A direct adaptive neural-network control for unknown nonlinear systems and its application

Authors:
J. R. Noriega;Hong Wang
Affiliations:
Dept. of Paper Sci., Univ. of Manchester Inst. of Sci. & Technol.;-
Venue:
IEEE Transactions on Neural Networks
Year:
1998

Citing 0
Cited 13

Using a New Model of Recurrent Neural Network for Control

Neural Processing Letters
Neural Control of the Movements of a Wheelchair

Journal of Intelligent and Robotic Systems
Direct neural network-based self-tuning control for a class of nonlinear systems

International Journal of Systems Science
Enhance Computational Efficiency of Neural Network Predictive Control Using PSO with Controllable Random Exploration Velocity

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Model-based Control for 6-DOF Parallel Manipulator

CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Design of a hybrid adaptive CMAC with supervisory controller for a class of nonlinear system

Neurocomputing
Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems

IEEE Transactions on Neural Networks
An adaptive dynamic evolution feedforward neural network on modified particle swarm optimization

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Quality modeling of chemical product based on a new chaotic Elman neural network

ICNC'09 Proceedings of the 5th international conference on Natural computation
Indirect sliding mode neural-network control for holonomic constrained robot manipulators

International Journal of Intelligent Systems Technologies and Applications
A neural network-based sliding-mode control for rotating stall and surge in axial compressors

Applied Soft Computing
A predictive control approach for nonlinear systems

TELE-INFO'06 Proceedings of the 5th WSEAS international conference on Telecommunications and informatics
A new constructive neural network method for noise processing and its application on stock market prediction

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a direct adaptive neural-network control strategy for unknown nonlinear systems is presented. The system considered is described by an unknown NARMA model, and a feedforward neural network is used to learn the system. Taking the neural network as a neural model of the system, control signals are directly obtained by minimizing either the instant difference or the cumulative differences between a set point and the output of the neural model. Since the training algorithm guarantees that the output of the neural model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the set point. An application to a flow-rate control system is included to demonstrate the applicability of the proposed method and desired results are obtained