A simplified adaptive neural dynamic surface control approach is proposed for a class of completely non-affine
pure-feedback nonlinear systems. By using implicit function theorem and mean value theorem, unknown non-affine input
functions can be transformed to partially affine forms. The simplified neural networks are used to approximate the unknown
nonlinearities in systems, and for a ??-th order strict feedback nonlinear system, only one parameter is needed to be estimated
on-line. The problem of explosion of terms in traditional backstepping design is eliminated by utilizing dynamic surface
control. It is proved that the developed method can guarantee the semi-global stability of the close-loop system. Simulation
results show the effectiveness of the proposed approach.