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

Accacio Ferreira Dos Santos Neto1, Murillo Ferreira Dos Santos1, Mathaus Ferreira da Silva2

  • 1Department of Electroelectronics, Federal Center of Technological Education of Minas Gerais (CEFET-MG), Leopoldina 36700-001, Brazil.

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Summary
This summary is machine-generated.

This study compares meta-heuristics for optimal signal design in nonlinear systems. The Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology, based on Particle Swarm Optimization (PSO), was evaluated using an Autonomous Surface Vessel (ASV) case study.

Keywords:
Autonomous Surface VehiclesOptimal Signal Designmeta-heuristicsparametric estimation

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Area of Science:

  • Robotics and Control Systems
  • Computational Intelligence
  • Signal Processing

Background:

  • Parameter estimation in nonlinear systems is crucial for accurate modeling and control.
  • Meta-heuristic algorithms offer powerful tools for complex optimization problems.
  • Optimal signal design enhances the efficiency and robustness of parameter estimation.

Purpose of the Study:

  • To comparatively evaluate the performance of various meta-heuristics for optimal signal design in nonlinear systems.
  • To introduce and assess the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology.
  • To provide insights for selecting optimal meta-heuristics for parameter estimation tasks.

Main Methods:

  • Implementation of the rSOESGOPE methodology, derived from Particle Swarm Optimization (PSO).
  • Comparative analysis of diverse meta-heuristic algorithms.
  • Application to a real-life case study of an Autonomous Surface Vessel (ASV) with three Degrees of Freedom (DoFs).

Main Results:

  • Demonstration of the effectiveness of different meta-heuristics in optimizing parameter estimation for nonlinear systems.
  • Evaluation of the rSOESGOPE methodology's performance in a practical ASV scenario.
  • Identification of superior meta-heuristics for the specific problem context.

Conclusions:

  • The study offers valuable insights into selecting appropriate meta-heuristics for parameter estimation in nonlinear systems.
  • The rSOESGOPE methodology and comparative analysis aid researchers in making informed decisions.
  • Findings support the advancement of optimal signal design techniques in autonomous systems.