Preassigned Finite-Time Control of High-Order Nonlinear Systems with Time-Varying Powers and Full-State Constraints

Abstract

This paper studies adaptive preassigned finite-time control of high-order nonlinear systems with unknown timevarying powers and full-state constraints. Neural network approximation is employed to remove frequently used assumptions imposed on unknown system nonlinearities. By introducing lower and higher powers, finite-time performance functions and nonlinear transformed functions into dynamic surface control design, full-state constrained controller is designed without imposing feasibility conditions on virtual controllers in traditional barrier Lyapunov function-based control methods. It is proved that all the closed-system signals are semi-globally bounded, full-state constraints are not violated, and system states converge to prescribed small regions around zero after a preassigned finite-time.