S-Variable Approach to LMI-Based Robust Control S-Variable Approach to LMI-Based Robust Control
Communications and Control Engineering

S-Variable Approach to LMI-Based Robust Control

Yoshio Ebihara et autres
    • 87,99 €
    • 87,99 €

Description de l’éditeur

This book shows how the use of S-variables (SVs) in enhancing the range of problems that can be addressed with the already-versatile linear matrix inequality (LMI) approach to control can, in many cases, be put on a more unified, methodical footing. Beginning with the fundamentals of the SV approach, the text shows how the basic idea can be used for each problem (and when it should not be employed at all). The specific adaptations of the method necessitated by each problem are also detailed. The problems dealt with in the book have the common traits that: analytic closed-form solutions are not available; and LMIs can be applied to produce numerical solutions with a certain amount of conservatism. Typical examples are robustness analysis of linear systems affected by parametric uncertainties and the synthesis of a linear controller satisfying multiple, often  conflicting, design specifications. For problems in which LMI methods produce conservative results, the SV approach is shown to achieve greater accuracy.

The authors emphasize the simplicity and easy comprehensibility of the SV approach and show how it can be implemented in programs without difficulty so that its power becomes readily apparent. The S-Variable Approach to LMI-Based Robust Control is a useful reference for academic control researchers, applied mathematicians and graduate students interested in LMI methods and convex optimization and will also be of considerable assistance to practising control engineers faced with problems of conservatism in their systems and controllers.

GENRE
Professionnel et technique
SORTIE
2014
16 octobre
LANGUE
EN
Anglais
LONGUEUR
263
Pages
ÉDITIONS
Springer London
TAILLE
7,2
Mo

Autres livres de cette série

H-Systems H-Systems
2023
Input-to-State Stability Input-to-State Stability
2023
Learning and Robust Control in Quantum Technology Learning and Robust Control in Quantum Technology
2023
Regularized System Identification Regularized System Identification
2022
Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory
2022
Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems
2020