El-Osery / Prevost Control and Systems Engineering

A Report on Four Decades of Contributions
1. Auflage 2015
ISBN: 978-3-319-14636-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 27, 380 Seiten

Reihe: Studies in Systems, Decision and Control

This book is a tribute to 40 years of contributions by Professor Mo Jamshidi who is a well known and respected scholar, researcher, and educator. Mo Jamshidi has spent his professional career formalizing and extending the field of large-scale complex systems (LSS) engineering resulting in educating numerous graduates specifically, ethnic minorities. He has made significant contributions in modeling, optimization, CAD,  control and applications of large-scale systems leading to his current global role in formalizing  system of systems engineering (SoSE), as a new field. His books on complex LSS and SoSE have filled a vacuum in cyber-physical systems literature for the 21st Century. His contributions to ethnic minority engineering education commenced with his work at the University of New Mexico (UNM, Tier-I Hispanic Serving Institution) in 1980 through a NASA JPL grant. Followed by several more major federal grants, he formalized a model for educating minorities, called VI-P Pyramid where K-12 students(bottom of pyramid) to doctoral (top of pyramid) students form a seamless group working on one project. Upper level students mentor lower ones on a sequential basis. Since 1980, he has graduated over 114 minority students consisting of 62 Hispanics, 34 African Americans., 15 Native Americans, and 3 Pacific Islanders. This book contains contributed chapters from colleagues, and former and current students of Professor Jamshidi. Areas of focus are: control systems, energy and system of systems, robotics and soft computing.

Autoren/Hrsg.

