Information Theory and Cooperative Control in Networked Multi-Agent Systems with Applications to Smart Grid

This dissertation focuses on information theoretic aspects of and cooperative control techniques in networked multi-agent systems (NMAS) with communication constraints. In the first part of the dissertation, information theoretic limitations of tracking problems in networked control systems, especially leader-follower systems with communication constraints, are studied. Necessary conditions on the data rate of each communication link for tracking of the leader-follower systems are provided. By considering the forward and feedback channels as one cascade channel, we also provide a lower bound for the data rate of the cascade channel for the system to track a reference signal such that the tracking error has finite second moment. Finally, the aforementioned results are extended to the case in which the leader system and follower system have different system models. In the second part, we propose an easily scalable hierarchical decision-making and control architecture for smart grid with communication constraints in which distributed customers equipped with renewable distributed generation (RDG) interact and trade energy in the grid. We introduce the key components and their interactions in the proposed control architecture and discuss the design of distributed controllers which deal with short-term and long-term grid stability, power load balancing and energy routing. At microgrid level, under the assumption of user cooperation and inter-user communications, we propose a distributed networked control strategy to solve the demand-side management problem in microgrids. Moreover, by considering communication delays between users and microgrid central controller, we propose a distributed networked control strategy with prediction to solve the demand-side management problem with communication delays. In the third part, we consider the disturbance attenuation and stabilization problem in networked control systems. To be specific, we consider the string stability in a large group of interconnected systems over a communication network. Its potential applications could be found in formation tracking control in groups of robots, as well as uncertainty reduction and disturbance attenuation in smart grid. We propose a leader-following consensus protocol for such interconnected systems and derive the sufficient conditions, in terms of communication topology and control parameters, for string stability. Simulation results and performance in terms of disturbance propagation are also given. In the fourth part, we consider distributed tracking and consensus in networked multi-agent systems with noisy time-varying graphs and incomplete data. In particular, a distributed tracking with consensus algorithm is developed for the space-object tracking with a satellite surveillance network. We also intend to investigate the possible application of such methods in smart grid networks. Later, conditions for achieving distributed consensus are discussed and the rate of convergence is quantified for noisy time-varying graphs with incomplete data. We also provide detailed simulation results and performance comparison of the proposed distributed tracking with consensus algorithm in the case of space-object tracking problem and that of distributed local Kalman filtering with centralized fusion and centralized Kalman filter. The information theoretic limitations developed in the first part of this dissertation provide guildlines for design and analysis of tracking problems in networked control systems. The results reveal the mutual interaction and joint application of information theory and control theory in networked control systems. Second, the proposed architectures and approaches enable scalability in smart grid design and allow resource pooling among distributed energy resources (DER) so that the grid stability and optimality is maintained. The proposed distributed networked control strategy with prediction provides an approach for cooperative control at RDG-equipped customers within a self-contained microgrid with different feedback delays. Our string stability analysis in the third part of this dissertation allows a single networked control system to be extended to a large group of interconnected subsystems while system stability is still maintained. It also reveals the disturbance propagation through the network and the effect of disturbance in one subsystem on other subsystems. The proposed leader-following consensus protocol in the constrained communication among users reveals the effect of communication in stabilization of networked control systems and the interaction between communication and control over a network. Finally, the distributed tracking and consensus in networked multi-agent systems problem shows that information sharing among users improves the quality of local estimates and helps avoid conflicting and inefficient distributed decisions. It also reveals the effect of the graph topologies and incomplete node measurements on the speed of achieving distributed decision and final consensus accuracy

Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Envirosuite: An Environmentally-Immersive Programming Framework for Wireless Sensor Networks

