book

Multiagent Systems

Name: Multiagent Systems
Author: Magdi S. Mahmoud
ISBN: 9781000078107

by Magdi S. Mahmoud

March 2020

Intermediate to advanced

414 pages

10h 46m

English

CRC Press

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Cover
Title Page
Copyright Page
Dedication
Preface
Acknowledgement
Table of Contents
Author Biography
1. Introduction
1.1 Overview1.2 Elements of Graph Theory1.2.1 Basic results1.2.2 Laplacian spectrum of graphs1.2.3 Properties of adjacency matrix1.2.4 Nonlinear stochastic dynamical systems1.2.5 Complex dynamical systems1.2.6 Delay effects1.2.7 Sampled-data framework1.3 Multiagent System Approach1.3.1 Practical examples1.3.2 Some relevant definitions1.4 Mathematical Models for Agent Dynamics1.4.1 Single integrator model1.4.2 Double integrator model1.4.3 Uncertain fully actuated model1.4.4 Non-holonomic unicycle model1.5 Coordination and Control Problems1.5.1 Aggregation and social foraging1.5.2 Flocking and rendezvous1.5.3 Synchronization of coupled nonlinear oscillators1.6 Scope and Book Layout
2. Theoretical Background
2.1 Preliminaries of Distributed Systems2.1.1 Problem description2.1.2 Control design scheme2.1.3 Without communication delays2.1.4 With communication delays2.2 Networked Multiagent Systems2.2.1 Consensus in networks2.2.2 The f-consensus problem2.2.3 Iterative consensus and Markov chains2.3 Applications2.3.1 Synchronization of coupled oscillators2.3.2 Flocking theory2.3.3 Fast consensus in small-worlds2.3.4 Rendezvous in space2.3.5 Distributed sensor fusion in sensor networks2.3.6 Distributed formation control2.4 Information Consensus2.4.1 Algebraic connectivity and spectral properties2.4.2 Convergence analysis for directed networks2.4.3 Consensus in discrete-time2.4.4 Performance of consensus algorithms2.4.5 Alternative forms of consensus algorithms2.4.6 Weighted-average consensus2.4.7 Consensus under communication time-delays2.5 Consensus in Switching Networks2.6 Cooperation in Networked Control Systems2.6.1 Collective dynamics of multivehicle formation2.6.2 Stability of relative dynamics2.7 Simulation Studies2.7.1 Consensus in complex networks2.7.2 Multivehicle formation control2.8 Notes

