DocumentCode
498852
Title
Model set identification for H∞ robust control of Hydraulic Flight Motion Simulator
Author
Wang, Ben-yong ; Dong, Yan-liang ; Zhao, Ke-ding
Author_Institution
Sch. of Mech. Eng., Heilongjiang Inst. of Sci. & Technol., Harbin, China
Volume
4
fYear
2009
fDate
12-15 July 2009
Firstpage
2118
Lastpage
2122
Abstract
A model set identification approach is presented for Hinfin robust control design of Hydraulic Flight Motion Simulator servo system, which suffers from various uncertainties, such as parameter uncertainty, high frequency unmodelled dynamic uncertainty, and so on. The identification approach consists of two steps. To be exact, the first step is nominal model parameter identification, and then model uncertainty estimation. The nominal model parameter identification is based on the grey-box method. The model uncertainty estimation is implemented with model error modeling. Both nominal model and uncertainty bound are indispensable to Hinfin robust control design. Compared to the usual modeling method with math derivation and simulation, the proposed identification approach provides more accurate nominal model and uncertainty bound, and is more suitable for Hinfin robust control design of Hydraulic Flight Motion Simulator servo system. The identification procedure is illustrated with the Hydraulic Flight Motion Simulator inner gimbal servo system.
Keywords
Hinfin control; aerospace simulation; control system synthesis; parameter estimation; robust control; servomechanisms; Hinfin robust control design; grey-box method; hydraulic flight motion simulator; model set identification; nominal model parameter identification; servo system; uncertainty estimation; Aerospace simulation; Control system synthesis; Cybernetics; Machine learning; Parameter estimation; Robust control; Sampling methods; Servomechanisms; Uncertainty; Valves; Grey-box method; Hydraulic Flight Motion Simulator; Model error modeling; Model set identification; Model uncertainty; Nominal model;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212206
Filename
5212206
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