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Multiagent Systems: Introduction and Coordination Control [Pehme köide]

(King Fahd University for Petroleum and Minerals, Saudi Arabia)
  • Formaat: Paperback / softback, 400 pages, kõrgus x laius: 234x156 mm, kaal: 689 g, 20 Tables, black and white; 10 Illustrations, color; 144 Illustrations, black and white
  • Ilmumisaeg: 30-Jun-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367509253
  • ISBN-13: 9780367509255
Teised raamatud teemal:
Multiagent Systems: Introduction and Coordination ControlSuurem pilt
  • Formaat: Paperback / softback, 400 pages, kõrgus x laius: 234x156 mm, kaal: 689 g, 20 Tables, black and white; 10 Illustrations, color; 144 Illustrations, black and white
  • Ilmumisaeg: 30-Jun-2021
  • Kirjastus: CRC Press
  • ISBN-10: 0367509253
  • ISBN-13: 9780367509255
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367509255.html
Märksõnad:

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.



Multiagent systems offer tremendous opportunities for development in computing and its applications. The objective of this book is to identify preliminary requirements and fundamental issues related to multiagent systems. The book is a comprehensive volume on the subject.

Preface iv
Acknowledgement vi
Author Biography xiii
1 Introduction
1(28)
1.1 Overview
1(1)
1.2 Elements of Graph Theory
2(15)
1.2.1 Basic results
3(1)
1.2.2 Laplacian spectrum of graphs
3(1)
1.2.3 Properties of adjacency matrix
4(3)
1.2.4 Nonlinear stochastic dynamical systems
7(5)
1.2.5 Complex dynamical systems
12(3)
1.2.6 Delay effects
15(1)
1.2.7 Sampled-data framework
16(1)
1.3 Multiagent System Approach
17(3)
1.3.1 Practical examples
17(1)
1.3.2 Some relevant definitions
18(2)
1.4 Mathematical Models for Agent Dynamics
20(3)
1.4.1 Single integrator model
21(1)
1.4.2 Double integrator model
21(1)
1.4.3 Uncertain fully actuated model
22(1)
1.4.4 Non-holonomic unicycle model
23(1)
1.5 Coordination and Control Problems
23(4)
1.5.1 Aggregation and social foraging
24(1)
1.5.2 Flocking and rendezvous
25(1)
1.5.3 Synchronization of coupled nonlinear oscillators
25(2)
1.6 Scope and Book Layout
27(2)
2 Theoretical Background
29(40)
2.1 Preliminaries of Distributed Systems
29(9)
2.1.1 Problem description
30(2)
2.1.2 Control design scheme
32(1)
2.1.3 Without communication delays
33(4)
2.1.4 With communication delays
37(1)
2.2 Networked Multiagent Systems
38(4)
2.2.1 Consensus in networks
40(1)
2.2.2 The f-consensus problem
41(1)
2.2.3 Iterative consensus and Markov chains
42(1)
2.3 Applications
42(3)
2.3.1 Synchronization of coupled oscillators
42(1)
2.3.2 Flocking theory
43(1)
2.3.3 Fast consensus in small-worlds
43(1)
2.3.4 Rendezvous in space
44(1)
2.3.5 Distributed sensor fusion in sensor networks
44(1)
2.3.6 Distributed formation control
44(1)
2.4 Information Consensus
45(14)
2.4.1 Algebraic connectivity and spectral properties
47(1)
2.4.2 Convergence analysis for directed networks
47(3)
2.4.3 Consensus in discrete-time
50(2)
2.4.4 Performance of consensus algorithms
52(2)
2.4.5 Alternative forms of consensus algorithms
54(3)
2.4.6 Weighted-average consensus
57(1)
2.4.7 Consensus under communication time-delays
