A Hybrid Model and MIMO Control for Intelligent Buildings Temperature Regulation over WSN

Witrant, Emmanuel; Mocanu, Stéphane; Sename, Olivier

doi:10.3182/20090901-3-ro-4009.00069

Cited by 15 publications

(11 citation statements)

References 7 publications

(6 reference statements)

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“…1, which consists of the electromechanical part (green box) and N building zones interconnected through doors and walls [Witrant et al (2009)]. The multi-zone HVAC system is regarded as a network of N + 1 interconnected subsystems Σ s , Σ (1) ,.…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems * *This work was supported by the European Research Council under the ERC Advanced Grant ERC-2011-AdG-291508

2017

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This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and airconditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology.

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Section: Problem Formulationmentioning

confidence: 99%

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems * *This work was supported by the European Research Council under the ERC Advanced Grant ERC-2011-AdG-291508

2017

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“…Fig. 1 depicts the multi-zone HVAC system with the electromechanical part (green box) and the N building zones, which are interconnected through doors [10]. Let us consider the HVAC system as a network of N + 1 interconnected subsystems, where the subsystem associated with the water temperature dynamics in the storage tank is denoted by Σ s , and Σ (i) represents the subsystem associated with the i-th zone temperature dynamics, i ∈ N , where N = {1,...,N}.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The coefficients a sz and a st (kJ/kg o C) represent the effectiveness of the heating coil and the heat loss of the storage tank due to exterior surfaces, respectively. Based on [8] and [10], the state space representation of the subsystem Σ (i) , i ∈ N , can be expressed as…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed adaptive sensor fault tolerant control for smart buildings

Papadopoulos

Reppa

Polycarpou

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper presents a model-based distributed fault-tolerant control (FTC) scheme with emphasis on compensating the effects of sensor faults in multi-zone heating, ventilating and air-conditioning (HVAC) systems. A bank of local adaptive FTC agents are designed to accommodate possible sensor faults in HVAC systems, modeled as a set of interconnected, nonlinear subsystems. In order to compensate the fault effects that may propagate in the neighboring subsystems, each local FTC utilizes the information generated by a local monitoring agent that diagnoses the sensor faults and performs an adaptive estimation of the isolated sensor faults. The local monitoring and control agents are allowed to exchange information with neighboring agents. Simulation results are used to illustrate the effectiveness of the proposed methodology applied to a sevenzone HVAC system.

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“…Studies related to the first and third item are found in, for example, [Camponogara et al, 2002, Speetjens et al, 2008, Witrant et al, 2009, Venkat et al, 2005. Though solutions on the third item of distributed control show promising results, they are currently limited to linear processes.…”

Section: Introductionmentioning

confidence: 99%

Localized climate control in greenhouses

Booij¹,

Sijs²,

Fransman

2012

IFAC Proceedings Volumes

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Strategies for controlling the indoor climate in greenhouses are based on a few sensors and actuators in combination with an assumption that climate variables, such as temperature, are uniform throughout the greenhouse. While this is already an improper assumption for conventional greenhouses, it especially does not hold for the new trend of growing crops at multiple layers. In addition, different temperature values are desired at the different layers, which turns out to give an uncontrollability issue with the current set of actuators. To solve this issue, fans are placed at each of the layers and an MPC strategy is employed for controlling this nonlinear MIMO system. A case study further shows that such a control strategy reduces the consumed energy of the greenhouse, while maintaining the desired temperature values.

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A Hybrid Model and MIMO Control for Intelligent Buildings Temperature Regulation over WSN

Cited by 15 publications

References 7 publications

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems * *This work was supported by the European Research Council under the ERC Advanced Grant ERC-2011-AdG-291508

Distributed Diagnosis of Actuator and Sensor Faults in HVAC Systems * *This work was supported by the European Research Council under the ERC Advanced Grant ERC-2011-AdG-291508

Distributed adaptive sensor fault tolerant control for smart buildings

Localized climate control in greenhouses

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