Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification
This paper presents a comparative study that explores the performance of various meta-heuristics employed for Optimal Signal Design, specifically focusing on estimating parameters in nonlinear systems. The study introduces the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Est...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/22/9085 |
| _version_ | 1797457815501012992 |
|---|---|
| author | Accacio Ferreira dos Santos Neto Murillo Ferreira dos Santos Mathaus Ferreira da Silva Leonardo de Mello Honório Edimar José de Oliveira Edvaldo Soares Araújo Neto |
| author_facet | Accacio Ferreira dos Santos Neto Murillo Ferreira dos Santos Mathaus Ferreira da Silva Leonardo de Mello Honório Edimar José de Oliveira Edvaldo Soares Araújo Neto |
| author_sort | Accacio Ferreira dos Santos Neto |
| collection | DOAJ |
| description | This paper presents a comparative study that explores the performance of various meta-heuristics employed for Optimal Signal Design, specifically focusing on estimating parameters in nonlinear systems. The study introduces the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology, which is originally derived from the well-known Particle Swarm Optimization (PSO) algorithm. Through a real-life case study involving an Autonomous Surface Vessel (ASV) equipped with three Degrees of Freedom (DoFs) and an aerial holonomic propulsion system, the effectiveness of different meta-heuristics is thoroughly evaluated. By conducting an in-depth analysis and comparison of the obtained results from the diverse meta-heuristics, this study offers valuable insights for selecting the most suitable optimization technique for parameter estimation in nonlinear systems. Researchers and experimental tests in the field can benefit from the comprehensive examination of these techniques, aiding them in making informed decisions about the optimal approach for optimizing parameter estimation in nonlinear systems. |
| first_indexed | 2024-03-09T16:28:23Z |
| format | Article |
| id | doaj.art-5892ce102d564b0598412de519033785 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:28:23Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5892ce102d564b0598412de5190337852023-11-24T15:05:17ZengMDPI AGSensors1424-82202023-11-012322908510.3390/s23229085Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter IdentificationAccacio Ferreira dos Santos Neto0Murillo Ferreira dos Santos1Mathaus Ferreira da Silva2Leonardo de Mello Honório3Edimar José de Oliveira4Edvaldo Soares Araújo Neto5Department of Electroelectronics, Federal Center of Technological Education of Minas Gerais (CEFET-MG), Leopoldina 36700-001, BrazilDepartment of Electroelectronics, Federal Center of Technological Education of Minas Gerais (CEFET-MG), Leopoldina 36700-001, BrazilFaculty of Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora 36036-900, BrazilFaculty of Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora 36036-900, BrazilFaculty of Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora 36036-900, BrazilSanto Antônio S.A., Hydroelectric Plant Santo Antônio, Porto Velho 76805-812, BrazilThis paper presents a comparative study that explores the performance of various meta-heuristics employed for Optimal Signal Design, specifically focusing on estimating parameters in nonlinear systems. The study introduces the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology, which is originally derived from the well-known Particle Swarm Optimization (PSO) algorithm. Through a real-life case study involving an Autonomous Surface Vessel (ASV) equipped with three Degrees of Freedom (DoFs) and an aerial holonomic propulsion system, the effectiveness of different meta-heuristics is thoroughly evaluated. By conducting an in-depth analysis and comparison of the obtained results from the diverse meta-heuristics, this study offers valuable insights for selecting the most suitable optimization technique for parameter estimation in nonlinear systems. Researchers and experimental tests in the field can benefit from the comprehensive examination of these techniques, aiding them in making informed decisions about the optimal approach for optimizing parameter estimation in nonlinear systems.https://www.mdpi.com/1424-8220/23/22/9085Optimal Signal Designparametric estimationmeta-heuristicsAutonomous Surface Vehicles |
| spellingShingle | Accacio Ferreira dos Santos Neto Murillo Ferreira dos Santos Mathaus Ferreira da Silva Leonardo de Mello Honório Edimar José de Oliveira Edvaldo Soares Araújo Neto Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification Sensors Optimal Signal Design parametric estimation meta-heuristics Autonomous Surface Vehicles |
| title | Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification |
| title_full | Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification |
| title_fullStr | Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification |
| title_full_unstemmed | Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification |
| title_short | Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification |
| title_sort | performance comparison of meta heuristics applied to optimal signal design for parameter identification |
| topic | Optimal Signal Design parametric estimation meta-heuristics Autonomous Surface Vehicles |
| url | https://www.mdpi.com/1424-8220/23/22/9085 |
| work_keys_str_mv | AT accacioferreiradossantosneto performancecomparisonofmetaheuristicsappliedtooptimalsignaldesignforparameteridentification AT murilloferreiradossantos performancecomparisonofmetaheuristicsappliedtooptimalsignaldesignforparameteridentification AT mathausferreiradasilva performancecomparisonofmetaheuristicsappliedtooptimalsignaldesignforparameteridentification AT leonardodemellohonorio performancecomparisonofmetaheuristicsappliedtooptimalsignaldesignforparameteridentification AT edimarjosedeoliveira performancecomparisonofmetaheuristicsappliedtooptimalsignaldesignforparameteridentification AT edvaldosoaresaraujoneto performancecomparisonofmetaheuristicsappliedtooptimalsignaldesignforparameteridentification |