Hof / Scherer / Heuberger Model-Based Control:

Bridging Rigorous Theory and Advanced Technology
1. Auflage 2009
ISBN: 978-1-4419-0895-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 239 Seiten

Model-Based Control will be a collection of state-of-the-art contributions in the field of modelling, identification, robust control and optimization of dynamical systems, with particular attention to the application domains of motion control systems (high-accuracy positioning systems) and large scale industrial process control systems.The book will be directed to academic and industrial people involved in research in systems and control, industrial process control and mechatronics.

Autoren/Hrsg.

Scherer, Carsten

Hof, Paul M. J. van den

Heuberger, Peter S. C.

Inhaltsverzeichnis

1;Foreword;5
1.1;Acknowledgements;5
2;Preface;9
3;Contents;11
4;List of Contributors;13
5;Part I_Fundamentals;16
5.1;Linear Systems in Discrete Time;17
5.1.1;1 Introduction;17
5.1.2;2 Linear dynamical systems;18
5.1.3;3 Polynomial annihilators;19
5.1.4;4 Input/output representations;20
5.1.5;5 Representations with rational symbols;21
5.1.6;6 Integer invariants;22
5.1.7;7 Latent variables;22
5.1.8;8 Controllability;23
5.1.9;9 Rational annihilators;24
5.1.10;10 Stabilizability;25
5.1.11;11 Autonomous systems;25
5.1.12;References;25
5.2;Robust Controller Synthesis is Convex forSystems without Control Channel Uncertainties;27
5.2.1;1 Introduction;27
5.2.2;2 System Interconnections and Performance Specification;29
5.2.3;3 Robust Performance Analysis;31
5.2.4;4 Parametric-Dynamic Feasibility Problems;33
5.2.4.1;4.1 Analysis;35
5.2.4.2;4.2 Synthesis;36
5.2.4.3;4.3 Elimination;40
5.2.5;5 A Sketch of Further Applications;41
5.2.6;6 Conclusions;42
5.2.7;7 Appendix: Proof of Lemma 1;42
5.2.8;References;44
5.3;Conservation Laws andLumped System Dynamics;45
5.3.1;1 Introduction;45
5.3.2;2 Kirchhoff’s laws on graphs and circuit dynamics;46
5.3.2.1;2.1 Graphs;46
5.3.2.2;2.2 Kirchhoff’s laws for graphs;47
5.3.2.3;2.3 Kirchhoff’s laws for open graphs;49
5.3.2.4;2.4 Constraints on boundary currents and invariance of boundarypotentials;51
5.3.2.5;2.5 Interconnection of open graphs;52
5.3.2.6;2.6 Constitutive relations and port-Hamiltonian circuit dynamics;53
5.3.3;3 Conservation laws on higher-dimensional complexes;55
5.3.3.1;3.1 Kirchhoff behavior on k-complexes;55
5.3.3.2;3.2 Open k-complexes;57
5.3.4;4 Port-Hamiltonian dynamics on k-complexes;58
5.3.4.1;4.1 Example: Heat transfer on a 2-complex;59
5.3.5;5 Conclusions;60
5.3.6;References;61
5.4;Polynomial Optimization Problems areEigenvalue Problems;63
5.4.1;1 Introduction;63
5.4.2;2 General Theory;64
5.4.2.1;2.1 Introduction;64
5.4.2.2;2.2 Polynomial Optimization is Polynomial System Solving;65
5.4.2.3;2.3 Solving a System of Polynomial Equations is Linear Algebra;66
5.4.2.3.1;2.3.1 Motivational Example;66
5.4.2.3.2;2.3.2 Preliminary Notions;66
5.4.2.3.3;2.3.3 ConstructingMatrices Md;68
5.4.2.4;2.4 Determining the Number of Roots;70
5.4.2.5;2.5 Finding the Roots;71
5.4.2.5.1;2.5.1 Realization Theory;72
5.4.2.5.2;2.5.2 The Stetter-M¨oller Eigenvalue Problem;73
5.4.2.6;2.6 Finding the Minimizing Root as a Maximal Eigenvalue;74
5.4.2.7;2.7 Algorithms;78
