ISBN-10:
0817639543
ISBN-13:
9780817639549
Pub. Date:
07/28/1999
Publisher:
Birkhauser Verlag
Applied and Computational Control, Signals, and Circuits: Volume 1 / Edition 1

Applied and Computational Control, Signals, and Circuits: Volume 1 / Edition 1

by Biswa N. Datta

Hardcover

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Product Details

ISBN-13: 9780817639549
Publisher: Birkhauser Verlag
Publication date: 07/28/1999
Edition description: 1999
Pages: 539
Product dimensions: 6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

1 Discrete Event Systems: The State of the Art and New Directions.- 1.1 Introduction.- 1.2 DES Modeling Framework.- 1.3 Review of the State of the Art in DES Theory.- 1.3.1 Supervisory Control.- 1.3.2 Max-Plus Algebra.- 1.3.3 Sample Path Analysis and Performance Optimization.- 1.4 New Directions in DES Theory.- 1.5 Decentralized Control and Optimization.- 1.5.1 Some Key Issues.- 1.5.2 Decentralized Optimization Problem Formulation.- 1.5.3 Distributed Estimation.- 1.5.4 Weak Convergence Analysis.- 1.6 Failure Diagnosis.- 1.6.1 Statement of the Problem.- 1.6.2 Survey of Recent Literature.- 1.6.3 Presentation of One Approach to Failure Diagnosis.- 1.6.4 Some Issues for Future Research.- 1.7 Nondeterministic Supervisory Control.- 1.7.1 Nondeterminism and Semantics of Untimed Models.- 1.7.2 The Failure Semantics.- 1.7.3 The Trajectory Semantics.- 1.7.4 The Bisimulation Semantics.- 1.7.5 The Isomorphism Semantics.- 1.7.6 Discussion.- 1.8 Hybrid Systems and Optimal Control.- 1.8.1 Statement of the Problem.- 1.8.2 Using Optimal Control in Systems with Event-Driven Dynamics.- References.- 2 Array Algorithms forH2andH?Estimation.- 2.1 Introduction.- 2.2H2Square Root Array Algorithms.- 2.2.1 Kalman Filtering.- 2.2.2 Square Root Arrays.- 2.3HO?Square Root Array Algorithms.- 2.3.1H?Filtering.- 2.3.2 A Krein Space Formulation.- 2.3.3 J-Unitary Transformations.- 2.3.4 Square Root Array Algorithms.- 2.3.5 The Central Filters.- 2.4H2Fast Array Algorithms.- 2.5HO?Fast Array Algorithms.- 2.5.1 The General Case.- 2.5.2 The Central Filters.- 2.6 Conclusion.- References.- 2.A Unitary and Hyperbolic Rotations.- 2.A.1 Elementary Householder Transformations.- 2.A.2 Elementary Circular or Givens Rotations.- 2.A.3 Fast Givens Transformations.- 2.A.4 Hyperbolic Transformations.- 2.B Krein Spaces.- 2.B.1 A Geometric Interpretation.- 3 Nonuniqueness, Uncertainty, and Complexity in Modeling.- 3.1 Introduction.- 3.2 Issues of Models and Modeling.- 3.3 Nonuniqueness.- 3.4 Uncertainty.- 3.5 Complexity.- 3.6 Formulation of Model Set Identification.- 3.7 Learning or Optimization?.- 3.8 Conclusion.- References.- 4 Iterative Learning Control: An Expository Overview.- 4.1 Introduction.- 4.2 Generic Description of ILC.- 4.3 Two Illustrative Examples of ILC Algorithms.- 4.3.1 A Linear Example.- 4.3.2 An Adaptive ILC Algorithm for a Robotic Manipulator.- 4.4 The Literature, Context, and Terminology of ILC.- 4.4.1 Classifications of ILC Literature.- 4.4.2 Connections to Other Control Paradigms.- 4.5 ILC Algorithms and Results.- 4.5.1 Basic Ideas.- 4.5.2 Nonlinear Systems.- 4.5.3 Robotics and Other Applications.