Nanayakkara / Sahin / Jamshidi Intelligent Control Systems with an Introduction to System of Systems Engineering

1. Auflage 2010
ISBN: 978-1-4200-7925-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 441 Seiten

From aeronautics and manufacturing to healthcare and disaster management, systems engineering (SE) now focuses on designing applications that ensure performance optimization, robustness, and reliability while combining an emerging group of heterogeneous systems to realize a common goal.
Use SoS to Revolutionize Management of Large Organizations, Factories, and Systems
Intelligent Control Systems with an Introduction to System of Systems Engineering integrates the fundamentals of artificial intelligence and systems control in a framework applicable to both simple dynamic systems and large-scale system of systems (SoS). For decades, NASA has used SoS methods, and major manufacturers—including Boeing, Lockheed-Martin, Northrop-Grumman, Raytheon, BAE Systems—now make large-scale systems integration and SoS a key part of their business strategies, dedicating entire business units to this remarkably efficient approach.
Simulate Novel Robotic Systems and Applications
Transcending theory, this book offers a complete and practical review of SoS and some of its fascinating applications, including:

- Manipulation of robots through neural-based network control

- Use of robotic swarms, based on ant colonies, to detect mines

- Other novel systems in which intelligent robots, trained animals, and humans cooperate to achieve humanitarian objectives

Training engineers to integrate traditional systems control theory with soft computing techniques further nourishes emerging SoS technology. With this in mind, the authors address the fundamental precepts at the core of SoS, which uses human heuristics to model complex systems, providing a scientific rationale for integrating independent, complex systems into a single coordinated, stabilized, and optimized one. They provide readers with MATLAB® code, which can be downloaded from the publisher's website to simulate presented results and projects that offer practical, hands-on experience using concepts discussed throughout the book.

Zielgruppe

Those working in aeronautics, aerospace, transportation, healthcare, systems engineering, industrial engineering, robotics, manufacturing, etc.

Autoren/Hrsg.

Nanayakkara, Thrishantha

Sahin, Ferat

Jamshidi, Mo

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise

Inhaltsverzeichnis

Introduction


Elements of a Classical Control System
How the Model of a Dynamic System Can Help to Control It
Control of Robot Manipulators
Stability


System of Systems Simulation
SoS in a Nutshell
An SoS Simulation Framework
SoS Simulation Framework Examples
Agent-in-the-Loop Simulation of an SoS
Conclusion
Acknowledgment


Observer Design and Kalman Filtering
State Space Methods for Model-Based Control
Observing and Filtering Based on Dynamic Models
Derivation of the Discrete Kalman Filter
Worked Out Project on the Inverted Pendulum
Particle Filters


Fuzzy Systems—Sets, Logic, and Control
Classical Sets
Classical Set Operations
Properties of Classical Set
Fuzzy Sets
Fuzzy Set Operations
Properties of Fuzzy Sets
Classical Relations versus Fuzzy Relations
Predicate Logic
Fuzzy Logic
Approximate Reasoning
Fuzzy Control
Conclusions


Neural Network-Based Control
NN-Based Identification of Dynamics of a Robot Manipulator
Structure of NNs
Generating Training Data for an NN
Dynamic Neurons
Attractors, Strange Attractors, and Chaotic Neurons
Cerebellar Networks and Exposition of Neural Organization to Adaptively Enact Behavior


Introduction to System of Systems
Definitions of SoS
Challenging Problems in SoS
Conclusions


Control of System of Systems
Hierarchical Control of SoS
Decentralized Control of SoS
Other Control Approaches
Conclusions


Reward-Based Behavior Adaptation
Markov Decision Process
Temporal Difference-Based Learning
Extension to Q Learning
Exploration versus Exploitation
Vector Q Learning


An Automated System to Induce and Innovate Advanced Skills in a Group of Networked Machine Operators
Visual Inspection and Acquisition of Novel Motor Skills
Experimental Setup
Dynamics of Successive Improvement of Individual Skills
Proposed Model of Internal Model Construction and Learning
Discussion and Conclusion


A System of Intelligent Robots-TrainedAnimals-Humans in a Humanitarian Demining Application
A Novel Legged Field Robot for Landmine Detection
Combining a Trained Animal with the Robot
Simulations on Multirobot Approaches to Landmine Detection


Robotic Swarms for Mine Detection System of Systems Approach
SoS Approach to Robotic Swarms
Designing System of Swarm Robots: GroundScouts
Mine Detection with Ant Colony-Based Swarm Intelligence
Conclusion
Acknowledgment


Index

Über Autor(innen)

Thrishantha Nanayakkara was a postdoctoral research fellow at the Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland. From 2003 to 2007, he was a faculty member at the University of Moratuwa, Sri Lanka, and was the principal investigator of the "Laboratory for Intelligent Field Robots" at the Department of Mechanical Engineering. Dr. Nanayakkara was also the founding general chair of the International Conference on Information and Automation, and is an associate editor of the Journal of Control and Intelligent Systems. At present, he is a fellow in the Radcliffe Institute, Harvard University, and a research affiliate of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology.

Ferat Sahin currently serves as the deputy editor-in-chief for International Journal of Computers and Electrical Engineering and an associate editor for IEEE Systems Journal and AutoSoft Journal, as also serves as the technical cochair of the IEEE SMC International Conference on System of Systems Engineering (SOSE 2008 and SOSE 2009). He is also the director of the Multi Agent Bio-Robotics Laboratory at Rochester Institute of Technology (RIT), where he is also currently an associate professor.
Mo M. Jamshidi has served in various capacities with the U.S. Air Force Research Laboratory, the U.S. Department of Energy, NASA Headquarters, NASA JPL, Oak Ridge National Laboratory, and the Los Alamos National Laboratory. He has also served in various academic and industrial positions at various national and international organizations including IBM and GM Corporation. In 1999, he was a NATO Distinguished Professor in Portugal, and in 2008, he was a UK Royal Academy of Engineering fellow in the UK. Dr. Jamshidi is currently the Lutcher Brown endowed chair professor at the University of Texas, San Antonio.