Hybrid Model Predictive Control of Chiller Systems via Collaborative Neurodynamic Optimization

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.