This study proposes a novel saturated prescribed performance (PP) controller for Euler–Lagrange dynamic systems. The design of bounded-input controller is facilitated by means of introduction of auxiliary dynamics in the closed-loop system. The controller takes advantages of using an adaptive multilayer neural network and a robust term to deal with uncertainties of the system. Through the stability analysis, it is shown that PPwith saturated control command is feasible having certain constraints on initial conditions and external disturbances. The proposed controller is validated through experimental and simulation studies conducted on a lower-limb robotic exoskeleton.