Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relati...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-05, Vol.22 (11), p.4050
Main Authors: Rodríguez-Abreo, Omar, Rodríguez-Reséndiz, Juvenal, Álvarez-Alvarado, José Manuel, García-Cerezo, Alfonso
Format: Article
Language:English
Subjects:
Algorithms
Cuckoo Search
DC motor
Dynamic response
Grey Wolf Optimizer
Heuristic
Jaya
metaheuristic
Methods
Motors
Parameter estimation
Parameter identification
Parameter modification
Random variables
Search algorithms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterations.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22114050