<p>This paper proposes an adaptive practical prescribed-time tracking control scheme for a nonlinear helicopter system with unknown dynamics and state constraints. A backstepping-based control framework is developed by integrating a radial basis function neural network (RBF-NN) with an integral barrier Lyapunov function (iBLF). The RBF-NN is used to approximate lumped system uncertainties online, while the iBLF ensures that the constrained states remain within prescribed safety bounds. By introducing a prescribed-time scaling function into the controller and adaptive laws, the proposed method guarantees that the tracking errors converge to compact residual sets within a prescribed time. This prescribed time can be specified in advance by the user and is independent of the system initial conditions. The rigorous Lyapunov analysis proves the boundedness of all closed-loop signals and establishes semi-global practical prescribed-time stability. Comparative simulations and experiments on the Quanser Aero platform show that, compared with finite-time control, the proposed method achieves faster and more predictable convergence, smaller residual tracking errors, and better constraint satisfaction.</p>