Imen Zaidi, Mohamed Chtourou and Mohamed Djemel. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing, vol. 16, no. 2, pp. 213-225, 2019. DOI: 10.1007/s11633-017-1062-2
Citation: Imen Zaidi, Mohamed Chtourou and Mohamed Djemel. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing, vol. 16, no. 2, pp. 213-225, 2019. DOI: 10.1007/s11633-017-1062-2

Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm

  • This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system afiected by parametric uncertainties. According to the control scheme, in the flrst step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best conflguration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the efiectiveness of the proposed control strategy.
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