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Formation Control of Multi-Agent Systems: A Graph Rigidity Approach Marcio de Queiroz, Louisiana State University, USA Xiaoyu Cai, FARO Technologies, USA Matthew Feemster, U.S. Naval Academy, USA A comprehensive guide to formation control of multi-agent systems using rigid graph theory This book is the first to provide a comprehensive and unified treatment of the subject of graph rigidity-based formation control of multi-agent systems. Such systems are relevant to a variety of emerging engineering applications, including unmanned robotic vehicles and mobile sensor networks. Graph theory, and rigid graphs in particular, provides a natural tool for describing the multi-agent formation shape as well as the inter-agent sensing, communication, and control topology. Beginning with an introduction to rigid graph theory, the contents of the book are organized by the agent dynamic model (single integrator, double integrator, and mechanical dynamics) and by the type of formation problem (formation acquisition, formation manoeuvring, and target interception). The book presents the material in ascending level of difficulty and in a self-contained manner; thus, facilitating reader understanding. Key features: Uses the concept of graph rigidity as the basis for describing the multi-agent formation geometry and solving formation control problems. Considers different agent models and formation control problems. Control designs throughout the book progressively build upon each other. Provides a primer on rigid graph theory. Combines theory, computer simulations, and experimental results. Formation Control of Multi-Agent Systems: A Graph Rigidity Approach is targeted at researchers and graduate students in the areas of control systems and robotics. Prerequisite knowledge includes linear algebra, matrix theory, control systems, and nonlinear systems.

This book covers the most important issues from classical and robust control, deterministic and stochastic control, system identification, and adaptive and iterative control strategies. It covers most of the known control system methodologies using a new base, the Youla parameterization (YP). This concept is introduced and extended for TDOF control loops. The Keviczky-Bányász parameterization (KP) method developed for closed loop systems is also presented. The book is valuable for those who want to see through the jungle of available methods by using a unified approach, and for those who want to prepare computer code with a given algorithm.

Drawing on the authors' two decades of experience in applied modeling and data mining, this self-contained book presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It explains the algorithmic details behind each technique, including underlying assumptions and mathematical formulations, and discusses a variety of practical topics that are frequently missing from similar texts. Software and examples are available at www.DataMinerXL.com.

A comprehensive introduction to fuzzy control and identification, covering both Mamdani and Takagi-Sugeno fuzzy systemsA fuzzy control system is a control system based on fuzzy logic, which is a mathematical system that makes decisions using human reasoning processes. This book presents an introductory-level exposure to two of the principal uses for fuzzy logic—identification and control. Drawn from the author's lectures presented in a graduate-level course over the past decade, this volume serves as a holistically suitable single text for a fuzzy control course, compiling the information often found in several different books on the subject into one.Starting with explanations of fuzzy logic, fuzzy control, and adaptive fuzzy control, the book introduces the concept of expert knowledge, which is the basis for much of fuzzy control. From there, the author covers:Basic concepts of fuzzy sets such as membership functions, universe of discourse, linguistic variables, linguistic values, support, a-cut, and convexityBoth Mamdani and Takagi-Sugeno fuzzy systems, showing how an effective controller can be designed for many complex nonlinear systems without mathematical models or knowledge of control theory while also suggesting several approaches to modeling of complex engineering systems with unknown modelsHow PID controllers can be made fuzzy and why this is usefulPosition-form and incremental-form fuzzy controllersHow nonlinear systems can be modeled as fuzzy systems in several formsHow fuzzy tracking control and model reference control can be realized for nonlinear systems using parallel distributed techniquesThe estimation of nonlinear systems using the batch least squares, recursive least squares, and gradient methodsThe creation of direct and indirect adaptive fuzzy controllersAlso included are many examples, exercises, and computer program listings, all class-tested. Fuzzy Control and Identification is intended for seniors and first-year graduate students, and is suitable for any engineering department. No knowledge specific to any particular branch of engineering is required, and no knowledge of electrical, chemical, or mechanical systems is necessary to read and understand the material.

<p><b>A BRIDGE BETWEEN THE APPLICATION OF SUBSPACE&#45;BASED METHODS FOR PARAMETER ESTIMATION IN SIGNAL PROCESSING AND SUBSPACE&#45;BASED SYSTEM IDENTIFICATION IN CONTROL SYSTEMS</b> <p><i>Model&#45;Based Processing: An Applied Subspace Identification Approach</i> provides expert insight on developing models for designing model&#45;based signal processors &#40;MBSP&#41; employing subspace identification techniques to achieve model&#45;based identification &#40;MBID&#41; and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. <p>The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles&#151;all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real&#45;world solutions to a variety of model development problems, this text demonstrates how model&#45;based subspace system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: <ul> <li>Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters</li> <li>Practical processor designs including comprehensive methods of performance analysis</li> <li>Provides a link between model development and practical applications in model&#45;based signal processing</li> <li>Offers in&#45;depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications</li> <li>Enables readers to bridge the gap from statistical signal processing to subspace identification</li> <li>Includes appendices, problem sets, case studies, examples, and notes for MATLAB</li> </ul> <p><i>Model&#45;Based Processing: An Applied Subspace Identification Approach</i> is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia.

Simulation and modeling contribute to a broad range of applications in computational science and robotics technology, often addressing important design and control problems.This book presents a selection of papers from the International Workshop on Simulation and Modeling related to Computational Science and Robotics Technology (SiMCTR 2011), held at Kobe University, Japan, in November 2011.The workshop provided a forum for discussing recent developments in the growing field of engineering science and mathematical sciences, and brought together a diverse group of researchers in these areas to share and compare the different approaches to simulation and modeling in computational science and robotics technology. The workshop was also aimed at establishing collaborative links between engineering researchers of information and robotics technology (IRT) and applied mathematicians working in modeling and computational methods for design and control.

<p>A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, this book showcases recent advances and industrially relevant applications. Readers are first given a comprehensive overview of the intersection between ILC and MAS, then introduced to a range of topics that include both basic and advanced theoretical discussions, rigorous mathematics, engineering practice, and both linear and nonlinear systems. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as power grids, communication and sensor networks, intelligent transportation systems, and formation control. Readers will gain a roadmap of the latest advances in the fields and can use their newfound knowledge to design their own algorithms.</p> <ul> <li>Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS) </li> <li>Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks, and control processes</li> <li>Covers basic theory and rigorous mathematics as well as engineering practice</li> </ul><br> <p>Written by experienced researchers, Iterative Learning Control for Multi-agent Systems Coordination will appeal to researchers and graduate students of multi-agent systems. Industrial practitioners whose work involves system engineering, system control, system biology, and computing science will also find it useful.

* Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena * Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance * Includes case studies of several nonlinear stochastic systems * Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations * Considers both time-invariant systems and time-varying systems * Exploits newly developed techniques to handle the emerging mathematical and computational challenges