Chadli / Bououden / Zelinka Recent Advances in Electrical Engineering and Control Applications

1. Auflage 2017
ISBN: 978-3-319-48929-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 411, 419 Seiten, eBook

Reihe: Lecture Notes in Electrical Engineering

This book of proceedings includes papers presenting the state of art in electrical engineering and control theory as well as their applications. The topics focus on classical as well as modern methods for modeling, control, identification and simulation of complex systems with applications in science and engineering. The papers were selected from the hottest topic areas, such as control and systems engineering, renewable energy, faults diagnosis—faults tolerant control, large-scale systems, fractional order systems, unconventional algorithms in control engineering, signals and communications.

The control and design of complex systems dynamics, analysis and modeling of its behavior and structure is vitally important in engineering, economics and in science generally science today. Examples of such systems can be seen in the world around us and are a part of our everyday life. Application of modern methods for control, electronics, signal processing and more can be found in our mobile phones, car engines, home devices like washing machines is as well as in such advanced devices as space probes and systems for communicating with them. All these technologies are part of technological backbone of our civilization, making further research and hi-tech applications essential.

The rich variety of contributions appeals to a wide audience, including researchers, students and academics.

Zielgruppe

Research

Autoren/Hrsg.

Chadli, Mohammed

Bououden, Sofiane

Zelinka, Ivan

Inhaltsverzeichnis

1;Foreword;6
2;Contents;8
3;Control and Systems Engineering (CSE);12
4;Power Quality Improvement Based on Five-Level NPC Series APF Using Fuzzy Control Scheme;13
4.1;Abstract;13
4.2;1 Introduction;13
4.3;2 Series APF Configuration System;14
4.4;3 Control Strategies;17
4.5;4 Fuzzy Logic Control;19
4.6;5 Simulation Results and Discussion;24
4.7;6 Conclusion;25
4.8;References;25
5;Adaptive Backstepping Control Using Combined Direct and Indirect \sigma –Modification Adaptation;27
5.1;Abstract;27
5.2;1 Introduction;27
5.3;2 Identification Based x-Swapping;28
5.4;3 Direct/Indirect Adaptive Backstepping Control with DSC;30
5.5;4 Stability Analysis;33
5.6;5 Numerical Example;35
5.7;6 Conclusion;39
5.8;References;39
6;Linear Stochastic Model Validation for Civil Engineering Structures Under Earthquakes;41
6.1;Abstract;41
6.2;1 Introduction;41
6.3;2 Dynamic Model of the Structure;42
6.4;3 Seismic Dynamic Model;44
6.5;4 ARMAX Model of the Structure;46
6.6;5 ARMA Model Identification;49
6.7;6 Simulation Results;51
6.8;7 Conclusions;53
6.9;References;54
7;Adaptive Fuzzy Control-Based Projective Synchronization Scheme of Uncertain Chaotic Systems with Input Nonlinearities;55
7.1;Abstract;55
7.2;1 Introduction;55
7.3;2 Problem Statements and Preliminaries;56
7.4;3 Design of Fuzzy Adaptive Controller;60
7.5;4 Simulation Results;65
7.6;5 Conclusion;67
