A Set-Theoretic Model Reference Adaptive Control Architecture with Partially Adjustable Strict Performance Guarantees: A Command Governor Approach
Abstract
A new set-theoretic model reference adaptive control architecture is reported with partially adjustable strict performance guarantees on a subset of the system error vector. The proposed framework involves a two-level constructive design framework. First, we introduce an auxiliary state dynamics and develop the auxiliary system error vector between the states of an uncertain dynamical system and the states of this auxiliary dynamics. This allows a control designer to independently weight each auxiliary system error vector element while enforcing strict performance guarantees on the norm of this auxiliary system error vector. Next, we utilize a command governor mechanism. This mechanism drives a feasible user-selected system subset states to a close and user-controllable neighborhood of their equivalent reference model subset states in order to achieve easily adjustable strict performance guarantees on partial system errors. A numerical example complements the presented architecture.
Recommended Citation
E. Arabi et al., "A Set-Theoretic Model Reference Adaptive Control Architecture with Partially Adjustable Strict Performance Guarantees: A Command Governor Approach," Proceedings of the 2018 Annual American Control Conference (2018, Milwaukee, WI), pp. 5436 - 5441, Institute of Electrical and Electronics Engineers (IEEE), Jun 2018.
The definitive version is available at https://doi.org/10.23919/ACC.2018.8430748
Meeting Name
2018 Annual American Control Conference, AAC (2018: Jun. 27-29, Milwaukee, WI)
Department(s)
Mechanical and Aerospace Engineering
International Standard Book Number (ISBN)
978-1-5386-5428-6
International Standard Serial Number (ISSN)
2378-5861
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jun 2018
Comments
This research was supported in part by the National Aeronautics and Space Administration under Grant NNX15AM51A.