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In a supply-following “smart” grid scenario, buildings can exploit remotely controllable thermostats and “smart” meters to communicate with energy providers, trade energy in real-time and offer frequency regulation services, by leveraging the flexibility in the energy consumption of their heating, ventilation and air conditioning (HVAC) systems. The realization of such a scenario is, however, strongly dependent on our ability to radically re-think the way both the grid and the building control algorithms are designed. In this work, we regard the grid as an integrated, distributed, cyber-physical system, and propose a compositional framework for the deployment of an optimal supply-following strategy. We use the concept of assume-guarantee contracts to formalize the requirements of the grid and the building subsystem as well as their interface. At the building level, such formalization leads to the development of an optimal control mechanism to determine the HVAC energy flexibility while maximizing the monetary incentive for it. At the grid level, it allows formulating a model predictive control scheme to optimally control the ancillary service power flow from buildings, while integrating constraints such as ramping rates of ancillary service providers, maximum available ancillary power, and load forecast information. Simulation results illustrate the effectiveness of the proposed design methodology and the improvements brought by the proposed control strategy with respect to the state of the art.

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.

This paper describes current work on framing the model predictive control (MPC) of cyber-physical systems as synthesis from signal temporal logic (STL) specifications. We provide a case study using a simplified power grid model with uncertain demand and generation; the model-predictive control problem here is that of the ancillary service power flow from the buildings. We show how various relevant constraints can be captured using STL formulas, and incorporated into an MPC framework. We also provide preliminary simulation results to illustrate the promise of the proposed approach.

Aircraft Electric Power Systems (EPS) route power from generators to vital avionic loads by configuring a set of electronic control switches denoted as contactors. In this paper, we address the problem of designing a hierarchical optimal control strategy for the EPS contactors in the presence of system faults. We first formalize the system connectivity, safety and performance requirements in terms of mathematical constraints. We then show that the EPS control problem can be formulated as a Mixed-Integer Linear Program (MILP) and efficiently solved to yield load shedding, source allocation, contactor switching and battery charging policies, while optimizing a number of performance metrics, such as the number of used generators and shed loads. This solution is then integrated into a hierarchical control scheme consisting of two layers of controllers. The high-level controller provides control optimality by solving the MILP within a receding horizon approach. The low-level controller handles system faults, by directly actuating the EPS contactors, and implements the solution from the high-level controller only if it is safe. Simulation results confirm the effectiveness of the proposed approach.

We present a mathematical programming-based method for model predictive control of discrete-time cyberphysical 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 model predictive control framework. We present experimental results for controller synthesis for building energy and climate control.

A hierarchical control architecture for balancing comfort and energy consumption in buildings is presented. The control design is based on a simplified, yet accurate model of the temperature within each room of the building. The model is validated against real measurements. The control architecture comprises a first level that regulates low level quantities such as air flow, and a second level that balances comfort (i.e. distance between the desired and actual temperature) and energy consumption (i.e. total energy consumed for the required level of comfort). We show the effectiveness of our approach by simulation using validated models.

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation ” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

This paper deals with the problem of robust model predictive control of an uncertain linearized model of a building envelope and HVAC system. Uncertainty of the model is due to the imperfect  predictions of internal and external heat gains of the building. The Open-Loop prediction formulation of the Robust Model Predictive Control (OL-RMPC) is known to be unnecessarily over-conservative in practice. Therefore, we adopt a Closed-Loop prediction formulation of Robust Model Predictive Control (CL-RMPC) which exploits an uncertainty feedback parameterization  of the control sequence and results in a tractable formulation of the problem. To improve on the efficiency of CLRMPC we propose a new uncertainty feedback parameterization of the control input, which leads to a number of decision variables linear  in time horizon as opposed to quadratic  as in previous approaches. To assess our approach we compare three different robust optimal control strategies: nominal MPC which does not have a priori information of the uncertainty, OL-RMPC and CL-RMPC. We show results from a quantitative  analysis of performance of these controllers at different prediction error values of the disturbance. Simulations show that CL-RMPC provides a higher level of comfort with respect to OL-RMPC while consuming 36% less energy. Moreover, CL-RMPC maintains perfect comfort level for up to 75% error in the disturbance prediction. Finally, the newly proposed parameterization maintains the performance of CL-RMPC while reducing the simulation time by an average of 30%.

