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MULTIAGENT SYSTEMS : introduction and coordination control.
Multiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel appro...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified]
CRC Press,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a MULTIAGENT SYSTEMS : |b introduction and coordination control. |
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| 520 | |a Multiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel approach of handling multiplayer interactive systems. In particular, the multiagent system approach is adapted to model, control, manage or test the operations and management of several system applications including multi-vehicles, microgrids, multi-robots, where agents represent individual entities in the network. Each participant is modeled as an autonomous participant with independent strategies and responses to outcomes. They are able to operate autonomously and interact pro-actively with their environment. In recent works, the problem of information consensus is addressed, where a team of vehicles communicate with each other to agree on key pieces of information that enable them to work together in a coordinated fashion. The problem is challenging because communication channels have limited range and there are possibilities of fading and dropout. The book comprises chapters on synchronization and consensus in multiagent systems. It shows that the joint presentation of synchronization and consensus enables readers to learn about similarities and differences of both concepts. It reviews the cooperative control of multi-agent dynamical systems interconnected by a communication network topology. Using the terminology of cooperative control, each system is endowed with its own state variable and dynamics. A fundamental problem in multi-agent dynamical systems on networks is the design of distributed protocols that guarantee consensus or synchronization in the sense that the states of all the systems reach the same value. It is evident from the results that research in multiagent systems offer opportunities for further developments in theoretical, simulation and implementations. This book attempts to fill this gap and aims at presenting a comprehensive volume that documents theoretical aspects and practical applications. | ||
| 545 | 0 | |a MagdiSadek Mahmoud obtained PhD. in systems engineering from Cairo University in 1974. He has been a professor of engineering since 1984 and he is now Distinguished Professor at KFUPM, Saudi Arabia. He served on the faculty at universities worldwide: Egypt (CU, AUC), Kuwait (KU), UAE (UAEU), UK (UMIST), USA (Pitt, Case Western), Singapore (Nanyang) and Australia (Adelaide). He lectured in Venezuela (Caracas), Germany (Hanover), UK (Kent, UCL), USA (UoSA), Canada (Montreal), and China (BIT, Yanshan). Dr. Mahmoud is the principal author of 30 books, 25 book-chapters and the author/co-author of more than 600 peer-reviewed papers. He is the recipient of two national, one regional and several university prizes for outstanding research in engineering and applied mathematics. | |
| 505 | 0 | |a Cover -- Title Page -- Copyright Page -- Dedication -- Preface -- Acknowledgement -- Table of Contents -- Author Biography -- 1: Introduction -- 1.1 Overview -- 1.2 Elements of Graph Theory -- 1.2.1 Basic Results -- 1.2.2 Laplacian Spectrum of Graphs -- 1.2.3 Properties of Adjacency Matrix -- 1.2.4 Nonlinear Stochastic Dynamical Systems -- 1.2.5 Complex Dynamical Systems -- 1.2.6 Delay Effects -- 1.2.7 Sampled-Data Framework -- 1.3 Multiagent System Approach -- 1.3.1 Practical Examples -- 1.3.2 Some Relevant Definitions -- 1.4 Mathematical Models for Agent Dynamics | |
| 505 | 8 | |a 1.4.1 Single Integrator Model -- 1.4.2 Double Integrator Model -- 1.4.3 Uncertain Fully Actuated Model -- 1.4.4 Non-Holonomic Unicycle Model -- 1.5 Coordination and Control Problems -- 1.5.1 Aggregation and Social Foraging -- 1.5.2 Flocking and Rendezvous -- 1.5.3 Synchronization of Coupled Nonlinear Oscillators -- 1.6 Scope and Book Layout -- 2: Theoretical Background -- 2.1 Preliminaries of Distributed Systems -- 2.1.1 Problem Description -- 2.1.2 Control Design Scheme -- 2.1.3 Without Communication Delays -- 2.1.4 With Communication Delays -- 2.2 Networked Multiagent Systems | |
| 505 | 8 | |a 2.2.1 Consensus in Networks -- 2.2.2 The f-Consensus Problem -- 2.2.3 Iterative Consensus and Markov Chains -- 2.3 Applications -- 2.3.1 Synchronization of Coupled Oscillators -- 2.3.2 Flocking Theory -- 2.3.3 Fast Consensus in Small-Worlds -- 2.3.4 Rendezvous in Space -- 2.3.5 Distributed Sensor Fusion in Sensor Networks -- 2.3.6 Distributed Formation Control -- 2.4 Information Consensus -- 2.4.1 Algebraic Connectivity and Spectral Properties -- 2.4.2 Convergence Analysis for Directed Networks -- 2.4.3 Consensus in Discrete-Time -- 2.4.4 Performance of Consensus Algorithms | |
| 505 | 8 | |a 2.4.5 Alternative Forms of Consensus Algorithms -- 2.4.6 Weighted-Average Consensus -- 2.4.7 Consensus Under Communication Time-Delays -- 2.5 Consensus in Switching Networks -- 2.6 Cooperation in Networked Control Systems -- 2.6.1 Collective Dynamics of Multivehicle Formation -- 2.6.2 Stability of Relative Dynamics -- 2.7 Simulation Studies -- 2.7.1 Consensus in Complex Networks -- 2.7.2 Multivehicle Formation Control -- 2.8 Notes -- 3: Distributed Intelligence in Power Systems -- 3.1 Introduction to MAS Technology -- 3.1.1 Autonomous Microgrid System -- 3.1.2 A State-Space Model | |
| 505 | 8 | |a 3.1.3 Heuristic Dynamic Programming -- 3.1.4 Discrete-Time Bellman Equation -- 3.1.5 Value Iteration Algorithm -- 3.1.6 Adaptive Critics Implementation -- 3.1.7 Actor-Critic Implementation -- 3.1.8 Simulations Results -- 3.1.9 Actor-Critic Tuning Results -- 3.1.10 Robustness of the Proposed Controller -- 3.2 Operation in Islanded Mode -- 3.2.1 Autonomous Microgrid -- 3.2.2 Primary Control -- 3.2.3 Fixed Gain Distributed Secondary Control -- 3.2.4 Neural Network Distributed Secondary Control -- 3.2.5 Stage 1: Selection of Training Data -- 3.2.6 Stage 2: Selection of Artificial Neural Network | |
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Multiagent systems. | |
| 650 | 6 | |a Systèmes multiagents (Intelligence artificielle) | |
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| 650 | 7 | |a COMPUTERS |x Programming |x Systems Analysis & Design. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Data Modeling & Design. |2 bisacsh | |
| 650 | 7 | |a Multiagent systems. |2 fast |0 (OCoLC)fst01749717 | |
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