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Model-Based Control: Bridging Rigorous Theory and Advanced Technology

:	Paul M.J. Hof, Carsten Scherer, Peter S.C. Heuberger
:	Paul M.J. van den Hof, Carsten Scherer, Peter S.C. Heuberger
:	Model-Based Control: Bridging Rigorous Theory and Advanced Technology
:	Springer-Verlag
:	9781441908957
:	1
:	CHF 85.30
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	239
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

< >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.

	Foreword	5
	Acknowledgements	5
	Preface	9
	Contents	11
	List of Contributors	13
	Part I_Fundamentals	16
	Linear Systems in Discrete Time	17
	1 Introduction	17
	2 Linear dynamical systems	18
	3 Polynomial annihilators	19
	4 Input/output representations	20
	5 Representations with rational symbols	21
	6 Integer invariants	22
	7 Latent variables	22
	8 Controllability	23
	9 Rational annihilators	24
	10 Stabilizability	25
	11 Autonomous systems	25
	References	25
	Robust Controller Synthesis is Convex forSystems without Control Channel Uncertainties	27
	1 Introduction	27
	2 System Interconnections and Performance Specification	29
	3 Robust Performance Analysis	31
	4 Parametric-Dynamic Feasibility Problems	33
	4.1 Analysis	35
	4.2 Synthesis	36
	4.3 Elimination	40
	5 A Sketch of Further Applications	41
	6 Conclusions	42
	7 Appendix: Proof of Lemma 1	42
	References	44
	Conservation Laws andLumped System Dynamics	45
	1 Introduction	45
	2 Kirchhoff’s laws on graphs and circuit dynamics	46
	2.1 Graphs	46
	2.2 Kirchhoff’s laws for graphs	47
	2.3 Kirchhoff’s laws for open graphs	49
	2.4 Constraints on boundary currents and invariance of boundarypotentials	51
	2.5 Interconnection of open graphs	52
	2.6 Constitutive relations and port-Hamiltonian circuit dynamics	53
	3 Conservation laws on higher-dimensional complexes	55
	3.1 Kirchhoff behavior on k-complexes	55
	3.2 Open k-complexes	57
	4 Port-Hamiltonian dynamics on k-complexes	58
	4.1 Example: Heat transfer on a 2-complex	59
	5 Conclusions	60
	References	61
	Polynomial Optimization Problems areEigenvalue Problems	63
	1 Introduction	63
	2 General Theory	64
	2.1 Introduction	64
	2.2 Polynomial Optimization is Polynomial System Solving	65
	2.3 Solving a System of Polynomial Equations is Linear Algebra	66
	2.3.1 Motivational Example	66
	2.3.2 Preliminary Notions	66
	2.3.3 ConstructingMatrices Md	68
	2.4 Determining the Number of Roots	70
	2.5 Finding the Roots	71
	2.5.1 Realization Theory	72
	2.5.2 The Stetter-M¨oller Eigenvalue Problem	73
	2.6 Finding the Minimizing Root as a Maximal Eigenvalue	74
	2.7 Algorithms	78
	3 Applications in Systems Theory and Identification	78
	4 Conclusions and Future Work	80
	References	81
	Part II_Bridging Theory and Applied Technology	83
	Designing Instrumentation for Control	84
	1 Motivation	84
	2 Definition of Information Architecture	86
	3 Background	86
	4 Contributions of this Paper	87
	5 Problem Statement	88
	6 Solution to the General Integrated Sensor/Actuator Selectionand Control Design Problem	90
	7 Particular Cases of the Integrated Sensor/Actuator Selectionand Control Design Problem	91
	7.1 State feedback control	91
	7.2 Estimation	92
	7.3 Economic design problem	93
	8 Discrete-time systems	93
	9 Sensor and Actuator Selection	95
	10 Examples	96
	11 Economic sensor/actuator selection	99
	12 Conclusion	100
	References	101
	Uncertain Model Set Calculation fromFrequency Domain Data	102
	1 Introduction	102
	2 Uncertainty Models	103
	2.1 Application to covering a family of models	105
	2.2 Containment Metrics	106
	3 Application of Over-Bound Uncertainty Modeling to NASAGTM Aircraft	107
	3.1 Lateral-Directional GTM Aircraft Linear Model	107
	3.2 Generation of Frequency Response Data Sets	108
	3.3 Over-Bounding as a LMI Feasibility Problem	110
	3.3.1 Data Set I	110
	3.3.2 Data Set IP	112
	3.3.3 Data Set IPN	114
	3.4 Effect of System Directionality	114
	3.5 Containment Metric	116
	4 Conclusions	117
	References	118
	Robust Estimation for Automatic ControllerTuning with Application to Active Noise Control	119
	1 Introduction	119
	2 Approach to Automatic Controller Tuning	120
	2.1 Simultaneous Perturbation of Plant and Controller	120
	2.2 Disturbance Model	122
	2.3 Overview of REACT	122
	3 REACT Algorithm	123
	3.1 Defining an Error Function	123
	3.2 Derivation of Algorithm	124
	4 Stability and Convergence of the Tuning Algorithm	125
	4.1 Stability of the Feedback System	125
	4.2 Convergence of the Tuning Algorithm	127
	5 Application to ANC	132
	5.1 Description of System	132
	5.2 Identification of Plant Model	133
	5.3 Experimental Results	133
	6 Conclusions	135
	References	135
	Identification of Parameters in Large ScalePhysical Model Structures, for the Purpose ofModel-Based Operations	137
	1 Introduction	138
	2 Identifiability - the starting point	139
	3 Testing local identifiability in identification	141
	3.1 Introduction	141
	3.2 Analyzing local identifiability in q	141
	3.3 Approximating the identifiable parameter space	142
	4 Parameter scaling in identifiability	144
	5 Relation with controllability and observability	145
	6 Cost function minimization in identification	146
	7 A Bayesian approach	148
	8 Structural identifiability	150
	9 Examples	151
	10 Conclusions	153
	References	154
	Part III_Applications in Motion Control Systemsand Industrial Process Control	156
	Recovering Data from Cracked Optical Discsusing Hankel Iterative Learning Control	157
	1 Introduction	157
	2 Experimental setup	160
	2.1 Optical storage principle	160