Application of optimization heuristics in tuning decentralized PID controllers

This paper aims to make the tuning of PID Controllers in multivariable systems, using two optimization heuristics, namely: the GA (Genetic Algorithm) and the PSO (Particle Swarm Optimization). Two multivariable processes with two inputs and two outputs (TITO), in a decentralized control strategy, are analyzed. The first process is the Quadruple-Tank and the second process is the Wood-Berry Distillation Column. The PID tuning is modeled as an optimization problem, which cost function seeks to improve the dynamic response of the system, while forcing decoupling of the loops. Results shown that both GA and PSO were able to find good PID parameters in order to provide a reasonable dynamic behavior and a good loop decoupling.