Comparative Studies on Decentralized Multiloop PID Controller Design Using Evolutionary Algorithms

TL;DR

This paper compares the effectiveness of three evolutionary algorithms in designing decentralized multiloop PID controllers for multivariable systems, optimizing both tracking performance and control effort.

Contribution

It introduces a systematic framework for tuning decentralized PID controllers using GA, ES, and CA, with comprehensive simulation comparisons on benchmark processes.

Findings

GA outperforms ES and CA in tracking accuracy

CA provides the best balance between error minimization and control effort

All algorithms successfully achieve desired control objectives

Abstract

Decentralized PID controllers have been designed in this paper for simultaneous tracking of individual process variables in multivariable systems under step reference input. The controller design framework takes into account the minimization of a weighted sum of Integral of Time multiplied Squared Error (ITSE) and Integral of Squared Controller Output (ISCO) so as to balance the overall tracking errors for the process variables and required variation in the corresponding manipulated variables. Decentralized PID gains are tuned using three popular Evolutionary Algorithms (EAs) viz. Genetic Algorithm (GA), Evolutionary Strategy (ES) and Cultural Algorithm (CA). Credible simulation comparisons have been reported for four benchmark 2x2 multivariable processes.