Nonlinearities and system uncertainties are the most important difficulties in designing controllers that ensure stability and acceptable closed-loop performance. Many significant results on the stability and robust control of uncertain nonlinear systems using the TS fuzzy model have been reported over the past decades, and considerable advances have been made. However, as stated many approaches for stability and robust control of uncertain systems are often characterized by conservatism when dealing with uncertainties. It has been well known that the TS fuzzy model is very effective representation of complex nonlinear systems. In the TS fuzzy model, the state space of a nonlinear system is divided into different fuzzy regions with a local linear model being used in each region. The overall model output is obtained by defuzzification using the center of gravity (COG) method. Once the fuzzy model is obtained, control design can be carried out via the so-called parallel and distributed computing (PDC) approach, which employs multiple linear controllers corresponding to the locally linear plant models carried out via the so-called parallel and distributed computing (PDC). However, in this chapter, our goal is to present three different proposed approaches, namely, robust fuzzy fault-tolerant control (RFFTC), robust fuzzy scheduled fault-tolerant control (RFSFTC) and robust dynamic fuzzy fault-tolerant control (RDFFTC) for TS models subject to time varying actuator faults, sensor faults and parameter uncertainties, and maximize the output power from the wind turbine (WT) approach, which employs multiple linear controllers corresponding to the locally linear plant models (Boukhezzar et al. 2006).