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

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Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5226))

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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.

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© 2008 Springer-Verlag Berlin Heidelberg

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Sun, M. (2008). On Initial Rectifying Learning for Linear Time-Invariant Systems with Rank-Defective Markov Parameters. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_91

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  • DOI: https://doi.org/10.1007/978-3-540-87442-3_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87440-9

  • Online ISBN: 978-3-540-87442-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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