Moving on from earlier stochastic and robust control paradigms, this book introduces the reader to the fundamentals of probabilistic methods in the analysis and design of uncertain systems. It significantly reduces the computational cost of high-quality control and the complexity of the algorithms involved.
Inhaltsverzeichnis
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
