Abstract

In this paper, we propose a command governor-based adaptive control architecture for stabilizing uncertain dynamical systems with not only matched but also unmatched uncertainties and achieving the desired command following performance of a user-defined subset of the accessible states. In our proposed solution, online least-squares solutions for the matched and unmatched parameters are obtained through integration method and they are employed in the adaptive control framework. Specifically, the matched uncertainty is identified and its effect upon the system behavior is entirely attenuated. Moreover, using the unmatched uncertainty approximation obtained through radial basis function neural networks, the command governor signal is designed to achieve the desired command following performance of the user-defined subset of the accessible states. With this command governor-based model reference adaptive control architecture, the tracking error of the selected states can be made arbitrarily small by judiciously tuning the design parameters. In addition to the analysis of the closed-loop system stability using methods from the Lyapunov theory, our findings are also illustrated through numerical examples.

Department(s)

Mechanical and Aerospace Engineering

Publication Status

Full Access

Comments

National Aeronautics and Space Administration, Grant NNX15AM51A

Keywords and Phrases

adaptive control; command governor; matched and unmatched uncertainties; neural networks; stability and command following

International Standard Serial Number (ISSN)

1099-1115; 0890-6327

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Wiley, All rights reserved.

Publication Date

01 Aug 2018

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