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Hybrid state-space self-tuning control of uncertain linear systemsThe paper presents a hybrid state-space self-tuner using a new dual-rate sampling scheme for digital adaptive control of continuous-time uncertain linear systems. A state-space-based recursive least-squares algorithm, together with a variable forgetting factor, is used for direct estimations of both the equivalent discrete-time uncertain linear system parameters and the associated discrete-time state of a continuous-time uncertain linear system from the sampled input and output data. An analogue optimal regional pole-placement design method is used for designing an optimal observer-based analogue controller. A suboptimal observer-based digital controller is then designed from the designed analogue controller using digital redesign technique. To enhance the robustness of parameter identification and state estimation algorithms, a dynamic bound for a class of uncertain bilinear parameters and a fast-rate digital controller are developed at each fast-sampling period. Also, to accommodate computation loads and computation delay for developing the advanced hybrid self-tuner, the designed analogue controller and observer gains are both updated at each slow-sampling period. This control technique has been successfully applied to benchmark control problems.
Document ID
19930053025
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Shieh, L. S.
(NASA Lyndon B. Johnson Space Center Houston, TX, United States)
Wang, Y. J.
(Houston Univ. TX, United States)
Sunkel, J. W.
(NASA Johnson Space Center Houston, TX, United States)
Date Acquired
August 16, 2013
Publication Date
March 1, 1993
Publication Information
Publication: IEE Proceedings, Part D - Control Theory and Applications
Volume: 140
Issue: 2
ISSN: 0143-7054
Subject Category
Cybernetics
Accession Number
93A37022
Funding Number(s)
CONTRACT_GRANT: DAAL03-91-G-0106
CONTRACT_GRANT: NAG9-380
Distribution Limits
Public
Copyright
Other

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