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

The updated edition of this classic text reflects new developments in estimation theory and design techniques. The major feature of this text 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. The book 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 MATLAB code allows readers to gain hands-on experience.

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 RevisitedOptimal 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 EngineersThis 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 MATLAB® 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.

OPTIMAL ESTIMATIONClassical Estimation TheoryMean-Square Estimation Maximum-Likelihood Estimation The Cramer-Rao Bound Recursive Estimation Wiener FilteringProblemsDiscrete-Time Kalman FilterDeterministic State Observer Linear Stochastic Systems The Discrete-Time Kalman Filter Discrete Measurements of Continuous-Time SystemsError Dynamics and Statistical Steady State Frequency Domain Results Correlated Noise and Shaping Filters Optimal SmoothingProblemsContinuous-Time Kalman FilterDerivation 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 SmoothingProblemsKalman Filter Design and ImplementationModeling Errors, Divergence, and Exponential Data Weighting Reduced-Order Filters and Decoupling Using Suboptimal Gains Scalar Measurement UpdatingProblemsEstimation for Nonlinear SystemsUpdate of the Hyperstate General Update of Mean and Covariance Extended Kalman Filter Application to Robotics and Adaptive SamplingProblemsROBUST ESTIMATIONRobust Kalman FilterSystems with Modeling Uncertainties Robust Finite Horizon Kalman A Priori Filter Robust Stationary Kalman A Priori Filter Convergence AnalysisLinear Matrix Inequality Approach Robust Kalman Filtering for Continuous-Time SystemsProblemsH-Infinity Filtering of Continuous-Time SystemsH-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 ApproachProblemsH-Infinity Filtering of Discrete-Time SystemsDiscrete-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 EqualizationProblemsOPTIMAL STOCHASTIC CONTROLStochastic Control for State Variable SystemsDynamic Programming Approach Continuous-Time Linear Quadratic Gaussian ProblemDiscrete-Time Linear Quadratic Gaussian ProblemProblemsStochastic Control for Polynomial SystemsPolynomial Representation of Stochastic Systems Optimal Prediction Minimum Variance Control Polynomial Linear Quadratic Gaussian RegulatorProblemsAppendix A: Review of Matrix AlgebraBasic Definitions and Facts Partitioned Matrices Quadratic Forms and Definiteness Matrix CalculusReferencesIndex