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

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mathematics (Basel) Ročník 12; číslo 11; s. 1625
Hlavní autoři: Sanin-Villa, Daniel, Rodriguez-Cabal, Miguel Angel, Grisales-Noreña, Luis Fernando, Ramirez-Neria, Mario, Tejada, Juan C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2024
Témata:
Accuracy
Algorithms
Automation
Business metrics
Comparative analysis
Confidence intervals
continuous genetic algorithm
Control algorithms
Controllers
dynamic model
Dynamical systems
Efficiency
Engineering
Errors
Fuzzy control
Fuzzy logic
Genetic algorithms
Heuristic methods
Heuristic programming
inverted pendulum
Investigations
Mathematical models
metaheuristic algorithms
Methods
Optimization algorithms
Optimization techniques
Parameter estimation
Particle swarm optimization
Pendulum
Pendulums
Performance evaluation
Physical properties
Predictive control
Proportional integral derivative
Robotics
Statistical analysis
Surveillance
System dynamics
Unmanned aerial vehicles
Mexico
ISSN:2227-7390, 2227-7390
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math12111625