Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation

Yongfeng Lv, Jing Na, Qinmin Yang, Guido Herrmann

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    In this paper, an alternative solution for adaptive optimal tracking control of nonlinear completely unknown systems is proposed. Firstly, an adaptive identifier is used to estimate the unknown system dynamics. Then, a recently developed system augmentation approach is adopted to design the optimal control, where the reference signal is incorporated into the augmented system. Thus, both the feedforward control and feedback control can be obtained simultaneously. Then, a critic neural network (NN) is used to estimate the augmented performance index, and calculate the optimal control action. Thus, the widely used actor NN is not needed. Finally, a new adaptive law recently proposed by the authors is used to online update the NN weight. The closed-loop stability and the convergence of the optimal control are all proved. The feasibility of the suggested approach is demonstrated by a simulation example.
    Original languageEnglish
    Number of pages6
    DOIs
    Publication statusPublished - Feb 2017
    Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
    Duration: 24 Jul 201629 Jul 2016

    Conference

    Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
    Country/TerritoryCanada
    CityVancouver
    Period24/07/1629/07/16

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