scholarly journals A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3608
Author(s):  
Yang Yuan ◽  
Neng Zhu ◽  
Haizhu Zhou ◽  
Hai Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Water System ◽  
Energy Performance ◽  
Effective Control ◽  
New Model ◽  
Chilled Water ◽  
Chilled Water System ◽  
Hydraulic Oscillation

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.

Application of Neural Network for Nonlinear Predictive Control

2012 ◽  
Vol 562-564 ◽  
pp. 1964-1967 ◽  
Author(s):  
Zhi Cheng Xu ◽  
Bin Zhu ◽  
Qing Bin Jiang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Nonlinear Process ◽  
Effective Control ◽  
Nonlinear Predictive Control ◽  
The Stability

A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.

EXERGOECONOMIC ANALYSIS OF A CHILLED WATER SYSTEM IN A SHOPPING MALL

2020 ◽  
Author(s):  
Renata Portela de Abreu ◽  
Victor Hugo Lobo Correia ◽  
Adriano Marques ◽  
monica carvalho
Keyword(s):  
Water System ◽  
Shopping Mall ◽  
Chilled Water ◽  
Chilled Water System ◽  
Exergoeconomic Analysis

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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