Adaptive Critic-Based Event-Triggered Control for HVAC System

Dhar, Narendra Kumar; Verma, Nishchal K.; Behera, Laxmidhar

doi:10.1109/tii.2017.2725899

Cited by 82 publications

(24 citation statements)

References 28 publications

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“…In addition to the above mentioned, many other cases have been studied on HVAC [18][19][20][21][22][23][24][25][26][27]. In the present paper, a model predictive control (MPC) method has been used for controlling temperature and humidity which was previously only introduced for controlling temperature in [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

“…In order to model the interference between humidity and temperature, the exponential equation of A.L. Buck [50] is used according to the following equation: e * w = 1.0007 + 3.46 × 10 −6 P (6.1121)e 17.502T 240.97+T (23) where e * w is the pressure equilibrium pressure in millibars, T is the dry bubble temperature in degrees Celsius, and P is the absolute pressure in millibar. A.L.…”

Section: Study Of Physical Equations For Temperature and Humidity Govmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy.

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Section: Related Workmentioning

confidence: 99%

Section: Study Of Physical Equations For Temperature and Humidity Govmentioning

confidence: 99%

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

et al. 2019

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“…Thus, there exist an irreconcilable discrepancy between tracking error and energy consumption under the current investigations constructed within the continuous-time framework for MEMS gyroscope. To avoid the unnecessary resources consumption, eventtriggered strategies, by which the sampling instants are only determined by the evolution of system performance satisfying triggered conditions, are presented and have been employed in many pioneering researches [21]- [27], [38], [40]. In [21], fixed threshold triggering mechanisms are blended in the controller design for motor system with nonlinearity, where the existence of Zeno behavior can be avoided.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

2020

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This paper investigates an event-triggered output feedback control problem with guaranteed tracking performance for microelectromechanical system (MEMS) gyroscope subject to the lumped disturbances, including parameter uncertainties, coupling between driving mode and sensing mode, and unknown external disturbances. Firstly, to make the system output tracking errors evolve within the predetermined performance envelopes, a prescribed performance control (PPC) is employed in our controller design, such that a prescribed trajectory tracking with preselected settling time and steady-state tracking accuracy can be ensured. Besides, to simultaneously realize disturbance rejection and estimation of the immeasurable velocity state, linear extended state observers (LESOs) are designed to reconstruct the lumped disturbances and immeasurable velocity states for both MEMS gyroscope driving and sensing modes. In general, excessively energy and resource consumption are fundamentally neglected in most of the existing output feedback controllers for MEMS gyroscope, which will dramatically reduce the operational time of the close-loop system. To address this problem, we embed a fixed threshold event-triggered mechanism into the controller design procedure, such that the communication data size can be drastically reduced without sacrificing the tracking accuracy. Finally, to obtain the close-loop stability for MEMS gyroscope, robust control laws are designed to compensate for the measurement error and estimation error, meanwhile Zeno phenomena can be effectively eliminated. The simulation results and comparisons validate the effectiveness of the proposed scheme. INDEX TERMS Output feedback, event-triggered control, prescribed performance, MEMS gyroscope.

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“…These mechanisms are included within the event–triggered schemes. Currently, there is much interest in these types of schemes in several fields of interest, for instance, in industrial event–oriented control [ 12 , 13 ], cooperative control of environmental monitoring [ 14 ], tracking trajectories of vehicles and robots [ 15 , 16 ], rule-based control of Smart–Homes [ 17 ], etc. Currently, large advances have been obtained with diverse variations of the event–triggered schemes, for example, by dynamic adaptive delta mechanisms [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks

León‐García

Muñoz

Olivares

2018

Sensors

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The reduction of sensor network traffic has become a scientific challenge. Different compression techniques are applied for this purpose, offering general solutions which try to minimize the loss of information. Here, a new proposal for traffic reduction by redefining the domains of the sensor data is presented. A configurable data reduction model is proposed focused on periodic duty–cycled sensor networks with events triggered by threshold. The loss of information produced by the model is analyzed in this paper in the context of event detection, an unusual approach leading to a set of specific metrics that enable the evaluation of the model in terms of traffic savings, precision, and recall. Different model configurations are tested with two experimental cases, whose input data are extracted from an extensive set of real data. In particular, two new versions of Send–on–Delta (SoD) and Predictive Sampling (PS) have been designed and implemented in the proposed data–domain reduction for threshold–based event detection (D2R-TED) model. The obtained results illustrate the potential usefulness of analyzing different model configurations to obtain a cost–benefit curve, in terms of traffic savings and quality of the response. Experiments show an average reduction of 76% of network packages with an error of less than 1%. In addition, experiments show that the methods designed under the proposed D2R–TED model outperform the original event–triggered SoD and PS methods by 10% and 16% of the traffic savings, respectively. This model is useful to avoid network bottlenecks by applying the optimal configuration in each situation.

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Adaptive Critic-Based Event-Triggered Control for HVAC System

Cited by 82 publications

References 28 publications

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

Modeling and Controlling of Temperature and Humidity in Building Heating, Ventilating, and Air Conditioning System Using Model Predictive Control

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks

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