International Journal of Automation and Safety
Volume 4, Numéro 1, Pages 26-31
2026-07-23
Multi-objective Particle Swarm Optimization For Predictive Controller Design Of The Wood–berry Distillation Column
Authors : Boumegoura Rami . Bendib Riad . Menighed Kamel .
Abstract
Model Predictive Control (MPC) is a powerful advanced control strategy whose performance is heavily dependent on the proper tuning of its parameters, such as prediction and control horizons and weighting matrices. This tuning process often involves a trade-off between competing objectives, primarily reference tracking accuracy and control effort smoothness. This paper presents a systematic, multi-objective framework for the automatic tuning of an offset-free MPC controller. The tuning problem is formulated as a bi-objective optimization, minimizing both output tracking error and input movement. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed to efficiently explore the tuning parameter space and generate a Pareto front, representing the optimal trade-off between the two objectives. A knee-point selection method is then used to extract a single, balanced controller design from the Pareto-optimal set. The proposed methodology is demonstrated and validated on the classic Wood-Berry binary distillation column benchmark. Simulation results clearly illustrate the trade-off, showing that the selected knee-point controller achieves a favorable compromise, offering a significant reduction in control effort with only a minor sacrifice in tracking performance compared to the aggressive tuning extreme.
Keywords
Model Predictive Control (MPC) ; Multi-Objective Optimization ; Particle Swarm Optimization (PSO) ; MIMO Process Control ; Wood–Berry Distillation Column
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