El-Osery, Aly

Prevost, Jeff

Inhaltsverzeichnis

1;Forewords;1
2;Preface;1
3;Contents;1
4;Reflection on Four Decades of Contributions of My Graduate Students;25
4.1;1Introduction;25
4.2;2Graduate Students;26
4.2.1;2.1MS Students;26
4.2.2;2.2PhD Students;31
4.2.3;2.3Visiting Overseas Ph.D. Students;36
4.3;3Conclusions;36
5;Proportional-Integral Observer in Robust Control, Fault Detection, and Decentralized Control of Dynamic Systems;37
5.1;1Introduction;37
5.2;2Observer Fundamentals;38
5.2.1;2.1Full-Order Observer and State Feedback;39
5.2.2;2.2Reduced-Order Observer;40
5.2.3;2.3Functional Observer;42
5.3;3Robustness with Observers;46
5.3.1;3.1P-Observer and Loop Transfer Recovery;46
5.3.2;3.2PI-Observer and Robustness;47
5.4;4Disturbance Estimation and Fault Detection with Observers;50
5.4.1;4.1Disturbance Observer (DO);50
5.4.2;4.2Unknown Input Observer (UIO);51
5.4.3;4.3Proportional-Integral Observer (PIO);52
5.4.4;4.4Fault Detection;54
5.5;5Decentralized PI-Observer Design;55
5.6;6Decentralized PI-Observer-Based Control Design;60
5.6.1;6.1bserver Case;61
5.6.2;6.2PI-Observer Case;62
5.7;7Conclusion;64
6;New Application of an Adaptive Controller Based on Robust Fixed Point Transformations;68
6.1;1Introduction;68
6.2;2Nonlinear Order Reduction in Adaptive Control Based on Approximate Dynamic Model;70
6.3;3Immediate Antecedents in WMR Control;71
6.4;4The More General Model;73
6.4.1;4.1The Physical Basis;73
6.4.2;4.2The Sign Conventions and the Final Results;74
6.4.3;4.3Combination of the Dynamic Models of the DC Motor and the Cart;76
6.4.4;4.4Anisotropic and Dynamically Varying Resolution of the Non-Holonomic Constraints;77
6.5;5The RFPT-Based Adaptivity;79
6.6;6Simulation Results;80
6.7;7Conclusions;86
7;Hybrid Functions Approach for Variational Problems and Optimal Control of Delay Systems;90
7.1;1Introduction;90
7.2;2Properties of Hybrid Functions;93
7.2.1;2.1Hybrid of Block-Pulse and Bernoulli Polynomials;93
7.2.2;2.2Function Approximation;94
7.2.3;2.3Integration of B(t)BT(t);95
7.2.4;2.4Operational Matrix of Integration;96
7.2.5;2.5The Operational Matrix of Product;96
7.2.6;2.6The Operational Matrix of Delay;97
7.3;3The Numerical Methods;97
7.3.1;3.1Problem (a);97
7.3.2;3.2Problem (b);99
7.4;4Main Feature of the Method;102
7.5;5Illustrative Examples;102
7.5.1;5.1Example 1;102
7.5.2;5.2Example 2;104
7.5.3;5.3Example 3;105
7.5.4;5.4Example 4;106
7.5.5;5.5Example 5;107
7.5.6;5.6Example 6;108
7.6;6Conclusion;110
8;Punctuated Anytime Learningfor Autonomous Agent Control;112
8.1;1 Introduction;112
8.2;2 Punctuated Anytime Learning;113
8.2.1;2.1 The Co-Evolution of Model Parameters;115
8.2.2;2.2 Fitness Biasing;115
8.3;3 Using the Co-Evolution of Model Parameters for HexapodRobot Gait Development;116
8.3.1;3.1 Robot and Model;117
8.3.2;3.2 Cyclic Genetic Algorithm;118
8.3.3;3.3 Using the Co-Evolution of Model Parameters to Learn Gaits;118
8.3.4;3.4 Results;120
8.4;4 Using Fitness Biasing for Xpilot Game Agent Control;122
8.4.1;4.1 Xpilot-AI;124
8.4.2;4.2 Fitness Biasing Applied to Xpilot-AI;125
8.4.3;4.3 Results;126
8.5;5 Discussion;127
8.6;6 Conclusion;128
8.7;References;129
9;Big Data Analytic: Cases for Communications SystemsModeling and Renewable Energy Forecast;131
9.1;1 Cost-Efficient Approach to ROF Communications SystemsDesign for CATV Channels over WDM Network and Fuzzy-GA Estimation;132
9.1.1;1.1 Introduction;132
9.1.2;1.2 Optimized System Design to Overcome DM Laser Limitations;133
9.1.3;1.3 System Analysis;133
9.1.4;1.4 Simulation Results;134
9.1.5;1.5 Fuzzy-GA Estimation;136
9.2;2 Big Data Analytic for 24-hour Ahead Solar Energy Forecast;140
9.2.1;2.1 Introduction;140
9.2.2;2.2 Problem Statement and Solar Forecasting State-of-the-art;141
9.2.3;2.3 Framework Development; Proposed Approach to Solar EnergyForecasting;142
9.2.4;2.4 Performance Evaluation Metrics;150
9.2.5;2.5 Forecast Results and Discussion;151
9.3;3 Conclusion and Remarks;153
9.4;References;154
10;Area Coverage in a Fixed-Obstacle Environment Using Mobile Sensor Networks;157
10.1;1Introduction;157
10.2;2Visibility-Aware Multiplicatively Weighted Voronoi Diagram;159
10.3;3Deployment Protocols;161
10.3.1;3.1Obstructed Farthest Point (OFP) Strategy;163
10.3.2;3.2Obstructed Minmax Point (OMP) Strategy;165
10.4;4Simulation Results;167
10.5;5Conclusions and Future Works;171
11;Energy Aware Load Prediction for Cloud Data Centers;174
11.1;1 Introduction;174
11.1.1;1.1 Related Research;175
11.1.2;1.2 Optimum Cloud Provisioning Method;176
11.1.3;1.3 Research Contributions and Paper Outline;177
11.2;2 Stochastic State Change Model;178
11.2.1;2.1 Determination of Probability Distribution Function;179
11.2.2;2.2 Definition of the SLA Cost Function;181
11.3;3 Dynamic Quantization Model;182
11.3.1;3.1 Determination of Cost Functions;182
11.3.2;3.2 Prediction Frequency Calculation;183
11.4;4 Linear Prediction Model;184
11.5;5 Simulation and Results;187
11.6;6 Conclusion;193
11.7;References;194
12;Behaving Nicely in a System of Systems –Harmonising with the Environment;196
12.1;1 Introduction;196
