Neural network based controller is used for controlling a mobile robot system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a mobile robot system controlled by a FEL neural network and PD controllers. This feedback error-learning controller benefits from both classic and adaptive controller proper...

The paper discusses about the Choquet Fuzzy Integral (CFI) based identification and control of a Real Time Dynamical System. The plant is modeled using Choquet fuzzy Integral based Neural Network with Gradient Descent as learning algorithm. This model is used for designing and simulating CFI based controller. Then this designed controller is implemented on real time system through MATLAB. Final...

A neural network (NN) based adaptive output feedback controller is proposed for a class of nonlinear systems. In this control scheme, the adaptive output feedback NN controller is proposed by using an observer to estimate the states of the system. The weights of the neural network can be adjusted in terms of Lyapnuov’s stability criterion. The proposed controller can be applied to nonlinear sys...

In this paper an Artificial Neural Network (ANN) based controller is presented for the Automatic Generation Control (AGC) of a five area interconnected power system with reheat turbines. The control scheme guarantees that steady state error of the area frequencies and inadvertent interchange of tielines are maintained in a given tolerance limit. ANN controller controls the inputs of each area i...

This paper proposes an adaptive neural predictive controller to guide a nonholonomic mobile robot during 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 feedforward multi-layer perceptron respectively. The modified...

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...

In this paper a new method for two linkrobotic manipulator systems control using Neural Network, The first method is based on Proportional-Integral-Derivative controller, the second method is based on artificial Neural Network by PID controller for Two linkrobot control with different load.

in this paper, a robust integral of the sign error (rise) feedback controller is designed for a rigid-link electrically driven (rled) robot manipulator actuated by direct current dc motor in presence of parametric uncertainties and additive disturbances. rise feedback with implicitly learning capability is a continuous control method based on the lyapunov stability analysis to compensate an add...

A parallel hardware neural network with on-chip learning ability is presented. The chip is used to perform real-time output feedback control on a nonlinear dynamic system. The non linear plant is a simulated unstable combustion process and is nonlinear enough that linear controllers give poor performance. Neural networks provide an adaptive sub-optimal control that does not need any prior knowl...

This paper aims to propose an efficient control algorithm for the mobile robot path control. A supervisory fuzzy-Gaussian-neural-network (SFGNN) controller is proposed. This controller includes a fuzzy-Gaussian-neural-network (FGNN) controller and a supervisory controller. The FGNN controller is constructed in a form of neural network with a Gaussian-type fuzzy membership function; and the para...