Robust design of nonlinear internal models without adaptation

Alberto Isidori, Lorenzo Marconi, Laurent Praly

    Research output: Contribution to journalArticlepeer-review

    Abstract

    We propose a solution to the problem of semiglobal output regulation for nonlinear minimum-phase systems driven by uncertain exosystems that does not rely upon conventional adaptation schemes to estimate the frequency of the exogenous signals. Rather, the proposed approach relies upon regression-like arguments used to derive a nonlinear internal model able to offset the presence of an unknown number of harmonic exogenous inputs of uncertain amplitude, phase and frequency. The design methodology guarantees asymptotic regulation if the dimension of the regulator exceeds a lower bound determined by the actual number of harmonic components of the exogenous input. If this is not the case, a bounded steady-state regulation error is ensured whose amplitude, though, can be arbitrarily decreased by acting on a design parameter of the regulator. We propose a solution to the problem of semiglobal output regulation for nonlinear minimum-phase systems driven by uncertain exosystems that does not rely upon conventional adaptation schemes to estimate the frequency of the exogenous signals. Rather, the proposed approach relies upon regression-like arguments used to derive a nonlinear internal model able to offset the presence of an unknown number of harmonic exogenous inputs of uncertain amplitude, phase and frequency. The design methodology guarantees asymptotic regulation if the dimension of the regulator exceeds a lower bound determined by the actual number of harmonic components of the exogenous input. If this is not the case, a bounded steady-state regulation error is ensured whose amplitude, though, can be arbitrarily decreased by acting on a design parameter of the regulator. © 2012 Elsevier Ltd. All rights reserved
    Original languageEnglish
    Pages (from-to)2409
    Number of pages11
    JournalAutomatica
    Volume48/2012
    DOIs
    Publication statusPublished - Jul 2012

    Fields of science

    • 101 Mathematics
    • 102 Computer Sciences
    • 202 Electrical Engineering, Electronics, Information Engineering
    • 202009 Electrical drive engineering
    • 202027 Mechatronics
    • 202034 Control engineering
    • 202036 Sensor systems
    • 203 Mechanical Engineering
    • 203033 Hydraulic drive technology

    JKU Focus areas

    • Mechatronics and Information Processing

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