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...

Optimization of PID controller parameters has been a hot issue in the fields of Automatic control. In the automatic control process, the controlled object has nonlinear and uncertainty characteristics. Traditional PID parameters methods are often time-consuming and difficult to obtain control effect, causing the control accuracy not high. In order to solve the optimization problem of PID contro...

An adaptive robust tracking controller is proposed for robot systems under plant uncertainties and external disturbances. Nonlinear robust control theory and neural network design are combined to construct a hybrid adaptive-robust tracking control scheme which ensures that the joint positions track the desired reference signals. Neural network is used to identify the uncertainties, and the effe...

Because simulation turntable servo system is highly nonlinear and uncertainty plants, a fuzzy neural network PID controller is proposed based on the Radial Basis Function (RBF). Up to now, various kinds of nonlinear PID controllers have been designed in order to satisfactorily control this system and some of them applied in actual systems with different degrees. Given this background, the step ...

In this paper an artificial neural network, which realizes a nonlinear adaptive control algorithm, has been applied in a control system of variable speed generating system. The speed is adjusted automatically as a function of load power demand. The controller employs a single layer neural network to estimate the unknown plant nonlinearities online. Optimization of the controller is difficult be...

This paper presents a Multilayer Neural Network controller for real time control applications. A model reference structure is developed and a neural network is used as a compensator in the closed loop system. This scheme can be used in the control of nonlinear systems and/or as an adaptive controller if desired.

We present two neural network controller learning schemes based on feedbackerror-learning and modular architecture for recognition and control of multiple manipulated objects. In the first scheme, a Gating Network is trained to acquire object-specific representations for recognition of a number of objects (or sets of objects). In the second scheme, an Estimation Network is trained to acquire fu...