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Swarming species such as flocks of birds or schools of fish exhibit fascinating collective behaviors during migration and predator avoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles (“vehicle swarms”) exhibit sophisticated collective behaviors while maneuvering. In this book we show how to model and control a wide range of such multi-agent dynamic systems and analyze their collective behavior using both stability theoretic and simulation-based approaches. In particular, we investigate problems such as group aggregation,…mehr
Swarming species such as flocks of birds or schools of fish exhibit fascinating collective behaviors during migration and predator avoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles (“vehicle swarms”) exhibit sophisticated collective behaviors while maneuvering. In this book we show how to model and control a wide range of such multi-agent dynamic systems and analyze their collective behavior using both stability theoretic and simulation-based approaches. In particular, we investigate problems such as group aggregation, social foraging, formation control, swarm tracking, distributed agreement, and engineering optimization inspired by swarm behavior.
Part I Basic Principles.- Part II Continuous Time Swarms.- Part III Discrete Time Swarms.- Part IV Swarm Based Optimization Methods.
Part I Basic Principles 1 Introduction 2 Swarm Coordination and Control Problems 2.1 Aggregation 2.2 Social Foraging 2.3 Formation Control 2.4 Swarm Tracking 2.5 Distributed Agreement Part II Continuous Time Swarms 3 Swarms of Single Integrator Agents 3.1 Single Integrator Agent Model 3.2 Aggregation 3.2.1 Potential Function Design 3.2.2 Analysis of Swarm Motion 3.2.3 Swarm Cohesion Analysis 3.2.4 Individuals with Finite Body Size 3.2.5 Simulation Examples 3.3 Social Foraging 3.3.1 Introduction 3.3.2 Swarm Cohesion Analysis 3.3.3 Swarm Motion in Various Resource Profiles 3.3.4 Analysis of Individual Behavior in a Cohesive Swarm 3.3.5 Simulation Examples 3.4 Formation Control 3.4.1 Simulation Examples 3.5 Swarm Tracking 3.5.1 Simulation Examples 3.6 Further Issues 3.6.1 General Neighborhood Topology 3.6.2 Non-Reciprocal Agent Interactions 3.6.3 For Further Reading 4 Swarms of Double Integrator Agents 4.1 Double IntegratorModel 4.2 Stability Analysis of Swarm Cohesion Properties 4.2.1 Controls and Error Dynamics 4.2.2 Cohesive Social Foraging with Noise1 4.2.3 Special Case: Identical Agents 4.3 Stability Analysis of Swarm Trajectory Following 4.4 Simulation Examples 4.4.1 No-Noise Case 4.4.2 Noise Case 4.5 Further Issues 4.5.1 Extensions and Generalizations 4.5.2 For Further Reading 5 Swarms of Fully Actuated Agents with Model Uncertainty 5.1 Fully Actuated Agent Model with Uncertainty 5.2 Controller Development 5.2.1 Aggregation, Foraging, and Formation Control 5.2.2 Swarm Tracking 5.3 Potential Functions and Bounds 5.3.1 Aggregation 5.3.2 Social Foraging 5.3.3 Formation Control 5.3.4 Swarm Tracking 5.4 Simulation Examples 5.4.1 Aggregation 5.4.2 Social Foraging 5.4.3 Formation Control 5.4.4 Swarm Tracking 5.5 Further Issues 5.5.1 Extensions and Generalizations 5.5.2 For Further Reading 6 Swarms of Non-Holonomic Unicycle Agents with Model Uncertainty 6.1 Non-Holonomic Unicycle Agent Model with Uncertainty 6.2 Controller Development 6.3 Potential Functions and Bounds 6.4 Simulation Examples 6.5 Further Issues 7 Formation Control using Nonlinear Servomechanism 7.1 General Non-Linear Agent Model 7.2 Controller Development 7.2.1 Full Information Controller 7.2.2 Error Feedback Controller . 7.3 Formation Reconfiguration 7.3.1 Expansion/Contraction. 7.3.2 Rotation 7.3.3 Topology Change 7.4 Illustrative Examples 7.5 Further Issues Part III Discrete Time Swarms 8 Asynchronous Distributed Agreement in Discrete Time Swarms 8.1 Model of the System 8.2 System Under Total Synchronism 8.3 Asynchronous System 8.4 Simulation Examples 8.5 Further Issues 9 Formation Control with Potential Functions and Newtons Iteration 9.1 Model of the System 9.2 Controller Development 9.3 Simulation Examples 9.4 Further Issues 10 Orientation Agreement in Swarms of Self-Propelled Particles 10.1 Self-Propelled Particle Agent Model 10.2 Strategies for Orientation Agreement 10.3 Turn Angle Restrictions 10.4 Simulation Examples 10.5 Further Issues Part IV Swarm Based OptimizationMethods 11 Biomimicry of Bacteria Foraging for Optimization 11.1 Bacterial Foraging by E. coli 11.1.1 Swimming and Tumbling 11.1.2 Chemotaxis and Climbing Nutrient Gradients 11.1.3 Underlying Sensing and Decision-Making Mechanisms 11.1.4 Elimination and Dispersal Events 11.2 E. coli Bacterial Foraging for Optimization 11.2.1 An Optimization Model for E. coli Bacterial Foraging 11.2.2 Bacterial Foraging Optimization Algorithm (BFOA) 11.2.3 Guidelines for Algorithm Parameter Choices 11.2.4 Relations to the Genetic Algorithm 11.3 Example: Function Optimization via E. coli Foraging 11.3.1 Nutrient Hill-Climbing: No Swarming 11.3.2 Swarming Effects 11.4 Further Issues 11.4.1 Extensions and Generalizations 11.4.2 For Further Reading 12 Particle Swarm Optimization 12.1 Synchronous PSO Algorithm 12.2 Decentralized Asynchronous PSO Formulation 12.3 Dyn
Part I Basic Principles.- Part II Continuous Time Swarms.- Part III Discrete Time Swarms.- Part IV Swarm Based Optimization Methods.
