This paper addresses quantized control of a heat ventilation and air conditioning system. The objective is to guarantee comfort, defined in terms of desired temperature and humidity, with a higher priority assigned to the temperature control. The system is described by a linear model with a stochastic input to account for model uncertainty. A chance-constrained control design strategy is proposed where constraints on the temperature and humidity ranges are enforced over some look-ahead time horizon with a predefined (high) probability with respect to the uncertain initial state and the stochastic input. Feasibility of the constraints is guaranteed by minimizing the temperature and humidity variability around the desired set-points, with the variability range on the humidity eventually enlarged when needed to squeeze the one on the temperature. The resulting quantized control is applied in a receding horizon fashion, leading to a closed-loop solution that integrates state filtering to reduce on the fly the uncertainty on the state.

A chance-constrained approach to the quantized control of a heat ventilation and air conditioning system with prioritized constraints

MANGANINI, GIORGIO;
2016

Abstract

This paper addresses quantized control of a heat ventilation and air conditioning system. The objective is to guarantee comfort, defined in terms of desired temperature and humidity, with a higher priority assigned to the temperature control. The system is described by a linear model with a stochastic input to account for model uncertainty. A chance-constrained control design strategy is proposed where constraints on the temperature and humidity ranges are enforced over some look-ahead time horizon with a predefined (high) probability with respect to the uncertain initial state and the stochastic input. Feasibility of the constraints is guaranteed by minimizing the temperature and humidity variability around the desired set-points, with the variability range on the humidity eventually enlarged when needed to squeeze the one on the temperature. The resulting quantized control is applied in a receding horizon fashion, leading to a closed-loop solution that integrates state filtering to reduce on the fly the uncertainty on the state.
978-1-5323-1358-5
Stochastic Control and Estimation
Linear Systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12571/7652
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