Green Hall, Room 0120
Runxin He, PhD Candidate, will present.
Dissertation advisor: Humberto Gonzalez
This seminar is in partial fulfillment of the Doctor of Philosophy degree.
Abstract: Efficiency, comfort, and convenience are three major aspects in the design of control systems for residential
Heating, Ventilation, and Air Conditioning (HVAC) units. However, current solutions for indoor climate control focus on
concentrated models, which lack the detail to fully model the energy consumed in air convection or to describe the
thermal comfort of the residents.
In this talk we present our results on distributed-model based optimal control strategies for HVAC systems. Our
formulation uses a Computer Fluid Dynamics (CFD) model, which is mathematically formulated by nonlinear partial
differential equations, to describe the interactions between temperature, pressure, and air flow. Due to the complexity of
the PDE-constrained optimal control problem, we develop several optimization-based numerical algorithms, each
applicable to different scenarios, such as improving the energy efficiency of HVAC system by maintaining temperature
inside a small target region around resident, developing an estimation algorithm to reconstruct indoor climate distribution
and doors configuration by only thermostats, developing a model predictive control to directly maintain indoor residential
Due to our dependence on distributed-parameter models, our algorithms for the control of HVAC systems result in largescale
numerical optimization problems. In order to balance the accuracy of the optimal solution and computation
complexity, we develop a new sampling-based numerical algorithm. This algorithm reduces the computational effort
when solving optimal control problems by approximating the dynamical vector field as a convex cone. This
approximation allows us to solve a sequence of convex optimization problems in parallel, even for constrained nonlinear
problems. Moreover, we show the consistency of our algorithm, as local minimizers converge to local minimizers of the
original infinite-dimensional optimal control problem. We validate the algorithm via simulations, the results open the
doors to potentially solve nonlinear PDE-constrained optimal control in real time.