Comparative Studies on Decentralized Multiloop PID Controller Design Using Evolutionary Algorithms
This work addresses controller tuning for multivariable systems, but it is incremental as it applies existing evolutionary methods to a known problem without new paradigms.
The paper tackled the design of decentralized PID controllers for multivariable systems by tuning gains with three evolutionary algorithms (GA, ES, CA) to minimize a weighted error and control output metric, reporting simulation comparisons on four benchmark processes.
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.