On Initial Rectifying Learning for Linear Time-Invariant Systems with Rank-Defective Markov Parameters

Authors:
Mingxuan Sun
Affiliations:
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China 310014
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

Citing 4
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Iterative learning control for a class of nonlinear systems

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Iterative solution of nonlinear equations in several variables

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Brief Iterative learning control with initial rectifying action

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Abstract

This paper presents an initial rectifying learning method for trajectory tracking of linear time-invariant systems with rank-defective Markov parameters. The initial shift problem is addressed through introduction of the initial rectifying action. The role of the rectifying action is examined in case of systems with row and column rank-defective Markov parameters, respectively. Sufficient conditions for convergence of the proposed learning algorithms are derived, by which the learning gains can be chosen. It is shown that the output trajectory converges to the desired one with a smooth transition. The merging of the transition to the desired trajectory occurs at a pre-specified time instant.