7.7;References;68
8;A Novel State Representation of Electric Powered Wheelchair;70
8.1;Abstract;70
8.2;1 Introduction;70
8.3;2 Descripting and Modeling;71
8.4;3 Dynamic Modeling;72
8.5;4 New State Representation;75
8.6;5 Decoupling System;76
8.7;6 EPW Control System;77
8.8;7 Results and Discussion;77
8.9;8 Conclusion;79
8.10;References;79
9;Single and Multi Objective Predictive Control of Mobile Robots;80
9.1;Abstract;80
9.2;1 Introduction;80
9.3;2 Model Predictive Control;81
9.4;3 Solution of the Multi Objective Predictive Control Problem;82
9.5;4 Application;83
9.6;5 Conclusion;88
9.7;References;88
10;Comparison Between Predictive Sliding Mode Control and Sliding Mode Control with Predictive Sliding Function;90
10.1;Abstract;90
10.2;1 Introduction;90
10.3;2 System Description;92
10.4;3 Synthesis of Discrete Predictive Sliding Mode Controller;92
10.5;4 Synthesis of Discrete Sliding Mode Controller with Predictive Sliding Function;96
10.6;5 Comparison Between PSMC and SMC-PSF;101
10.7;6 Conclusion;105
10.8;Acknowledgment;105
10.9;References;105
11;Discrete Variable Structure Model Reference Adaptive Control for Non Strictly Positive Real Systems Using Only I/O Measurements;108
11.1;Abstract;108
11.2;1 Introduction;108
11.3;2 Basic Definitions;109
11.4;3 The Modified Discrete Model Reference Adaptive Control;110
11.5;4 The Discrete Variable Structure Model Reference Adaptive Control Using Only Input-Output Measurements;113
11.6;5 Simulation Example;114
11.6.1;5.1 Mrac;116
11.6.2;5.2 D-Vs-Mrac-Io;119
11.6.3;5.3 Comparison Between Discrete MRAC and D-VS-MRACIO;120
11.7;6 Conclusion;121
11.8;Acknowledgment;122
11.9;References;122
12;Stable Adaptive Fuzzy Sliding-Mode Controller for a Class of Underactuated Dynamic Systems;124
12.1;Abstract;124
12.2;1 Introduction;124
12.3;2 System Description and Problem Formulation;125
12.4;3 Control System Design and Stability Analysis;126
12.5;4 Simulation Study;130
12.6;5 Conclusion;133
12.7;References;133
13;Indirect Robust Adaptive Fuzzy Control of Uncertain Two Link Robot Manipulator;135
13.1;Abstract;135
13.2;1 Introduction;135
13.3;2 Problem Formulation;136
13.4;3 Description of Fuzzy Systems;138
13.5;4 Indirect Adaptive Fuzzy Control;138
13.6;5 Simulation Results;145
13.7;6 Conclusion;148
13.8;References;148
14;Constrained Fuzzy Predictive Control Design Based on the PDC Approach;150
14.1;Abstract;150
14.2;1 Introduction;150
14.3;2 Backgrounds;151
14.3.1;2.1 Model Predictive Control;151
14.3.2;2.2 Fuzzy Discrete Time T-S Model;151
14.3.3;2.3 PDC Fuzzy Control Law;152
14.4;3 Robust T-S Predictive Control Model Using PDC Controller;152
14.5;4 Simulation Results;157
14.5.1;4.1 Example 1;157
14.5.2;4.2 Example 2;158
14.6;5 Conclusion;163
14.7;References;164
15;Renewable Energy (RE);165
16;Control of Grid-Connected Photovoltaic System with Batteries Storage;166
16.1;Abstract;166
16.2;1 Introduction;166
16.3;2 Photovoltaic Model;167
16.4;3 Storage System;170
16.5;4 Topology of the System;171
16.6;5 Control Strategy;171
16.6.1;5.1 Boost Converter Control;172
16.6.2;5.2 Inverter Control;172
16.7;6 Simulation Results;174
16.8;7 Conclusion;178
