Neural network solution for suboptimal control of non-holonomic chained form system

    Authors

    T. Cheng; H. X. Sun; Z. H. Qu;F. L. Lewis

    Comments

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    Abbreviated Journal Title

    Trans. Inst. Meas. Control

    Keywords

    Constrained input systems; finite-horizon optimal control; Hamilton-Jacobi-Bellman; neural network control; non-holonomic systems; CONTINUOUS-TIME SYSTEMS; SATURATING ACTUATORS; NONLINEAR-SYSTEMS; FEEDBACK-CONTROL; LINEAR-SYSTEMS; Automation & Control Systems; Instruments & Instrumentation

    Abstract

    In this paper, we develop fixed-final time nearly optimal control laws for a class of non-holonomic chained form systems by using neural networks to approximately solve a Hamilton-Jacobi-Bellman equation. A certain time-folding method is applied to recover uniform complete controllability for the chained form system. This method requires an innovative design of a certain dynamic control component. Using this time-folding method, the chained form system is mapped into a controllable linear system for which controllers can systematically be designed to ensure exponential or asymptotic stability as well as nearly optimal performance. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. The results of this paper are demonstrated in an example.

    Journal Title

    Transactions of the Institute of Measurement and Control

    Volume

    31

    Issue/Number

    6

    Publication Date

    1-1-2009

    Document Type

    Article

    Language

    English

    First Page

    475

    Last Page

    494

    WOS Identifier

    WOS:000272679100002

    ISSN

    0142-3312

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