Adaptive Practical Finite-Time Stabilization for Uncertain Nonstrict Feedback Nonlinear Systems With Input Nonlinearity

This paper investigates the adaptive practical finite-time stabilization for a class of nonstrict feedback nonlinear systems. The nonlinear systems under consideration contain unknown nonlinearities and control coefficients, and unknown deadzone and saturation input nonlinearities. Without imposing any conditions on the unknown nonlinearities, neural networks are utilized as the approximators to cope with these unknown nonlinear functions. The adding a power integrator technique is employed to construct controller and adaptive laws. The stability of the corresponding closed-loop system is proved with the help of the finite-time Lyapunov theory. Finally, two simulation examples are provided to show the validity of the proposed design method.