Abstract

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which is represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: 1) a NN observer to estimate the system states with the input-output data, and 2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem in the discrete-time backstepping design is avoided by using the universal NN approximator. The persistence excitation (PE) condition is relaxed both in the NN observer and NN controller design. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is shown.

Meeting Name

2004 American Control Conference, 2004

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Sponsor(s)

National Science Foundation (U.S.)

Keywords and Phrases

Closed Loop Systems; Control System Synthesis; Neurocontrollers; Non-Linear Control Systems

Library of Congress Subject Headings

Adaptive control systems
Discrete-time systems

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.