- 4.5.4 Some New Approaches to ILC Algorithms.- 4.6 Example: Combining Some New ILC Approaches.- 4.6.1 GMAW Model.- 4.6.2 ILC-Based Control Strategy.- 4.7 Conclusion: The Past, Present, and Future of ILC.- References.- 5 FIR Filter Design via Spectral Factorization and Convex Optimization.- 5.1 Introduction.- 5.2 Spectral Factorization.- 5.3 Convex Semi-infinite Optimization.- 5.4 Lowpass Filter Design.- 5.5 Log-Chebychev Approximation.- 5.6 Magnitude Equalizer Design.- 5.7 Linear Antenna Array Weight Design.- 5.8 Conclusions.- References.- 5.A Appendix: Spectral Factorization.- 6 Algorithms for Subspace State-Space System Identification: An Overview.- 6.1 System Identification: To Measure Is To Know’.- 6.2 Linear Subspace Identification: An Overview.- 6.2.1 Rediscovering the State.- 6.2.2 The Subspace Structure of Linear Systems.- 6.2.3 The Two Basic Steps in Subspace Identification.- 6.3 Comparing PEM with Subspace Methods.- 6.4 Statistical Consistency Results.- 6.5 Extensions.- 6.5.1 Deterministic Systems.- 6.5.2 Closed-loop Subspace System Identification.- 6.5.3 Frequency Domain Subspace Identification.- 6.5.4 Subspace Identification of Bilinear Systems.- 6.6 Software and DAISY.- 6.7 Conclusions and Open Research Problems.- References.- 7 Iterative Solution Methods for Large Linear Discrete Ill-Posed Problems.- 7.1 Introduction.- 7.2 Krylov Subspace Iterative Methods.- 7.2.1 The Standard Conjugate Gradient Algorithm.- 7.2.2 Conjugate Gradient Methods for Inconsistent Systems.- 7.3 Tikhonov Regularization.- 7.3.1 Factorization Methods.- 7.3.2 Algorithms Based on the Conjugate Gradient Method.- 7.3.3 Explicit Approximation of the Filter Function.- 7.3.4 A Comparison of Conjugate Gradient and Expansion Methods.- 7.3.5 Methods Based on the Total Variation Norm.- 7.4 An Exponential Filter Function.- 7.5 Iterative Methods Based on Implicitly Defined Filter Functions.- 7.5.1 Landweber Iteration.- 7.5.2 Truncated Conjugate Gradient Iteration.- 7.5.3 Regularizing Preconditioned Conjugate Gradient Methods.- 7.6 Toward a Black Box.- 7.6.1 Computation of the Regularization Parameter.- 7.6.2 Two Algorithms for Tikhonov Regularization.- 7.7 Computed Examples.- References.- 8 Wavelet-Based Image Coding: An Overview.- 8.1 Introduction.- 8.1.1 Image Compression.- 8.2 Quantization.- 8.2.1 Vector Quantization.- 8.2.2 Optimal Vector Quantizers.- 8.2.3 Sphere Covering and Density Shaping.- 8.2.4 Cross-Variable Dependencies.- 8.2.5 Fractional Bitrates.- 8.3 Transform Coding.- 8.3.1 The Karhunen-Loève Transform.- 8.3.2 Optimal Bit Allocation.- 8.3.3 Optimality of the Karhunen-Loève Transform.- 8.3.4 The Discrete Cosine Transform.- 8.3.5 Subband Transforms.- 8.4 Wavelets: A Different Perspective.- 8.4.1 Multiresolution Analyses.- 8.4.2 Wavelets.- 8.4.3 Recurrence Relations.- 8.4.4 Wavelet Transforms vs. Subband Decompositions.- 8.4.5 Wavelet Properties.- 8.5 A Basic Wavelet Image Coder.- 8.5.1 Choice of Wavelet Basis.- 8.5.2 Boundaries.- 8.5.3 Quantization.- 8.5.4 Entropy Coding.- 8.5.5 Bit Allocation.- 8.5.6 Perceptually Weighted Error Measures.- 8.6 Extending the Transform Coder Paradigm.- 8.7 Zerotree Coding.- 8.7.1 The Shapiro and Said-Pearlman Coders.