An efficient approach for controlling processes with completely unknown dynamics subject to input nonlinearity and in the absence of state measurements is presented. To handle the system input nonlinearity, this constrain has been included in the system unknown dynamics and a new input has been defined. An unknown Lyapunov function has been considered, and the terms appearing in its time derivative are estimated by neural networks and the stability of the closed-loop system has been established. The effectiveness of the proposed control scheme has been demonstrated by applying the scheme to different bioreactors with unknown dynamics under input saturation. The performances of designed controllers in set-point tracking, load rejection, and model mismatch have been evaluated via a simulation study.