2nd Edition

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

By Frank L. Lewis, Lihua Xie, Dan Popa Copyright 2008
    552 Pages 125 B/W Illustrations
    by CRC Press

    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.

    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


    Lewis, Frank L.; Xie, Lihua; Popa, Dan