Part I Basic Principles 1 Introduction 2 Swarm Coordination and Control Problems 2.1 Aggregation 2.2 Social Foraging 2.3 Formation Control 2.4 Swarm Tracking 2.5 Distributed Agreement Part II Continuous Time Swarms 3 Swarms of Single Integrator Agents 3.1 Single Integrator Agent Model 3.2 Aggregation 3.2.1 Potential Function Design 3.2.2 Analysis of Swarm Motion 3.2.3 Swarm Cohesion Analysis 3.2.4 Individuals with Finite Body Size 3.2.5 Simulation Examples 3.3 Social Foraging 3.3.1 Introduction 3.3.2 Swarm Cohesion Analysis 3.3.3 Swarm Motion in Various Resource Profiles 3.3.4 Analysis of Individual Behavior in a Cohesive Swarm 3.3.5 Simulation Examples 3.4 Formation Control 3.4.1 Simulation Examples 3.5 Swarm Tracking 3.5.1 Simulation Examples 3.6 Further Issues 3.6.1 General Neighborhood Topology 3.6.2 Non-Reciprocal Agent Interactions 3.6.3 For Further Reading 4 Swarms of Double Integrator Agents 4.1 Double IntegratorModel 4.2 Stability Analysis of Swarm Cohesion Properties 4.2.1 Controls and Error Dynamics 4.2.2 Cohesive Social Foraging with Noise1 4.2.3 Special Case: Identical Agents 4.3 Stability Analysis of Swarm Trajectory Following 4.4 Simulation Examples 4.4.1 No-Noise Case 4.4.2 Noise Case 4.5 Further Issues 4.5.1 Extensions and Generalizations 4.5.2 For Further Reading 5 Swarms of Fully Actuated Agents with Model Uncertainty 5.1 Fully Actuated Agent Model with Uncertainty 5.2 Controller Development 5.2.1 Aggregation, Foraging, and Formation Control 5.2.2 Swarm Tracking 5.3 Potential Functions and Bounds 5.3.1 Aggregation 5.3.2 Social Foraging 5.3.3 Formation Control 5.3.4 Swarm Tracking 5.4 Simulation Examples 5.4.1 Aggregation 5.4.2 Social Foraging 5.4.3 Formation Control 5.4.4 Swarm Tracking 5.5 Further Issues 5.5.1 Extensions and Generalizations 5.5.2 For Further Reading 6 Swarms of Non-Holonomic Unicycle Agents with Model Uncertainty 6.1 Non-Holonomic Unicycle Agent Model with Uncertainty 6.2 Controller Development 6.3 Potential Functions and Bounds 6.4 Simulation Examples 6.5 Further Issues 7 Formation Control using Nonlinear Servomechanism 7.1 General Non-Linear Agent Model 7.2 Controller Development 7.2.1 Full Information Controller 7.2.2 Error Feedback Controller . 7.3 Formation Reconfiguration 7.3.1 Expansion/Contraction. 7.3.2 Rotation 7.3.3 Topology Change 7.4 Illustrative Examples 7.5 Further Issues Part III Discrete Time Swarms 8 Asynchronous Distributed Agreement in Discrete Time Swarms 8.1 Model of the System 8.2 System Under Total Synchronism 8.3 Asynchronous System 8.4 Simulation Examples 8.5 Further Issues 9 Formation Control with Potential Functions and Newtons Iteration 9.1 Model of the System 9.2 Controller Development 9.3 Simulation Examples 9.4 Further Issues 10 Orientation Agreement in Swarms of Self-Propelled Particles 10.1 Self-Propelled Particle Agent Model 10.2 Strategies for Orientation Agreement 10.3 Turn Angle Restrictions 10.4 Simulation Examples 10.5 Further Issues Part IV Swarm Based OptimizationMethods 11 Biomimicry of Bacteria Foraging for Optimization 11.1 Bacterial Foraging by E. coli 11.1.1 Swimming and Tumbling 11.1.2 Chemotaxis and Climbing Nutrient Gradients 11.1.3 Underlying Sensing and Decision-Making Mechanisms 11.1.4 Elimination and Dispersal Events 11.2 E. coli Bacterial Foraging for Optimization 11.2.1 An Optimization Model for E. coli Bacterial Foraging 11.2.2 Bacterial Foraging Optimization Algorithm (BFOA) 11.2.3 Guidelines for Algorithm Parameter Choices 11.2.4 Relations to the Genetic Algorithm 11.3 Example: Function Optimization via E. coli Foraging 11.3.1 Nutrient Hill-Climbing: No Swarming 11.3.2 Swarming Effects 11.4 Further Issues 11.4.1 Extensions and Generalizations 11.4.2 For Further Reading 12 Particle Swarm Optimization 12.1 Synchronous PSO Algorithm 12.2 Decentralized Asynchronous PSO Formulation 12.3 Dyn
Rezensionen
From the reviews: "The book is intended as a textbook for a course on multiagent dynamic systems; as a supplementary book in courses on nonlinear control systems, optimization, or discrete-time control systems; or as a reference book for graduate students, researchers, and engineers performing studies or implementation work in the area of multiagent dynamic systems." (IEEE Control Systems Magazine, Vol. 31, October, 2011)
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