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Adaptive NN Tracking Control for Stochastic Pure-Feedback Nonlinear Systems in Non-Strict-Feedback form

Conference Paper


Abstract


  • This work is dedicated to solving the neural network-based tracking control problem for stochastic pure-feedback nonlinear systems in non-strict-feedback form. In order to deal with the difficulty caused by the non-strict-feedback structure, the structural characteristics of neural networks is employed by a lemma. At the same time, the pure-feedback structure in the system is also dealt with by the mean-value theorem. Based on the backstepping technology and neural networks, the adaptive neural tracking controller has been successfully constructed. And it guarantees all signals of closed-loop stochastic nonlinear systems are bounded in probability.

Publication Date


  • 2020

Citation


  • Li, R. B., Feng, Z., Cao, Y., & Cui, Y. (2020). Adaptive NN Tracking Control for Stochastic Pure-Feedback Nonlinear Systems in Non-Strict-Feedback form. In 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020 (pp. 124-129). doi:10.1109/ICCSS52145.2020.9336860

Scopus Eid


  • 2-s2.0-85100891158

Web Of Science Accession Number


Start Page


  • 124

End Page


  • 129

Abstract


  • This work is dedicated to solving the neural network-based tracking control problem for stochastic pure-feedback nonlinear systems in non-strict-feedback form. In order to deal with the difficulty caused by the non-strict-feedback structure, the structural characteristics of neural networks is employed by a lemma. At the same time, the pure-feedback structure in the system is also dealt with by the mean-value theorem. Based on the backstepping technology and neural networks, the adaptive neural tracking controller has been successfully constructed. And it guarantees all signals of closed-loop stochastic nonlinear systems are bounded in probability.

Publication Date


  • 2020

Citation


  • Li, R. B., Feng, Z., Cao, Y., & Cui, Y. (2020). Adaptive NN Tracking Control for Stochastic Pure-Feedback Nonlinear Systems in Non-Strict-Feedback form. In 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020 (pp. 124-129). doi:10.1109/ICCSS52145.2020.9336860

Scopus Eid


  • 2-s2.0-85100891158

Web Of Science Accession Number


Start Page


  • 124

End Page


  • 129