- 8.7.2 Zerotrees and Rate-Distortion Optimization.- 8.8 Frequency and Space-Frequency Adaptive Coders.- 8.8.1 Wavelet Packets.- 8.8.2 Frequency Adaptive Coders.- 8.8.3 Space-Frequency Adaptive Coders.- 8.9 Utilizing Intra-band Dependencies.- 8.9.1 Trellis Coded Quantization.- 8.9.2 TCQ Subband Coders.- 8.9.3 Mixture Modeling and Estimation.- 8.10 Future Trends.- 8.11 Summary and Conclusion.- References.- 9 Reduced-Order Modeling Techniques Based on Krylov Subspaces and Their Use in Circuit Simulation.- 9.1 Introduction.- 9.2 Reduced-Order Modeling of Linear Dynamical Systems.- 9.2.1 Linear Dynamical Systems.- 9.2.2 Reduced-Order Modeling.- 9.2.3 Reduction to One Matrix.- 9.3 Linear Systems in Circuit Simulation.- 9.3.1 General Circuit Equations.- 9.3.2 Linear Subcircuits and Linearized Circuits.- 9.3.3 Linear RLC Circuits.- 9.4 Krylov Subspaces and Moment Matching.- 9.4.1 Assumptions and a Convention.- 9.4.2 Single Starting Vectors.- 9.4.3 Connection to Moment Matching.- 9.4.4 Multiple Starting Vectors.- 9.5 The Lanczos Process.- 9.5.1 The Classical Algorithm for Single Starting Vectors.- 9.5.2 A Lanczos-Type Algorithm for Multiple Starting Vectors.- 9.5.3 Exploiting Symmetry.- 9.6 Lanczos-Based Reduced-Order Modeling.- 9.6.1 The Classical Lanczos-Padé Connection.- 9.6.2 The Multi-Input Multi-Output Case.- 9.6.3 Stability and Passivity.- 9.6.4 PVL7r: Post-Processing of PVL.- 9.6.5 Passive Reduced-Order Models from SyMPVL.- 9.6.6 How to Achieve Passivity in Practice.- 9.6.7 Two Other Lanczos-Based Approaches.- 9.7 The Arnoldi Process.- 9.8 Arnoldi-Based Reduced-Order Modeling.- 9.9 Circuit-Noise Computations.- 9.9.1 The Problem.- 9.9.2 Reformulation as a Transfer Function.- 9.9.3 A PVL Simulation.- 9.10 Concluding Remarks.- References.- 10 SLICOT—A Subroutine Library in Systems and Control Theory.- 10.1 Introduction.- 10.2 Why Do We Need More Than MATLAB Numerics?.- 10.2.1 Limitations of MATLAB.- 10.2.2 The Need for Production Quality Numerical Software.- 10.2.3 Low-Level Reusability of Fortran Libraries.- 10.2.4 Structure Preserving Algorithms.- 10.3 Retrospect.- 10.3.1 Short History of Control Subroutine Libraries.- 10.3.2 Standard Libraries RASP and SLICOT: Present Status.- 10.3.3 RASP/SLICOT Mutual Compatibility Concept.- 10.4 The Design of SLICOT.- 10.4.1 Structure of the Library.- 10.4.2 Choice of Algorithms.- 10.4.3 User Manual.- 10.4.4 Implementation and Documentation Standards.- 10.4.5 Benchmarks.- 10.5 Contents of SLICOT.- 10.5.1 Current Contents of the Library.- 10.5.2 Development of the Public Release of SLICOT.- 10.5.3 In the Queue.- 10.6 Performance Results.- 10.7 The Future — NICONET.- 10.7.1 Objectives and Exploratory Phase of NICONET.- 10.7.2 Development of Performant Numerical Software for CACSD.- 10.7.3 Integration of Software in a User-Friendly Environment.- 10.7.4 Benchmarking and Testing the Software in an Industrial Environment.- 10.7.5 Information Dissemination and Access to Control Software.- 10.7.6 Implementation Phase.- 10.8 Concluding Remarks.- References.- 10.A Contents of SLICOT Release 3.0.- 10.B Electronic Access to the Library and Related Literature.

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