to use a DOI?
- Keywords
- Whale optimization algorithm; Model order reduction; integral squared error; single-input single-output systems
- Abstract
For the determination of accurate and stable decreasing-order model, Heuristic search method is used. Whale optimization algorithm is used to optimize stable higher order systems. By lowering the objective function (E) value, this approach builds the best reduced-order model. The First function determines measure of integral squared error between the original HOS step response and the decreasing-order model. The second term in the objective function assesses the reduced-order model’s ability to retain the original system’s full impulse response energy. By reducing objective function ‘E’, the suggested method ensures that the original system’s correctness, stability, and passivity are preserved in the decreasing-order model. The method’s validity is verified using eighth-order and ninth-order SISO systems. The results of integral squared error show that the suggested technique is superior to the existing decreasing methods that have been existing in literature.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - V. Nagababu AU - D. Vijay Arun AU - M. Siva Kumar AU - B. Dasu AU - R. Srinivasa Rao PY - 2022 DA - 2022/12/05 TI - A Novel Model Reduction Approach for Linear Time-Invariant Systems via Whale Optimization Algorithm BT - Proceedings of the International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES 2022) PB - Atlantis Press SP - 218 EP - 226 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-074-9_19 DO - 10.2991/978-94-6463-074-9_19 ID - Nagababu2022 ER -