Distributed optimization for control and learning

Large scale multi-agent networked systems are becoming increasingly popular in industry and academia as they can be applied to represent systems in diverse application areas, such as intelligent surveillance and reconnaissance, mobile robotics, transportation networks and complex buildings. In such systems, issues related to control and learning have been significant technical challenges to affect system performance and overall cost. While centralized optimization approaches have been widely used by the engineering and computer science community, advanced and effective distributed optimization techniques have not been explored sufficiently and thoroughly in this regard. This study explores various categories of centralized and distributed optimization methods that have been applied or may be applicable for diverse engineering and science problems. The performance of centralized or distributed optimization schemes significantly depends on various factors including the types of objective functions, constraints, step sizes, and communication networks, etc. In this context, the focus of this dissertation is towards developing novel distributed optimization algorithms in order to solve challenging control and learning problems in various domains such as large-scale building energy systems and robotic networks. Specifically, we develop a generalized gossip-based subgradient method for solving distributed optimization problems in large-scale networked systems, e.g., larger-scale commercial building energy systems. Different from previous work, a user-defined control parameter is introduced to control a spectrum from globally optimal solution to suboptimal solutions and the trade-off between the solution accuracy and temporal convergence. We test and validate our proposed algorithm on a real testbed involving multiple zones incorporating a distributed control and sensing platform. In addition, we extend the distributed optimization to the deep learning area for solving an emerging topic, i.e., distributed deep learning, in fixed topology networks. While some previous work exists on this topic, the data parallelism and distributed computation are still not sufficiently explored. Therefore, we propose a class of distributed deep learning methods to tackle such issues by combining the consensus protocol and stochastic gradient descent approach. Moreover, to address the consensus-optimality trade-offs in distributed convex and nonconvex optimization, especially in deep learning when the training datasets for agents are non-balanced (non-iid), we propose and develop new approaches in this research, namely, incremental consensus-based distributed stochastic gradient descent and generalized consensus-based distributed (stochastic) gradient descent approach

Airborne Contaminant Dispersal in Critical Built Environments

The Indoor Air Quality (IAQ), being one of the most significant exposures to human beings, encompasses the concepts of comfort and safety from unwanted contaminants. Whereas the thermal comfort is controlled through proper conditioning and distribution of ventilated air, controlling the airborne contaminants requires careful investigation of the flow characteristics. IAQ translates to different requirements, depending on the intended use of the indoor environment. In critical indoor spaces such as Operating Rooms and Cleanrooms, the principal focus of IAQ is to remove/contain/divert contaminants flowing with the airstream to maintain the required sterility, as contamination can lead to adverse patient/product outcomes. The airborne contaminants, generally submicron-sized particles, are controlled by directional airflow through differential pressure, depending on whether the space needs to exfiltrate (e.g., Operating Room – positive pressure) or contain (e.g., Isolation Room – negative pressure) the airborne contaminants. The current design paradigm that determines such pressure differential assumes steady-state conditions. Theoretically, during the steady-state, the rate of flow velocity change is zero, resulting in a constant flow field in time, and the distribution of contaminants in the space can be modeled using ordinary differential equations. Therefore, the steady-state assumption must hold to explain the contamination dispersal. However, in practice, transient occupant interventions like a door opening and walking through the steady-state flow fields alter the flow characteristics. In response, this dissertation examines how occupant-introduced transient events affect the steady-state flow. This study aims to quantify and identify patterns of the changes in the flow characteristics for different scenarios of realistic door openings and human walks under a range of ventilation rates through controlled experiments and numerical simulations. Through specifically designed experiments, the impacts of door operation and occupant walking were characterized and quantified based on different levels of supply flow rates from the ventilation system. The results of the experiments suggested that special considerations were required to control for the transient phenomena and the pressure differential. The walking and door opening experiments also found distinguishable changes in the flow characteristics under each separate interaction between the indoor environment and the occupant. It was interesting to note that even though the magnitude of the effects was different for different levels of initial condition and intervention types, the changes in the flow properties exhibited identical patterns that were possible to model and make predictions. Thus, this dissertation considers the sporadic transient interventions from the occupants (e.g., - door opening and walking) as events and discusses an approximation method called ‘Event-Based Modeling’ (EBM) using the collected data through these experiments. Two-dimensional numerical models were developed to obtain additional data on the changes in airflow characteristics and were used to model and test the accuracy of EBM’s prediction capabilities. The results demonstrated that the predictions from EBM were accurate, and the computational efficiency is improved compared to the traditional numerical simulation approach. This method can eliminate parallel modeling of the same phenomena, providing alternatives to simulate complex and computationally intensive transient events repeatedly. As a potential application, the changes in flow velocities from human-environment interactions in a critical indoor environment like an operating room can be predicted using the EBM method. This way, the ventilation systems can be designed as occupant-centric and energy-efficient by considering the impacts of the transient events instead of only considering the steady-state events