16.9;References;178
17;The Development of Empirical Photovoltaic/Thermal Collector;180
17.1;Abstract;180
17.2;1 Introduction;180
17.3;2 Concept of Hybrid PVT Collector;181
17.3.1;2.1 Block Diagram of a PV/T System for the Production of Energy;181
17.3.2;2.2 Constitution of the Hybrid Collector;182
17.4;3 Thermal Analysis;182
17.4.1;3.1 Schematic of Heat Transfer;182
17.5;4 Results and Discussions;185
17.6;5 Experimental Study;186
17.7;6 Conclusion;188
17.8;References;188
18;A Mathematical Model to Determine the Shading Effects in the I-V Characteristic of a Photovoltaic Module;190
18.1;Abstract;190
18.2;1 Introduction;190
18.3;2 Model and Simulation Procedure;191
18.3.1;2.1 Model of Practical PV in First Quadrant;191
18.3.2;2.2 Modeling of Reverse Characteristics of PV Cell;193
18.4;3 Study of Partial Shadowing Effects in the Solar PV;195
18.5;4 Simulation Results;195
18.5.1;4.1 Influence of the Amount of Shading with Bypass Diode;196
18.6;5 Conclusion;197
18.7;References;198
19;Hybrid Systems Using Thermal/Biomass Sources;200
19.1;Abstract;200
19.2;1 Introduction;200
19.3;2 An Experimental Hybrid System;202
19.4;3 Energy Resources Estimation;202
19.4.1;3.1 Estimation of Annual Thermal Energy;202
19.4.2;3.2 Estimation of Annual Biogas Energy;203
19.5;4 Modeling the Hybrid System;204
19.5.1;4.1 Continuous Energy Case;204
19.5.2;4.2 Discrete Energy Case;204
19.6;5 Application;205
19.6.1;5.1 Continuous Energy Case;205
19.6.2;5.2 Discrete Energy Case;205
19.7;6 Results;205
19.8;7 Graphic;207
19.9;8 Conclusion;208
19.10;References;209
20;A Neural and Fuzzy Logic Based Control Scheme for a Shunt Active Power Filter;210
20.1;Abstract;210
20.2;1 Introduction;210
20.3;2 Proposed Control Strategy;212
20.4;3 Adaptive Linear Neural Networks Principle;212
20.5;4 ADALINE as Harmonic Estimator;213
20.6;5 Design of the DC-bus Fuzzy Logic Controller;214
20.7;6 Simulations and Analysis of the Results;216
20.8;7 Conclusion;219
20.9;References;219
21;Faults Diagnosis-Faults Tolerant Control (FTC);221
22;Robust Fault Detection Filter Design for Discrete-Time Fuzzy Models;222
22.1;Abstract;222
22.2;1 Introduction;222
22.3;2 Preliminaries on T-S Fuzzy Systems;223
22.4;3 Problem Statement;225
22.5;4 Robustness Conditions;226
22.6;5 Fault Sensitivity Conditions;229
22.7;6 Mixed {{{{\bf H}}_{ - } } \mathord{\left/ {\vphantom {{{{\bf H}}_{ - } } {{{\bf H}}_{\infty } }}} \right. \kern-0pt} {{{\bf H}}_{\infty } }} Fault Detection Observer Design;232
22.8;7 Simulation Example;232
22.9;8 Conclusion;237
22.10;Appendix A;238
22.11;References;239
23;Feature Selection for Enhancement of Bearing Fault Detection and Diagnosis Based on Self-Organizing Map;240
23.1;Abstract;240
23.2;1 Introduction;240
23.3;2 Theoretical Background;242
23.3.1;2.1 Feature Selection;242
23.3.2;2.2 ReliefF Feature Selection;242
23.3.3;2.3 Minimum Redundancy and Maximum Relevancy (MRMR);243
23.3.4;2.4 Self-Organizing Map (SOM);243
23.4;3 Proposed Fault Diagnosis System;245
23.4.1;3.1 Feature Extraction;245
23.5;4 Experimental Implementation;248
23.5.1;4.1 Experimental Data;248
23.5.2;4.2 Results and Discussion;249
23.6;5 Conclusion;251
