Advanced controller auto-tuning and its application in HVAC systems

Bi, Qiang; Cai, Wenjian; Wang, Qing‐Guo; Hang, Chang-Chieh; Lee, Eng-Lock; Sun, Yijun; Liu, Ke-Dian; Zhang, Yong; Zou, Biao

doi:10.1016/s0967-0661(99)00198-7

Cited by 129 publications

(53 citation statements)

References 6 publications

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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%

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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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Section: Office Building Temperature Controlmentioning

confidence: 99%

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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“…To evaluate the effectiveness of the proposed AIFPI controller, its control performance is compared with a fixed well-tuned PI controller, which adopts the PI/PID tuning rules proposed by Wang [13]. It has been proved that the auto-tuning controller using the PI/PID tuning rules has good performance for HVAC systems [14]. Their behavior is all evaluated with MATLAB/Simulink simulation using the HVAC system model described by Eq.…”

Section: Application Of the Aifpi Controller To The Hvac Systemmentioning

confidence: 99%

Development an Adaptive Incremental Fuzzy PI Controller for a HVAC System

Bai¹

2013

INT J COMPUT COMMUN

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This paper presents an adaptive incremental fuzzy PI controller (AIFPI) for a heating, ventilating, and air conditioning (HVAC) system capable of maintaining comfortable conditions under varying thermal loads. The HVAC system has two subsystems and is used to control indoor temperature and humidity in a thermal zone. As the system has strong-coupling and non-linear characteristics, fixed PI controllers have poor control performance and more energy consumption. Aiming to solve the problem, fuzzy control and PI control are combined together organically. In the proposed control scheme, the error of the system output and its derivative are taken as two parameters necessary to adapt the proportional (P) and integral (I) gains of the PI controller based on fuzzy reasoning according to practical control experiences. To evaluate the effectiveness of the proposed control methods in the HVAC system, it is compared with a fixed well-tuned PI controller. The results demonstrate that the AIFPI controller has more superior performance than the latter.

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“…These parameters are compared with ZeiglerNichols tuned parameters and model based gain scheduling is additionally employed to cope with the operating point dependent system gain. In [6], auto-tuners for PI/PID control of HVAC systems are designed based on a combination of relay and step tests. The auto-tuners show better performance than manual tuning and standard relay auto-tuning.…”

Section: Introductionmentioning

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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Advanced controller auto-tuning and its application in HVAC systems

Cited by 129 publications

References 6 publications

Learning/repetitive control for building systems with nearly periodic disturbances

Learning/repetitive control for building systems with nearly periodic disturbances

Development an Adaptive Incremental Fuzzy PI Controller for a HVAC System

Evaporator superheat control With one temperature sensor using qualitative system knowledge

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