Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance
作者：
Highlights：
• This is the first work to handle the ATC issue for uncertain nonstrict-feedback switched systems with unknown system functions via incorporating the estimation ability of RBF NNs into the adaptive DSC framework.
• Different from the traditional adaptive control methods, for an n-order nonlinear system, our controller contains only two adaptive parameters that need to be updated online, instead of n parameters. This greatly reduces the computation burden and has practical value.
• The approximation error caused by the use of radial basis function (RBF) neural networks (NNs) is compensated by an online updated parameter.
• The nonstrict-feedback form is handled by adopting the inherent properties of RBF NNs.
摘要
•This is the first work to handle the ATC issue for uncertain nonstrict-feedback switched systems with unknown system functions via incorporating the estimation ability of RBF NNs into the adaptive DSC framework.•Different from the traditional adaptive control methods, for an n-order nonlinear system, our controller contains only two adaptive parameters that need to be updated online, instead of n parameters. This greatly reduces the computation burden and has practical value.•The approximation error caused by the use of radial basis function (RBF) neural networks (NNs) is compensated by an online updated parameter.•The nonstrict-feedback form is handled by adopting the inherent properties of RBF NNs.
论文关键词：Switched nonlinear systems,Asymptotic tracking,Nonstrict-feedback form,Dynamic surface control
论文评审过程：Received 13 February 2021, Revised 8 April 2021, Accepted 2 May 2021, Available online 24 May 2021, Version of Record 24 May 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126344
