Hybrid Model Predictive Control of Chiller Systems via Collaborative Neurodynamic Optimization

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 7; pp. 9539 - 9547
• Main Authors: Chen, Zhongying, Wang, Jun, Han, Qing-Long
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Chiller system; Chillers; collaborative neurodynamic optimization; Control systems; Effectiveness; Heuristic methods; HVAC; HVAC system; Mixed integer; model predictive control; Neural networks; Neurodynamics; Optimization; Power consumption; Power demand; Predictive control; Space heating; Thermodynamics
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3383908

This article addresses the hybrid model predictive control of chiller systems via collaborative neurodynamic optimization. A mixed-integer optimization problem is formulated for the model predictive control of chiller systems to minimize power consumption, subject to various constraints including thermodynamic and energy-conservation constraints. It is then decomposed into a global and a binary optimization subproblem. A collaborative neurodynamic optimization approach is proposed to solve the subproblems sequentially. The approach is based on multiple pairs of projection neural networks and discrete Hopfield networks, assisted with a metaheuristic rule. The effectiveness of the approach is demonstrated through experiments based on the parameters and specifications of a chiller system.