23.7;References;251
24;Small Signal Fractional Order Modeling of PN Junction Diode;254
24.1;Abstract;254
24.2;1 Introduction;254
24.3;2 Diode AC Small Signal Impedance-Experimental Setup;255
24.3.1;2.1 Diode Elements;255
24.3.2;2.2 Integer Order Models;257
24.4;3 Fractional Order Models;258
24.5;4 Experimental Results;259
24.5.1;4.1 Analytical and Classical Models;259
24.5.2;4.2 Fractional Model with One Zero and One Pole;260
24.5.3;4.3 Fractional Model with One Zero and Two Poles;261
24.6;5 Conclusion;261
24.7;References;262
25;Fractional Order Systems (Sofa);263
26;Rational Function Approximation of a Fundamental Fractional Order Transfer Function;264
26.1;Abstract;264
26.2;1 Introduction;264
26.3;2 Rational Function Approximation;266
26.3.1;2.1 Case 1: 0  lessthan  ?  lessthan  0.5;266
26.3.2;2.2 Case 2: ? = 0.5;269
26.4;3 Time Responses;270
26.4.1;3.1 Case 1: 0  lessthan  ?  lessthan  0.5;270
26.4.2;3.2 Case 2: ? = 0.5;271
26.5;4 Illustrative Example;272
26.6;5 Conclusion;279
26.7;References;279
27;Robust Adaptive Fuzzy Control for a Class of Uncertain Nonlinear Fractional Systems;281
27.1;Abstract;281
27.2;1 Introduction;281
27.3;2 Basic Definitions and Preliminaries for Fractional Order Systems;282
27.4;3 Description of the T–S Fuzzy Systems;285
27.5;4 Adaptive H^{\infty } Control of Uncertain Fractional Order Systems;286
27.6;5 Simulation Results;292
27.7;6 Conclusion;297
27.8;References;297
28;Signal and Communications (SC);300
29;A Leaky Wave Antenna Based on SIW Technology for Ka Band Applications;301
29.1;Abstract;301
29.2;1 Introduction;301
29.3;2 Parameters of Substrate Integrated Waveguide;302
29.3.1;2.1 Feed Design;303
29.3.2;2.2 Microstrip Transition Lines in Substrate Integrated Waveguide;304
29.4;3 SIW Leaky-Wave Antenna Design;306
29.5;4 Conclusion;308
29.6;References;309
30;Selective Filters Design Based Two-Dimensional Photonic Crystals: Modeling Using the 2D-FDTD Method;310
30.1;Abstract;310
30.2;1 Introduction;310
30.3;2 Filtering in Two-Dimensional Photonic Crystals;311
30.4;3 Two Dimensional FDTD 2D;312
30.5;4 Selective Filter Design;315
30.5.1;4.1 First Filter Topology Based on Three Cascaded Waveguide in Triangular Lattices;315
30.5.2;4.2 Second Filter Topology Based on Three Cascaded Wave Guides in Square and Triangular Lattices;316
30.6;5 Conclusions;319
30.7;References;319
31;Writer’s Gender Classification Using HOG and LBP Features;321
31.1;Abstract;321
31.2;1 Introduction;321
31.3;2 Gender Classification System;322
31.3.1;2.1 Local Binary Patterns;322
31.3.2;2.2 Histogram of Oriented Gradients;324
31.3.3;2.3 Support Vector Machines;325
31.4;3 Experimental Results;325
31.4.1;3.1 Results Obtained for the First Training Set;326
31.4.2;3.2 Results Obtained for the Second Training Set;327
31.5;4 Conclusion and Future Work;328
31.6;References;328
32;Speech Recognition System Based on OLLO French Corpus by Using MFCCs;330
32.1;Abstract;330
32.2;1 Introduction;330
32.3;2 The Mel-Frequency Cepstrum Coefficient (MFCC);331
32.4;3 Coprus;331
32.5;4 Experimental Results and Analyse;333
32.6;5 Conclusion;335
32.7;References;335
33;Wavelets Based Image De-Noising: Application to EFTEM Imaging;336
