Adaptive Neural Network Dynamic Surface Control for Nonlinear Stochastic Systems in the Strict-Feedback Form with Prandtl-Ishlinskii Hysteresis in the Actuator

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Aghajary Mohammad Mahdi; Hashemi mahnaz

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Journal: Journal of Intelligent Procedures in Electrical Technology Year:2020 | Volume:11 | Issue:42 Page(s): 15-27

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Title

Adaptive Neural Network Dynamic Surface Control for Nonlinear Stochastic Systems in the Strict-Feedback Form with Prandtl-Ishlinskii Hysteresis in the Actuator

Author(s)

Aghajary Mohammad Mahdi | Hashemi mahnaz | Issue Writer Certificate

Keywords

Actuator nonlinearityQ1

adaptive RBF neural networksQ1

dynamic surface controlQ1

nonlinear stochastic systemsQ1

Prandtl-Ishlinskii hysteresisQ1

Abstract

Using the adaptive radial basis function (RBF) neural network dynamic surface control (DSC) design method, a controller design approach is presented in order to the stabilization of strict-feedback nonlinear stochastic systems subjected to Prandtl-Ishlinskii nonlinearity in the actuator. This method is capable to be applied to nonlinear stochastic systems with any unknown dynamics. According to the universal approximation capability, the RBF neural networks make it possible to approximate the unknown dynamics of the nonlinear stochastic systems. Using the minimal-learning-parameters algorithm the approximation procedure is done with a minimum complexity and required calculations. The stability of the proposed control system is proven analytically and its results are demonstrated using a simulation example. It is shown that the proposed design approach guarantees the boundedness in probability for adaptive control system, and in turn the uniformly ultimately boundedness of all closed-loop signals. It is also shown, that using this method the tracking error can be made arbitrarily small.

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APA: Copy

Aghajary, Mohammad Mahdi, & Hashemi, mahnaz. (2020). Adaptive Neural Network Dynamic Surface Control for Nonlinear Stochastic Systems in the Strict-Feedback Form with Prandtl-Ishlinskii Hysteresis in the Actuator. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, 11(42 ), 15-27. SID. https://sid.ir/paper/389687/en

Vancouver: Copy

Aghajary Mohammad Mahdi, Hashemi mahnaz. Adaptive Neural Network Dynamic Surface Control for Nonlinear Stochastic Systems in the Strict-Feedback Form with Prandtl-Ishlinskii Hysteresis in the Actuator. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY[Internet]. 2020;11(42 ):15-27. Available from: https://sid.ir/paper/389687/en

IEEE: Copy

Mohammad Mahdi Aghajary, and mahnaz Hashemi, “Adaptive Neural Network Dynamic Surface Control for Nonlinear Stochastic Systems in the Strict-Feedback Form with Prandtl-Ishlinskii Hysteresis in the Actuator,” JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, vol. 11, no. 42 , pp. 15–27, 2020, [Online]. Available: https://sid.ir/paper/389687/en

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