An artificial neural network (ANN) can easily detect and adapt variations in complex and nonlinear systems. This ability enhances the robustness of the estimation model. On the other hand, the sliding mode controller (SMC) ensures that the system stays on a predefined sliding surface, making it less sensitive to uncertainties, disturbances, and nonlinear changes. Thus, an ANN observer–based SMC is proposed and analyzed for a category of nonlinear systems. The ANN observer is designed to estimate unknown states and unknown disturbances. The observer is developed using the structure of a neural network comprising three layers. The ANN observer precisely estimated the states, and the estimation error converges to 0. The SMC controller is developed based on an ANN observer, and convergence of the law is also demonstrated through the Lyapunov theorem. The presented law improves the system's performance. The effectiveness of the controller is demonstrated through simulations on a single‐link robot dynamical model.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Artificial Neural Network–Based Sliding Mode Controller for a Class of Nonlinear System


    Contributors:


    Publication date :

    2025-06-24


    Size :

    14 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Nonlinear Flight Neuro-Controller Design Using Sliding Mode Based Neural Network

    Shin, D. / Kim, Y. / Kim, D. et al. | British Library Conference Proceedings | 2000


    Integral sliding mode nonlinear controller of electrical-hydraulic flight simulator based on neural network

    Songshan, H. / Zhongxia, J. / Chengwen, W. et al. | British Library Online Contents | 2014