A Comparative Analysis of Metaheuristic Algorithms for Enhanced Parameter Estimation on Inverted Pendulum System Dynamics

Daniel Sanin-Villa, Miguel Angel Rodriguez-Cabal, Luis Fernando Grisales-Noreña, Mario Ramirez-Neria () and Juan C. Tejada
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Daniel Sanin-Villa: Department of Mechatronics and Electromechanics, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
Miguel Angel Rodriguez-Cabal: Department of Mechatronics and Electromechanics, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
Luis Fernando Grisales-Noreña: Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile
Mario Ramirez-Neria: InIAT Instituto de Investigación Aplicada y Tecnología, Universidad Iberoamericana, Ciudad de México 01219, Mexico
Juan C. Tejada: Artificial Intelligence and Robotics Research Group (IAR), Universidad EIA, Envigado 055428, Colombia

Mathematics, 2024, vol. 12, issue 11, 1-17

Abstract: This research explores the application of metaheuristic algorithms to refine parameter estimation in dynamic systems, with a focus on the inverted pendulum model. Three optimization techniques, Particle Swarm Optimization (PSO), Continuous Genetic Algorithm (CGA), and Salp Swarm Algorithm (SSA), are introduced to solve this problem. Through a thorough statistical evaluation, the optimal performance of each technique within the dynamic methodology is determined. Furthermore, the efficacy of these algorithms is demonstrated through experimental validation on a real prototype, providing practical insights into their performance. The outcomes of this study contribute to the advancement of control strategies by integrating precisely estimated physical parameters into various control algorithms, including PID controllers, fuzzy logic controllers, and model predictive controllers. Each algorithm ran 1000 times, and the SSA algorithm achieved the best performance, with the most accurate parameter estimation with a minimum error of 0.01501 N m and a mean solution error of 0.01506 N m. This precision was further underscored by its lowest standard deviation in RMSE (1.443 99 × 10 −6 N m), indicating remarkable consistency across evaluations. The 95% confidence interval for error corroborated the algorithm’s reliability in deriving optimal solutions.

Keywords: parameter estimation; metaheuristic algorithms; dynamic model; inverted pendulum; particle swarm optimization; continuous genetic algorithm; salp swarm algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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