###### Abstract  
Robust control techniques for power converters are becoming more attractive because they can meet with most demanding control goals like uncertainties. In this sense, the Takagi-Sugeno (T-S) fuzzy controller based on linear matrix inequalities (LMI) is a linear control by intervals that has been relatively unexplored for the output-voltage regulation problem in switching converters. Through this technique it is possible to minimize the disturbance rejection level, satisfying constraints over the decay rate of state variables as well as the control effort. Therefore, it is possible to guarantee, a priori, the stability of the large-signal converters in a broad operation domain. This work presents the design of a fuzzy control synthesis based on a T-S fuzzy model for non-minimum phase dc-dc converters, such as  
consider the non-linearities of the converter. With the aim to overcome these limitations, researchers have been exploring other control techniques that contemplate the non-linearities and uncertainty parameters in switching DC-DC converters. For instance, robust control has been widely used during the last years attracting the interest in the power electronics field [4; 5; 6]. Unlike non-linear control and conventional linear control, robust control ensures a minimum of features regarding uncertainties in DC-DC converters. In [7; 8], the design of a robust state-feedback control for a minimum-phase buck converter is proposed. The works showed the use of simple conditions for LMIs guaranteeing the stability of the system as well as the disturbance rejection for the close-loop control. Nevertheless, they do not describe any experimental results and only verify the effectiveness of the proposed method through simulations. Similarly, in [9] it is also presented the design of a LMI-based robust control law by state-feedback, but in this case, applied to a non-minimum phase boost converter. This control method takes into consideration uncertainties and non-linearities of the converter, which are modeled like a convex polytope. This allows LMIs constraints to robustly guarantee a certain level of disturbances rejection and a specific location region for the poles. Contrarily to [7; 8; 9], presents precise experimental results in a boost prototype which are in agreement with the design requirements in spite of the uncertainties. Finally, in [10; 11; 12] other powerful LMI approaches are proposed for a boost regulator control, which allows to consider uncertainty in the converter and ensure its stability among different operation points due to the inclusion of a bilinear dynamic. The accuracy of these approaches is verified through an experimental prototype, which shows good similarity with theoretical predictions. Moreover, over the last decades, the application of fuzzy logic in control systems has drawn the attention of the scientific community as well as numerous technicians of industrial processes. This methodology can be very useful when processes are rather complex for their analysis through conventional techniques, or when the available information is inaccurately or uncertainly interpreted. Despite the advantages of the fuzzy logic based controls there is a lack of tools for the prediction of the stability and robustness for closed-loop systems, generating some criticism in the automatic control field. For this reason, in the last few years, the design of more accurately fuzzy controllers, particularly the Takagi-Sugeno (T-S) based model, have registered significant breakthroughs thanks to the Linear Matrix Inequalities theory (LMI) and Lyapunov stability analysis [13; 14]. In control engineering, the Model-Based Fuzzy Control (MBFC), which uses the concept of parallel distributed compensation, has been catalogued as an effective and systematic approach for highly non-linear control systems.