Neural Network Boundary Approximation for Uncertain Nonlinear Spatiotemporal Systems and Its Application of Tracking Control

This brief addresses the neural network (NN) approximation problem for uncertain nonlinear systems with time-varying parameters (that is, unknown nonlinear spatiotemporal systems). Due to the fact that the unknown spatiotemporal functions cannot be directly approximated by NNs, a so-called time-vary...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2024-05, Vol.35 (5), p.7238-7243
Main Authors: Zhang, Faxiang, Chen, Yang-Yang, Zhang, Ya
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive systems
Approximation
Artificial neural networks
Boundary function
Control theory
Mathematical analysis
Neural networks
neural networks (NNs)
nonlinear spatiotemporal system
Nonlinear systems
Parameter extraction
Parameter uncertainty
Parameters
Spatiotemporal phenomena
time-varying parameters
Time-varying systems
Tracking
Tracking control
Tracking errors
Trajectory
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This brief addresses the neural network (NN) approximation problem for uncertain nonlinear systems with time-varying parameters (that is, unknown nonlinear spatiotemporal systems). Due to the fact that the unknown spatiotemporal functions cannot be directly approximated by NNs, a so-called time-varying parameter extraction is given to separate time-varying parameters from uncertain nonlinear spatiotemporal functions. By using the supremum of Euler norm of the extracted time-varying parameters, the nonlinear spatiotemporal function is mapped to an unknown state-based boundary function, which can be approximated by NNs. Based on the time-varying parameter extraction, an adaptive neural tracking control law is designed for uncertain strict-feedback nonlinear spatiotemporal systems, which guarantees the convergence of the tracking error with a trajectory performance. The effectiveness of the designed method is verified by simulations.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3212696