Supervisory GPC and evolutionary PI controller for web transport systems

Abstract

Web Transport Systems (WTS) are used in material processing industries to maintain constant tension on the transported material (web), that is required for assuring material integrity and to reduce production down-time. Maintaining constant web tension in the presence of disturbance is a challenging task. This investigation presents a cascade control design, with online supervisory web tension control using Generalized Predictive Controller (GPC) in major loop and offline evolutionary optimization based PI controller in the inner loop. Two algorithms are used to tune the inner PI controller: Real coded Genetic Algorithm (RGA) and Bacterial Foraging Particle Swarm Optimization (BF-PSO), due to their ability to solve non-linear optimization problems and convergence to global optimum. Our results indicate that the GPC –BF-PSO cascaded control design shows better performance by regulating tension without violating physical constraints in the presence of process and external disturbances, when compared to GPC – RGA cascaded control design.