A fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled building automation systems

Internet of Things (IoT) technologies are increasingly implemented in buildings as the cost-effective smart sensing infrastructure of building automation systems (BASs). They are also dispersed computing resources for novel distributed optimal control approaches. However, wireless communication networks are critical to fulfill these tasks with the performance influenced by inherent uncertainties in networks, e.g., unpredictable occurrence of link failures. Centralized and hierarchical distributed approaches are vulnerable against link failure, while the robustness of fully distributed approaches depends on the algorithms adopted. This study therefore proposes a fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled BASs. The distributed algorithm is adopted that agents know their out-neighbors only. Agents directly coordinate with the connected neighbors for global optimization. Tests are conducted to test and validate the proposed approach by comparing with existing approaches, i.e., the centralized, the hierarchical distributed and the fully distributed approaches. Results show that different approaches are vulnerable against to uncertainties of communication link to different extents. The proposed approach always guarantees the optimal control performance under normal conditions and conditions with link failures, verifying its high robustness. It also has low computation complexity and high optimization efficiency, thus applicable on IoT-enabled BASs

Final Causality in the Thought of Thomas Aquinas

Throughout his corpus, Thomas Aquinas develops an account of final causality that is both philosophically nuanced and interesting. The aim of my dissertation is to provide a systematic reconstruction of this account of final causality, one that clarifies its motivation and appeal. The body of my dissertation consists of four chapters. In Chapter 1, I examine the metaphysical underpinnings of Aquinas’s account of final causality by focusing on how Aquinas understands the causality of the final cause. I argue that Aquinas holds that an end is a cause because it is the determinate effect toward which an agent’s action is directed. I proceed by first presenting the general framework of causality within which Aquinas understands final causality. I then consider how Aquinas justifies the reality of each of the four kinds of cause, placing special emphasis on the final cause. In Chapter 2, I consider final causality from the perspective of goodness and explore the reasons why Aquinas thinks that the end of an action is always good. For even if one was convinced that the end of an action is indeed a cause, one might still resist attributing any normative or evaluative properties to the end, much less a positively-valenced normative property like goodness. In this chapter, I show how, given Aquinas’s metaphysics of powers and his characterization of goodness as that which all desire, it follows that every action is for the sake of some good. In Chapter 3, I consider Aquinas’s account of the relation between final causality and cognition. In many passages throughout his corpus—most famously in the fifth of his Five Ways—Aquinas advances the claim that cognition plays an essential role in final causality. In this chapter, I explore Aquinas’s account of the relation between final causality and cognition by reconstructing his Fifth Way and investigating the metaphysical foundations on which it rests. While the first three chapters of my dissertation focus on Aquinas’s account of final causality from the perspective of the ends of individual agents, in Chapter 4 I broaden my focus to consider the way in which the account of final causality developed in these earlier chapters shapes Aquinas’s philosophical cosmology. I argue that, on Aquinas’s view, when an individual agent acts for an end, it is plays a role in a larger system, e.g. a polis, an ecosystem, or the universe itself

A Distributed Model Predictive Control Approach for Optimal Coordination of Multiple Thermal Zones in a Large Open Space

Model Predictive Control (MPC) based approaches have recently seen a significant increase in applications to the supervisory control of building heating, ventilation and air-conditioning (HVAC) systems, thanks to their ability to incorporate weather, occupancy, and utility price information in the optimization of heating/cooling strategy while satisfying the physical constraints of HVAC equipment. Many of the proposed MPC solution approaches are centralized ones that often have difficulty in dealing with large-scale building clusters or even a single building consisting of multiple thermal zones due to the high computational cost caused by the large number of decision variables and the overhead in information gathering and distribution. This paper investigates a decomposition technique based on a variant of the Alternating Direction Method of Multipliers (ADMM) that can significantly reduce the computational and communication costs of centralized solutions, and more importantly, facilitates a plug-and-play implementation. The proposed Distributed Model Predictive Control (DMPC) framework takes advantage of parallel computation and collaborations among multiple agents, each of which is assigned to address a smaller dimensional optimization problem. The proposed method is general in that it can accommodate some fairly general types of couplings in agent dynamics as well as in the cost function; therefore it can be used for a broad class of HVAC optimal operation problems. A case study on one of the Purdue Living Labs is carried out to demonstrate the effectiveness of the proposed method. The Living Lab considered is an open office space served by one central air handling unit (AHU) with multiple diffusers whose openings can be controlled individually. The office space is partitioned into multiple thermal zones with individual thermostat controls. In view of significant thermal couplings due to direct air exchange and noticeable load gradient between zones, a multiple thermal zone coordination problem is formulated with the objective of optimally scheduling the different thermostat setpoints for energy minimization and comfort delivery while satisfying actuation constraints. Preliminary results show that the proposed method successfully converges to the optimal solution for the problem concerned in this case study. The optimal solutions demonstrate significant opportunities of inter-zonal coordination and energy savings potentials