3. Distributed Intelligence in Power Systems
3.1 Introduction to MAS Technology3.1.1 Autonomous microgrid system3.1.2 A state-space model3.1.3 Heuristic dynamic programming3.1.4 Discrete-time Bellman equation3.1.5 Value iteration algorithm3.1.6 Adaptive critics implementation3.1.7 Actor-critic implementation3.1.8 Simulations results3.1.9 Actor-critic tuning results3.1.10 Robustness of the proposed controller3.2 Operation in Islanded Mode3.2.1 Autonomous microgrid3.2.2 Primary control3.2.3 Fixed gain distributed secondary control3.2.4 Neural network distributed secondary control3.2.5 Stage 1: Selection of training data3.2.6 Stage 2: Selection of artificial neural network3.2.7 Stage 3: Neural network training3.2.8 Simulation results I3.3 Multiagent Coordination for Distributed Energy Resources3.3.1 Introduction3.3.2 Advantages of MAS approach3.3.3 Agent platform3.3.4 Software system analysis3.3.5 Distributed control system3.3.6 Simulation studies I3.3.7 Coordination between agents3.3.8 Checking reliability3.3.9 Simulation results II3.4 Notes
4. Consensus for Heterogeneous Systems with Delays
4.1 Introduction4.2 Multiagent Leader-Follower Consensus Problem4.3 Distributed Adaptive Control Design4.4 Illustrative Example4.5 Tracking and Coordination Using Sensor Networks4.6 Target Tracking in Sensor Networks4.7 Control System Architecture4.7.1 Sensor network and models4.7.2 Multitarget tracking4.7.3 Agent dynamics and coordination objective
4.8 Control System Implementation
4.8.1 Multisensor fusion module4.8.2 Multitarget tracking and multitrack fusion modules4.8.3 Multiagent coordination module4.9 Experimental Results4.9.1 Platform4.9.2 Live demonstration4.10 Notes
5. Secure Control of Distributed Multiagent Systems
5.1 Introduction5.2 Problem Formulation5.3 Main Results5.4 Illustrative Examples5.5 Notes
6. Advanced Consensus Algorithms
6.1 Event-Triggered Control for Multiagent Systems6.1.1 Introduction6.1.2 System model and problem statement6.1.3 Design tracking results6.1.4 Numerical example6.2 Pinning Coordination Control of Networked Systems6.2.1 Networked multi-vehicle systems6.2.2 Fixed communication topology6.2.3 Case of general graphs6.2.4 Example 6.16.2.5 Strongly connected and balanced graphs
6.2.6 Selection of the pinned nodes
6.2.7 Pinning control with variable topology6.2.8 Simulation examples6.3 Distributed Consensus Control6.3.1 Consensus with observer-type protocol6.3.2 Dynamic consensus6.3.3 Consensus region6.3.4 Consensus with neutrally stable matrix6.3.5 Consensus with prescribed convergence speed6.3.6 Illustrative example 6.26.3.7 Consensus with static protocols6.3.8 Formation control6.3.9 Illustrative example 6.36.4 Consensus Control for Time-Delay Systems6.4.1 Problem formulation6.4.2 Fixed interconnection topology6.4.3 Switched interconnection topology6.4.4 Illustrative example 6.46.4.5 Illustrative example 6.5
6.5 Robust Consensus of Multiagent Systems
6.5.1 Problem description6.5.2 Analytic results6.5.3 Illustrative example 6.66.6 Notes
7. Cooperative Control of Networked Power Systems
7.1 Coordinated Model Predictive Power Flows7.1.1 Introduction7.1.2 System architecture7.1.3 Wind power generation7.1.4 Photovoltaic module generators7.1.5 Energy storage system dynamics7.1.6 Loads7.1.7 Energy management unit7.1.8 Power price mechanism7.2 Power Scheduling in Networked MG7.2.1 Networked topology7.2.2 GCC of networked MG7.2.3 MPC-based power scheduling7.2.4 Optimization problem formulation7.2.5 State equations and constraints7.2.6 Case studies7.2.7 Simulation setup7.2.8 Case study 17.2.9 Case study 27.2.10 Case study 37.3 Distributed Robust Control in Smart Microgrids7.3.1 A microgrid model7.3.2 Microgrid group model7.3.3 Problem formulation7.3.4 Robust group control7.3.5 Distributed information models7.3.6 Simulation study7.3.7 Solution procedure A7.3.8 Solution procedure B7.3.9 Solution procedure C7.3.10 Solution procedure D7.4 Notes
8. Dynamic Graphical Games
8.1 Constrained Graphical Games8.1.1 Reinforcement learning8.1.2 Synchronization control problem8.1.3 Performance evaluation of the game8.1.4 Optimality conditions8.1.5 Bellman equations8.1.6 The Hamiltonian function8.1.7 Coupled IRL-Hamilton-Jacobi theory8.1.8 Coupled IRL-HJB equations8.1.9 Nash equilibrium solution8.1.10 Stability analysis8.2 Value Iteration Solution and Implementation8.2.1 Value iteration algorithm8.2.2 Graph solution implementation8.2.3 Online actor-critic neural networks tuning8.2.4 Simulation results I8.2.5 Simulation case 18.2.6 Simulation case 28.2.7 Simulation case 38.3 Multiagent Reinforcement Learning for Microgrids8.3.1 Microgrid control requirements8.3.2 Features of MAS technology8.3.3 A multiagent reinforcement learning method8.3.4 Critical operation in island mode8.3.5 Simulation results II8.4 Notes
References
Index

Content preview from Multiagent Systems

4.8 Control System Implementation

4.8.1 Multisensor fusion module

1. Signal-Strength Sensor Model: Consider the signal-strength sensor model described in Section III-A. Recall that z_i is the signal strength measured by node i. For each node i, if z_i ≥ θ, where θ is a threshold set for appropriate values of detection and false-positive probabilities, the node transmits z_i to its neighboring nodes, which are, at most, 2R_s away from s_i, and listens to incoming messages from neighboring nodes within a 2R_s radius. We assume that the communication range of each node is larger than a 2R_s. For a node i, if z_i is larger than all incoming messages, z_i1, …, z_i−k, and z_ik = z_i, then the position of an object is estimated by

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ISBN: 9781000078107

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Multiagent Systems

by Magdi S. Mahmoud

4.8 Control System Implementation

4.8.1 Multisensor fusion module

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