Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems

This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree struc...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2016-09, Vol.27 (9), p.1864-1877
Main Authors: Zhang, Yanjun, Tao, Gang, Chen, Mou
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive control
Adaptive systems
Approximation
Approximation methods
Computer simulation
Control design
Control systems
Design engineering
Dynamical systems
feedback linearization
Mathematical analysis
neural network system models
Neural networks
noncanonical nonlinear systems
Nonlinear dynamics
Nonlinear systems
output tracking
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2015.2461001