US 11,808,472 B2
Controlling thermal state of conditioned environment based on multivariable optimization
Saleh Nabi, Arlington, MA (US); Ankush Chakrabarty, Bedford, MA (US); Mouhacine Benosman, Boston, MA (US); and Sanjana VijayShankar, Boston, MA (US)
Assigned to Mitsubishi Electric Research Laboratories Inc., Cambridge, MA (US)
Filed by Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed on Mar. 26, 2021, as Appl. No. 17/213,277.
Prior Publication US 2022/0316736 A1, Oct. 6, 2022
Int. Cl. F24F 11/63 (2018.01); G05D 23/19 (2006.01); G06F 17/13 (2006.01); G06F 30/20 (2020.01); G06F 30/28 (2020.01); G06F 30/17 (2020.01); F24F 11/46 (2018.01)
CPC F24F 11/63 (2018.01) [F24F 11/46 (2018.01); G05D 23/1917 (2013.01); G06F 17/13 (2013.01); G06F 30/17 (2020.01); G06F 30/20 (2020.01); G06F 30/28 (2020.01)] 8 Claims
OG exemplary drawing
 
1. A system for controlling an operation of heating, ventilating, and air-conditioning (HVAC) system arranged to condition an environment, comprising: at least one processor; and memory having instructions stored thereon that, when executed by the at least one processor, cause the system to:
receive data indicative of the operation of the HVAC system controlled by a controller of the HVAC system;
optimize a cost function to reduce a difference between a full thermal state of the conditioned environment simulated from the received data based on a physical model of airflow including a partial differential equation (PDE) and a thermal state of the conditioned environment reconstructed by an observer using a reduced order model (ROM) of airflow including an ordinary differential equation (ODE) from values of the full thermal state at a set of locations, wherein optimization of the cost function is a multivariable optimization that jointly and interdependently optimizes the set of locations for measuring the thermal state, a structure of the observer, and a structure of the ODE, wherein the multivariable optimization is a constrained optimization of the structure of the ODE subject to a constraint imposed on the structure of the observer, and wherein the constrained optimization is subject to a stability constraint imposed on the structure of the ODE and subject to an observability constraint imposed on the structure of the observer, wherein the processor optimizes the cost function using semi-definite programming (SDP) that estimates the structure of the observer and the structure of the ODE in dependence on each other; and
modify the controller of the HVAC system with the optimized structure of the observer and the optimized structure of the ODE causing the operation of the HVAC system controlled by the modified controller of the HVAC system based on the optimized set of locations for measuring the thermal state.