In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems.


    Access

    Download


    Export, share and cite



    Title :

    Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback



    Publication date :

    2015-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    An adaptive full order sliding mode controller for mismatched uncertain systems

    Mondal, S. / Ghommam, J. / Saad, M. | British Library Online Contents | 2017


    Integral sliding mode control for a class of nonlinear mismatched uncertain systems

    Qun Zong, / Zhanshan Zhao, / Liqian Dou, et al. | IEEE | 2008


    Fuzzy Dynamic Output Feedback Control for a Class of Uncertain Nonlinear Systems

    Tong, S.-c. / Zhou, J. | British Library Online Contents | 2001