in this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. in order to eliminate the effect of these uncertain...

This paper presents a new neural network based controller design for multivariable systems. The proposed controller is designed using radial basis function (RBF) neural network. Weight update equation using classical least mean square principle is derived for the RBF network. The controller generates optimal control signals abiding by constraints, if any, on the control signals. Simulation resu...

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...

this paper presents a new damping controller design based on fuzzy wavelet neural network (fwnn) to damp the multi-machine power system low frequency oscillations. the error between the desired system output and the output of control object is directly utilized to tune the network parameters. the orthogonal least square (ols) algorithm is used to purify the wavelets for each rule and determine ...

Problem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN) approach for efficient controlling of an Active Magnetic Bearing (AMB) system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited in d...

In this paper, a new neural network controller for the constrained robot manipulators in task space is presented. The neural network will be used for adaptive compensation of the structured and unstructured uncertainties. The controller consisted of a model-based term and a neural network on-line adaptive compensation term. It is shown that the neural network adaptive compensation is universall...

During recent years there has been growing interest in unmanned aerial vehicles (UAVs). Moreover, the necessity to control and navigate these vehicles has attracted much attention from researchers in this field. This is mostly due to the fact that the interactions between turbulent airflows apply complex aerodynamic forces to the system. Since the dynamics of a quadrotor are non-linear and the ...

this paper addresses a nonlinear observer based control scheme to synchronize chaotic systems subject to uncertainties and external disturbances. it is assumed that the dynamic of slave system is not completely known. in order to compensate for the system perturbation resulting from parameter variations and mismodeling phenomena, an adaptive neural network observer is employed to handle this pr...

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with...

The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on intelligent controller of spike neural networks(SNN). This is applied to predict subsequent step flow, consequently reducing overcrowding that might happen. centralized acts as reactive managing clustering head process Software Defined Network layer proposed model. simulation results show capabil...