Linear System Theory

Frank M. Callier

ISBN 10: 146126961X / ISBN 13: 9781461269618

Editore: Springer, Springer US, 2012

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - This book is the result of our teaching over the years an undergraduate course on Linear Optimal Systems to applied mathematicians and a first-year graduate course on Linear Systems to engineers. The contents of the book bear the strong influence of the great advances in the field and of its enormous literature. However, we made no attempt to have a complete coverage. Our motivation was to write a book on linear systems that covers finite dimensional linear systems, always keeping in mind the main purpose of engineering and applied science, which is to analyze, design, and improve the performance of phy sical systems. Hence we discuss the effect of small nonlinearities, and of perturbations of feedback. It is our on the data; we face robustness issues and discuss the properties hope that the book will be a useful reference for a first-year graduate student. We assume that a typical reader with an engineering background will have gone through the conventional undergraduate single-input single-output linear systems course; an elementary course in control is not indispensable but may be useful for motivation. For readers from a mathematical curriculum we require only familiarity with techniques of linear algebra and of ordinary differential equations.

Codice articolo 9781461269618

Dati bibliografici

• Titolo: Linear System Theory
• Editore: Springer, Springer US
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 146126961X
• ISBN 13: 9781461269618
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 29 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 29 millimetri di profondità
• Peso: 791 grammi

Descrizione articolo

Riassunto

This book is the result of our teaching over the years an undergraduate course on Linear Optimal Systems to applied mathematicians and a first-year graduate course on Linear Systems to engineers. The contents of the book bear the strong influence of the great advances in the field and of its enormous literature. However, we made no attempt to have a complete coverage. Our motivation was to write a book on linear systems that covers finite­ dimensional linear systems, always keeping in mind the main purpose of engineering and applied science, which is to analyze, design, and improve the performance of phy­ sical systems. Hence we discuss the effect of small nonlinearities, and of perturbations of feedback. It is our on the data; we face robustness issues and discuss the properties hope that the book will be a useful reference for a first-year graduate student. We assume that a typical reader with an engineering background will have gone through the conventional undergraduate single-input single-output linear systems course; an elementary course in control is not indispensable but may be useful for motivation. For readers from a mathematical curriculum we require only familiarity with techniques of linear algebra and of ordinary differential equations.

