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.

A sliding mode controller integrated moment of inertia observer of web handling systems

“…The goal of every controller is to ensure the system's quality, such as the velocity and tension of the materials segment. That is especially true for the tension control of the materials segment because it affects the quality of the output product, leading to economic losses [5][6][7]. Due to their popularity and financial efficiency, proportionalintegral-derivative (PID) controllers are an effective way to solve the tension control problem.…”

Model‐free hierarchical control with fractional‐order sliding surface for multisection web machines

“…A new structure to make the WTSs more adaptable by exploiting decentralized PI controller tuning based on overlapping decomposition using Genetic Algorithms (GA). In, 14 a cascade control structure using a Generalized Predictive Controller (GPC) combined with an offline optimized PI controller relied on Bacterial Foraging Particle Swarm Optimization (BF‐PSO) algorithm, 15 which possesses the ability of handling nonlinear objectives and computation optimization to achieve efficient tension adjustment and eliminate arising disturbance of the single‐span WTSs. The significant impact of the recommended control scheme is believed to be superior when compared with the GPC structure combined with Real code GA‐tuned PI.…”