In many engineering design and optimisation problems, the presence of uncertainty in the data is a central and critical issue. Different fields of engineering use different ways to describe this uncertainty and adopt a variety of techniques to devise designs that are at least partly insensitive or robust to uncertainty.

Probabilistic and Randomized Methods for Design under Uncertainty examines uncertain systems in control engineering and general decision or optimisation problems for which data is not known exactly. Gathering contributions from the worlda (TM)s leading researchers in optimisation and robust control; this book highlights the interactions between these two fields, and focuses on new randomised and probabilistic techniques for solving design problems in the presence of uncertainty:

• Part I describes general theory and solution methodologies for probability-constrained and stochastic optimization problems, including chance-constrained optimisation, stochastic optimization and risk measures;
• Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimisation problems by probabilistic techniques such as constraint sampling and scenario-based optimisation;
• Part III details the theory and applications of randomised techniques to the analysis and design of robust control systems.

Probabilistic and Randomized Methods for Design under Uncertainty will be of interest to researchers, academics and postgraduate students in control engineering and operations research as well as professionals working in operations reasearch who are interested in decision-making, optimisation and stochastic modelling.

The 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

"

The 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

In many engineering design and optimisation problems, the presence of uncertainty in the data is a central and critical issue. Different fields of engineering use different ways to describe this uncertainty and adopt a variety of techniques to devise designs that are at least partly insensitive or robust to uncertainty.

Probabilistic and Randomized Methods for Design under Uncertainty examines uncertain systems in control engineering and general decision or optimisation problems for which data is not known exactly. Gathering contributions from the world s leading researchers in optimisation and robust control; this book highlights the interactions between these two fields, and focuses on new randomised and probabilistic techniques for solving design problems in the presence of uncertainty:

• Part I describes general theory and solution methodologies for probability-constrained and stochastic optimization problems, including chance-constrained optimisation, stochastic optimization and risk measures;
• Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimisation problems by probabilistic techniques such as constraint sampling and scenario-based optimisation;
• Part III details the theory and applications of randomised techniques to the analysis and design of robust control systems.

Probabilistic and Randomized Methods for Design under Uncertainty will be of interest to researchers, academics and postgraduate students in control engineering and operations research as well as professionals working in operations reasearch who are interested in decision-making, optimisation and stochastic modelling."

Moving on from earlier stochastic and robust control paradigms, this book introduces the fundamentals of probabilistic methods in the analysis and design of uncertain systems. The use of randomized algorithms, 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 in congestion control of high-speed communications networks and the stability of quantized sampled-data systems.

This monograph will be of interest to theorists concerned with robust and optimal control techniques and to all control engineers dealing with system uncertainties."