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 ...

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 ...

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...