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• W2923956963 startingPage "3565" @default.
• W2923956963 abstract "In this paper, a robust and chattering-free sliding-mode control strategy using recurrent neural networks (RNNs) and H ∞ approach for a class of nonlinear systems with uncertainties is proposed. The dynamic and algebraic models of the RNN are extracted based on the nominal model of the system and formulation of a quadratic programming problem. For tuning the parameters of the sliding surface, the performance index and the switching coefficient, a robust approach based on the H ∞ method is developed. To this end, the control law is divided into two parts: (1) the main term, which includes the feedback error and (2) other terms, which include the network states, the reference input and its derivatives and the effects of the uncertainties. The feedback error gain is tuned by solving a linear matrix inequality. The neural optimizer determines the sliding-mode control law without being directly affected by the uncertainties. By applying the proposed method to the continuous-stirred reactor tank and the inverted pendulum problems, the performance of the proposed controller has been evaluated in terms of the tracking accuracy, elimination of the chattering, robustness against the uncertainties and feasibility of the control signals. Moreover, the results are compared with the conventional and twisting sliding-mode control methods." @default.
• W2923956963 created "2019-04-01" @default.
• W2923956963 creator A5068893907 @default.
• W2923956963 creator A5084539076 @default.
• W2923956963 date "2019-03-21" @default.
• W2923956963 modified "2023-10-10" @default.
• W2923956963 title "Robust chattering-free optimal sliding-mode control using recurrent neural networks: An <i>H_∞</i>-based approach" @default.
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• W2923956963 doi "https://doi.org/10.1177/0142331219832953" @default.
• W2923956963 hasPublicationYear "2019" @default.
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