Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems

• 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. In this article, an event-triggered adaptive neural command-filter-based dynamic surface control (CFDSC) is discussed for states constrained nonstrict-feedback nonlinear systems with dynamic uncertainties. The order-reduced compensation signals are designed to compensate the tracking error of the filter in original dynamic surface control (DSC). The hyperbolic tangent function is adopted as the invertible mapping to deal with the full state constraints. An auxiliary signal is employed to estimate the dynamical uncertainties produced by the unmodeled dynamics. The unknown smooth nonlinear terms are approximated by radial basis function neural networks (RBFNNs) at recursive each step. An event-triggered input is constructed in this novel CFDSC framework. With the help of the defined compact set in the stability analysis, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the adaptive closed-loop system is proved. Moreover, each state can be strictly limited within the time-varying state conditions. Two simulation verifications are employed to verify and clarify the theoretical findings.