Computational Complexity of Robust Stability and Regularity in Families of Linear Systems
Author: Gregory E. Coxson
Publisher:
Published: 1993
Total Pages: 406
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Gregory E. Coxson
Publisher:
Published: 1993
Total Pages: 406
ISBN-13:
DOWNLOAD EBOOKAuthor:
Publisher: European Control Association
Published: 1995-09-05
Total Pages: 582
ISBN-13: 9783952417300
DOWNLOAD EBOOKProceedings of the European Control Conference 1995, Rome, Italy 5-8 September 1995
Author: Joe H. Chow
Publisher: Springer Science & Business Media
Published: 1995-02-24
Total Pages: 436
ISBN-13: 9780387944388
DOWNLOAD EBOOKThe articles in this volume cover power system model reduction, transient and voltage stability, nonlinear control, robust stability, computation and optimization and have been written by some of the leading researchers in these areas. This book should be of interest to power and control engineers, and applied mathematicians.
Author:
Publisher:
Published: 2006
Total Pages: 764
ISBN-13:
DOWNLOAD EBOOKAuthor:
Publisher:
Published: 1992
Total Pages: 796
ISBN-13:
DOWNLOAD EBOOKAuthor: University of Wisconsin--Madison. Department of Electrical and Computer Engineering
Publisher:
Published: 1992
Total Pages: 466
ISBN-13:
DOWNLOAD EBOOKAuthor: Sanjeev Arora
Publisher: Cambridge University Press
Published: 2009-04-20
Total Pages: 609
ISBN-13: 0521424267
DOWNLOAD EBOOKNew and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Author: Roberto Tempo
Publisher: Springer Science & Business Media
Published: 2012-10-21
Total Pages: 363
ISBN-13: 1447146093
DOWNLOAD EBOOKThe presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of systems subject to deterministic and stochastic uncertainty. The approach propounded by this text guarantees a reduction in the computational complexity of classical control algorithms and in the conservativeness of standard robust control techniques. The second edition has been thoroughly updated to reflect recent research and new applications with chapters on statistical learning theory, sequential methods for control and the scenario approach being completely rewritten. Features: · self-contained treatment explaining Monte Carlo and Las Vegas randomized algorithms from their genesis in the principles of probability theory to their use for system analysis; · development of a novel paradigm for (convex and nonconvex) controller synthesis in the presence of uncertainty and in the context of randomized algorithms; · comprehensive treatment of multivariate sample generation techniques, including consideration of the difficulties involved in obtaining identically and independently distributed samples; · applications of randomized algorithms in various endeavours, such as PageRank computation for the Google Web search engine, unmanned aerial vehicle design (both new in the second edition), congestion control of high-speed communications networks and stability of quantized sampled-data systems. Randomized Algorithms for Analysis and Control of Uncertain Systems (second edition) is certain to interest academic researchers and graduate control students working in probabilistic, robust or optimal control methods and control engineers dealing with system uncertainties. The present book is a very timely contribution to the literature. I have no hesitation in asserting that it will remain a widely cited reference work for many years. M. Vidyasagar
Author: Stephen Boyd
Publisher: SIAM
Published: 1994-01-01
Total Pages: 203
ISBN-13: 9781611970777
DOWNLOAD EBOOKIn this book the authors reduce a wide variety of problems arising in system and control theory to a handful of convex and quasiconvex optimization problems that involve linear matrix inequalities. These optimization problems can be solved using recently developed numerical algorithms that not only are polynomial-time but also work very well in practice; the reduction therefore can be considered a solution to the original problems. This book opens up an important new research area in which convex optimization is combined with system and control theory, resulting in the solution of a large number of previously unsolved problems.