Adaptive Neural Control of High-order Uncertain Nonaffine Systems: A Transformation to Affine Systems Approach

Abstract

This brief investigates the adaptive neural network (NN) control of a class of high-order nonaffine nonlinear systems with completely unknown dynamics. Since the control terms appear within the unknown nonlinearity, traditional control schemes and stability analysis are usually rendered extremely complicated. Our main contribution includes a novel system transformation that converts the nonaffine system into an affine system through a combination of a low-pass filter and state transformation. As a result, the state-feedback control of the nonaffine system can be viewed as the output-feedback control of an affine system in normal form. The transformed system becomes linear with respect to the new input while the traditional backstepping approach is not needed thus allowing the synthesis to be extremely simplified. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results are provided to demonstrate the performance of the developed controller. © 2014 Elsevier Ltd. All rights reserved.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

National Natural Science Foundation of China, Grant 2012AA062201

Keywords and Phrases

Adaptive neural control; Nonlinear nonaffine systems; Unknown dynamics

International Standard Serial Number (ISSN)

0005-1098

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier; International Federation of Automatic Control (IFAC), All rights reserved.

Publication Date

01 Jan 2014

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