There is an increasing demand for dynamic systems to become safer and more reliable. This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital. It is clear that fault diagnosis is becoming an important subject in modern control theory and practice. Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework. It contains many important topics and methods; however, total coverage and completeness is not the primary concern. The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches. In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications. The first two chapters are of tutorial value and provide a starting point for newcomers to this field. The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications. This will certainly appeal to experts in this field. Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research. The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed. Although this is a research monograph, it will be an important text for postgraduate research students world-wide. The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world.
