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Adaptive neural design frame for uncertain stochastic nonlinear non-lower triangular pure-feedback systems with input constraint

Journal Article


Abstract


  • This paper dedicates to dealing with the adaptive neural design problem for uncertain stochastic nonlinear systems with non-lower triangular pure-feedback form and input constraint. On the basis of the mean-value theorem, the pure-feedback structure is first transformed into the desired affine structure, and then the well-known backstepping technology is applied to construct the actual input signal of the controller. Although the considered system has a fairly complex structure, a new adaptive neural tracking controller design frame is established via the flexible application of radial basis function (RBF) neural networks’ (NNs’) structural characteristics. The proposed design frame guarantees the control objective of this paper can be achieved. Finally, a simulation example is given to further illustrate the availability of the proposed control scheme.

Publication Date


  • 2019

Citation


  • Li, R. B., Niu, B., Feng, Z. G., Li, J. Q., Duan, P. Y., & Yang, D. (2019). Adaptive neural design frame for uncertain stochastic nonlinear non-lower triangular pure-feedback systems with input constraint. Journal of the Franklin Institute, 356(16), 9545-9564. doi:10.1016/j.jfranklin.2019.09.019

Scopus Eid


  • 2-s2.0-85072712478

Web Of Science Accession Number


Start Page


  • 9545

End Page


  • 9564

Volume


  • 356

Issue


  • 16

Abstract


  • This paper dedicates to dealing with the adaptive neural design problem for uncertain stochastic nonlinear systems with non-lower triangular pure-feedback form and input constraint. On the basis of the mean-value theorem, the pure-feedback structure is first transformed into the desired affine structure, and then the well-known backstepping technology is applied to construct the actual input signal of the controller. Although the considered system has a fairly complex structure, a new adaptive neural tracking controller design frame is established via the flexible application of radial basis function (RBF) neural networks’ (NNs’) structural characteristics. The proposed design frame guarantees the control objective of this paper can be achieved. Finally, a simulation example is given to further illustrate the availability of the proposed control scheme.

Publication Date


  • 2019

Citation


  • Li, R. B., Niu, B., Feng, Z. G., Li, J. Q., Duan, P. Y., & Yang, D. (2019). Adaptive neural design frame for uncertain stochastic nonlinear non-lower triangular pure-feedback systems with input constraint. Journal of the Franklin Institute, 356(16), 9545-9564. doi:10.1016/j.jfranklin.2019.09.019

Scopus Eid


  • 2-s2.0-85072712478

Web Of Science Accession Number


Start Page


  • 9545

End Page


  • 9564

Volume


  • 356

Issue


  • 16