This thesis concerns the cooperative control of networked vehicles. Autonomous driving is a topic that is currently being discussed with great interest from researchers, vehicle manufacturers and the corresponding media. Future autonomous vehicles should bring the passengers to their desired destination while improving both safety and efficiency compared to current human-driven vehicles. The inherent problem of all vehicle coordination tasks is to guarantee collision avoidance in every situation. To this end, autonomous vehicles have to share information with each other in order to perform traffic manoeuvres that require the cooperation of multiple vehicles. The fundamental problem of vehicle platooning is studied extensively which describes the task of arranging a set of vehicles so that they drive with a common velocity and a prescribed distance. Local design objectives are derived that have to be satisfied by the vehicle controllers. In particular, it is shown that the vehicles have to be externally positive to achieve collision avoidance. As an abstraction from real traffic scenarios, swarms of networked vehicles are considered. The main difference between swarming and traffic problems is that a communication structure that has been appropriate in the beginning might become unsuited for the control task due to the relative movement of the vehicles. To solve this problem, this thesis proposes to use the Delaunay triangulation as a switching communication structure.

Stability theory has allowed us to study both qualitative and quantitative properties of dynamical systems, and control theory has played a key role in designing numerous systems. Contemporary sensing and communication n- works enable collection and subscription of geographically-distributed inf- mation and such information can be used to enhance signi?cantly the perf- manceofmanyofexisting systems. Throughasharedsensing/communication network,heterogeneoussystemscannowbecontrolledtooperaterobustlyand autonomously; cooperative control is to make the systems act as one group and exhibit certain cooperative behavior, and it must be pliable to physical and environmental constraints as well as be robust to intermittency, latency and changing patterns of the information ?ow in the network. This book attempts to provide a detailed coverage on the tools of and the results on analyzing and synthesizing cooperative systems. Dynamical systems under consideration can be either continuous-time or discrete-time, either linear or non-linear, and either unconstrained or constrained. Technical contents of the book are divided into three parts. The ?rst part consists of Chapters 1, 2, and 4. Chapter 1 provides an overview of coope- tive behaviors, kinematical and dynamical modeling approaches, and typical vehicle models. Chapter 2 contains a review of standard analysis and design tools in both linear control theory and non-linear control theory. Chapter 4 is a focused treatment of non-negativematrices and their properties,multipli- tive sequence convergence of non-negative and row-stochastic matrices, and the presence of these matrices and sequences in linear cooperative systems.

Assuming only neighbor-neighbor interaction among vehicles, this monograph develops distributed consensus strategies that ensure that the information states of all vehicles in a network converge to a common value. Readers learn to deal with groups of autonomous vehicles in aerial, terrestrial, and submarine environments. Plus, they get the tools needed to overcome impaired communication by using constantly updated neighbor-neighbor interchange.

The paradigm of ‘multi-agent’ cooperative control is the challenge frontier for new control system application domains, and as a research area it has experienced a considerable increase in activity in recent years. This volume, the result of a UCLA collaborative project with Caltech, Cornell and MIT, presents cutting edge results in terms of the “dimensions” of cooperative control from leading researchers worldwide. This dimensional decomposition allows the reader to assess the multi-faceted landscape of cooperative control. Cooperative Control of Distributed Multi-Agent Systems is organized into four main themes, or dimensions, of cooperative control: distributed control and computation, adversarial interactions, uncertain evolution and complexity management. The military application of autonomous vehicles systems or multiple unmanned vehicles is primarily targeted; however much of the material is relevant to a broader range of multi-agent systems including cooperative robotics, distributed computing, sensor networks and data network congestion control. Cooperative Control of Distributed Multi-Agent Systems offers the reader an organized presentation of a variety of recent research advances, supporting software and experimental data on the resolution of the cooperative control problem. It will appeal to senior academics, researchers and graduate students as well as engineers working in the areas of cooperative systems, control and optimization.

Are there universal principles of coordinated group motion and if so what might they be? This carefully edited book presents how natural groupings such as fish schools, bird flocks, deer herds etc. coordinate themselves and move so flawlessly, often without an apparent leader or any form of centralized control. It shows how the underlying principles of cooperative control may be used for groups of mobile autonomous agents to help enable a large group of autonomous robotic vehicles in the air, on land or sea or underwater, to collectively accomplish useful tasks such as distributed, adaptive scientific data gathering, search and rescue, or reconnaissance.

Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented. Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.

An invaluable addition to the literature on UAV guidance and cooperative control, Cooperative Path Planning of Unmanned Aerial Vehicles is a dedicated, practical guide to computational path planning for UAVs. One of the key issues facing future development of UAVs is path planning: it is vital that swarm UAVs/ MAVs can cooperate together in a coordinated manner, obeying a pre-planned course but able to react to their environment by communicating and cooperating. An optimized path is necessary in order to ensure a UAV completes its mission efficiently, safely, and successfully. Focussing on the path planning of multiple UAVs for simultaneous arrival on target, Cooperative Path Planning of Unmanned Aerial Vehicles also offers coverage of path planners that are applicable to land, sea, or space-borne vehicles. Cooperative Path Planning of Unmanned Aerial Vehicles is authored by leading researchers from Cranfield University and provides an authoritative resource for researchers, academics and engineers working in the area of cooperative systems, cooperative control and optimization particularly in the aerospace industry.