Finite-Time Prescribed Performance Control for a 2-DOF Helicopter System with Asymmetric Input Saturation

This paper investigates prescribed performance tracking control for nonlinear systems with asymmetric input saturation under a finite-time framework. A novel performance function is constructed to explicitly regulate the convergence time without restrictive initial conditions. To address saturation effects, a violation-based characterization is introduced, and an adaptive auxiliary dynamic framework is developed, where the auxiliary gains are adjusted online according to the saturation level. Moreover, the auxiliary states are incorporated into the performance function, thereby enabling saturation-aware dynamic reshaping of the prescribed performance bounds. Neural networks are employed to approximate unknown nonlinear dynamics, and a composite control law is designed to guarantee closed-loop stability. It is rigorously proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of the origin within a prescribed finite time. Simulation results on a 2-DOF helicopter system demonstrate improved convergence speed, higher tracking accuracy, and enhanced robustness against input saturation.