Uncertain Models and Robust Control

AvAlexander Weinmann

Häftad, Engelska, 2012

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Beskrivning

Control systems particularly designed to manage uncertainties are called robust control system. Choosing appropriate design methods, the influence of uncertainties on the closed-loop behaviour can be reduced to a large extent.Most of the important areas of robust control are covered. The aim of the book is to provide an introduction to the theory and methods of robust control system design, to present a coherent body of knowledge, to clarify and unify presentation of significant derivations and proofs. The book contains a thorough treatment of important material of uncertainties and robust control which is scattered throughout the literature.

Produktinformation

Utgivningsdatum:2012-10-29
Mått:170 x 244 x 40 mm
Vikt:1 240 g
Format:Häftad
Språk:Engelska
Antal sidor:723
Förlag:Springer Verlag GmbH
ISBN:9783709173909

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Innehållsförteckning

I Introduction.- II Differential Sensitivity. Small-Scale Perturbation.- III Robustness in the Time Domain.- IV Robustness in the Frequency Domain.- V Coprime Factorization and Minimax Frequency Optimization.- VI Robustness Via Approximative Models.- A Matrix Algebra and Control.- A.1 Matrix Multiplication.- A.2 Properties of Matrix Operations.- A.3 Diagonal Matrices.- A.4Triangular Matrices.- A.5 Column Matrices (Vectors) and Row Matrices.- A.6 Reduced Matrix, Minor, Cofactor, Adjoint.- A.7 Similar Matrices.- A.8 Some Properties of Determinants.- A.9 Singularity.- A.10 System of Linear Equations.- A.11 Stable Matrices.- A.12 Range Space. Rank. Null Space.- A.13 Trace.- A.14 Matrix Functions.- A.15 Metzler Matrices.- A.16 Projectors.- A.17 Projectors and Rank.- A.18 Projectors. Left-Inverse and Right-Inverse.- A.19 Trigonal Operator.- A.20 Transfer Function Zeros and Initial Step Transients.- A.21 Convolution Sum and TrigonalOperator.- B Eigenvalues and Eigenvectors.- B.1 Right-Eigenvectors.- B.2 Left-Eigenvectors.- B.3 Complex-Conjugate Eigenvalues.- B.4 Modal Matrix of Eigenvectors.- B.5 Complex Matrices.- B.6 Modal Decomposition.- B.7 Linear Differential Equations and Modal Transformations.- B.8 Eigenvalue Assignment.- B.9 Eigensystem Assignment.- B.10 Complete Modal Synthesis.- B.11 Vandermonde Matrix.- B.12 Decompostion into Eigenvectors.- B.13 Properties of Eigenvalues.- B.13.1 Smallest and Largest Eigenvalue of Symmetrie Matrices.- B.13.2 Eigenvalues and Trace.- B.13.3 Maximum Real Part of an Eigenvalue.- B.13.5 Adding the Identity Matrix.- B.13.6 Eigenvalues of Matrix Products.- B.13.7 Eigenvalue of a Matrix Polynomial.- B.13.8 Weyl Inequality.- B.14 Rayleigh’s Theorem.- B.15 Eigenvalues and Eigenvectors of the Inverse.- B.16 Dyadic Decomposition (SpectralRepresentation).- B.17 Spectral Representation of the Exponential Matrix.- B.18 Perron-Frobenius Theorem.- B.19 Multiple Eigenvalues. Generalized Eigenvectors.- B.20 Jordan Canonical Form and Jordan Blocks.- B.21 Special Cases.- B.22 Fundamental Matrix.- B.23 Eigenvector Assignment.- B.23.1 Assignable Subspaces. Parametrization of Controllers.- B.23.2 Single Real or Complex-Conjugate Eigenvalues.- B.23.3 Multiple Eigenvalues and Linearly Independent Eigenvectors.- B.23.4 Multiple Eigenvalues and Generalized Eigenvectors.- B.23.5 Assignable Subspace. Concluding Remarks.- C Matrix Inversion.- C.1 Matrix Inversion Using Cayley-Hamilton Theorem.- C.2 Matrix Inversion Lemma.- C.3 Simplified Version of the Matrix Inversion Lemma.- C.4 Matrices in Partitioned Form.- C.4.1 Algebraic Properties.- C.4.2 Inversion of a Partitioned Matrix.- C.4.3 Inversion of a Partitioned Matrix. Nonsingular Submatrices.- C.4.4 Inversion of a Block-Diagonal Matrix.- C.4.5 Determinants of Matrices in Partitioned Form.- C.4.6 Reducible Matrix.- C.5 Right-Inverse.- C.6 Left-Inverse.- C.7 Pseudo-Inverse.- C.7.1 General Pseudo-Inverse.- C.7.2 General Pseudo-Inverse and a General Matrix Equation.- C.7.3 Right-Pseudo-Inverse.- C.7.4 Left-Pseudo-Inverse.- C.8 General System Inverse.- C.9 Pseudo-Inverse and Singular-Value Decomposition.- C.1O Pseudo-Inverse of a Matrix Partitioned into Submatrices.- C.11 Pseudo-Inverse of a Matrix Partitioned into Columns.- C.12 Successive Application of Right and Left-Pseudo-Inverse Operator.- C.13 Conditioning and Scaling.- C.13.1 Condition Number of a Matrix.- C.13.2 General Spectral Decomposition.- C.13.3 Eigenvalue Decomposition.- C.13.4 Orthogonal Transformation.- C.13.5 Scaled Decomposition.- C.13.6 Square Root Decomposition.- C.13.7 Cholesky Decomposition.- C.14 Orthogonalizing.- D Linear Regression and Estimation.- D.1 Parameter Demarcation.- D.2 Interpolation.- D.3 Weighted Least Squares Approximation.- D.4 Ordinary Least Squares Approximation.- D.5 Left Inverse and Right Inverse. Mnemonic Aid.- D.7 Sum of Errors and Residual Sum in Parameter Space.- D.8 Successive Estimation in Large-Scale Systems.- D.9 Recursive Least-Squares Estimation.- D.10 Recursive Instrumental Variable Method.- D.11 Linear Estimation.- D.11.1 Parametrie Models. Markov Processes.- D.11.2 Observation as a Random Process.- D.11.3 Minimum Variance Estimator. Gauss-Markov Theorem.- D.11.4 Estimation Sensitivity.- E Notations.- E.1 General Conventions.- E.2 Abbreviations and General Symbols.- E.3 Superscripts.- E.4 Subscripts.- E.5 Glossary of Symbols in Alphabetic Order.- F Author Index.- G Index.