Advanced Filtering of Nonlinear Systems via T-S Fuzzy Models in Finite Frequency Domain with Application to Fault Diagnosis
DescriptionThe filtering technique plays an important role in a wide range of applications such as target tracking, image processing, signal processing, control, fault diagnosis, and so on. Fault diagnosis, as one of important applications of the filtering technique, becomes more and more critical in a variety of engineering applications such as automation engineering, nuclear engineering, aerospace engineering, and robotic engineering, due to an ever-increasing demand on reliability and safety of these engineering systems. Great effort has been devoted to study of filtering and fault diagnosis for many different kinds of systems during the past decades. Techniques of filtering and fault diagnosis have been well developed for linear systems, and still enjoy significant development for nonlinear systems due to the challenge in dealing with nonlinearities. On the other hand, it is well known that many practical engineering systems can be described or approximated by a special form of nonlinear models, that is, the so-called Takagi Sugeno (TS) fuzzy model. Due to their special features, T-S fuzzy models have been widely exploited for development of filtering and fault diagnosis techniques in addition to control techniques for nonlinear systems in the past decade and many results have been obtained. However, how to reduce design conservatism of filtering and fault diagnosis and how to improve the performance of filtering and fault diagnosis for T-S fuzzy systems are two challenging issues yet to be well addressed. This project will investigate and develop novel approaches to filtering and fault diagnosis to address those two challenging issues for T-S fuzzy systems. In particular, this project will focus on advanced memory filtering techniques in finite frequency domain, and their application to fault diagnosis for T-S fuzzy systems. The outcome of this project is expected to be a number of novel advanced filtering and fault diagnosis approaches with better performance and reduced conservatism in real-world applications. These approaches will enrich theoretical foundation of filtering and fault diagnosis for a class of nonlinear systems described by T-S fuzzy systems, and provide engineers with more tools for filtering and fault diagnosis design.
|Effective start/end date||1/01/19 → 10/01/23|