33.1;Abstract;336
33.2;1 Introduction;336
33.3;2 Noise in EM Images;337
33.4;3 Concrete Steps of Wavelets De-Noising Algorithm in EM;338
33.4.1;3.1 Basic Assumption;338
33.4.2;3.2 Concrete Steps of De-noising EM Images;340
33.5;4 Results;340
33.5.1;4.1 Experimental Test Data;340
33.5.2;4.2 Performance Evaluation;340
33.5.3;4.3 Results of the De-Noising Algorithm;343
33.6;5 Concluding Remarques;344
33.7;References;346
34;New Front End Based on Multitaper and Gammatone Filters for Robust Speaker Verification;348
34.1;Abstract;348
34.2;1 Introduction;348
34.3;2 The Proposed Multitaper Gammatone Cepstral Coefficient MGCC;349
34.4;3 Gammatone Filter;350
34.5;4 Multitaper Spectrum Estimation;351
34.6;5 Experiment;352
34.6.1;5.1 Experimental Setup;352
34.6.2;5.2 Experimental Results Using GMM-UBM;353
34.6.3;5.3 Experimental Results Using I-Vector;356
34.7;6 Conclusion;357
34.8;References;357
35;Comparative Study of Time Frequency Analysis Application on Abnormal EEG Signals;359
35.1;Abstract;359
35.2;1 Introduction;359
35.3;2 Methods;360
35.3.1;2.1 Time-Frequency Analysis;360
35.3.2;2.2 Rényi Entropy;361
35.4;3 Materials and EEG Data;362
35.5;4 Experimental Results;363
35.5.1;4.1 Time-Frequency Analysis Using Rényi Entropy;363
35.5.2;4.2 Peak Seizure Characterisation;364
35.6;5 Conclusion;370
35.7;References;370
36;Performance Evaluation of Segmentation Algorithms Based on Level Set Method: Application to Medical Images;373
36.1;Abstract;373
36.2;1 Introduction;373
36.3;2 Level Set Method in Image Segmentation;374
36.3.1;2.1 Level Sets;374
36.3.2;2.2 Performance Evaluation;378
36.4;3 Experimental Results;380
36.5;4 Concluding Remarques;383
36.6;References;383
37;Design of Antipodal Linearly Tapered Slot Antennas (ALTSA) Arrays in SIW Technology for UWB Imaging;385
37.1;Abstract;385
37.2;1 Introduction;385
37.3;2 Single Antenna Element;386
37.4;3 SIW Bends Design;388
37.5;4 Design of SIW Power 2-Way Divider with ALTSA;388
37.6;5 Resultants and Simulation;390
37.7;6 Present Electromagnetic Fields in SIW Bends;391
37.8;7 Conclusion;392
37.9;References;392
38;Large Scale Systems (SI03);394
39;Optimized Sliding Mode Control of DC-DC Boost Converter for Photovoltaic System;395
39.1;Abstract;395
39.2;1 Introduction;395
39.3;2 System Configuration and Sliding Mode Control Strategy;396
39.3.1;2.1 Validity of the Control Methodology;398
39.4;3 Optimization of the Sliding Mode Control Strategy;402
39.4.1;3.1 Simplex Method to Delimitate Sliding Mode Controller Gains;402
39.4.2;3.2 PSO-Based Optimization of Sliding Mode Controller Gains;404
39.5;4 Simulation Results;405
39.6;5 Conclusion;407
39.7;References;408
40;Modeling of MOSFET Transistor by MLP Neural Networks;409
40.1;Abstract;409
40.2;1 Introduction;409
40.3;2 The Metal Oxide Semiconductor (MOS) Transistor;410
40.4;3 Artificial Neuron Networks ANN;411
40.4.1;3.1 Structure of ANN;411
40.4.2;3.2 Training of an ANN;412
40.5;4 Genetic Algorithms GA;412
40.6;5 Applying of Genetic Algorithms for Neural Network Training;413
40.7;6 Simulation Results;414
40.8;7 Conclusion;416
40.9;References;416
41;Author Index;418