A new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. We use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. Then, we linearized this identified model at each sampling time to have an approximate linear time varying system. In order to stabilize...

A Petri net is an abstract formal model of the behavior of a system and information flow. The properties, concepts, and techniques of Petri net are so as to present it as a simple and strong method for describing and analyzing information flow and systems control. Fuzzy Petri Net (FPN) is an appropriate powerful model to emulate knowledge base systems using fuzzy rules. Yet, FPN model does not ...

Two phenomena can produce chattering: switching of input control signal and the large amplitude of this switching (switching gain). To remove the switching of input control signal, dynamic sliding mode control (DSMC) is used. In DSMC switching is removed due to the integrator which is placed before the plant. However, in DSMC the augmented system (system plus the integrator) is one dimension bi...

Distributed flexible AC- transmission system (D-FACTS) is a recently advanced FACTS device with high flexibility and smaller size. The DPFC can control power flow in transmission lines, regulate bus voltages and it can also enhance stability margin in power grids. Adaptive-neural network-based fuzzy inference system (ANFIS) combines features of artificial neural network and fuzzy controller. Th...

Based on nonlinear parametric fuzzy systems, an adaptive backstepping controller is proposed for a class of strict-feedback nonlinear systems. The nonlinear parametric fuzzy systems are capable of automatically learning their membership functions and tuning their weightings. Since the adjustable parameters of the membership functions nonlinearly appear in the fuzzy systems, the adaptive laws ar...

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Chapter . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Working memory studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Adaptive system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Fuzzy system . . . . . . . . . . . . ...

| Fuzzy adaptive systems may be viewed as a framework for solving the problems of nonlinear system control. In the paper a novel model reference fuzzy adaptive control system is introduced which is based on the fuzzy model of the process. The combination of adaptive control theory based on models obtained by fuzzy basis function expansion results in fuzzy direct model-reference adaptive control...