Optimal Tuning of a Bounded e-Modified Adaptive Control Law using a Particle Swarm Optimization algorithm

Olga Lidia Jiménez-Morales; Diego  Tristán-Rodriguez; Rubén  Garrido; Efrén  Mezura-Montes

doi:10.29057/icbi.v11iEspecial2.10713

Olga Lidia Jiménez-Morales Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional https://orcid.org/0000-0003-2106-4946
Diego Tristán-Rodriguez Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional https://orcid.org/0009-0004-4856-7222
Rubén Garrido Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional https://orcid.org/0000-0002-6227-2779
Efrén Mezura-Montes Centro de Inteligencia Artificial de la Universidad Veracruzana https://orcid.org/0000-0002-1565-5267

DOI: https://doi.org/10.29057/icbi.v11iEspecial2.10713

Keywords: Optimization, PSO, DC servomotor, adaptive, control

Abstract

This paper presents the gain tuning of an adaptive control law by means of Particle Swarm Optimization (PSO). The restrictions imposed on the particles in the PSO are obtained from the stability analysis of the adaptive control law. In this way, the PSO produces particles associated with optimal gains that simultaneously guarantee closed-loop stability and the minimization of the Fitness Function. The adaptive controller employs the velocity and acceleration of the desired trajectory signal for constructing the regressor vector used in the updated law. In addition, a new bounding technique is proposed for the estimated parameters allowing them to remain within certain prescribed limits. The performance of the adaptive law tuned using the PSO is evaluated by experiments on a low-cost servo system.

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