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...

Rice, as one of the significant grain products across world, features a wide range varieties in terms usability and efficiency. It may be known with various regional names depending on specific locations. To specify particular rice type, different are considered, such shape color. This study uses an available dataset Turkey consisting five varieties: Ipsala, Arborio, Basmati, Jasmine, Karacadag...

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...

This paper proposes a Fuzzy Logic and Neural Network control system that is able to guide the Mobile robots (AmigoBot and P3DX) traverse through a maze with arbitrary obstacles and computed data and algorithm in Personal Computer (PC) in wireless system. For input data, controller (Fuzzy Logic and Neural Network) receives data from sensors (sonar and laser range finder). This paper checked the ...

Two-layered neural networks are well known as autoencoders (AEs) in order to reduce the dimensionality of data. AEs are successfully employed as pre-trained layers of neural networks for classification tasks. Most of the existing studies conceived real-valued AEs in real-valued neural networks. This study investigated complexand quaternion-valued AEs for complexand quaternion-valued neural netw...

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...

This paper presents the system identification and design of a neural network based Proportional, Integral and Derivative (PID) controller for a two degree of freedom (2DOF), quarter-car active suspension system. The controller design consists of a PID controller in a feedback loop and a neural network feedforward controller for the suspension travel to improve the vehicle ride comfort and handl...

this paper presents a new observer-based control scheme for a class of nonlinear systems. in the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. due to lack of availability of the complex system ...