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

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 in

SUBMITTER: Santos Neto AFD 

PROVIDER: S-EPMC10674472 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature