Content area

Abstract

This paper uses a nature-inspired optimization algorithm to discuss the automatic selection of control structure parameters. The commonly used quality indicators are presented and analyzed for the optimization process of the control system. Moreover, the possibilities of formulating objective functions for nature-inspired optimization algorithms that can be successfully used to solve multi-objective constrained optimization problems are presented. The proposed general methodology was presented and discussed in detail using an example, which is published in the open-source repository Mathwork FileExchange. Theoretical aspects are validated in the case studies for automatic tuning of the hysteresis and PI controller.

Details

1009240
Business indexing term
Title
Objective Function Formulation to Optimize Control Structure Parameters Using Nature-Inspired Optimization Algorithms
Publication title
Volume
8
Issue
3
First page
79
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
e-ISSN
25715577
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-06-11
Milestone dates
2025-05-01 (Received); 2025-06-07 (Accepted)
Publication history
 
 
   First posting date
11 Jun 2025
ProQuest document ID
3223869790
Document URL
https://www.proquest.com/scholarly-journals/objective-function-formulation-optimize-control/docview/3223869790/se-2?accountid=208611
Copyright
© 2025 by the authors. Published by MDPI on behalf of the International Institute of Knowledge Innovation and Invention. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-06-25
Database
ProQuest One Academic