An Iterative Learning Control for a Class of Partially Feedback Linearizable Systems

A class of single-input single-output nonlinear systems which are partially linearizable by state feedback is considered: feedback linearizable systems are included in such a class; no parametrization is required for the uncertainties which are required to satisfy the matching condition. Periodic re...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2009-08, Vol.54 (8), p.1991-1996
Main Authors: Marino, R., Tomei, P.
Format: Article
Language:English
Subjects:
Adaptative systems
Adaptive control
Applied sciences
Asymptotic properties
Computer science
control theory
systems
Control systems
Control theory
Control theory. Systems
Exact sciences and technology
Feedback
Feedback linearization
Fourier series
Iterative algorithms
iterative learning
Iterative methods
Learning
Linear feedback control systems
Nonlinear systems
Programmable control
Robotics
Robots
State feedback
uncertain systems
Uncertainty
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Summary:A class of single-input single-output nonlinear systems which are partially linearizable by state feedback is considered: feedback linearizable systems are included in such a class; no parametrization is required for the uncertainties which are required to satisfy the matching condition. Periodic reference signals with known period T are to be tracked by the output. Provided that known bounding functions on the uncertainties are available, a state feedback iterative learning control is designed which achieves asymptotic output tracking and guarantees bounded closed loop signals from any intial condition. The novel control tecnhique is illustrated for a single-link robot arm.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2009.2023967