Neural Network Model-Based Adaptive Control of a VAV-HVAC&R System

M. Ning; M. Zaheeruddin

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Neural Network Model-Based Adaptive Control of a VAV-HVAC&R System

机译：基于神经网络模型的VAV-HVAC＆R系统自适应控制

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摘要

A dynamic system model of a two-zone variable air volume heating, ventilation and air conditioning and refrigeration (VAV-HVAC&R) system is considered. The system model consists of two environmental zones, an HVAC system and a water-cooled vapor compression chiller. Five adaptive controllers were designed to achieve good tracking control of set points of zone air temperatures, discharge air temperature, chilled water supply temperature and static pressure of the VAV-HVAC&R system. The PI controller gains were updated online using adaptive neural networks and an auto-tuning algorithm. Simulation results show that adaptive PI control gave faster response and less overshoot compared to conventional constant gain PI control. The control responses tracked set-points closely and remained stable over a typical day simulation of building operation under variable load conditions.

机译：考虑了两区可变风量供暖，通风，空调和制冷（VAV-HVAC＆R）系统的动态系统模型。系统模型包括两个环境区域，一个HVAC系统和一个水冷式蒸气压缩冷却器。设计了五个自适应控制器，以实现对区域空气温度，排气温度，冷冻水供应温度和VAV-HVAC＆R系统的静压设定点的良好跟踪控制。使用自适应神经网络和自动调整算法在线更新PI控制器的增益。仿真结果表明，与传统的恒定增益PI控制相比，自适应PI控制具有更快的响应和更少的过冲。控制响应密切跟踪设定点，并且在可变负载条件下的典型建筑运行日模拟中保持稳定。

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《International journal of air-conditioning and refrigeration》 |2019年第1期|1950006.1-1950006.16|共16页
作者
M. Ning; M. Zaheeruddin;
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作者单位

Building, Civil and Environmental Engineering Concordia University, Montreal, Quebec, Canada;

Building, Civil and Environmental Engineering Concordia University, Montreal, Quebec, Canada;

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正文语种 eng
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关键词
Neural network; VAV system; HVACR system; optimal set points; adaptive control;

机译：神经网络;VAV系统;HVAC＆R系统;最佳设定点;自适应控制;

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Neural Network Model-Based Adaptive Control of a VAV-HVAC&R System

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