Model predictive control of VAV zone thermal systems concerning bi-linearity and gain nonlinearity

Huang, Gongsheng

doi:10.1016/j.conengprac.2011.03.005

Cited by 87 publications

(61 citation statements)

References 19 publications

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“…The MPC-based techniques have been employed in zone temperature control [124], damper process control [125], supply air temperature control [126], evaporator pressure and cooling set point generation [122], ventilation control [127]. In Ref.…”

Section: Scheduling and Model Predictive Control Methodsmentioning

confidence: 99%

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

2015

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Section: Scheduling and Model Predictive Control Methodsmentioning

confidence: 99%

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

2015

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“…Buildings, on the other hand, are widely accepted as displaying non-linear characteristics due to factors such as rate and output limited sub-systems (Huang, 2011;Afram & Janabi-Sharifi, 2014), and other causes.…”

Section: Creation Of the Predictive Modelmentioning

confidence: 99%

Model-predictive control for non-domestic buildings: a critical review and prospects

Rockett

Hathway

2016

Building Research & Information

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Model-predictive control (MPC) has recently excited a great deal of interest as a new control paradigm for non-domestic buildings. Since it is based on the notion of optimisation, MPC is, in principle, well-placed to deliver significant energy savings and reduction in carbon emissions compared to existing rule-based control systems. In this paper, we critically review the prospects for buildings MPC and, in particular, the central role of the predictive mathematical model that lies at its heart; our clear emphasis is on practical implementation rather than control-theoretic aspects, and covers the role of occupants as well as the form of the predictive model. The most appropriate structure for such a model is still an open question, which we discuss alongside the development of the initial model, and the process of updating the model during the building's operational life. The importance of sensor placement is highlighted alongside the possibility of updating the model with occupants' comfort perception. We conclude that there is an urgent need for research on the automated creation and updating of predictive models if MPC is to become an economically-viable control methodology for non-domestic buildings. Finally, more evidence through operating full scale buildings with MPC is required to demonstrate the viability of this method.

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“…In fact, this control package accepts also nonlinear models; in particular, using large-scale nonlinear programming solvers, such as the Advanced Process OPTimizer (APOPT) and Interior Point OPTimizer (IPOPT), which are available in the Optimization Toolbox in the MATLAB R and Simulink R environments. Therefore, this simulation code is able to implement the moving horizon estimation, dynamic optimisation and simulation, thus solving the nonlinear MPC problems [29]. The nonlinear input-output dynamic model used in simulation has been obtained again by exploiting the System Identification Toolbox in the MATLAB R environment.…”

Section: Is U(t) = [T I (T) V(t)]mentioning

confidence: 99%

“…Finally, it is worth observing that several research papers have already dealt with this issue in the past (see, e.g., [22,29]), even if they are limited to MPC solutions. However, this work recalls, analyses and implement different control solutions when applied to the thermal unit model already developed by the authors in [9].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

et al. 2017

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Abstract:The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically-meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.

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Model predictive control of VAV zone thermal systems concerning bi-linearity and gain nonlinearity

Cited by 87 publications

References 19 publications

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

Model-predictive control for non-domestic buildings: a critical review and prospects

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

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