Luigi Fortuna Libros

This new edition deals with advanced automatic control techniques, paying particular attention to robustness-the ability to guarantee stability in the presence of uncertainty. It explains advanced techniques for handling uncertainty and optimizing the control loop and details analytical strategies for obtaining reduced order models. Inhaltsverzeichnis 1. Modelling of Uncertain Systems and the Robust Control Problem. 2. Fundamentals of Stability. 3. Kalman Canonical Decomposition. 4. Singular Value Decomposition. 5. Open-loop Balanced Realization. 6. Reduced Order Models and Symmetric Systems. 7. Variational Calculus and Linear Quadratic Optimal Control. 8. Closed-loop Balanced Realization. 9. Positive-real, Bounded-real, and Negative-imaginary Systems. 10. Enforcing the Positive-real or the Negative-imaginary Property in a Linear Model. 11. H linear control. 12. Linear Matrix Inequalities for Optimal and Robust Control. 13. The Class of Stabilizing Controllers. 14. Formulation and Solution of Matrix Algebraic Problems Through Optimization Problems. 15. Time-delay Systems. Appendix A. Norms. Appendix B. Algebraic Riccati Equations. Appendix C. Invariance under Frequency Transformations.

The text aims to provide an advanced source of training for graduates in electrical engineering and physics and to give comprehensive instruction in the emerging areas of microelectronics.

The book presents a clear understanding of a new type of computation system, the Cellular Neural Network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations. The text describes how CNN will improve the soft-computation toolbox, and examines the many applications of soft computing to complex systems.