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应用于参数识别最优信号设计的元启发式算法性能比较

Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification.

作者信息

Santos Neto Accacio Ferreira Dos, Santos Murillo Ferreira Dos, Silva Mathaus Ferreira da, Honório Leonardo de Mello, Oliveira Edimar José de, Neto Edvaldo Soares Araújo

机构信息

Department of Electroelectronics, Federal Center of Technological Education of Minas Gerais (CEFET-MG), Leopoldina 36700-001, Brazil.

Faculty of Engineering, Federal University of Juiz de Fora (UFJF), Juiz de Fora 36036-900, Brazil.

出版信息

Sensors (Basel). 2023 Nov 10;23(22):9085. doi: 10.3390/s23229085.

DOI:10.3390/s23229085
PMID:38005473
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10674472/
Abstract

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.

摘要

本文提出了一项比较研究，探讨用于最优信号设计的各种元启发式算法的性能，特别关注非线性系统中的参数估计。该研究引入了鲁棒次优激励信号生成与最优参数估计（rSOESGOPE）方法，该方法最初源自著名的粒子群优化（PSO）算法。通过一个涉及配备三个自由度（DoF）和空中完整推进系统的自主水面舰艇（ASV）的实际案例研究，对不同元启发式算法的有效性进行了全面评估。通过对不同元启发式算法获得的结果进行深入分析和比较，本研究为选择非线性系统参数估计最合适的优化技术提供了有价值的见解。该领域的研究人员和实验测试可以从这些技术的全面检验中受益，帮助他们就非线性系统中优化参数估计的最佳方法做出明智的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/f228b352102a/sensors-23-09085-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/d0d89054e961/sensors-23-09085-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/46f2a5d2ad5c/sensors-23-09085-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/e9095d6a35d3/sensors-23-09085-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/d098b96bed08/sensors-23-09085-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/36f53f3a3d0d/sensors-23-09085-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/d37af1341537/sensors-23-09085-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/6ca865991651/sensors-23-09085-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/f388e7a09824/sensors-23-09085-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/3ca336a6aefd/sensors-23-09085-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/f228b352102a/sensors-23-09085-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/d0d89054e961/sensors-23-09085-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/6e33a68c983c/sensors-23-09085-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/3dafc5599f98/sensors-23-09085-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/179ced5d1273/sensors-23-09085-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/47c79c78feb5/sensors-23-09085-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/46f2a5d2ad5c/sensors-23-09085-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/e9095d6a35d3/sensors-23-09085-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/d098b96bed08/sensors-23-09085-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/36f53f3a3d0d/sensors-23-09085-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/d37af1341537/sensors-23-09085-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/6ca865991651/sensors-23-09085-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/f388e7a09824/sensors-23-09085-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/3ca336a6aefd/sensors-23-09085-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/212a/10674472/f228b352102a/sensors-23-09085-g014.jpg

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本文引用的文献

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Sensors (Basel). 2021 Jan 15;21(2):571. doi: 10.3390/s21020571.
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A Robotic Cognitive Architecture for Slope and Dam Inspections.用于边坡和大坝检查的机器人认知架构。
Sensors (Basel). 2020 Aug 15;20(16):4579. doi: 10.3390/s20164579.
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Unmanned Surface Vehicle Simulator with Realistic Environmental Disturbances.无人水面艇模拟器与真实环境干扰。
Sensors (Basel). 2019 Mar 2;19(5):1068. doi: 10.3390/s19051068.
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Persistently-exciting signal generation for Optimal Parameter Estimation of constrained nonlinear dynamical systems.持续激励信号生成在约束非线性动力系统最优参数估计中的应用。
ISA Trans. 2018 Jun;77:231-241. doi: 10.1016/j.isatra.2018.03.024. Epub 2018 Apr 14.
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Optimal input design for multibody systems by using an extended adjoint approach.基于扩展伴随方法的多体系统最优输入设计
Multibody Syst Dyn. 2017;40(1):43-54. doi: 10.1007/s11044-016-9541-8. Epub 2016 Oct 5.