In this paper it is investigated whether neural networks are able to improve the performance of a PI controller when controlling a combustion engine. The idea is not to replace but to assist a PI controller by a neural co-controller. Three different neural approaches are investigated for this use: Dynamic RBF (DRBF), Adaptive Time-Delay Neural Network (ATNN), and Local Ellipsoidal Model Network...

This paper presents the hardware implementation of a neural network controller for a nonlinear system. As a learning algorithm for a neural network, the reference compensation technique has been implemented on a low cost micro-controller unit (MCU), while PID controllers with counters and PWM generators are implemented on an FPGA chip. Interface between an MCU and a field programmable gate arra...

This paper presents an approach for acquisition and transfer of an experienced driver’s skills to an automatic parking controller. The controller processes visual input information from a video sensor and generates the corresponding steering commands. Two neural control architectures are considered. In the direct neural control architecture the controller is a single artificial neural network. ...

Navigation of multiple mobile robots using neuro-fuzzy controller has been discussed in this paper. In neuro-fuzzy controller the output from the neural network is fed as an input to fuzzy controller and the final outputs from the fuzzy controller are used for motion control of robots. The inputs to the neural network are obtained from the robot sensors (such as left, front, right obstacle dist...

This paper presents the application of a neural network controller to the problem of active drag reduction in a fully turbulent 3D fluid flow regime. The neural network learns a function nearly identical to an analytically derived control law. We then demonstrate the ability of a neural controller to maintain a drag-reduced flow in a fully turbulent fluid simulation. Finally we examine the amou...

This paper presents a survey of neural network controllers for AUVs. A direct adaptive neural net controller incorporating integral action was designed for the heave motion of the ODIN underwater vehicle. The neural net controller is trained on-line by parallel recursive error prediction method and the critic equation. The influence of the various design parameters of the neural net controller ...

In this paper, the use of a proposed recurrent neural network control system to control a four-legged walking robot is investigated. The control system consists of a neural controller, standard PD controller and the walking robot. The robot is a planar four-legged walking robot. The proposed Neural Network (NN) is employed as an inverse controller of the robot. The NN has three layers, which ar...

 Abstract—several neural networks controllers for robotics manipulators have been developed during the last decades due to their capability to learn the dynamics properties and the improvements in the global stability of the system. In this paper, two control and identification schemes for a two links robotic manipulator implementing neural networks are presented. A multilayer feedforward neu...

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, a neural network controller for constrained robot manipulators is presented. A feedforward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the feed-forward neural network controller. The neural network weights are tuned on-line, with no off-line learning phase required. It is shown that the controller ...