In response to the problem that the traditional air-conditioning refrigeration control system is prone to os-cillation and cannot achieve overall system performance optimization,this paper proposed a nonlinear predictive control strategy for air-conditioning refrigeration.The optimization objective function was designed to meet the building cooling demand while improving the overall energy efficiency of the system as much as possible.To solve the contradictory re-lationship between the above two optimization objectives,an optimization objective weight adaptive module is designed using fuzzy logic to find the weight factor optimal solution in real time.In order to solve the difficult problem of online optimization of nonlinear systems,this paper proposed a nonlinear rolling optimization algorithm based on neural network,using neural network as the feedback optimization controller,and using the system optimization objective function as the online optimization performance index,combining Euler-Lagrange method and stochastic gradient descent method for on-line optimization of controller weights and thresholds.The algorithm is computationally small,occupies moderate storage space,and facilitates the use of low-cost field controllers for predictive control of air-conditioning refrigeration.The sim-ulation experimental results show that the predictive control strategy proposed in this paper improves the average energy efficiency ratio of the system by about 32.5％compared with the proportional-integral-derivative(PID)control without the addition of the optimal objective function weight adaptive module;after performing the optimal objective function weight adaptive optimization search,the average energy efficiency of the system improves by about 39.43％.
维普
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