An Agent-based Control Implementation for the Optimal Coordination of Multiple Rooftop Units

Model predictive control (MPC) has been a popular advanced supervisory control approach for optimizing the operations of building heating, ventilation and air-conditioning (HVAC) systems, with the objectives of reducing energy consumption and delivering better comfort. However, centralized MPC designs are often 1) not scalable to the increasing sizes of the building systems, 2) not adaptive to subsystem addition/attrition, i.e., ‘Plug-and-Play’ implementation. Agent-based approaches, such as distributed model predictive control (DMPC), are attractive alternative. In this paper, taking a multiple rooftop units (RTU) coordination problem as a case study, we experimentally investigate the energy saving potential by implementing an agent-based DMPC strategy to coordinate the operations of multiple ‘virtual’ variable-speed RTUs with diverse unit efficiencies (COP) in an open space with multiple sub-zones. The operations of three RTUs are emulated by three groups of variable air volume (VAV) diffusers that can be individually controlled to provide continuously changing sensible cooling loads into respective zones. A multi-zone model that accurately captures the thermal interactions of different zones for control purposes is developed. This model takes the sensible cooling loads provided by the three ‘virtual’ RTUs as controllable inputs, and ambient temperature, solar radiation, internal heat gains (occupancy, plug, lighting and equipment loads) as exogenous inputs. Three laptop computers are dispatched into the three thermal zones as local agents. A server computer connected to both the Building Automation System (BAS) and the outside internet is responsible for predicting various exogenous inputs and exchanging information with the local agents. Experimental results show that the proposed agent-based DMPC design and implementation are able to achieve over 20% cost savings, in terms of electricity consumption charge with Time-of-Use pricing schedules, while at the same time maintaining local occupancy comfort. The savings can be further broken down into two parts: 1) utilizing the RTUs with higher overall unit efficiency 2) shifting the aggregate cooling load of the room into periods with lower electricity price or higher RTUs’ COP

Ten questions concerning integrating smart buildings into the smart grid

Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be ‘prosumers’ on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and non-technical areas

Limited-Communication Distributed Model Predictive Control for HVAC Systems

This dissertation proposes a Limited-Communication Distributed Model Predictive Control algorithm for networks with constrained discrete-time linear processes as local subsystems. The introduced algorithm has an iterative and cooperative framework with neighbor-to-neighbor communication structure. Convergence to a centralized solution is guaranteed by requiring coupled subsystems with local information to cooperate only. During an iteration, a local controller exchanges its predicted effects with local neighbors (which are treated as measured input disturbances in local dynamics) and receives the neighbor sensitivities for these effects at next iteration. Then the controller minimizes a local cost function that counts for the future effects to neighbors weighted by the received sensitivity information. Distributed observers are employed to estimate local states through local input-output signals. Closed-loop stability is proved for sufficiently long horizons. To reduce the computational loads associated with large horizons, local decisions are parametrized by Laguerre functions. A local agent can also reduce the communication burden by parametrizing the communicated data with Laguerre sequences. So far, convergence and closed-loop stability of the algorithm are proven under the assumptions of accessing all subsystem dynamics and cost functions information by a centralized monitor and sufficient number of iterations per sampling. However, these are not mild assumptions for many applications. To design a local convergence condition or a global condition that requires less information, tools from dissipativity theory are used. Although they are conservative conditions, the algorithm convergence can now be ensured either by requiring a distributed subsystem to show dissipativity in the local information dynamic inputs-outputs with gain less than unity or solving a global dissipative inequality with subsystem dissipativity gains and network topology only. Free variables are added to the local problems with the object of having freedom to design such convergence conditions. However, these new variables will result into a suboptimal algorithm that affects the proposed closed-loop stability. To ensure local MPC stability, therefore, a distributed synthesis, which considers the system interactions, of stabilizing terminal costs is introduced. Finally, to illustrate the aspects of the algorithm, coupled tank process and building HVAC system are used as application examples