Optimal Weighting Factor Design of Finite Control Set Model Predictive Control Based on Multiobjective Ant Colony Optimization

• Published in: IEEE transactions on industrial electronics (1982) Vol. 71; no. 7; pp. 1 - 11
• Main Authors: Hu, Linqiang, Lei, Wanjun, Zhao, Jiaqi, Sun, Xing
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Ant colony optimization (ACO); Capacitors; current balance control; dc–dc converter; Design factors; Genetic algorithms; model predictive control (MPC); Multiple objective analysis; Optimization; Particle swarm optimization; Power converters; Predictive control; Predictive models; Switches; Tuning; Weighting; weighting factor (WF)
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2023.3301534

In this article, an improved multiobjective ant colony optimization (ACO) algorithm is proposed to design the weighting factors (WFs) in the model predictive control of power converters. First, the principle of the multiobjective ACO algorithm is introduced. Then, the WF design process based on the multiobjective ACO algorithm is given in both the single-function mode and the Pareto mode. Finally, improvement measures are proposed for the multiobjective ACO algorithm to reduce the calculation and accelerate the convergence. Simulations and experiments are carried out on a parallel three-level dc-dc converter. The results show that the proposed method is faster and less-computational than the traditional ACO algorithm, and is more accurate than the particle swarm optimization algorithm. With the proposed method, higher solution diversity and smaller control error can be achieved. In addition, the proposed method can also be used for WF online tuning, which will bring more benefits when the converter parameters are mismatched.