(Ebook PDF) Distributed Optimization Based Control of Multi Agent Networks in Complex Environments 1st edition by Minghui Zhu, Sonia Martínez 3319190725 9783319190723 full chapters

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ISBN-10 ‏ : ‎3319190725 

ISBN-13 ‏ : ‎ 9783319190723

Author :  Minghui Zhu, Sonia Martínez 

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.

Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments 1st Table of contents:

1 Preliminaries
1.1 Basic Notations
1.2 The Consensus Problem
1.2.1 Algebraic Graph Theory
1.2.2 Network Model
1.2.3 The Static Average Consensus Problem
1.2.4 The Dynamic Average Consensus Problem
1.3 Convex Optimization
1.3.1 Convex Analysis
1.3.2 Constrained Optimization
1.3.3 Duality Theory
1.4 Noncooperative Game Theory
1.4.1 Potential Games
1.4.2 Constrained Games
1.5 Markov Chains
1.5.1 Stochastic Stability
1.6 Notes
References
2 Distributed Cooperative Optimization
2.1 Introduction
2.2 Problem Formulation
2.2.1 Subgradient Notions and Notations
2.3 Case (i): Absence of Equality Constraint
2.3.1 The Distributed Lagrangian Primal-Dual Subgradient Algorithm
2.3.2 A Numerical Example for the Distributed Lagrangian Primal-Dual Subgradient Algorithm
2.4 Case (ii): Identical Local Constraint Sets
2.4.1 The Distributed Penalty Primal-Dual Subgradient Algorithm
2.4.2 A Numerical Example for the Distributed Penalty Primal-Dual Subgradient Algorithm
2.5 Appendix
2.5.1 Convergence Analysis of the Distributed Lagrangian Primal-Dual Subgradient Algorithm
2.5.2 Convergence Analysis of the Distributed Penalty Primal-Dual Subgradient Algorithm
2.6 Notes
References
3 Game Theoretic Optimal Sensor Deployment
3.1 Introduction
3.2 Problem Formulation
3.2.1 Coverage Game
3.2.2 Our Objective
3.2.3 Notations
3.3 Distributed Learning Algorithms
3.3.1 The Coverage Learning Algorithm
3.3.2 The Asynchronous Coverage Learning Algorithm
3.4 Convergence Analysis
3.4.1 Convergence Analysis of the Coverage Learning Algorithm
3.4.2 Convergence Analysis of the Asynchronous Coverage Learning Algorithm
3.5 Numerical Examples
3.5.1 A Numerical Example of the Coverage Learning Algorithm
3.5.2 A Numerical Example of the Asynchronous Coverage Learning Algorithm
3.6 Notes
References
4 Distributed Resilient Formation Control
4.1 Introduction
4.2 Problem Formulation
4.2.1 The Operator-Vehicle Network
4.2.2 Model of Adversaries
4.2.3 A Motivating Scenario
4.2.4 Prior Information About Adversaries and Objective
4.3 Preliminaries
4.3.1 A Coordinate Transformation
4.3.2 A Constrained Multiparametric Program
4.4 Distributed Attack-Resilient Algorithm
4.4.1 Algorithm Statement
4.4.2 The Resilience Properties
4.5 Convergence Analysis
4.6 Discussion
4.6.1 The Special Case of Consensus
4.6.2 Resilience to Denial-of-Service Attacks
4.6.3 The Issue of Solving the n-OC
4.6.4 Pros and Cons
4.6.5 Tradeoff Between Computation, Memory, and Communication Costs
4.7 Numerical Examples
4.8 Notes

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