Buch, Englisch, 426 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 668 g
Buch, Englisch, 426 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 668 g
Reihe: Systems & Control: Foundations & Applications
ISBN: 978-1-4612-6599-3
Verlag: Birkhäuser Boston
and communication theory. This introductory text on the subject is
fairly self-contained, and consists of a wide range of topics that
include realization problems, linear-quadratic optimal control,
stability theory, stochastic modeling and recursive estimation
algorithms in communications and control, and distributed system
modeling.
In the early chapters methods based on Wiener--Hopf integral
equations are utilized. The fundamentals of both linear control
systems as well as stochastic control are presented in a unique way so
that the methods generalize to a useful class of distributed parameter
and nonlinear system models. The control of distributed parameter
systems (systems governed by PDEs) is based on the framework of linear
quadratic Gaussian optimization problems.
Additionally, the important notion of state space modeling of
distributed systems is examined. Basic results due to Gohberg and
Krein on convolution are given and many results are illustrated with
some examples that carry throughout the text.
The standard linear regulator problem is studied in the continuous
and discrete time cases, followed by a discussion of (dual) filtering
problems. Later chapters treat the stationary regulator and filtering
problems using a Wiener--Hopf approach. This leads to spectral
factorization problems and useful iterative algorithms that follow
naturally from the methods employed. The interplay between time and
frequency domain approaches is emphasized.
"Foundations of Deterministic and Stochastic Control" is geared
primarily towards advanced mathematics and engineering students in
various disciplines.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
- Mathematik | Informatik Mathematik Stochastik
- Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen
Weitere Infos & Material
1 State Space Realizations.- 1.1 Linear Models.- 1.2 Realizations.- 1.3 Constructing Time Invariant Realizations.- 1.4 An Active Suspension Model.- 1.5 A Model Identification Problem.- 1.6 Simulating Recursive Identification.- 1.7 Discrete Time Models.- Problems.- 2 Least Squares Control.- 2.1 Minimum Energy Transfers.- 2.2 The Output Regulator.- 2.3 Linear Regulator Tracking Problems.- 2.4 Dynamic Programming.- Problems.- 3 Stability Theory.- 3.1 Introduction.- 3.2 Introduction to Lyapunov Theory.- 3.3 Definitions.- 3.4 Classical Lyapunov Theorems.- 3.5 The Invariance Approach.- 3.6 Input-Output Stability.- Problems.- 4 Random Variables and Processes.- 4.1 Introduction.- 4.2 Random Variables.- 4.3 Sample Spaces and Probabilities.- 4.4 Densities.- 4.5 Expectations, Inner Products and Variances.- 4.6 Linear Minimum Variance Estimates.- 4.7 Gramians and Covariance Matrices.- 4.8 Random Processes.- 4.9 Gaussian Variables.- Problems.- 5 Kalman-Bucy Filters.- 5.1 The Model.- 5.2 Estimation Criterion.- 5.3 The One Step Predictor.- Problems.- 6 Continuous Time Models.- 6.1 Introduction.- 6.2 Stochastic Integrals.- 6.3 Stochastic Differential Equations.- 6.4 Linear Models.- 6.5 Second Order Results.- 6.6 Continuous White Noise.- 6.7 Continuous Time Kalman-Bucy Filters.- Problems.- 7 The Separation Theorem.- 7.1 Stochastic Dynamic Programming.- 7.2 Dynamic Programming Algorithm.- 7.3 Discrete Time Stochastic Regulator.- 7.4 Continuous Time.- 7.5 The Time Invariant Case.- 7.6 Active Suspension.- Problems.- 8 Luenberger Observers.- 8.1 Full State Observers.- 8.2 Reduced Order Observers.- Problems.- 9 Nonlinear and Finite State Problems.- 9.1 Introduction.- 9.2 Finite State Machines.- 9.3 Finite Markov Processes.- 9.4 Hidden Markov Models.- Problems.- 10 Wiener-Hopf Methods.- 10.1Wiener Filters.- 10.2 Spectral Factorization.- 10.3 The Scalar Case - Spectral Factorization.- 10.4 Discrete Time Factorization.- 10.5 Factorization in The Vector Case.- 10.6 Finite Dimensional Symmetric Problems.- 10.7 Spectral Factors and Optimal Gains.- 10.8 Linear Regulators and The Projection Theorem.- Problems.- 11 Distributed System Regulators.- 11.1 Open Loop Unstable Distributed Regulators.- 11.2 The “Wiener-Hopf” Condition.- 11.3 Optimal Feedback Gains.- 11.4 Matched Filter Evasion.- Problems.- 12 Filters Without Riccati Equations.- 12.1 Introduction.- 12.2 Basic Problem Formulation.- 12.3 Spectral Factors.- 12.4 Closed Loop Stability.- 12.5 Realizing The Optimal Filter.- Problems.- 13 Newton’s Method for Riccati Equations.- 13.1 Newton’s Method.- 13.2 Continuous Time Riccati Equations.- 13.3 Discrete Time Riccati Equations.- 13.4 Convergence of Newton’s Method.- 14 Numerical Spectral Factorization.- 14.1 Introduction.- 14.2 An Intuitive Algorithm Derivation.- 14.3 A Convergence Proof for the Continuous Time Algorithm.- 14.4 Implementation.- 14.5 The Discrete Case.- 14.6 Numerical Comments.- A Hilbert and Banach Spaces and Operators.- A.1 Banach and Hilbert Spaces.- A.2 Quotient Spaces.- A.3 Dual Spaces.- A.4 Bounded Linear Operators.- A.5 Induced Norms.- A.6 The Banach Space G(X, Y).- A.7 Adjoint Mappings.- A.8 Orthogonal Complements.- A.9 Projection Theorem.- A.10 Abstract Linear Equations.- A.11 Linear Equations and Adjoints.- A.12 Minimum Miss Distance Problems.- A.13 Minimum Norm Problems.- A.14 Fredholm Operators.- A.15 Banach Algebras.- A.15.1 Inverses and Spectra.- A.15.2 Ideals, Transforms, and Spectra.- A.15.3 Functional Calculus.- B Measure Theoretic Probability.- B.1 Measure Theory.- B.2 Random variables.- B.3 Integrals and Expectation.-B.4 Derivatives and Densities.- B.5 Conditional Probabilities and Expectations.- B.5.1 Conditional Probability.- B.5.2 Conditional Expectations.- References.
