Second Order Consensus Of Continuous Time Multi Agent Systems

Second-Order Consensus of Continuous-Time Multi-Agent Systems focuses on the characteristics and features of second-order agents, communication networks, and control protocols/algorithms in continuous consensus of multi-agent systems. The book provides readers with background on consensus control of multi-agent systems and introduces the intrinsic characteristics of second-order agents’ behavior, including the development of continuous control protocols/algorithms over various types of underlying communication networks, as well as the implementation of computation- and communication-efficient strategies in the execution of protocols/algorithms. The book's authors also provide coverage of the frameworks of stability analysis, algebraic criteria and performance evaluation. On this basis, the book provides an in-depth study of intrinsic nonlinear dynamics from agents’ perspective, coverage of unbalanced directed topology, random switching topology, event-triggered communication, and random link failure, from a communication networks’ perspective, as well as leader-following control, finite-time control, and global consensus control, from a protocols/algorithms’ perspective. Finally, simulation results including practical application examples are presented to illustrate the effectiveness and the practicability of the control protocols and algorithms proposed in this book. Introduces the latest and most advanced protocols and algorithms in second-order consensus of continuous time, multi-agent systems with various characteristics Provides readers with in-depth methods on how to construct the frameworks of stability analysis, algebraic criteria, and performance evaluation, thus helping users develop novel consensus control methods Includes systematic introductions and detailed implementations on how control protocols and algorithms solve problems in real world, second-order, multi-agent systems, including solutions for engineers in related fields

Swarming, or aggregations of organisms in groups, can be found in nature in many organisms ranging from simple bacteria to mammals. Such behavior can result from several different mechanisms. For example, individuals may respond directly to local physical cues such as concentration of nutrients or distribution of some chemicals as seen in some bacteria and social insects, or they may respond directly to other individuals as seen in fish, birds, and herds of mammals. In this dissertation, we consider models for aggregating and social foraging swarms and perform rigorous stability analysis of emerging collective behavior. Moreover, we consider formation control of a general class of multi-agent systems in the framework of nonlinear output regulation problem with application on formation control of mobile robots. First, an individual-based continuous time model for swarm aggregation in an n-dimensional space is identified and its stability properties are analyzed. The motion of each individual is determined by two factors: (i) attraction to the other individuals on long distances and (ii) repulsion from the other individuals on short distances. It is shown that the individuals (autonomous agents or biological creatures) will form a cohesive swarm in a finite time. Moreover, explicit bounds on the swarm size and time of convergence are derived. Then, the results are generalized to a more general class of attraction/repulsion functions and extended to handle formation stabilization and uniform swarm density. After that, we consider social foraging swarms. We assume that the swarm is moving in an environment with an "attractant/repellent" profile (i.e., a profile of nutrients or toxic substances) which also affects the motion of each individual by an attraction to the more favorable or nutrient rich regions (or repulsion from the unfavorable or toxic regions) of the profile. The stability properties of the collective behavior of the swarm for different profiles are studied and conditions for collective convergence to more favorable regions are provided. Then, we use the ideas for modeling and analyzing the behavior of honey bee clusters and in-transit swarms, a phenomena seen during the reproduction of the bees. After that, we consider one-dimensional asynchronous swarms with time delays. We prove that, despite the asynchronism and time delays in the motion of the individuals, the swarm will converge to a comfortable position with comfortable intermember spacing. Finally, we consider formation control of a multi-agent system with general nonlinear dynamics. It is assumed that the formation is required to follow a virtual leader whose dynamics are generated by an autonomous neutrally stable system. We develop a decentralized control strategy based on the nonlinear output regulation (servomechanism) theory. We illustrate the procedure with application to formation control of mobile robots.

This book focuses on analysis and design problems for high-order linear time-invariant (LTI) swarm systems (multi-agent systems) to achieve consensus, formation, containment and formation-containment. As a first step, the concepts of practical consensus and formation-containment are introduced. Unlike previous research, the formation in this book can be time-varying. A general framework for consensus, consensus tracking, formation, containment and state formation-containment is presented for the first time. Sufficient/necessary and sufficient conditions, and approaches to designing the protocols for swarm systems to achieve these control objectives, are respectively proposed. Autonomous time-varying formation experiments using five quadrotor unmanned aerial vehicles (UAVs) are conducted in an outdoor setting to demonstrate the theoretical results.

This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.

A detailed and systematic introduction to the distributed cooperative control of multi-agent systems from a theoretical, network perspective Features detailed analysis and discussions on the distributed cooperative control and dynamics of multi-agent systems Covers comprehensively first order, second order and higher order systems, swarming and flocking behaviors Provides a broad theoretical framework for understanding the fundamentals of distributed cooperative control

The aim of this handbook is to summarize the recent rapidly developed real-time computing technologies, from theories to applications. This handbook benefits the readers as a full and quick technical reference with a high-level historic review of technology, detailed technical descriptions and the latest practical applications. In general, the handbook is divided into three main parts (subjected to be modified): theory, design, and application covering different but not limited to the following topics: - Real-time operating systems - Real-time scheduling - Timing analysis - Programming languages and run-time systems - Middleware systems - Design and analysis tools - Real-time aspects of wireless sensor networks - Energy aware real-time methods

This book describes an effective approach to the cooperative and coordinated control of multivehicle systems. This rigorous analytic approach guarantees the stability of coordinated and cooperating vehicles using distributed protocols and uses low-energy, event-triggered mechanisms for networked vehicle control. The text covers: design of a cooperative protocol to achieve consensus for multivehicle systems, allowing cooperation that is resistant to the effects of packet loss and/or adversarial attack; analysis and synthesis of an event-triggering mechanism for cooperative multivehicle systems over uncertain networks; and the problem of distributed leader-following consensus and methods for compelling multivehicle systems to reach consensus. Throughout the book, cooperation problems are transformed into stability problems. Lyapunov theory is used to guarantee cooperation among agents. The distributed approach is applied to triggering mechanisms, the cooperation process, and the impact of cyber-attacks. Discrete-time analysis shows how the event-based structure can be designed to match the performance of continuous-time counterparts. The book details applications and computer simulation with several practical examples. This book is of interest to a wide audience from the graduate student, through the academic researcher to the industrial practitioner, all of them sharing a common interest in the stability and security of multiagent systems.