Selecting PID Control Gains for Nonlinear HVAC&amp;R Systems

Lim, Dong-Won; Rasmussen, Bryan P.; Darbha, Swaroop

doi:10.1080/10789669.2009.10390876

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…The selected worst case operating point is supported by e.g. [13]. The slope R was measured to be -8.08 and -0.95, for the air conditioning system and refrigeration system, respectively, and the lag L was 23.6 and 27.6.…”

Section: Startup Proceduresmentioning

confidence: 99%

Evaporator superheat control With one temperature sensor using qualitative system knowledge

Vinther

Lyhne

Sørensen

et al. 2012

2012 American Control Conference (ACC)

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Abstract-This paper proposes a novel method for superheat control using only a single temperature sensor at the outlet of the evaporator, while eliminating the need for a pressure sensor. An inner loop controls the outlet temperature and an outer control loop provides a reference set point, which is based on estimation of the evaporation pressure and suitable reference logic. The pressure is approximated as being linear and proportional to the opening degree of the expansion valve. This gain and the reference logic is based on calculation of the variance in the outlet temperature, which have shown to increase at low superheat. The parameters in the proposed controller structure can automatically be chosen based on two open loop tests. Results from tests on two different refrigeration systems indicate that the proposed controller can control the evaporator superheat to a low level giving close to optimal filling of the evaporator, with only one temperature sensor. No a priori model knowledge was used and it is anticipated that the method is applicable on a wide variety of refrigeration systems.

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Section: Startup Proceduresmentioning

confidence: 99%

Evaporator superheat control With one temperature sensor using qualitative system knowledge

Vinther

Lyhne

Sørensen

et al. 2012

2012 American Control Conference (ACC)

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“…These classical methods have been investigated extensively in HVAC lower-loop controls [4][5][6], such as room temperature control [7][8][9] and supply air temperature control [10][11][12]. Despite their popularity, these classical methods do not perform optimally.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

2017

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Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using SketchUp. Using OpenStudio, the HVAC system is installed in the office. The control schemes (ideal thermal comfort, a traditional control and the RL control) are implemented in MATLAB. Using the Building Control Virtual Test Bed (BCVTB), the control of the thermostat schedule during each sample time is implemented for the office in EnergyPlus alongside local weather data. Results from training and validation indicate that the RL controller improves thermal comfort by an average of 15% and energy efficiency by an average of 2.5% as compared to other strategies mentioned.

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“…with simple on/off hysteresis based control, PI/PID control, or more advanced model based control. In most cases one of the first two methods is used [1], [2], [3], [4], primarily due to their simple implementations and relatively simple tuning procedures. Fig.…”

Section: Office Building Temperature Controlmentioning

confidence: 99%

“…The temperature is often controlled with a heating ventilation and air-conditioning (HVAC) system using either on/off type hysteresis control or variations of PI/PID control due to their simple implementations [1], [2]. However, they seldom perform well at all possible operating conditions because the control is typically tuned only for a selected nominal situation; also the control might not be tuned properly in the first place [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Learning/repetitive control for building systems with nearly periodic disturbances

Vinther

Chandan

Alleyne

2013

2013 European Control Conference (ECC)

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Abstract-In this paper, learning/repetitive control is proposed for improvement of existing feedback control loops for temperature regulation in buildings. A single zone office building is used as an example, with real weather data for Phoenix Arizona and realistic occupancy load schedules. Simulations have shown a decrease in the average set point tracking error of more than 50%, even without additional energy consumption. This can be achieved in situations where the load disturbances have enough repeatability and a repeatable-to-nonrepeatable ratio can be computed to determine if learning should be used and at which frequencies. Furthermore, the increased tightness in reference tracking could be used to lower energy consumption by moving the reference set point closer to the boundaries of the allowable temperature range.

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Selecting PID Control Gains for Nonlinear HVAC&R Systems

Cited by 18 publications

References 18 publications

Evaporator superheat control With one temperature sensor using qualitative system knowledge

Evaporator superheat control With one temperature sensor using qualitative system knowledge

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

Learning/repetitive control for building systems with nearly periodic disturbances

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