Abstract

A crucial aspect that could facilitate the applications of adaptive control systems in aerospace applications is the development of effective validation and verification procedures. Most of the existing analysis and design frameworks for adaptive controllers are based on the Lyapunov direct method. One well-known drawback of this approach is the conservatism in the estimation of the uniform ultimate boundedness region with little practical utility. To overcome this limitation, a probabilistic framework for the design of uniform ultimate boundedness regions is proposed where uncertain parameters and adaptive controls are considered as random variables. In this framework, the design is translated into a stochastic convex optimization. This brings the benefit that (probabilistic) linear matrix inequality constraints can be derived without the need of matrix majorizations resulting therefore in less conservative conditions. Although the results are probabilistic, the level of confidence in the violation of linear matrix inequality constraints can be effectively established at the design level, exploiting the recent results of the probabilistic scenario design method. The approach is here applied for the design of uniform ultimate boundedness regions with prespecified component wise error requirements for a model reference adaptive control scheme in the presence of matched and input uncertainty. The approach is validated using the short-period longitudinal dynamics of an F-16 aircraft.

Department(s)

Mechanical and Aerospace Engineering

Publication Status

Full Access

International Standard Serial Number (ISSN)

1533-3884; 0731-5090

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.

Publication Date

01 Jan 2017

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