Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition / Edition 2

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More than a decade ago, world-renowned control systems authority Frank L. Lewis introduced what would become a standard textbook on estimation, under the title Optimal Estimation, used in top universities throughout the world. The time has come for a new edition of this classic text, and Lewis enlisted the aid of two accomplished experts to bring the book completely up to date with the estimation methods driving today's high-performance systems.

A Classic Revisited
Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition reflects new developments in estimation theory and design techniques. As the title suggests, the major feature of this edition is the inclusion of robust methods. Three new chapters cover the robust Kalman filter, H-infinity filtering, and H-infinity filtering of discrete-time systems.

Modern Tools for Tomorrow's Engineers
This text overflows with examples that highlight practical applications of the theory and concepts. Design algorithms appear conveniently in tables, allowing students quick reference, easy implementation into software, and intuitive comparisons for selecting the best algorithm for a given application. In addition, downloadable MATLABĀ® code allows students to gain hands-on experience with industry-standard software tools for a wide variety of applications.

This cutting-edge and highly interactive text makes teaching, and learning, estimation methods easier and more modern than ever.

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Product Details

  • ISBN-13: 9780849390081
  • Publisher: Taylor & Francis
  • Publication date: 8/27/2007
  • Series: Automation and Control Engineering Series, #29
  • Edition description: REV
  • Edition number: 2
  • Pages: 552
  • Product dimensions: 6.20 (w) x 9.20 (h) x 1.40 (d)

Table of Contents

Classical Estimation Theory
Mean-Square Estimation
Maximum-Likelihood Estimation
The Cramer-Rao Bound
Recursive Estimation
Wiener Filtering
Discrete-Time Kalman Filter
Deterministic State Observer
Linear Stochastic Systems
The Discrete-Time Kalman Filter
Discrete Measurements of Continuous-Time Systems
Error Dynamics and Statistical Steady State
Frequency Domain Results
Correlated Noise and Shaping Filters
Optimal Smoothing
Continuous-Time Kalman Filter
Derivation from Discrete Kalman Filter
Some Examples
Derivation from Wiener-Hopf Equation
Error Dynamics and Statistical Steady State
Frequency Domain Results
Correlated Noise and Shaping Filters
Discrete Measurements of Continuous-Time Systems
Optimal Smoothing
Kalman Filter Design and Implementation
Modeling Errors, Divergence, and Exponential Data Weighting
Reduced-Order Filters and Decoupling
Using Suboptimal Gains
Scalar Measurement Updating
Estimation for Nonlinear Systems
Update of the Hyperstate
General Update of Mean and Covariance
Extended Kalman Filter
Application to Robotics and Adaptive Sampling
Robust Kalman Filter
Systems with Modeling Uncertainties
Robust Finite Horizon Kalman A Priori Filter
Robust Stationary Kalman A Priori Filter
Convergence Analysis
Linear Matrix Inequality Approach
Robust Kalman Filtering for Continuous-Time Systems
H-Infinity Filtering of Continuous-Time Systems
H-Infinity Filtering Problem
Finite Horizon H-Infinity Linear Filter
Characterization of All Finite Horizon H-Infinity Linear Filters
Stationary H-Infinity Filter-Riccati Equation Approach
Relationship with the Kalman Filter
Convergence Analysis
H-Infinity Filtering for a Special Class of Signal Models
Stationary H-Infinity Filter-Linear Matrix Inequality Approach
H-Infinity Filtering of Discrete-Time Systems
Discrete-Time H-Infinity Filtering Problem
H-Infinity A Priori Filter
H-Infinity A Posteriori Filter
Polynomial Approach to H-Infinity Estimation
J-Spectral Factorization
Applications in Channel Equalization
Stochastic Control for State Variable Systems
Dynamic Programming Approach
Continuous-Time Linear Quadratic Gaussian Problem
Discrete-Time Linear Quadratic Gaussian Problem
Stochastic Control for Polynomial Systems
Polynomial Representation of Stochastic Systems
Optimal Prediction
Minimum Variance Control
Polynomial Linear Quadratic Gaussian Regulator
Appendix A: Review of Matrix Algebra
Basic Definitions and Facts
Partitioned Matrices
Quadratic Forms and Definiteness
Matrix Calculus

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