in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

this paper presents a fuzzy search controller approach along with the look-up tables, to optimize the efficiency of a pump-induction motor drive. the induction motor and the converter are modeled based on thermal loss; and for modeling the pump and the hydraulic system, which are non-linear and relatively complicated, the neural network is used. in the designed system, in order to optimize the ...

tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...

 The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure-free optimization process stipulated a relatively small region of the asymptotic stability of the neural controller, which is concentrated around the regulation point. The presented hyb...

This paper a new method for the control of Two linkrobotic manipulator systems using Neural Network has been presented, The first method is based on Proportional-Derivative controller, The second method is based on Proportional-IntegralDerivative controller, the third method is based on artificial Neural Network by PD controller, and the forth method is based on artificial Neural Network by PID...

The aim of this paper was to present an optimized method in order to use maximum capacity of the photovoltaic panels. In this regard, we presented a method for the maximum power point tracking in the photovoltaic systems by using the neural networks and adaptive controller. In the proposed system, we estimated an error by using neural network. If this error is lower than the allowable systems e...

In this paper, stabilization of the structural system against earthquake is presented. Because the conventional PD controller is popular and simple in design, our controller is based on PD controller. The main problem of such a controller is its inability to produce desired response and instability against variations in the properties of the structural system. To obviate this issue, the neu...