This work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. Considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. By linearizing this mode, a sliding mode controller is designed. The linearized mode is subject to uncertainties. The uncertainties are generated in the process of linear...

Dr. Mohammed Yousif Hassan* Received on:28/6/2009 Accepted on:3/12/2009 Abstract Pneumatic circuits are widely used in industrial automation, such as drilling, sawing, squeezing, gripping, and spraying. Furthermore, they are used in motion control of materials and parts handling, packing machines, machine tools, foodprocessing industry and in robotics. In this paper, a Neural Network based Fuzz...

This chapter shares with Chapter 9 the adoption of a model predictive control (MPC) framework for flight control applications, but the details differ substantially. In particular, the control feedback in this case is a superposition of a neural-network-based nonlinear mapping and a nonlinear state-dependent Riccati equation (SDRE) controller. The neural network is optimized (trained) online for...

Neural networks and fuzzy systems have been the subject of much interest in recent years for the control of nonlinear processes. Much research has been directed at indirect control schemes where, basically, the neural network or fuzzy system has been used to identify the process, and a controller has been synthesised from this model. An alternative approach is that of direct control where the n...

The active magnetic bearing (A solution for all the technical problems of the since it ensures the total levitation of a eliminating any mechanical contact between stator. The goal of our work is to show the co a magnetic sustention, characterized by its using neural networks (NN). In this paper controller for a magnetic bearing under a control is presented. Keywords-active magnetic bearing; al...

Induction Motors are widely used in Industries, because of the low maintenance and robustness. Speed Control of Induction motor can be obtained by maximum torque and efficiency. Apart from other techniques Artificial Intelligence (AI) techniques, particularly the neural networks, improves the performance & operation of induction motor drives. This paper presents dynamic simulation of induction ...

This study applies a neural-network-based optimal TCSC controller for damping oscillations. Optimal neural network controller is related to model-reference adaptive control, the network controller is developed based on the recursive “pseudo-linear regression”. Problem statement: The optimal NN controller is designed to damp out the low frequency local and inter-area oscillations of the large po...

Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent...

A neural network based robust control system design for the yaw angle of autonomous underwater vehicle (AUV) is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as w...

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.