Probabilistic and Randomized Methods for Design Under Uncertainty

In many engineering design and optimization problems, the presence of uncertainty in the data is a critical issue. There are different ways to describe this uncertainty and to devise designs that are partly insensitive or robust to it.

This book examines uncertain systems in control engineering and general decision or optimization problems for which data is uncertain. Written by leading researchers in optimization and robust control; it highlights the interactions between these two fields.

• Part I describes theory and solution methods for probability-constrained and stochastic optimization problems;
• Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimization problems by probabilistic techniques;
• Part III details the theory and applications of randomized techniques to the analysis and design of robust control systems.

It will interest researchers, academics and postgraduates in control engineering and operations research as well as professionals working in operations research.

Drs. Giuseppe Calafiore and Fabrizio Dabbene work at the Politecnico di Torino, Italy, where Dr. Calafiore is an associate professor and Dr. Dabbene is a research fellow. Dr. Calafiore is an associate editor of IEEE Transactions on Systems, Man and Cybernetics and Dr. Dabbene is an associate editor of the conference editorial board of the IEEE Control Systems Society. Dr. Calafiore has published 60+ journal papers and both editors are co-authors of Randomized Algorithms for Analysis and Control of Uncertain Systems (Tempo, Calafiore and Dabbene, Springer-Verlag London, 2004).

In this edited work, Calafiore and Dabbene have brought together contributions from the world's leading experts in randomised methods as applied to robust design from both control and optimisation angles. The selection of authors is fully international and includes 15 from the United States.