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

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.