In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3-kW induction m...

The growth in the demand of electrical energy is leading to load on the Power system which increases the occurrence of frequent oscillations in the system. The reason for the oscillations is due to the lack of damping torque which is required to dominate the disturbances of Power system. By using FACT devices, such as Unified Power Flow Controller (UPFC) can control power flow, reduce sub-synch...

In this paper, an Adaptive Neuro Fuzzy Interference System (ANFIS) is proposed for eliminating voltage harmonics present in the multilevel inverter. It is achieved by reducing the THD present in the multilevel inverter output voltage. Here, the voltage variation of the multilevel inverter is determined from the actual load voltage and the reference voltage. The voltage variation at different ti...

Random early detection (RED) is the most popular active queue management algorithm that is used by the Internet routers. This paper proposes a neuro-fuzzy controller which enhances the network performance by dynamically tuning of RED's maxp parameter. The controller first learns the network behavior against maxp variations and then adjusts maxp. Simulation results in ns-2 environment show that,...

In the present study, fuzzy control of vibration of a hybrid smart composite beam actuated by electro-rheological fluids (ERF) and piezoceramics actuators is investigated. A carbon fiber reinforced plastics (CFRP) cantilevered beam containing ERF with bonded piezoceramics is prepared and vibrated forcibly with sinusoidal external excitation. Fuzzy model of controlled element containing two actu...

In this paper an algorithm based on the adaptive neuro-fuzzy controller is provided to enhance the tipover stability of mobile manipulators when they are subjected to predefined trajectories for the end-effector and the vehicle. The controller creates proper configurations for the manipulator to prevent the robot from being overturned. The optimal configuration and thus the most favorable contr...

A new model-based neuro-fuzzy controller for non-linear systems is proposed. The neural network provides a non-linear model of the process being controlled. The nonlinear space of the network is divided into small linear regions. For each of these regions linear controllers can be designed automatically. To ensure a smooth transition between different regions fuzzy membership functions are used...

In an interconnected power system, low frequency electromechanical oscillations are initiated by normal small changes in system loads, and they become much worse following a large disturbance. Flexible AC Transmission System (FACTS) devices are widely recognized as powerful controllers for damping power system oscillations. The standard FACTS controllers are linear controllers which may not gua...

This work presents a hybrid soft-computing methodology approach for intelligent maximum power point tracking (MPPT) techniques of photovoltaic (PV) system under any expected operating conditions using artificial neural network-fuzzy (neuro-fuzzy). The proposed technique predicts the calculation duty cycle ensuring optimal transfer between PV generator and load. neuro-fuzzy method combines netwo...

This paper outlines a hybrid approach of neuro-fuzzy based learning and classification approach based on the online learning systems. The effect of fault diagnosis for the suggested fault location tool is evaluated over the conventional fault diagnosis based approaches. The method of fault location based on the conventional offline neuro controller approach is compared with the suggested hybrid...