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 Hybrid Direct Neural Controller (HDNC) with Linear Feedback Compensator (LFBC) has been developed. Proper initialization of neural network weights is a critical problem. This paper presents two different neural network configurations with unity and random weight initialization while using it as a direct controller and linear feedback compensator. The performances of these controll...

As traditional PID controller has mature technique, it is applied widely. But the design of it depends on mathematical model of the controlled object. But the parameters of PID controller use engineering tuning method, it requires much time and effort and the parameters are only effect in a specific range. It is not suitable for complicated nonlinear and time-variable system. In recent year, pe...

The purpose of this study is to design an intelligent control system to guide the overtaking maneuver with a higher performance than the existing systems. Unlike the existing models which consider constant values for some of the effective variables of this behavior, in this paper, a neural network model is designed based on the real overtaking data using instantaneous values for variables. A fu...

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and fee...

This paper deals with the use of artificial neural networks employed as an on-line trained controller for a real process and simulation model control. Well-known back-propagation method is used as a learning algorithm intended to minimize the difference between the plant’s actual response and the desired reference signal. The influence of neural network’s parameters on a controlled plant output...

In this paper, adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main tracking controller, which i...

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...

This paper deals with the designing of neural PID speed controller of Permanent Magnet synchronous Motor (PMSM). The conventional Proportional-Integral-Derivative (PID) controller is largely used in industry because of the robustness this regulator procures. ANN is developed controller in this work, offer inherent advantages over conventional PID controller for PMSM, Reduction of the effects of...

A neural network robust controller (NNRC) to enhance the robustness of the conventional feedback controller is proposed in this paper. The control system consists of an optimal conventional feedback controller and a neural network robust controller in parallel to the controlled system. The optimal controller is used to guarantee the stability of the whole control system and present the optimal ...