58(1)
2.5 Consensus in Switching Networks
59(2)
2.6 Cooperation in Networked Control Systems
61(3)
2.6.1 Collective dynamics of multivehicle formation
62(1)
2.6.2 Stability of relative dynamics
63(1)
2.7 Simulation Studies
64(3)
2.7.1 Consensus in complex networks
64(2)
2.7.2 Multivehicle formation control
66(1)
2.8 Notes
67(2)
3 Distributed Intelligence in Power Systems
69(48)
3.1 Introduction to MAS Technology
69(12)
3.1.1 Autonomous microgrid system
71(1)
3.1.2 A state-space model
71(2)
3.1.3 Heuristic dynamic programming
73(1)
3.1.4 Discrete-time Bellman equation
74(1)
3.1.5 Value iteration algorithm
75(1)
3.1.6 Adaptive critics implementation
76(1)
3.1.7 Actor-critic implementation
77(1)
3.1.8 Simulations results
78(1)
3.1.9 Actor-critic tuning results
78(2)
3.1.10 Robustness of the proposed controller
80(1)
3.2 Operation in Islanded Mode
81(22)
3.2.1 Autonomous microgrid
85(1)
3.2.2 Primary control
86(3)
3.2.3 Fixed gain distributed secondary control
89(2)
3.2.4 Neural network distributed secondary control
91(1)
3.2.5 Stage 1: Selection of training data
91(3)
3.2.6 Stage 2: Selection of artificial neural network
94(1)
3.2.7 Stage 3: Neural network training
95(1)
3.2.8 Simulation results I
96(7)
3.3 Multiagent Coordination for Distributed Energy Resources
103(10)
3.3.1 Introduction
103(1)
3.3.2 Advantages of MAS approach
104(1)
3.3.3 Agent platform
105(1)
3.3.4 Software system analysis
106(1)
3.3.5 Distributed control system
107(1)
3.3.6 Simulation studies I
108(2)
3.3.7 Coordination between agents
110(2)
3.3.8 Checking reliability
112(1)
3.3.9 Simulation results II
113(1)
3.4 Notes
113(4)
4 Consensus for Heterogeneous Systems with Delays
117(52)
4.1 Introduction
117(1)
4.2 Multiagent Leader-Follower Consensus Problem
118(2)
4.3 Distributed Adaptive Control Design
120(9)
4.4 Illustrative Example
129(1)
4.5 Tracking and Coordination Using Sensor Networks
130(5)
4.6 Target Tracking in Sensor Networks
135(1)
4.7 Control System Architecture
136(9)
4.7.1 Sensor network and models
138(2)
4.7.2 Multitarget tracking
140(3)
4.7.3 Agent dynamics and coordination objective
143(2)
4.8 Control System Implementation
145(16)
4.8.1 Multisensor fusion module
145(5)
4.8.2 Multitarget tracking and multitrack fusion modules
150(5)
4.8.3 Multiagent coordination module
155(6)
4.9 Experimental Results
161(7)
4.9.1 Platform
163(2)
4.9.2 Live demonstration
165(3)
4.10 Notes
168(1)
5 Secure Control of Distributed Multiagent Systems
169(14)
5.1 Introduction
169(2)
5.2 Problem Formulation
171(1)
5.3 Main Results
172(5)
5.4 Illustrative Examples
177(3)
5.5 Notes
180(3)
6 Advanced Consensus Algorithms
183(90)
6.1 Event-Triggered Control for Multiagent Systems
183(15)
6.1.1 Introduction
183(2)
6.1.2 System model and problem statement
185(3)
6.1.3 Design tracking results
188(7)
6.1.4 Numerical example
195(3)
6.2 Pinning Coordination Control of Networked Systems
198(23)
6.2.1 Networked multi-vehicle systems
201(5)
6.2.2 Fixed communication topology
206(1)
6.2.3 Case of general graphs
207(2)
6.2.4 Example 6.1
209(2)
6.2.5 Strongly connected and balanced graphs
211(3)
6.2.6 Selection of the pinned nodes
214(4)
6.2.7 Pinning control with variable topology
218(1)