12.2;2 Systems of Systems Engineering;197
12.2.1;2.1 Definition and Characterization of Systems of Systems;197
12.2.2;2.2 Types of System of Systems;198
12.2.3;2.3 Interoperability;200
12.2.4;2.4 Summary of SoS Basics;200
12.3;3 Global Drivers for Improved SoS Design and Management;201
12.3.1;3.1 Increasing Interconnectivity, Increasing Complexity, and IncreasedInflexibility;201
12.3.2;3.2 A Perfect Storm for Europe;201
12.3.3;3.3 Agility;203
12.4;4 The SoS Research Agenda for Europe;204
12.4.1;4.1 Characterization and Description of SoS;205
12.4.2;4.2 Theoretical Foundations;206
12.4.3;4.3 Emergence;206
12.4.4;4.4 Multi-level Modelling;206
12.4.5;4.5 Measurement and Metrics;207
12.4.6;4.6 Evaluation of SoS;207
12.4.7;4.7 Definition and Evolution of SoS Architecture;208
12.4.8;4.8 Prototyping SoS;208
12.4.9;4.9 Trade-Off;209
12.4.10;4.10 Security;209
12.4.11;4.11 Human Aspects;209
12.4.12;4.12 Energy;210
12.5;5 Conclusions;210
12.6;References;211
13;Design Considerations of Dexterous Telerobotics;214
13.1;1 Introduction;214
13.2;2 Local Site Design Considerations and Challenges;215
13.2.1;2.1 Sensing (Action Identification).;215
13.2.2;2.2 Sensation Generation (Feedback);217
13.3;2 Local Site Design Considerations and Challenges;215
13.3.1;2.1 Sensing (Action Identification).;215
13.3.2;2.2 Sensation Generation (Feedback);217
13.4;3 Remote Site Design Considerations and Challenges;219
13.4.1;3.1 Action Generation;219
13.4.2;3.2 Sensing;221
13.5;4 Conclusions;221
13.6;References;222
14;Real-Time Neural Control of Mobile Robots;225
14.1;1Introduction;225
14.2;2Decentralized Systems;226
14.3;3Nonlinear System Identification;228
14.3.1;3.1Neural Identification;228
14.3.2;3.2The EKF Training Algorithm;230
14.4;4Inverse Optimal Control;231
14.5;5Shrimp Robot Application;234
14.6;6Vision Systems Application;238
14.6.1;8.1Stereo Vision;239
14.6.2;8.2Kinematic Planer;242
14.7;7 Results;242
14.8;8Conclusions;246
15;Low-Cost Inertial Navigation;250
15.1;1Notation;250
15.2;2Inertial Navigation;250
15.2.1;2.1Navigation Coordinate Frames;251
15.2.2;2.2Inertial Sensor Technology;252
15.2.3;2.3Inertial Sensor Errors;254
15.3;3Inertial Sensor Performance Comparison;258
15.3.1;3.1 Description of an ISA, IMU, IRU, and INS;259
15.3.2;3.2INS-Only Based Navigation;260
15.3.3;3.3An INS–Only Simulation Example;262
15.3.4;3.4INS Error Modeling;266
15.4;4GPS-Only Based Navigation;266
15.5;5GPS/INS–Based Navigation;268
15.5.1;5.1Introduction;268
15.5.2;5.2Uncoupled GPS/INS Integration;269
15.5.3;5.3Loosely-Coupled GPS/INS Integration;269
15.5.4;5.4Tightly-Coupled GPS/INS Integration;273
15.5.5;5.5Deeply-Coupled GPS/INS;273
15.6;6Conclusion;277
16;Hardware Implementation of Fuzzy Logic Controller-Designability, Stability and Robustness-;279
16.1;1 Introduction;279
16.2;2 Practical Hardware Systems;280
16.2.1;2.1 Current Mode Integrated Circuits1,2,3,4,5,6,7,8,9,10,11,12,13,14;280
16.2.2;2.2 Membership Function Generator in Voltage Mode15;281
16.2.3;2.3 Fuzzy Inference Engine Board of Voltage Mode Circuit16;282
16.2.4;2.4 Commercial Hardware for Fuzzy Logic Control in Voltage Mode17,18,19;283
16.2.5;2.5 Wine Glass/Mouse Stabilization by Employing the Fuzzy LogicController20;286
16.2.6;2.6 Fuzzy Chips –Rule Chip and Defuzzifier Chip–;288
16.3;3 Applications;289
16.4;4 Conclusions;290
16.5;References;290
17;Decision Making under Z-Information;292
17.1;1 Critical Analysis of the Existing Decision Theories;292
17.2;2 Isights for Development of New Theory of Decisions;294
17.3;3 The Evolut?on of Model?ng Dec?s?on-Relevant Informat?on;294
17.4;4 Basic Principles of the Suggested New Theory of Decisions;295
17.4.1;4.1 Z-Restriction;295
17.4.2;4.2 Combined States;295
17.4.3;4.3 Short Description of a New Theory;296
17.5;5 Statement of the Problem in the Suggested Theory ofDecisions;297
17.6;6 Model;299
17.7;7 Solving Methodology for a Suggested Theory of Decisions;303
17.8;8 Conclusion;304
17.9;References;304
18;Agencies of Intelligence: From the Macro to the Nano;305
18.1;1 Introduction;305
18.2;2 What Is Intelligence? A Macro Perspective;307
18.2.1;2.1 Generalization;307
18.2.2;2.2 Optimization by Nature;313
18.2.3;2.3 Learning from Psychology;315
18.3;3 What Is Intelligence? A Micro Perspective;316
18.3.1;3.1 Confabulation-Inspired Association Rule Mining (CARM);317
18.4;4 Social Intelligence? Intelligence Is as a Result of Interaction(Cooperation/Competition) of Several Beings;318
18.4.1;4.1 Fault Detection and Isolation by a Hybrid Fuzzy and Neuro Approach;319
18.4.2;4.2 Distributed Urban Traffic Control and Modeling;319
18.4.3;4.3 What about Shared Autonomy? Social Intelligence with the Human in theLoop;320
18.5;5 What Is Intelligence? The Intelligence of the Many;321
18.5.1;5.1 Spin Glasses for Optimization and Portfolio Selection;321
18.5.2;5.2 Swarm Control for Atherosclerosis and Cancer;322
18.6;6 Conclusion;325
18.7;References;326
19;Erratum: Control and Systems Engineering;328
20;Personal Notes;329
21;Appendix: Mo Jamshidi Publication List;339
22;Name Index;1