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Computer Science - Theoretical Computer Science | Randomized Algorithms for Analysis and Control of Uncertain Systems

Randomized Algorithms for Analysis and Control of Uncertain Systems

Tempo, Roberto, Calafiore, Giuseppe, Dabbene, Fabrizio

Softcover reprint of hardcover 1st ed. 2005

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  • Will give the reader tools for dealing with uncertainty in control systems which are more advanced and flexible than either traditional optimal control or robust control
  • Reduces the computational cost of high-quality control and the complexity of the algorithms involved making similar results achievable with less effort by the user

The presence of uncertainty in a system description has always been a critical issue in control. Moving on from earlier stochastic and robust control paradigms, the main objective of this book is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of uncertain systems. Using so-called "randomized algorithms", this emerging area of research guarantees a reduction in the computational complexity of classical robust control algorithms and in the conservativeness of methods like H-infinity control.

Features:

• self-contained treatment explaining randomized algorithms from their genesis in the principles of probability theory to their use for robust analysis and controller synthesis;

• comprehensive treatment of sample generation, including consideration of the difficulties involved in obtaining independent and identically distributed samples;

• applications of randomized algorithms in congestion control of high-speed communications networks and the stability of quantized sampled-data systems.

Randomized Algorithms for Analysis and Control of Uncertain Systems will be of certain interest to control theorists concerned with robust and optimal control techniques and to all 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

Content Level » Research

Keywords » Analysis - Control - Control Theory - Linear Systems - Monte Carlo method - Probabilistic Design Methods - Probabilistic Methods for Systems and Control - Randomized Algorithms - Robust Control - STATISTICA - Sample Generation Techniques - Uncertain Systems - algorithms - complexity - linear optimization

Related subjects » Applications - Control Engineering - Signals & Communication - Theoretical Computer Science

Table of contents 

Overview.- Elements of Probability Theory.- Uncertain Linear Systems and Robustness.- Linear Robust Control Design.- Some Limits of the Robustness Paradigm.- Probabilistic Methods for Robustness.- Monte Carlo Methods.- Randomized Algorithms in Systems and Control.- Probability Inequalities.- Statistical Learning Theory and Control Design.- Sequential Algorithms for Probabilistic Robust Design.- Sequential Algorithms for LPV Systems.- Scenario Approach for Probabilistic Robust Design.- Random Number and Variate Generation.- Statistical Theory of Radial Random Vectors.- Vector Randomization Methods.- Statistical Theory of Radial Random Matrices.- Matrix Randomization Methods.- Applications of Randomized Algorithms.

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