A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.

In this paper, we propose an integral-type T-S fuzzy control scheme to deal with the regulation problem of buck converters without current sensors. This current sensorless control of converters provides the output voltage to achieve zero steady-state error and is with high robust performance. The stability of the overall closed-loop system is rigorously analyzed by using Lyapunov's method. Based on an appropriate assumption, the separation principle can still succeed in the control problems. Hence, the controller and observer gains can be separately obtained by solving LMIs via Matlab's toolbox. The observer-based controller is realized with Simulink and digital signal processors (DSPs). The simulation and experimental results verify the feasibility of the proposed schemes and show the satisfactory performance for the power converters.