NDLI: Worst-case analysis based adaptive control design for SISO linear systems with plant and actuation uncertainties

Content Provider	IEEE Xplore Digital Library
Author	Zeng, Sheng
Copyright Year	2011
Description	Author affiliation: CareFusion Company, USA (Zeng, Sheng)
Abstract	We study the adaptive controller design for SISO linear systems subjected to plant and actuation uncertainties simultaneously. We first formulate the actuation and plant components of the linear system as two subsystems sequentially interconnected with additional feedback, and then convert the robust adaptive control problem as a nonlinear $H^{∞}$ control problem under imperfect state measurement. We derive the estimators and identifiers of the two subsystems using cost-to-come analysis, and then apply integrator backstepping methodology to obtain the control law. The controller guarantees the boundedness of closed-loop signals with bounded exogenous disturbances, and achieves desired disturbance attenuation level with respect to the unmeasured exogenous disturbance inputs and arbitrary positive or zero disturbance attenuation level with respect to the measured disturbance inputs. In addition, for the measured disturbances that the controller can achieve zero disturbance attenuation level, the asymptotic tracking objective is achieved even if they are only uniformly bounded without being of finite energy.
Starting Page	6349
Ending Page	6354
File Size	257976
Page Count	6
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6160744
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Adaptive control Robustness Attenuation Estimation Uncertainty Control design adaptive control Nonlinear H^∞ control cost-to-come function analysis measured disturbances
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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