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

Cihan Turhan, Silvio Simani, Ivan Zajic and Gulden Gokcen Akkurt
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Cihan Turhan: Mechanical Engineering, Izmir Institute of Technology, Gulbahce Campus, Urla, 35430 Izmir, Turkey
Silvio Simani: Dipartimento di Ingegneria, Università degli Studi di Ferrara. Via Saragat 1E, 44122 Ferrara (FE), Italy
Ivan Zajic: Control Theory and Applications Centre, Coventry University, Coventry CV1 5FB, UK
Gulden Gokcen Akkurt: Energy Engineering Program, Izmir Institute of Technology, Gulbahce Campus, Urla, 35430 Izmir, Turkey

Energies, 2017, vol. 10, issue 1, 1-20

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.

Keywords: modelling and simulation for control; advanced control design; model-based and data-driven approaches; artificial intelligence; thermal unit nonlinear system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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