6.2.8 Simulation examples
219(2)
6.3 Distributed Consensus Control
221(17)
6.3.1 Consensus with observer-type protocol
224(1)
6.3.2 Dynamic consensus
225(2)
6.3.3 Consensus region
227(1)
6.3.4 Consensus with neutrally stable matrix
228(2)
6.3.5 Consensus with prescribed convergence speed
230(2)
6.3.6 Illustrative example 6.2
232(1)
6.3.7 Consensus with static protocols
233(2)
6.3.8 Formation control
235(1)
6.3.9 Illustrative example 6.3
236(2)
6.4 Consensus Control for Time-Delay Systems
238(12)
6.4.1 Problem formulation
238(3)
6.4.2 Fixed interconnection topology
241(4)
6.4.3 Switched interconnection topology
245(3)
6.4.4 Illustrative example 6.4
248(1)
6.4.5 Illustrative example 6.5
249(1)
6.5 Robust Consensus of Multiagent Systems
250(20)
6.5.1 Problem description
251(2)
6.5.2 Analytic results
253(8)
6.5.3 Illustrative example 6.6
261(9)
6.6 Notes
270(3)
7 Cooperative Control of Networked Power Systems
273(32)
7.1 Coordinated Model Predictive Power Flows
273(6)
7.1.1 Introduction
274(1)
7.1.2 System architecture
275(1)
7.1.3 Wind power generation
276(1)
7.1.4 Photovoltaic module generators
277(1)
7.1.5 Energy storage system dynamics
277(1)
7.1.6 Loads
278(1)
7.1.7 Energy management unit
278(1)
7.1.8 Power price mechanism
278(1)
7.2 Power Scheduling in Networked MG
279(14)
7.2.1 Networked topology
279(1)
7.2.2 GCC of networked MG
279(1)
7.2.3 MPC-based power scheduling
279(1)
7.2.4 Optimization problem formulation
280(1)
7.2.5 State equations and constraints
281(1)
7.2.6 Case studies
282(1)
7.2.7 Simulation setup
283(1)
7.2.8 Case study 1
283(7)
7.2.9 Case study 2
290(2)
7.2.10 Case study 3
292(1)
7.3 Distributed Robust Control in Smart Microgrids
293(9)
7.3.1 A microgrid model
293(1)
7.3.2 Microgrid group model
293(2)
7.3.3 Problem formulation
295(1)
7.3.4 Robust group control
296(2)
7.3.5 Distributed information models
298(1)
7.3.6 Simulation study
299(1)
7.3.7 Solution procedure A
300(1)
7.3.8 Solution procedure B
301(1)
7.3.9 Solution procedure C
301(1)
7.3.10 Solution procedure D
301(1)
7.4 Notes
302(3)
8 Dynamic Graphical Games
305(40)
8.1 Constrained Graphical Games
305(15)
8.1.1 Reinforcement learning
306(1)
8.1.2 Synchronization control problem
307(1)
8.1.3 Performance evaluation of the game
308(1)
8.1.4 Optimality conditions
309(1)
8.1.5 Bellman equations
310(1)
8.1.6 The Hamiltonian function
311(2)
8.1.7 Coupled IRL-Hamilton-Jacobi theory
313(2)
8.1.8 Coupled IRL-HJB equations
315(2)
8.1.9 Nash equilibrium solution
317(1)
8.1.10 Stability analysis
318(2)
8.2 Value Iteration Solution and Implementation
320(11)
8.2.1 Value iteration algorithm
321(1)
8.2.2 Graph solution implementation
321(2)
8.2.3 Online actor-critic neural networks tuning
323(1)
8.2.4 Simulation results I
323(1)
8.2.5 Simulation case 1
324(1)
8.2.6 Simulation case 2
324(1)
8.2.7 Simulation case 3
325(6)
8.3 Multiagent Reinforcement Learning for Microgrids
331(13)
8.3.1 Microgrid control requirements
332(1)
8.3.2 Features of MAS technology
333(2)
8.3.3 A multiagent reinforcement learning method
335(3)
8.3.4 Critical operation in island mode
338(2)
8.3.5 Simulation results II
340(4)
8.4 Notes
344(1)
References 345(50)
Index 395
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.