Muutke küpsiste eelistusi

Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments 2015 ed. [Pehme köide]

  • Formaat: Paperback / softback, 124 pages, kõrgus x laius: 235x155 mm, kaal: 2488 g, 23 Illustrations, color; 1 Illustrations, black and white; XIII, 124 p. 24 illus., 23 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Electrical and Computer Engineering
  • Ilmumisaeg: 27-Jun-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319190717
  • ISBN-13: 9783319190716
Teised raamatud teemal:
  • Pehme köide
  • Hind: 48,70 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 57,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 124 pages, kõrgus x laius: 235x155 mm, kaal: 2488 g, 23 Illustrations, color; 1 Illustrations, black and white; XIII, 124 p. 24 illus., 23 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Electrical and Computer Engineering
  • Ilmumisaeg: 27-Jun-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319190717
  • ISBN-13: 9783319190716
Teised raamatud teemal:
This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi-agent networks and their performance analysis. It synthesizes and analyzes distributed strategies for three collaborative tasks: distributed cooperative optimization, mobile sensor deployment and multi-vehicle formation control. The book integrates miscellaneous ideas and tools from dynamic systems, control theory, graph theory, optimization, game theory and Markov chains to address the particular challenges introduced by such complexities in the environment as topological dynamics, environmental uncertainties, and potential cyber-attack by human adversaries.The book is written for first- or second-year graduate students in a variety of engineering disciplines, including control, robotics, decision-making, optimization and algorithms and with backgrounds in aerospace engineering, computer science, electrical engineering, mechanical engineering

and operations research. Researchers in these areas may also find the book useful as a reference.

