Asymptotic learning control for a class of cascaded nonlinear uncertain systems

    Authors

    Z. H. Qu;J. X. Xu

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Autom. Control

    Keywords

    learning control; Lyapunov design; periodic function; stability; uncertain system; ROBOTIC MANIPULATORS; DYNAMIC-SYSTEMS; Automation & Control Systems; Engineering, Electrical & Electronic

    Abstract

    In this note, the problem of learning unknown functions in a class of cascaded nonlinear systems will be studied. The functions to be learned are those functions that are imbedded in the system dynamics and are of known period of time. In addition to the unknown periodic time functions, nonlinear uncertainties bounded by known functions of the state are also admissible. The objective of the note is to find an iterative learning control under which the class of nonlinear systems are globally stabilized (in the sense of being uniform bounded), their outputs are asymptotically convergent, and a combination of the time functions contained in system dynamics are asymptotically learned. To this end, a new type of differential-difference learning law is utilized to generate the proposed learning control that yields both asymptotic stability of the system output and asymptotic convergence of the learning error. The design is carried out by applying the Lyapunov direct method and the backward recursive design method.

    Journal Title

    Ieee Transactions on Automatic Control

    Volume

    47

    Issue/Number

    8

    Publication Date

    1-1-2002

    Document Type

    Article

    Language

    English

    First Page

    1369

    Last Page

    1376

    WOS Identifier

    WOS:000177371500021

    ISSN

    0018-9286

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