Networked, embedded sensors allow for an instrumentation of the physical world at unprecedented granularities and from unimagined perspectives. The advent of a ubiquitous sensing era is evident. Yet, sensor network techniques are still far from entering mainstream adoption due to multiple unresolved research challenges, especially due to the high development cost of sensor network applications. Therefore, in this dissertation, we propose to design, implement, and evaluate an environmentally-immersive programming framework, called EnviroSuite, to reduce sensor network software development cost. The goal of our research is to create reusable sensor network development support for the community and reduce the adoption barriers for a broader category of users, ultimately leading to a transition of sensor networks from a research concept to a general-purpose technology available for use for a wide variety of research, government, industry, and everyday purposes. Current sensor network programming practice remains very cumbersome and inefficient for several reasons. First, most existing programming abstractions for sensor networks are either too low-level (thus too tedious and error-prone) or too high-level (unable to support the diversity of sensor network applications). Second, there is no clear separation between application-level programming and system-level programming. A significant concern is the lack of a general middleware library to isolate application developers from low-level details. Finally, testing sensor network systems is particularly challenging. Sensor systems interact heavily with a (non-repeatable) physical environment, making lab experiments not representative and on-site experiments very costly. This dissertation is targeted for a comprehensive solution that addresses all the above-mentioned problems. The EnviroSuite framework consists of (i) a new programming paradigm that exports environment-based abstractions, (ii) critical middleware services that support the abstractions and separate application programmers from tedious, low-level details, and (iii) testing tools geared for in-situ experimenting, debugging, and troubleshooting. First, we introduce a new programming paradigm, called environmentally-immersive programming (EIP), to capture the common characteristics of sensor network applications, the rich, distributed interactions with the physical environment. EIP refers to an object-based programming model in which individual objects represent physical elements in the external environment. It allows the programmer to think directly in terms of physical objects or events of interest. We provide language primitives for programmers to easily implement their environmental tracking and monitoring applications in EIP. A preprocessor translates such EIP code transparently into a library of support middleware services, central to which are object management algorithms, responsible for maintaining a unique mapping between physical and logical objects. The major outcome of sensor networks is observations of the instrumented environment, in other words, sensory data. Implementing an application mainly involves encoding how to generate, store, and collect such data. EIP object abstractions provide simple means for programmers to define how observations of the environment should be made via distributed coordination among multiple nodes, thus simplifying data generation. Yet, the next steps, namely, data storage and collection, remain complicated and fastidious. To isolate programmers from such concerns, we also include in the support library a set of data management services, comprising both network protocols and storage systems to allow data to be collected either in real-time or in a delay-tolerant manner. The final phase in sensor network software development life-cycle is testing, typically performed in-field, where the effects of environmental realities can be studied. However, physical events from the dynamic environment are normally asynchronous and non-repeatable. This lack of repeatability makes the last phase particularly difficult and costly. Hence, it is essential to have the capability to capture and replay sensing events, providing a basis not only for software testing, but also for realistic protocol comparison and parameter tuning. To achieve that, EnviroSuite also provides testing and debugging facilities that enable controllable and repeatable in-field experiments. Finally, to demonstrate the benefits of our framework, we build multiple representative applications upon EnviroSuite, drawn from both tracking systems such as military surveillance, and monitoring systems such as environmental acoustic monitoring. We install these applications into off-the-shelf hardware platforms and physically deploy the hardware into realistic environments. Empirical results collected from such deployments demonstrate the efficacy of EnviroSuite

Review of distributed control and optimization in energy internet: From traditional methods to artificial intelligence-based methods

Abstract: Energy internet (EI) can alleviate the arduous challenges brought about by the energy crisis and global warming and has aroused the concern of many scholars. In the research of EI control systems, the access of distributed energy causes the power system to exhibit complex nonlinearity, high uncertainty and strong coupling. Traditional control and optimization methods often have limited effectiveness in solving these problems. With the widespread application of distributed control technology and the maturity of artificial intelligence (AI) technology, the combination of distributed control and AI has become an effective method to break through current research bottlenecks. This study reviews the research progress of EI distributed control technologies based on AI in recent years. It can be found that AI‐based distributed control methods have many advantages in maintaining EI stability and achieving optimal energy management. This combination of AI and distributed control makes EI control systems more intelligent, safe and efficient, which will be an important direction for future research. The purpose of this study is to provide a reference as well as useful research ideas for the study of EI control systems

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space