NDLI: Uncertainty Identification for a Nominal LPV Vehicle Model Based on Experimental Data

Content Provider	IEEE Xplore Digital Library
Author	Rodonyi, G. Bokor, J.
Copyright Year	2005
Description	Author affiliation: Systems and Control Laboratory, Computer and Automation Research Institute of Hungarian Academy of Sciences. H-1518 Budapest P.O.Box 63, Hungary rodonyi@sztaki.hu (Rodonyi, G.)
Abstract	In this paper a practical method is presented for modelling uncertainty of a nominal linear parameter-varying (LPV) vehicle model. The aim with the uncertainty model is to bound the nominal model-error and satisfy robust stability and performance objectives during robust control design. Existing frequency-domain model-validation methods are applied to perform the first aim. The linear fractional uncertainty structure and the distribution of nominal model-error among the uncertainty blocks and disturbances are chosen to perform the second aim. The paper is motivated by the problem of steering a vehicle by alternately braking the front wheels in emergency situations. The identification is performed on real experiment data. The method and the results are demonstrated on a yaw-rate tracking problem and μ-controller design on constant scheduling variable of the LPV model. Using the proposed algorithm, on the supposition that nominal model error remains below the bound estimated from the validation data set, an unfalsified model is constructed for robust control guaranteeing robust performance against worstcase uncertainty and disturbance.
Starting Page	2682
Ending Page	2687
File Size	410525
Page Count	6
File Format	PDF
ISBN	0780395670
DOI	10.1109/CDC.2005.1582568
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	IEEE/EUCA
Subject Keyword	Uncertainty Robust control Adaptive control Frequency Intelligent vehicles Working environment noise Robust stability Wheels Error correction Centralized control
Content Type	Text
Resource Type	Article

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