Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 8 December 2016 / Approved: 9 December 2016 / Online: 9 December 2016 (03:17:38 CET)

A peer-reviewed article of this Preprint also exists.

Turhan, C.; Simani, S.; Zajic, I.; Gokcen Akkurt, G. Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model. Energies 2017, 10, 67. Turhan, C.; Simani, S.; Zajic, I.; Gokcen Akkurt, G. Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model. Energies 2017, 10, 67.

Journal reference: Energies 2017, 10, 67
DOI: 10.3390/en10010067

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 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.

Subject Areas

modelling and simulation for control; advanced control design; model–based and data-driven approaches; artificial intelligence; thermal unit nonlinear system

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