Direct Adaptive NN Control of Nonlinear Systems in Strict-Feedback Form Using Dynamic Surface Control

Abstract

In this paper, direct adaptive neural control is investigated for a class of strict-feedback nonlinear systems with both unknown system functions and virtual control gain functions. The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control (DSC) and introducing integral-type Lyapunov function. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.