5.4.3;3 Applications in Systems Theory and Identification;78
5.4.4;4 Conclusions and Future Work;80
5.4.5;References;81
6;Part II_Bridging Theory and Applied Technology;83
6.1;Designing Instrumentation for Control;84
6.1.1;1 Motivation;84
6.1.2;2 Definition of Information Architecture;86
6.1.3;3 Background;86
6.1.4;4 Contributions of this Paper;87
6.1.5;5 Problem Statement;88
6.1.6;6 Solution to the General Integrated Sensor/Actuator Selectionand Control Design Problem;90
6.1.7;7 Particular Cases of the Integrated Sensor/Actuator Selectionand Control Design Problem;91
6.1.7.1;7.1 State feedback control;91
6.1.7.2;7.2 Estimation;92
6.1.7.3;7.3 Economic design problem;93
6.1.8;8 Discrete-time systems;93
6.1.9;9 Sensor and Actuator Selection;95
6.1.10;10 Examples;96
6.1.11;11 Economic sensor/actuator selection;99
6.1.12;12 Conclusion;100
6.1.13;References;101
6.2;Uncertain Model Set Calculation fromFrequency Domain Data;102
6.2.1;1 Introduction;102
6.2.2;2 Uncertainty Models;103
6.2.2.1;2.1 Application to covering a family of models;105
6.2.2.2;2.2 Containment Metrics;106
6.2.3;3 Application of Over-Bound Uncertainty Modeling to NASAGTM Aircraft;107
6.2.3.1;3.1 Lateral-Directional GTM Aircraft Linear Model;107
6.2.3.2;3.2 Generation of Frequency Response Data Sets;108
6.2.3.3;3.3 Over-Bounding as a LMI Feasibility Problem;110
6.2.3.3.1;3.3.1 Data Set I;110
6.2.3.3.2;3.3.2 Data Set IP;112
6.2.3.3.3;3.3.3 Data Set IPN;114
6.2.3.4;3.4 Effect of System Directionality;114
6.2.3.5;3.5 Containment Metric;116
6.2.4;4 Conclusions;117
6.2.5;References;118
6.3;Robust Estimation for Automatic ControllerTuning with Application to Active Noise Control;119
6.3.1;1 Introduction;119
6.3.2;2 Approach to Automatic Controller Tuning;120
6.3.2.1;2.1 Simultaneous Perturbation of Plant and Controller;120
6.3.2.2;2.2 Disturbance Model;122
6.3.2.3;2.3 Overview of REACT;122
6.3.3;3 REACT Algorithm;123
6.3.3.1;3.1 Defining an Error Function;123
6.3.3.2;3.2 Derivation of Algorithm;124
6.3.4;4 Stability and Convergence of the Tuning Algorithm;125
6.3.4.1;4.1 Stability of the Feedback System;125
6.3.4.2;4.2 Convergence of the Tuning Algorithm;127
6.3.5;5 Application to ANC;132
6.3.5.1;5.1 Description of System;132
6.3.5.2;5.2 Identification of Plant Model;133
6.3.5.3;5.3 Experimental Results;133
6.3.6;6 Conclusions;135
6.3.7;References;135
6.4;Identification of Parameters in Large ScalePhysical Model Structures, for the Purpose ofModel-Based Operations;137
6.4.1;1 Introduction;138
6.4.2;2 Identifiability - the starting point;139
6.4.3;3 Testing local identifiability in identification;141
6.4.3.1;3.1 Introduction;141
6.4.3.2;3.2 Analyzing local identifiability in q;141
6.4.3.3;3.3 Approximating the identifiable parameter space;142
6.4.4;4 Parameter scaling in identifiability;144
6.4.5;5 Relation with controllability and observability;145
6.4.6;6 Cost function minimization in identification;146
6.4.7;7 A Bayesian approach;148
6.4.8;8 Structural identifiability;150
6.4.9;9 Examples;151
6.4.10;10 Conclusions;153
6.4.11;References;154
7;Part III_Applications in Motion Control Systemsand Industrial Process Control;156
7.1;Recovering Data from Cracked Optical Discsusing Hankel Iterative Learning Control;157
