Abstract: A physics model of a building is linearized around an operating point. Measurements received from sensors define a system state of the HVAC system. The linearized physics model is used as an equality constraint for a model predictive controller that determines a next control input to the HVAC system based on the system state by solving an optimization problem for a time horizon of size N. A constraint matrix H of the equality constraint is decomposed into factors of U and V matrices such that UUT and VT V are both diagonal matrices. An objective function of the model predictive controller is optimized by iteratively solving a linear system of equations that includes inverses of UUT and VT V to determining a sequence of inputs for a horizon of the model predictive controller. A first input of the sequence of inputs to is used to control the HVAC system.

Abstract: A physics model of a building is linearized around an operating point. Measurements received from sensors define a system state of the HVAC system. The linearized physics model is used as an equality constraint for a model predictive controller that determines a next control input to the HVAC system based on the system state by solving an optimization problem for a time horizon of size N. A constraint matrix H of the equality constraint is decomposed into factors of U and V matrices such that UUT and VTV are both diagonal matrices. An objective function of the model predictive controller is optimized by iteratively solving a linear system of equations that includes inverses of UUT and VTV to determining a sequence of inputs for a horizon of the model predictive controller. A first input of the sequence of inputs to is used to control the HVAC system.