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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 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.
Lewis, Frank L.; Xie, Lihua; Popa, Dan
OPTIMAL ESTIMATION. Classical Estimation Theory. Discrete-Time Kalman Filter. Continuous-Time Kalman Filter. Kalman Filter Design and Implementation. Estimation for Nonlinear Systems. ROBUST ESTIMATION. Robust Kalman Filter. H-Infinity Filtering of Continuous-Time Systems. H-Infinity Filtering of Discrete-Time Systems. OPTIMAL STOCHASTIC CONTROL. Stochastic Control for State Variable Systems. Stochastic Control for Polynomial Systems. Appendix A: Review of Matrix Algebra. References. Index.
