A robust model predictive control strategy for improving the control performance of air-conditioning systems

Huang, Gongsheng; Xu, Xin

doi:10.1016/j.enconman.2009.06.014

Cited by 78 publications

(37 citation statements)

References 19 publications

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“…Most research efforts have focused on optimal operating strategies in the context of VAV systems (optimal air flow rate and air temperature set-points) [12], [13], thermal-storage (optimal charging and discharging schemes) [14], and load-side analysis (optimal zone temperature and ventilation set-points) [14], [15]. The common underlying theme in these efforts is the supervisory control of the HVAC system or its constituent subsystems.…”

Section: Predictive Control Schemesmentioning

confidence: 99%

Predictive control of complex hydronic systems

Chandan

Mishra

Alleyne

2010

Proceedings of the 2010 American Control Conference

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The control of hydronic building systems is considered in this paper, using a simulated chilled water system as a case study. In this context, model-based predictive control strategies have been proposed and compared with traditional feedback control schemes. The advantages and limitations associated with these methodologies has been demonstrated. The cornerstone of this work is the development of a novel, distributed predictive scheme which provides the best compromise in the multidimensional evaluation framework of 'regulation', 'optimality', 'reliability' and 'computational complexity'.

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Section: Predictive Control Schemesmentioning

confidence: 99%

Predictive control of complex hydronic systems

Chandan

Mishra

Alleyne

2010

Proceedings of the 2010 American Control Conference

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“…This process is repeated at each time step and, as it is based on optimal control, it inevitably involves a forward simulation of the system states based on a model of the system. This has proved to be a highly useful control design methodology in a number of applications (see for example [1], [2], [3], [4], [5], [6], [7]). However, if the cost involves an unknown reference signal, this signal must be estimated in order to be able to evaluate the cost through forward simulation.…”

Section: Introductionmentioning

confidence: 98%

Adaptive time horizon optimization in model predictive control

Droge

Egerstedt

2011

Proceedings of the 2011 American Control Conference

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Whenever the control task involves the tracking of a reference signal the performance is typically improved if one knows the future behavior of this reference. However, in many applications, this is typically not the case, e.g., when the reference signal is generated by a human operator, and a remedy to this can be to try and model the reference signal over a short time horizon. In this paper, we address the problem of selecting this horizon in an adaptive fashion by minimizing a cost that takes into account the performance of the underlying control problem (that prefers longer time horizons) and the effectiveness of the reference signal model (that prefers shorter time horizons). The result is an adaptive time horizon controller that operates in a manner reminiscent of Model Predictive Control (MPC).

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“…Although these controllers improved the situation, improper choice of the gains in the PID controller could make the whole system unstable. Therefore, designers resorted to optimal [1][2][3][4][5], predictive, or adaptive control techniques [6][7][8][9]. In intelligent control systems, no mathematical model of the system is required.…”

Section: Introductionmentioning

confidence: 99%

“…The studies on the parameters of HVAC systems as temperature, volume and control strategies in the last 50 years were shown that the high performance of HVAC systems could be obtained by minimizing energy consumption. The different approaches to control systems for indoor building environments can be roughly classified into the following categories: conventional methods, and computational Intelligence techniques, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Soyguder et al [1] studied the classical PID control of HVAC system having two zones with different properties.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic control of air-conditioning system in residential buildings

Attia

Rezeka

Saleh

2015

Alexandria Engineering Journal

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There has been a rising concern in reducing the energy consumption in building. Heating ventilation and air condition system is the biggest consumer of energy in building. In this study, fuzzy logic control of the air conditioning system of building for efficient energy operation and comfortable environment is investigated. A theoretical model of the fan coil unit (FCU) and the heat transfer between air and coolant fluid is derived. The controlled variables are the room temperature and relative humidity and control consequents are the percentage of chilled and hot water flow rates at summer and the percentage of hot water and steam injected flow rates at winter. A computer simulation has been conducted and fuzzy control results are compared with that of conventional Proportional-Integral-Derivative control. It was found that the proposed control strategy satisfies the space load and at the same time to achieve the comfort zone, as defined by the ASHRAE code. Meanwhile PID control fails to adjust the room temperature at part-load operations. It has been demonstrated that fuzzy controller operation is more efficient and consumes less energy than PID control. ª 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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A robust model predictive control strategy for improving the control performance of air-conditioning systems

Cited by 78 publications

References 19 publications

Predictive control of complex hydronic systems

Predictive control of complex hydronic systems

Adaptive time horizon optimization in model predictive control

Fuzzy logic control of air-conditioning system in residential buildings

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