Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

0.00 Avg rating0 Votes
Article ID: iaor201527021
Volume: 79
Issue: 5
Start Page Number: 1745
End Page Number: 1753
Publication Date: Jan 2009
Journal: Mathematics and Computers in Simulation
Authors: , , ,
Keywords: control
Abstract:

A neural network‐based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unmodeled dynamics. By on‐line approximating the unknown nonlinear functions and unmodeled dynamics by radial basis function (RBF) networks, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. It is proved that with the proposed control law, the closed‐loop system is stable and the tracking error converges to zero in the presence of unmodeled dynamics and unknown nonlinearity. A simulation example is presented to demonstrate the method.

Reviews

Required fields are marked *. Your email address will not be published.