Specialists from all over the world contribute to a reference for graduate students, researchers, and engineers. Among their topics are algorithmic techniques in estimation and control for multirate sampled digital control systems, techniques in computational stochastic dynamic programming, the maximum likelihood estimation of the covariance matrix, and the discrete- time Kalman filter under uncertainty in noise covariances. Some chapters are double spaced. Annotation c. by Book News, Inc., Portland, Or.
Praise for the Series:
"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."
-IEEE Group Correspondence
"This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control."
--Control
Arvustused
This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory. --IEEE Group Correspondence "This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control. --CONTROL
I.S. Apostolakis, Algorithmic Techniques in Estimation and Control for
Multirate Sampled Digital Control Systems. G.G. Zhu and R.E. Skelton, Output
Covariance Constraint Problem for Periodic and Multirate Systems. G. De
Nicolao, R.R. Bitmead, and M. Gevers, Discrete-Time Fake Riccati Equations
for Kalman Filtering and Receding-Horizon Control. F.B. Hanson, Techniques in
Computational Stochastic Dynamic Programming. S.K. Pillai, Techniques in
ModelError by Means of Linear Kalman Filtering. X.R. Li, Hybrid Estimation
Techniques. H.E. Emara-Shabaik, Nonlinear Systems Modeling & Identification
Using Higher Order Statistics/Polyspectra. T. Westerlund, S. Karrila, P.M.
Makila, and A. Brink, Techniques in the Maximum Likelihood Estimation of the
Covariance Matrix. L. Campo, Y. Bar-Shalom, and X.R. Li, Control of
Discrete-Time Hybrid Stochastic Systems. S. Sangsuk-Iam and T.E. Bullock, The
Discrete-Time Kalman Filter under Uncertainty in Noise Covariances. Subject
Index.
Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.
