Based on the approximation capability of neural networks, adaptive dynamic surface control is presented for
a class of nonlinear systems in pure feedback form with unmodeled dynamics. By introducing the first order filter, the
explosion of complexity caused by the repeated differentiations of certain nonlinear functions such as virtual controls in
traditional backstepping design is avoided. By using Young’s inequality and integral-type Lyapunov function, the number
of adjustable parameters are effectively reduced and the derivative of the virtual control coefficients is avoided to be known.
Theoretical analysis shows that the closed-loop control system is semi-globally uniformly ultimately bounded.