Análisis comparativo de técnicas de optimización para la sintonización de controladores PID adaptativos

Abstract

This work presents a comparative analysis of four optimization techniques: genetic algorithms (AG), particle swarm optimization (PSO), Monte Carlo simulations (MC) and mean-variance mapping optimization (MVMO); using them to tune PID controllers in first order plants with time delay.

To make the comparison, the number of iterations required to converge, the execution time of the algorithms and performance indices ISE and TVu are taken as a reference. According to the results obtained from these parameters, one of the optimization methods is selected to be applied in tuning of adaptive control schemes in two non-linear chemical plants.

The adaptive schemes proposed for the case studies correspond to the type of control for gain scheduling, specifically continuous adjustment of controller gains, obtaining tuning equations based on optimization by average variance mapping for each process. In this way the parameters of the controllers are updated following the dynamics of the process, so the system operates in the desired conditions