| 摘要: |
| 在工业HVAC系统中，为了提高在大扰量下的控制精度，模型预测控制(MPC)被广泛应用。本文提出一种用于变风量(VAV)系统的非线性MPC。该非线性MPC采用具有外部输入的非线性自回归网络（NARX）和粒子群优化算法（PSO）。 NARX模型旨在预测VAV系统的受控参数（室温），PSO作为优化器，来获得VAV系统的最优控制变量。通过为成本函数的目标分配不同的权值，本文提出的非线性MPC能权衡VAV系统的控制精度和节能需求，以达到不同的控制效果。不同权值的两种方案在实验室的VAV系统中得到了验证，其中方案1仅考虑控制精度，方案2同时考虑了控制精度和节能性。分别将实验得到的两种方案的MPC的控制效果与基于PI控制器的定静压方法进行对比，实验结果表明：基于MPC的方案1可以实现室温稳定在设定值±0.5 ℃的控制精度范围；基于MPC的方案2显示出更好的节能特性，与定静压方法对比，节能率达到23.7%。 |
| 关键词: 非线性模型预测控制 变风量系统 神经网络模型 粒子群优化 |
| DOI： |
| 投稿时间：2018-08-22 修订日期：2018-12-10
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| 基金项目: |
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| Nonlinear Model Predictive Control for a Variable Air Volume Air-conditioning System |
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Chen Jiongde1, Wang Zixuan1, Yao Ye1, Wang Shaofan2, Feng Jingmei2, Zhao Pengsheng3
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| (1.Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University;2.School of Urban Construction and Safety Engineering, Shanghai Institute of Technology;3.Shanghai Geniuses Building Technology) |
| Abstract: |
| To improve the control precision of a control system under large and frequent perturbations, the model-based predictive control (MPC) has been developed for industrial heating, ventilation, and air conditioning (HVAC) systems. Because the thermal dynamic characteristics of HVAC systems are time-variant, nonlinear, and contain uncertainties during the control process, conventional controller methods face several challenges. In this study, a nonlinear MPC for variable air volume (VAV) systems is developed and investigated. A nonlinear autoregressive network with exogenous inputs (NARX) and particle swarm optimization (PSO) are employed for the nonlinear MPC. The NARX aims to predict the controlled parameter (room temperature) of the VAV system and the PSO serves as an optimizer to obtain the optimal control variables of the VAV system. By assigning different weight values to the objectives of the cost function, the proposed nonlinear MPC can generate different control solutions considering both the control precision and energy saving of a VAV system. Two scenarios of NARX-based MPC were investigated in an experimental VAV system. Scenario 1 only considers the control accuracy while scenario 2 considers both energy saving and control precision. The experimental results show that the NARX-based MPC under scenario 1 can achieve much higher control precision (±0.5 ℃) at room temperature than that of the constant static pressure (CSP) method with a PI controller. The NARX-based MPC under scenario 2 shows a better energy-saving characteristic, saving 23.7% of energy consumption, as compared to that of the CSP method with a PI controller. This work will contribute to the development of nonlinear MPC and its applications in HVAC systems. |
| Key words: model-based predictive control (MPC) variable-air-volume (VAV) system nonlinear autoregressive network with exogenous inputs (NARX) particle swarm optimization (PSO) |