7.1.1;1 Introduction;157
7.1.2;2 Experimental setup;160
7.1.2.1;2.1 Optical storage principle;160
7.1.2.2;2.2 Cracked disc;161
7.1.2.3;2.3 Motion system;162
7.1.3;3 Hankel ILC;164
7.1.3.1;3.1 System formulation;164
7.1.3.2;3.2 Hankel ILC control framework;166
7.1.3.2.1;3.2.1 Convergence;167
7.1.3.2.2;3.2.2 Performance;167
7.1.3.3;3.3 Hankel ILC control design;168
7.1.4;4 Implementation aspects;169
7.1.4.1;4.1 Trial-varying setpoint variations;169
7.1.4.2;4.2 Dealing with the DEFO;169
7.1.4.3;4.3 State transformation to physical coordinates;170
7.1.4.4;4.4 Resulting Hankel ILC scheme;172
7.1.5;5 Experimental results;173
7.1.6;6 Conclusions;173
7.1.7;References;175
7.2;Advances in Data-driven Optimization ofParametric and Non-parametric FeedforwardControl Designs with Industrial Applications;177
7.2.1;1 Introduction;177
7.2.2;2 Data-driven Feedforward Control Optimization;179
7.2.2.1;2.1 Objective Function;180
7.2.2.2;2.2 Optimization Algorithm;180
7.2.2.2.1;2.2.1 Convergence;182
7.2.2.2.2;2.2.2 Implementation;183
7.2.3;3 Parametric Feedforward Control Optimization for a WaferStage Application;183
7.2.3.1;3.1 Feedforward Controller Parameterization;184
7.2.3.2;3.2 Experimental Results;186
7.2.4;4 Non-parametric Feedforward Control Optimization for aDigital Light Projection Application;186
7.2.4.1;4.1 Feedforward Controller Parameterization;188
7.2.4.2;4.2 Non-parametric Feedforward Control Optimization and ILC;188
7.2.4.3;4.3 ILC Design for UHP Lamp Current Control;189
7.2.4.4;4.4 Experimental Results;190
7.2.5;5 Conclusions;192
7.2.6;References;192
7.3;Incremental Identification of Hybrid Models ofDynamic Process Systems;195
7.3.1;1 Introduction;196
7.3.2;2 Hybrid models;197
7.3.3;3 Model identification strategies;197
7.3.3.1;3.1 Incremental model development and refinement;198
7.3.3.2;3.2 Incremental model identification;199
7.3.3.3;3.3 An assessment of incremental identification;200
7.3.4;4 Incremental identification of a hybrid semi-batch reactor;201
7.3.4.1;4.1 Reactor model;201
7.3.4.2;4.2 Incremental identification approach for the hybrid semi-batchreactor example;202
7.3.4.3;4.3 Simulated isothermal reaction system;202
7.3.4.4;4.4 Experimental data;203
7.3.4.5;4.5 Various modeling scenarios;204
7.3.4.6;4.6 Validation of the hybrid reactor model;206
7.3.5;5 Incremental identification of generally structured hybridmodels;207
7.3.6;6 Conclusions;211
7.3.7;References;211
7.4;Front Controllability in Two-Phase PorousMedia Flow;213
7.4.1;1 Introduction;214
7.4.2;2 Front dynamics;214
7.4.3;3 Analytical solution;218
7.4.4;4 Numerical approximation;218
7.4.5;5 Controllability;220
7.4.5.1;5.1 Pressures and velocities at the front;220
7.4.5.2;5.2 Position of the front;223
7.4.6;6 Front control;224
7.4.7;7 Concluding remarks;225
7.4.8;Nomenclature;225
7.4.9;Appendix: Analytical expressions;227
7.4.10;References;228
8;Part IV_Appendix;230
8.1;PhD Supervision by Okko H. Bosgra;231
8.1.1;Delft University of Technology;231
8.1.2;Wageningen University and Research Centre;233
8.1.3;Eindhoven University of Technology;233
8.2;Okko H. Bosgra,Bibliographic Record;235
8.2.1;Journal Papers, Book Chapters and Book Reviews;235
8.2.2;Conference Papers;238
9;Index;245