Fault detection, isolation, andtolerant control of vehicles using soft computing methods

Research on fault detection and isolation (FDI) and fault tolerant control (FTC), from both theoretical and practical aspects, has received more attention in recent years. The latest results confirm that there still remain some challenging areas within FTC/FDI on methodologies and computational complexities, as well as the implementation for a large domain of applications such as automobile, civil transportation airplanes, unmanned aerial vehicles, launch vehicles and satellites. Therefore, soft computing methods have attracted considerable attention from both the academic and industrial communities, emerging globally into various control applications. They have shown to be effective approaches for the FDI/FTC of many complex systems, including nonanalytic systems. Moreover, numerous advanced ideas in FDI/FTC methodology, including neural network and fuzzy approaches, have been proposed. Among various modelbased fuzzy-control strategies, the Takagi??Sugeno (T-S) method is extensively exploited for model-based nonlinear control designs. Indeed, significant efforts have been devoted to stability analysis, controller/observer design and FDI/FTC methods of these dynamic systems.