Distributed consensus algorithms for collaborative temperature control in smart buildings

Gupta, Santosh K.; Kar, Koushik; Mishra, Sandipan; Wen, John T.

doi:10.1109/acc.2015.7172241

Cited by 13 publications

(3 citation statements)

References 16 publications

(22 reference statements)

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“…Flow networks (also known as distribution or transportation networks) consist of edges that are used to model the exchange of material (flow) between the nodes. The design and regulation of these networks received significant attention due to its many applications, including supply chains (Alessandri et al [2011]), heating, ventilation and air conditioning (HVAC) systems (Gupta et al [2015]), data networks (Moss and Segall [1982]), traffic networks (Iftar [1999], Coogan and Arcak [2015]) and compartmental systems (Blanchini et al [2016], Como [2017]). If the considered objective is static, the study of flow networks has a long history within the field of network optimization (Bertsekas [1998], Rockafellar [1984]).…”

Section: Introductionmentioning

confidence: 99%

Optimal regulation of flow networks with transient constraints

2019

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This paper investigates the control of flow networks, where the control objective is to regulate the measured output (e.g storage levels) towards a desired value. We present a distributed controller that dynamically adjusts the inputs and flows, to achieve output regulation in the presence of unknown disturbances, while satisfying given input and flow constraints. Optimal coordination among the inputs, minimizing a suitable cost function, is achieved by exchanging information over a communication network. Exploiting an incremental passivity property, the desired steady state is proven to be globally asymptotically attractive under the closed loop dynamics. Two case studies (a district heating system and a multi-terminal HVDC network) show the effectiveness of the proposed solution.

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Section: Introductionmentioning

confidence: 99%

Optimal regulation of flow networks with transient constraints

2019

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“…No such guarantees are known to hold for the existing approaches proposed in the related studies. To further contrast it with our recent work , the approach in this work is much simpler for the occupants as they just have to share their upper and lower limit temperatures (or provide binary input) without having to worry about pricing or penalty feedback from the building system. The collaborative (collaboration between occupants, declaring thermal comfort range in self‐interest) and coordination (the thermostat control is set in a coordinated manner, taking into account the multi‐zone thermal correlations) aspect of our proposed framework also distinguishes it more other smart thermostat solutions such as those by Google‐Nest and .…”

Section: Introductionmentioning

confidence: 99%

Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

Gupta

Kar

Mishra

et al. 2017

Asian Journal of Control

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In this work we propose a framework for incorporating occupant feedback towards temperature control of multi-occupant spaces, and analyze it using singular perturbation theory. Such a system would typically have to accommodate occupants with different temperature preferences, and incorporate that with thermal correlation among multiple zones to obtain optimal control for minimization of occupant discomfort and energy cost. In current practice, an acceptable temperature set point for the occupancy level of the zone is estimated, and the control law is designed to maintain temperature at the corresponding set point irrespective of the changes in occupancy and the preferences of multiple occupants. Proposed algorithm incorporates active occupant feedback to minimize aggregate user discomfort and total energy cost. Occupant binary feedback in the form of hot/cold or thermal comfort preference input is used by the control algorithm. The control algorithm also takes the energy cost into account, trading it off optimally with the aggregate occupant discomfort. For convergence to the optimal, sufficient separation between the occupant feedback frequency and the temperature dynamics of system is necessary; in absence of which, the occupant feedback provided do not correctly reflect the effect of current control input value on occupant discomfort. Under sufficient time scale separation, using singular perturbation theory, we establish the stability condition of the system and show convergence of the proposed solution to the desired temperature that minimizes energy cost plus occupant discomfort. The occupants are only assumed to be rational in that they choose their own comfort range to minimize individual thermal discomfort. Optimization for a multi-zone building also takes into account the thermal correlation among different zones. Simulation study with parameters based on our test facility, and experimental study conducted in the same building demonstrates performance of the algorithm under different occupancy and ambient conditions.

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“…Theorem [14] XZ are the minimizer of (5.17), respectively. Proof [30,32]: All elements of () m gX are proper, closed, and convex function. In addition, the Lagrangian is defined by (5.43)…”

Section: Convergence Propertymentioning

confidence: 99%

Application of linear multi-agent system approach to design building temperature control system

Pham

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Building temperature control system (BTCS) is a large-scale interconnected system with a high demand of energy consumption in building. BTCS is generally described by linear time-invariant system using electric-analogous devices such as resistors and capacitors. In view of graph theory, it is modeled as linear multi-agent system (MAS) subjected to undirected communication topology. The main goal of BTCS is to track the reference temperature trajectory which is equivalent to make the tracking error go to zero in finite time. Hence, BTCS properly suits with framework of distributed control, decentralized control, and centralized control. In this thesis, we focus on comparison of three design methods, namely, distributed consensus controllers (DCC), decentralized iterative learning control (ILC), and centralized ILC. First, the DCC is applied to BTCS by solving resource allocation problem with hard control input constraints. Next, we apply decentralized ILC using the derivative of tracking error (D-type). Lastly, centralized ILC design is formulated as minimization problem with a quadratic cost function subject to control input constraints. This problem is also called Q-ILC. We apply the Alternating Direction Method of Multipliers (ADMM) approach to Q-ILC design and derive analytical solution of control input in each iteration. The convergence property of ADMM and Q-ILC algorithm are given. Numerical results are provided to illustrate the effectiveness of these algorithms.

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Distributed consensus algorithms for collaborative temperature control in smart buildings

Cited by 13 publications

References 16 publications

Optimal regulation of flow networks with transient constraints

Optimal regulation of flow networks with transient constraints

Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

Application of linear multi-agent system approach to design building temperature control system

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