Showing papers by "Ron J. Patton published in 2011"

Robust Model-Based Fault Diagnosis for Dynamic Systems

Abstract: 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

A mixed H−/H1 LPV FDD observer for nonlinear aircraft

Abstract: This paper describes the development of a robust residual generator-based linear parameter varying (LPV) mixed H /H1 observer for non-linear systems, to facilitate the robust fault detection and diagnosis (FDD) task for flight control systems. The main design goal is to maximize the robustness of the residual signal to uncertainty and disturbances whilst also achieving the specific minimum sensitivity of the residual signal to faults. The specific minimum sensitivity index used is based on the H index concept and is extended to the LPV FDD system problem. This allows the fault signature for multiple sensor and actuator faults to be reconstructed simultaneously, facilitating the robust isolation of faults rather than just their detection. Instead of using constant gains, parameter-varying gains are used in the LPV observer and the free design parameters generated through using a generalized inverse, can be parameterized to improve the conservatism of finding the robust solution. This approach is combined within the standard affine quadratic LPV framework since it is well known that this framework leads to less conservative results, compared with the standard quadratic LPV case. The FDD approach developed is then applied in an example of a non-linear full force and moment aircraft system simulation to detect and isolate multiple actuator faults. The robust residuals generated approximate closely the actual fault signals.