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Firtual hardware-in-the-loop FMU CO-simulation based digital twins for heating, ventilation, and air-conditioning (HVAC) systems

Abrazeh, S ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1109/TETCI.2022.3168507
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2022
  4. Abstract:
  5. In this paper, a novel self-adaptive control method based on a digital twin is developed and investigated for a multi-input multi-output (MIMO) nonlinear system, which is a heating, ventilation, and air-conditioning system. For this purpose, hardware-in-loop (HIL) and software-in-loop (SIL) are integrated to develop the digital twin control concept in a straightforward manner. A nonlinear integral backstepping (NIB) model-free control technique is integrated with the HIL (implemented as a physical controller) and SIL (implemented as a virtual controller) controllers to control the HVAC system without the need for dynamic feature identification. The main goal is to design the virtual controller to minimize the distinction between system outputs in the SIL and HIL setups. For this purpose, Deep Reinforcement Learning (DRL) is applied to update the NIB controller coefficients of the virtual controller based on the measured data of the physical controller. Since the temperature and humidity of HVAC systems should be regulated, the NIB controllers in the HIL and SIL are designed by the DRL algorithm in a multi-objective scheme (MO). In particular, the simulations of the HIL and SIL environments are coupled by a new advanced tool: function mockup interface (FMI) standard. The Functional Mock-up Unit (FMU) is adopted into the FMI interface for data exchange. The extensive research of HIL and SIL controllers shows that the system outputs of the virtual controller are controlled exactly according to the physical controller. IEEE
  6. Keywords:
  7. Backstepping ; Control systems ; Deep reinforcement learning (DRL) ; Digital twin ; Digital twins ; Hardware-in-loop (HIL) ; Heating ; Humidity ; MIMO communication ; Nonlinear integral-backstepping (NIB) ; Space heating ; ventilating and air-conditioning (HVAC) ; Adaptive control systems ; Cooling ; Deep learning ; E-learning ; Hardware-in-the-loop simulation ; HVAC ; MIMO systems ; Mockups ; Synthetic apertures ; Deep reinforcement learning ; Hardware in loop ; Hardware-in-loop ; Heating ventilation and air conditioning ; Integral back-stepping ; Multi-input/multi-output communications ; Nonlinear integral ; Nonlinear integral-backstepping ; Ventilating and air-conditioning (heating, ventilation, and air-conditioning) ; Controllers
  8. Source: IEEE Transactions on Emerging Topics in Computational Intelligence ; 2022 , Pages 1-11 ; 2471285X (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9782098