This paper presents a neural-aided controller that enhances the fault tolerant capabilities of a high performance fighter aircraft during the landing phase when subjected to severe winds and failures such as stuck control surfaces. The controller architecture uses a neural controller aiding an existing conventional controller. The neural controller uses a feedback error learning mechanism and e...

This paper presents a neural network controller for permanent magnet synchronous motor (PMSM). The neural controller is used for torque ripple minimization of this type of motors. Two methods of neural controller design are used. The first method is based on two loop controllers (current controller and speed controller). The second method is based on estimation of torque constant and stator res...

In this paper, MLP network with sliding-mode controller and neural control is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Because the nonlinear nature robot, first the nonlinear and robust method sliding mode as a control theory for the system is chosen. Chattering phenomenon is always a problem of sliding...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

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...

This study proposes a soft computing speed controller where recurrent neural network speed controller is trained by genetic extended kalman filter, based on a predetermined genetic Proportional Integral (PI) controller. A genetic PI gives to this proposed speed controller exact values without mathematical equations, unlike the classical PI who is need for it necessary. With this proposed method...

A speed sensorless neuro-fuzzy controller is proposed for brush type DC motors. The actual speed of the DC machine is estimated using a feed-forward neural network. The inputs of the neural network are the armature current and voltage of the DC machine and their changes in time. Because DC machines are usually fed by 4-quadrant chopper, the measured armature voltage and current contains higher ...

|In this paper we present an approach to design a controller that enables a simulated car to drive autonomously around a race track. The input to the controller is the current speed of the car and several sensor signals indicating the properties of the race track, and as its output the controller is supposed to determine the car's change of direction and its change of speed in response to the i...

Using hydraulic interconnected suspension (HIS) system to improve the stability of the vehicles is a matter of recent interest of many scholars. In this paper, application of this kind of suspension system and its impact on the stability of the vehicle are studied. The governing dynamic relations of the system are presented, using free body diagram, Newton-Euler motion equations, and relations ...

This paper describes a neural network based approach for flight control design of commercial aircraft. A neural flight control system is used to actuate the gains of a linear controller for gain scheduling purposes without any explicit system identification. The neural network system uses reference models to specify desired handling qualities. Simulation results demonstrate the good tracking pe...