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Adaptive learning control for finite interval tracking based on constructive function approximation and wavelet.

Jian-Xin Xu¹, Rui Yan

¹Department of Electrical and Computer Engineering, National University of Singapore, Singapore. elexujx@nus.edu.sg

IEEE Transactions on Neural Networks

|May 12, 2011

Summary

This summary is machine-generated.

An adaptive learning control (ALC) approach uses a constructive function approximation network for tracking problems. This method allows flexible network structure tuning and precise function approximation for unknown nonlinear functions.

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Area of Science:

Control Engineering
Machine Learning
Applied Mathematics

Background:

Finite interval tracking problems require precise control strategies.
Traditional control methods often struggle with unknown nonlinear functions.
Function approximation networks offer a potential solution for complex control tasks.

Purpose of the Study:

To propose a novel adaptive learning control (ALC) approach for finite interval tracking.
To develop a constructive function approximation network with evolving structure.
To ensure precise function approximation and robust control for unknown nonlinear systems.

Main Methods:

Utilizing a constructive function approximation network with adaptable bases.
Implementing an adaptive learning mechanism for continuous parameter tuning.
Applying Lyapunov stability analysis to prove system convergence.
Employing wavelet networks as universal function approximators.

Main Results:

The proposed ALC system demonstrates guaranteed precision in function approximation.
The adaptive nature of the network avoids trial-and-error in structure selection.
The system's convergence properties are rigorously proven using Lyapunov methods.
The approach effectively handles both global and local L(2) unknown nonlinear functions.

Conclusions:

The developed ALC approach provides a flexible and precise solution for finite interval tracking.
The constructive function approximation network enhances control system adaptability.
This method offers a rigorous and effective way to manage unknown nonlinear dynamics.