Model Predictive Control for Signal Temporal Logic Specification

We present a mathematical programming-based method for model predictive control of cyber-physical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of a receding horizon control framework. We prove correctness of our algorithms, and present experimental results for controller synthesis for building energy and climate control

Model predictive control approach to online computation of demand-side flexibility of commercial buildings HVAC systems for Supply Following

Abstract — Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We investigate how to take advantage of this flexibility. We first propose a means to define and quantify the flexibility of a commercial building. We then propose a contractual framework that could be used by the building operator and the utility to declare flexibility on the one side and reward structure on the other side. We then design a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward, given the contractual framework. Finally, we perform at-scale experiments to demonstrate the feasibility of the proposed algorithm. I

Controlling Energy-Efficient Buildings in the Context of Smart Grid: A Cyber Physical System Approach

The building sector is responsible for about 40% of energy consumption, 40% of greenhouse gas emissions, and 70% of electricity use in the US. Over 50% of the energy consumed in buildings is directly related to space heating, cooling and ventilation. Optimal control of heating, ventilation and air conditioning (HVAC) systems is crucial for reducing energy consumption in buildings.We present a physics-based mathematical model of thermal behavior of buildings, along with a novel Parameter Adaptive Building (PAB) model framework to update the model parameters, as new measurements arrive, to reduce the model uncertainties. We then present a Model Predictive Control (MPC), and a Robust Model Predictive Control (RMPC) algorithm and a methodology for selecting a controller type, i.e. RMPC or MPC, versus Rule Based Control (RBC) as a function of model uncertainty.We then address the \Cyber-Physical" aspect of a building HVAC system in the design flow. We present a co-design framework that analyzes the interaction between the control algorithm and the embedded platform through a set of interface variables, and demonstrate how the design space is explored to optimize the energy cost and monetary cost, while satisfying the constraints for occupant comfort level.The last part of this dissertation is centered on the role of smart buildings in the context of the smart grid. Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We rst propose a means to dene and quantify the flexibility of a commercial building. We then present a contractual framework that could be used by the building operator and the utility company to declare flexibility on one side and reward structure on the other side. We also present a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward. We also present a Model Predictive Control (MPC) scheme to direct the ancillary service power flow from buildings to improve upon the classical Automatic Generation Control (AGC) practice. We show how constraints such as slow and fast ramping rates for various ancillary service providers, and shortterm load forecast information can be integrated into the proposed MPC framework. Finally, results from at-scale experiments are presented to demonstrate the feasibility of the proposed algorithm