Aperiodic disturbance rejection in a modified repetitive-control system with non-linear uncertainty

This study deals with the problem of aperiodic-disturbance rejection for a class of plants with non-linear state-dependent uncertainty in a modified repetitive-control system (MRCS). Since both the non-linear uncertainty and the disturbances are often unknown, an equivalent-input-disturbance (EID) e...

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Bibliographic Details
Published in:IET control theory & applications 2016-12, Vol.10 (18), p.2394-2402
Main Authors: Zhou, Lan, She, Jinhua, He, Yong, Li, Chaoyi
Format: Article
Language:English
Subjects:
aperiodic‐disturbance rejection problem
compensation
continuous‐discrete 2D model
control action preferential adjustment
design procedures
Disturbances
EID‐based MRCS
equivalent‐input‐disturbance estimator
Estimates
Estimators
full‐order state observer
learning action preferential adjustment
learning systems
linear matrix inequalities
linear matrix inequality
linear repetitive control law
Mathematical models
modified repetitive‐control system
nonlinear control systems
nonlinear state‐dependent uncertainty
Nonlinearity
observers
periodic control
Rejection
Repetitive control
Research Article
robust control
robust globally uniformly ultimately bounded stability
uncertain systems
Uncertainty
variant system matrix
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Summary:This study deals with the problem of aperiodic-disturbance rejection for a class of plants with non-linear state-dependent uncertainty in a modified repetitive-control system (MRCS). Since both the non-linear uncertainty and the disturbances are often unknown, an equivalent-input-disturbance (EID) estimator is constructed using a full-order state observer with variant system matrix to estimate them. The EID estimate, which exhibits the overall effect on the output of the non-linear uncertainty and all types of disturbances, is incorporated into a linear repetitive control law to compensate for the non-linear uncertainty and the disturbances. A continuous-discrete two-dimensional model of the EID-based MRCS is built that enables the preferential adjustment of the control and learning actions. A sufficient condition for the robust stability for the EID-based MRCS is given in terms of a linear matrix inequality. It yields the parameters of the repetitive controller and the EID estimator. Finally, a numerical example demonstrates the design procedures and illustrates the effectiveness of the method.
ISSN:1751-8644
1751-8652
1751-8652
DOI:10.1049/iet-cta.2016.0479