Model uncertainty is a significant challenge to more widespread use of model predictive controllers (MPC)for optimizing building energy consumption. This paper presents two methodologies to handle modeluncertainty for building MPC. First, we propose a modeling framework for online estimation of states andunknown parameters leading to a parameter-adaptive building (PAB) model. Second, we propose a robustmodel predictive control (RMPC) formulation to make a building controller robust to model uncertainties.The results from these two approaches are compared with those from a nominal MPC and a commonbuilding rule based control (RBC). The results are then used to develop a methodology for selecting acontroller type (i.e. RMPC, MPC, or RBC) as a function of building model uncertainty. RMPC is found to bethe superior controller for the cases with an intermediate level of model uncertainty (30–67%), while thenominal MPC is preferred for the cases with a low level of model uncertainty (0–30%). Further, a commonRBC outperforms MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).

This report focuses on modeling the thermal behavior of buildings and designing an optimal control algorithm for their HVAC systems. The problem of developing a good model to capture the heat storage and heat transmission properties of building thermal elements such as rooms and walls is addressed by using the lumped capacitance method. The equations governing the system dynamics are derived using the thermal circuit approach, and by dening equivalent thermal masses, thermal resistors and thermal capacitors. In the control design part, we have introduced a new hierarchical control algorithm which is composed of lower level PID controllers and a higher level LQR controller. The optimal tracking problem is solved in the higher level controller where the interconnection of all the rooms and the walls are taken into consideration. The LQR controller minimizes a quadratic cost function which has two quadratic terms. One takes into account the comfort level and the other represents the control eort, i.e. the energy consumed to operate the HVAC system. There are two tuning parameters as the weight matrices for each of these two terms by which the performance of the controller can be tuned in dierent operating conditions. Simulation results show how much energy can be saved using this algorithm.

A framework for the design and simulation of a building envelope and an HVAC system is used to compare advanced control algorithms in terms of energy efficiency, thermal comfort, and computational complexity. Building models are first captured in Modelica to leverage Modelica’s rich building component library and then imported into Simulink [15] to exploit Simulink’s strong control design environment. Four controllers with different computational complexity are considered and compared: a proportional (P) controller with time varying temperature bounds, a tracking linear quadratic regulator (LQR) controller with time varying tuning parameters, a tracking disturbance-aware linear quadratic regulator (d- LQR) controller with time varying tuning parameters which incorporates predictive disturbance information and a model predictive controller (MPC). We assess the performance of these controllers using two defined criteria, i.e. energy  and discomfort  measured with appropriate metrics. We show that the d-LQR and MPC, when compared to the P controller, manage to reduce energy by 41. 2%and 46%respectively, and discomfort from 3.8 to 0. While d-LQR and MPC have similar performance with respect to energy and discomfort, simulation time in the case of d-LQR is significantly less than the one of MPC.

The design of heating, ventilation and air conditioning (HVAC) systems is crucial for reducing energy consumption in buildings. As complex cyber-physical systems, HVAC systems involve three closely-related subsystems – the control algorithm, the physical building and environment and the embedded implementation platform. In the traditional topdown approach, the control algorithm and the embedded platform are in general designed separately leading to suboptimal systems. We propose a co-design approach that analyzes the interaction between the control algorithm and the embedded platform through a set of interface variables (in this paper we address in particular sensing accuracy). We present six control algorithms that take into account the sensing error, and model the relation of control performance and cost versus sensing error. We also capture the relation of embedded platform cost versus sensing error by analysis of the collected data from a testbed. Based on these models, we explore the co-design of the control algorithm and the temperature sensing subsystem of the embedded platform to optimize with respect to energy cost and monetary cost while satisfying the constraints for user comfort level.