Dimiter Driankov Livres

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as integral parts of a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers are an established practice for Japanese manufacturers, and are spreading in Europe and America. The main aim of this book is to show that fuzzy control is not totally ad hoc, that there exist formal techniques for the analysis of a fuzzy controller, and that fuzzy control can be implemented even when no expert knowledge is available. The book is mainly oriented to control engineers and theorists, although parts can be read without any knowledge of control theory and may interest AI people. This 2nd, revised edition incorporates suggestions from numerous reviewers and updates and reorganizes some of the material.

In the past decade, significant work utilizing fuzzy logic for autonomous vehicle navigation has emerged, but it remains scattered across various publications in different research communities, making it difficult for newcomers and specialists to access. This fragmentation can lead researchers to inadvertently duplicate efforts without awareness of existing contributions. We believe that the research and applications of fuzzy logic in autonomous vehicle navigation have matured sufficiently to warrant a comprehensive compilation for a broader audience of practitioners, researchers, and students. Our goal is to gather representative works into a single volume that offers more than just a random collection of papers, providing added value through context and organization. To achieve this, we have focused on selecting contributions that address a diverse range of validated problems and solutions applicable to real robots. Moreover, we have structured the individual contributions within a clear framework that highlights the key challenges in autonomous robotics that fuzzy logic solutions aim to address. This approach ensures that readers can better understand the landscape of fuzzy logic applications in the field.

Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.