Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems
作者：
Highlights：
• Event-triggered adaptive neural command-filter-based dynamic surface control is proposed for states constrained nonstrict-feedback nonlinear systems.
• Command filter is combined with dynamic surface control, and the compensation signals are used to design the virtual control and control signal.
• Hyperbolic tangent function is adopted as invertible mapping to deal with full state constraints.
• A subsidiary signal is introduced to estimate dynamical uncertainties produced by unmodeled dynamics.
• Unknown smooth nonlinear functions are approximated by radial basis function neural networks at recursive each step.
• The compensating signals are added to the whole Lyapunov function, and the semi-global uniform ultimate boundedness of all signals is strictly proved.
摘要
•Event-triggered adaptive neural command-filter-based dynamic surface control is proposed for states constrained nonstrict-feedback nonlinear systems.•Command filter is combined with dynamic surface control, and the compensation signals are used to design the virtual control and control signal.•Hyperbolic tangent function is adopted as invertible mapping to deal with full state constraints.•A subsidiary signal is introduced to estimate dynamical uncertainties produced by unmodeled dynamics.•Unknown smooth nonlinear functions are approximated by radial basis function neural networks at recursive each step.•The compensating signals are added to the whole Lyapunov function, and the semi-global uniform ultimate boundedness of all signals is strictly proved.
论文关键词：Nonlinear mapping,Command filter,Dynamic surface control,Time-varying constraint,Unmodeled dynamics,Event-triggered input
论文评审过程：Received 1 February 2022, Revised 31 May 2022, Accepted 22 July 2022, Available online 10 August 2022, Version of Record 10 August 2022.
论文官网地址：https://doi.org/10.1016/j.amc.2022.127440
