Modelling and data-based identification of heating element in continuous-time domain

Zajic, Ivan; Iten, Muriel; Burnham, Keith J.

doi:10.1088/1742-6596/570/1/012003

Cited by 3 publications

(20 citation statements)

References 11 publications

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“…The standard control strategy for HVAC systems uses a classic PI regulator, whose complete structure is recalled in Section 3.1 [14]. The optimal proportional and integral gains are determined using the automatic PID tuning procedure and settled to K p = 22 and K i = 36, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…4. The settling time achieved by the PI controller is T s = 2.92s., with an overshoot of S% = 40.19%, which are computed by applying a step change in the reference output temperature from 39 o C to 40 o C. Using the performance index (14), the tracking error is M SSE% = 0.51%. …”

Section: Simulation Resultsmentioning

confidence: 99%

“…The control strategy applied to the HE should determine the control input such that the controlled process is able to track a given reference r(t) [14]. It can be shown that the overall dynamic behaviour of the HE process (4) can be approximated with very good accuracy by a product of 2 second-order continuous-time transfer functions, as shown in [14]. On the other hand, the continuous-time linearised state-space description of the HE nonlinear process (4) is described by the system (5):…”

Section: Control Scheme Designsmentioning

confidence: 99%

“…Note finally that the increased popularity of AI control techniques for building temperature control is incontrovertible; however most of them do not give a comparison with extensive simulations. This paper is focussed on the design of different control strategies with application to the HE nonlinear model developed by one of the same authors in [14]. The simulations and the comparisons with the achieved performances have been implemented in the Matlab and Simulink environments.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

Turhan

Simani

Zajic

et al. 2017

J. Phys.: Conf. Ser.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Control Scheme Designsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

Turhan

Simani

Zajic

et al. 2017

J. Phys.: Conf. Ser.

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“…These control schemes are simple with low-cost implementations, but sometimes unable to achieve accurate solutions. Moreover, the TU module can include nonlinear functions [9], such as products between air temperature and mass flow rate, which can require advanced control strategies to achieve more complex thermal comfort indices and lower energy consumption. To overcome these problems, control strategies relying on artificial intelligence (AI) tools, namely artificial neural networks (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference systems (ANFIS) and model predictive controllers (MPC) have been proposed to obtain more advanced comfort issues in building applications [10][11][12][13].…”

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

Turhan

Simani

Zajic

et al. 2016

Preprint

Self Cite

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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 analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application 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.

show abstract

Modelling and data-based identification of heating element in continuous-time domain

Cited by 3 publications

References 11 publications

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

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

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

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Modelling and data-based identification of heating element in continuous-time domain

Cited by 3 publications

References 11 publications

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

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

Performance Analysis of Data&ndash;Driven and Model&ndash;Based Control Strategies Applied to a Thermal Unit Model

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