1 Preliminaries.- 1.1 Basic Notations.- 1.2 The Consensus Problem.- 1.3 Convex Optimization.- 1.4 Non-Cooperative Game Theory.- 1.5 Markov Chains.- 1.6 Notes.- 2 Distributed Cooperative Optimization.- 2.1 Introduction.- 2.2 Problem Formulation.- 2.3 Case (I): Absence of Equality Constraint.- 2.4 Case (Ii): Identical Local Constraint Sets.- 2.5 Appendix.- 2.6 Notes.- 3 Game Theoretic Optimal Sensor Deployment.- 3.1 Introduction.- 3.2 Problem Formulation.- 3.3 Distributed Learning Algorithms.- 3.4 Convergence Analysis.- 3.5 Numerical Examples.- 3.6 Notes.- 4 Distributed Resilient Formation Control.- 4.1 Introduction.- 4.2 Problem Formulation.- 4.3 Preliminaries.- 4.4 Distributed Attack-Resilient Algorithm.- 4.5 Convergence Analysis.- 4.6 Discussion.- 4.7 Numerical Examples.- 4.8 Notes.- Index.- References.
1 Preliminaries
1(20)
1.1 Basic Notations
1(1)
1.2 The Consensus Problem
1(6)
1.2.1 Algebraic Graph Theory
2(1)
1.2.2 Network Model
3(1)
1.2.3 The Static Average Consensus Problem
3(2)
1.2.4 The Dynamic Average Consensus Problem
5(2)
1.3 Convex Optimization
7(3)
1.3.1 Convex Analysis
7(1)
1.3.2 Constrained Optimization
8(1)
1.3.3 Duality Theory
8(2)
1.4 Noncooperative Game Theory
10(2)
1.4.1 Potential Games
11(1)
1.4.2 Constrained Games
12(1)
1.5 Markov Chains
12(2)
1.5.1 Stochastic Stability
13(1)
1.6 Notes
14(7)
References
16(5)
2 Distributed Cooperative Optimization
21(38)
2.1 Introduction
21(1)
2.2 Problem Formulation
22(2)
2.2.1 Subgradient Notions and Notations
23(1)
2.3 Case (i): Absence of Equality Constraint
24(7)
2.3.1 The Distributed Lagrangian Primal-Dual Subgradient Algorithm
28(1)
2.3.2 A Numerical Example for the Distributed Lagrangian Primal-Dual Subgradient Algorithm
29(2)
2.4 Case (ii): Identical Local Constraint Sets
31(7)
2.4.1 The Distributed Penalty Primal-Dual Subgradient Algorithm
34(2)
2.4.2 A Numerical Example for the Distributed Penalty Primal-Dual Subgradient Algorithm
36(2)
2.5 Appendix
38(18)
2.5.1 Convergence Analysis of the Distributed Lagrangian Primal-Dual Subgradient Algorithm
39(8)
2.5.2 Convergence Analysis of the Distributed Penalty Primal-Dual Subgradient Algorithm
47(9)
2.6 Notes
56(3)
References
57(2)
3 Game Theoretic Optimal Sensor Deployment
59(32)
3.1 Introduction
59(1)
3.2 Problem Formulation
60(5)
3.2.1 Coverage Game
60(4)
3.2.2 Our Objective
64(1)
3.2.3 Notations
64(1)
3.3 Distributed Learning Algorithms
65(3)
3.3.1 The Coverage Learning Algorithm
65(1)
3.3.2 The Asynchronous Coverage Learning Algorithm
66(2)
3.4 Convergence Analysis
68(12)
3.4.1 Convergence Analysis of the Coverage Learning Algorithm
69(5)
3.4.2 Convergence Analysis of the Asynchronous Coverage Learning Algorithm
74(6)
3.5 Numerical Examples
80(4)
3.5.1 A Numerical Example of the Coverage Learning Algorithm
81(2)
3.5.2 A Numerical Example of the Asynchronous Coverage Learning Algorithm
83(1)
3.6 Notes
84(7)
References
87(4)
4 Distributed Resilient Formation Control
91(28)
4.1 Introduction
91(1)
4.2 Problem Formulation
92(5)
4.2.1 The Operator-Vehicle Network
92(2)
4.2.2 Model of Adversaries
94(1)
4.2.3 A Motivating Scenario
95(1)
4.2.4 Prior Information About Adversaries and Objective
96(1)
4.3 Preliminaries
97(2)
4.3.1 A Coordinate Transformation
97(1)
4.3.2 A Constrained Multiparametric Program
97(2)
4.4 Distributed Attack-Resilient Algorithm
99(3)
4.4.1 Algorithm Statement
99(2)
4.4.2 The Resilience Properties
101(1)
4.5 Convergence Analysis
102(10)
4.6 Discussion
112(2)
4.6.1 The Special Case of Consensus
112(1)
4.6.2 Resilience to Denial-of-Service Attacks
112(1)
4.6.3 The Issue of Solving the n-OC
113(1)
4.6.4 Pros and Cons
113(1)
4.6.5 Tradeoff Between Computation, Memory, and Communication Costs
114(1)
4.7 Numerical Examples
114(1)
4.8 Notes
115(4)
References
116(3)
Series Editor's Biographies 119(2)
Algorithm Index 121(2)
Index 123
Minghui Zhu is the Dorothy Quiggle Assistant Professor in the Department of Electrical Engineering at the Pennsylvania State University. Prior to that, he was a postdoctoral associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. He received Ph.D. in Engineering Science (Mechanical Engineering) from the University of California, San Diego in 2011. His research interests lie in the design, analysis and control of multi-agent networks with applications in multi-vehicle networks, security and the smart grid. He was the recipient of the Powell fellowship and the Back fellowship at the University of California, San Diego in 2007. For his Ph.D. research, he received the award of Outstanding Graduate Student of Mechanical and Aerospace Engineering at the University of California, San Diego in 2011. He is an outstanding reviewer of Automatica in 2013 and 2014. Sonia Martínez is a Professor at the Department of Mechanical and Aerospace Engineering at the University of California, San Diego. Prof. Martínez received her Ph.D.  degree in Engineering Mathematics from the Universidad Carlos III de Madrid, Spain, in May 2002.  Following a year as a Visiting Assistant Professor of Applied Mathematics at the Technical University of Catalonia, Spain, she obtained a Postdoctoral Fulbright Fellowship and held appointments at the Coordinated Science Laboratory of the University of Illinois, Urbana-Champaign during 2004, and at the Center for Control, Dynamical systems and Computation (CCDC) of the University of California, Santa Barbara during 2005. From January 2006 to June 2010, and then from July 2010 to June 2014, she was an Assistant Professor, and then Associate Professor, with the department of Mechanical and Aerospace Engineering at the University of California, San Diego. Dr Martínez' main reseach interests include the control of network systems, multi-agent systems, nonlinear control theory, androbotics.  In particular, she has focused on the modeling and control of robotic sensor networks, the development of distributed coordination and estimation algorithms for groups of autonomous vehicles, and the geometric control of mechanical systems. She was the recipient of a NSF CAREER Award in 2007. For the paper "Motion coordination with Distributed Information," co-authored with Jorge Cortés and Francesco Bullo, she received the 2008 Control SystemsMagazine Outstanding Paper Award.