Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine

Stimulated by the growing demand for improving the reliability and performance of systems, fault-tolerant control has been receiving significant attention since its goal is to detect the occurrence of faults and achieve satisfactory system performance in the presence of faults. To develop an intelligent fault-tolerant control system, we begin by constructing a design model of the system using a hierarchical learning structure in the form of Takagi-Sugeno fuzzy systems. Afterwards, the fault-tolerant control scheme is designed based on stable adaptive fuzzy/neural control, where its online learning capabilities are used to capture the unknown dynamics caused by faults. Finally, the effectiveness of the proposed methods has been studied by extensive analysis of system zero dynamics and asymptotic tracking abilities for both indirect and direct adaptive control cases, and by “component level model” simulation of the General Electric XTE46 turbine engine