Adaptive control of a nonlinear dc motor drive using recurrent neural networks

Authors:
Khaled Nouri;Rached Dhaouadi;Naceur Benhadj Braiek
Affiliations:
Laboratoire d'Etude et de Commande Automatique de Processus (LECAP), Ecole Polytechnique de Tunisie, BP 743, 2078 La Marsa, Tunisia;Department of Electrical Engineering, School of Engineering, American University of Sharjah, United Arab Emirates;Laboratoire d'Etude et de Commande Automatique de Processus (LECAP), Ecole Polytechnique de Tunisie, BP 743, 2078 La Marsa, Tunisia
Venue:
Applied Soft Computing
Year:
2008

Citing 10
Cited 5

Training multilayer perceptrons with the extended Kalman algorithm

Advances in neural information processing systems 1
An efficient gradient-based algorithm for on-line training of recurrent network trajectories

Neural Computation
Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons

IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of models, analysis tools and compensation methods for the control of machines with friction

Automatica (Journal of IFAC)
A learning algorithm for continually running fully recurrent neural networks

Neural Computation
An algorithmic approach to adaptive state filtering using recurrent neural networks

IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks

IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks

IEEE Transactions on Neural Networks
Adaptive inverse control of linear and nonlinear systems using dynamic neural networks

IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks

IEEE Transactions on Neural Networks

Letters: Efficient online recurrent connectionist learning with the ensemble Kalman filter

Neurocomputing
Bounded neuro-control position regulation for a geared DC motor

Engineering Applications of Artificial Intelligence
Development of robust intelligent tracking control system for uncertain nonlinear systems using H∞ control technique

Applied Soft Computing
Adaptive self-constructing fuzzy neural network controller for hardware implementation of an inverted pendulum system

Applied Soft Computing
Adaptive dynamic RBF neural controller design for a class of nonlinear systems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system.