Contenuti

1 Introduction.- 1.1 Science and Engineering.- 1.2 Physical Systems, Models, and Representations.- 1.3 Robustness.- 2 The System RepresentationR(•) = [A(•),B(•),C(•),D(•)].- 2.1 Fundamental Properties ofR(•).- 2.1.1 Definitions.- 2.1.2 Structure ofR(•).- 2.1.3 State Transition Matrix.- 2.1.4 State Transition Map and Response Map.- 2.1.5 Impulse Response Matrix.- 2.1.6 Adjoint Equations.- 2.1.7 Linear-Quadratic Optimization.- 2.2 Applications.- 2.2.1 Variational Equation.- 2.2.2 Control Correction Example.- 2.2.3 Optimization Example.- 2.2.4 Periodically Varying Differential Equations.- 2d The Discrete-Time System RepresentationRd(•) = [A(•),B(•),C(•),D(•)].- 2d.1 Fundamental Properties ofRd(•).- 2d.2 Application: Periodically Varying Recursion Equations.- 3 The System RepresentationR= [A,B,C,D], Part I.- 3.1 Preliminaries.- 3.2 General Properties ofR= [A,B,C,D].- 3.2.1 Definition.- 3.2.2 State Transition Matrix.- 3.2.3 The State Transition and Response Map of R.- 3.3 Properties of R when A has a Basis of Eigenvectors.- 3d The Discrete-Time System Representation Rd = [A,B,C,D].- 3d.1 Preliminaries.- 3d.2 General Properties of Rd.- 3d.3 Properties of Rd when A has a Basis of Eigenvectors.- 4 The System Representation R = [A,B,C,D], Part II.- 4.1 Preliminaries.- 4.2 Minimal Polynomial.- 4.3 Decomposition Theorem.- 4.4 The Decomposition of a Linear Map.- 4.5 Jordan Form.- 4.6 Function of a Matrix.- 4.7 Spectral Mapping Theorem.- 4.8 The Linear Map X ? AX+XB.- 5 General System Concepts.- 5.1 Dynamical Systems.- 5.2 Time-Invariant Dynamical Systems.- 5.3 Linear Dynamical Systems.- 5.4 Equivalence.- 6 Sampled Data Systems.- 6.1 Relation BetweenL- and z-Transforms.- 6.2 D/A Converter.- 6.3 A/D Converter.- 6.4 Sampled-Data System.- 6.5 Example.- 7 Stability.- 7.1 I/O Stability.- 7.2 State Related Stability Concepts and Applications.- 7.2.1 Stability of x = A(t)x.- 7.2.2 Bounded Trajectories and Regulation.- 7.2.3 Response to T-Periodic Inputs.- 7.2.4 Periodically Varying System with Periodic Input.- 7.2.5 Slightly Nonlinear Systems.- 7d Stability: The Discrete-Time Case.- 7d.1 I/O Stability.- 7d.2 State Related Stability Concepts.- 7d.2.1 Stability of x(k+1) = A(k)x(k).- 7d.2.2 Bounded Trajectories and Regulation.- 7d.2.3 Response to q-Periodic Inputs.- 8 Controllability and Observability.- 8.1 Controllability and Observability of Dynamical Systems.- 8.2 Controllability of the Pair (A(•),B(•)).- 8.2.1 Controllability of the Pair (A(•),B(•)).- 8.2.2 The Cost of Control.- 8.2.3 Stabilization by Linear State Feedback.- 8.3 Observability of the Pair (C(•),A(•)).- 8.4 Duality.- 8.5 Linear Time-Invariant Systems.- 8.5.1 Observability Properties of the Pair (C,A).- 8.5.2 Controllability of the Pair (A,B).- 8.6 Kalman Decomposition Theorem.- 8.7 Hidden Modes, Stabilizability, and Detectability.- 8.8 Balanced Representations.- 8.9 Robustness of Controllability.- 8d Controllability and Observability: The Discrete-Time Case.- 8d.1 Controllability and Observability of Dynamical Systems.- 8d.2 Reachability and Controllability of the Pair (A(•),B(•)).- 8d.2.1 Controllability of the Pair (A(•),B(•)).- 8d.2.2 The Cost of Control.- 8d.3 Observability of the Pair (C(•),A(•)).- 8d.4 Duality.- 8d.5 Linear Time-Invariant Systems.- 8d.5.1 Observability of the Pair (C,A).- 8d.5.2 Reachability and Controllability of the Pair(A,B).- 8d.6 Kalman Decomposition Theorem.- 8d.7 Stabilizability and Detectability.- 9 Realization Theory.- 9.1 Minimal Realizations.- 9.2 Controllable Canonical Form.- 10 Linear State Feedback and Estimation.- 10.1 Linear State Feedback.- 10.2 Linear Output Injection and State Estimation.- 10.3 State Feedback of the Estimated State.- 10.4 Infinite Horizon Linear Quadratic Optimization.- 10d.4 Infinite Horizon Linear Quadratic Optimization. The Discrete-Time Case.- 11 Unity Feedback Systems.- 11.1 The Feedback System ?c.- 11.1.1 State Space Analysis.- 11.1.2 Special Case:R1andR2have no Unstable Hidden Modes.- 11.1.3 The Discrete-Time Case.- 11.2 Nyquist Criterion.- 11.2.1 The Nyquist Criterion.- 11.2.2 Remarks on the Nyquist Criterion.- 11.2.3 Proof of Nyquist Criterion.- 11.2.4 The Discrete-Time Case.- 11.3 Robustness.- 11.3.1 Robustness With Respect to Plant Perturbations.- 11.3.2 Robustness With Respect to Exogenous Disturbances.- 11.3.3 Robust Regulation.- 11.3.4 Bandwidth-Robustness Tradeoff.- 11.3.5 The Discrete-Time Case.- 11.4 Kharitonov’s Theorem.- 11.4.1 Hurwitz Polynomials.- 11.4.2 Kharitonov’s Theorem.- 11.5 Robust Stability Under Structured Perturbations.- 11.5.1 General Robustness Theorem.- 11.5.2 Special Case: Affine Maps and Convexity.- 11.5.3 The Discrete Time Case.- 11.6 Stability Under Arbitrary Additive Plant Perturbations.- 11.7 Transmission Zeros.- 11.7.1 Single-Input Single-Output Case.- 11.7.2 Multi-Input Multi-Output Case: Assumptions and Definitions.- 11.7.3 Characterization of the Zeros.- 11.7.4 Application to Unity Feedback Systems.- Appendix A Linear Maps and Matrix Analysis.- A.1 Preliminary Notions.- A.2 Rings and Fields.- A.3 Linear Spaces.- A4. Linear Maps.- AS. Matrix Representation.- A.5.1 The Concept of Matrix Representation.- A.5.2 Matrix Representation and Change of Basis.- A.5.3 Range and Null Space: Rank and Nullity.- A.5.4 Echelon Forms of a Matrix.- A.6 Notmed Linear Spaces.- A.6.1 Norms.- A.6.2 Convergence.- A.6.3 Equivalent Norms.- A.6.4 The Lebesgue Spaces 1P and LP [Tay.1].- A.6.5 Continuous Linear Transformations.- A.7 The Adjoint of a Linear Map.- A.7.1 Inner Products.- A.7.2 Adjoints of Continuous Linear Maps.- A.7.3 Properties of the Adjoint.- A.7.4 The Finite Rank Operator Fundamental Lemma.- A.7.5 Singular Value Decomposition (SVD).- Appendix B Differential Equations.- BA Existence and Uniqueness of Solutions.- B.1.1 Assumptions.- B.1.2 Fundamental Theorem.- B.1.3 Construction of a Solution by Iteration.- B.1.4 The Bellman-Gronwall Inequality.- B.1.5 Uniqueness.- B.2 Initial Conditions and Parameter Perturbations.- B.3 Geometric Interpretation and Numerical Calculations.- Appendix C Laplace Transforms.- C.1 Definition of the Laplace Transform.- C.2 Properties of Laplace Transforms.- Appendix D the z-Transform.- D.1 Definition of the z-Transform.- D.2 Properties of the z-Transform.- References.- Abbreviations.- Mathematical Symbols.