Based on the dynamic surface control (DSC), an adaptive neural network control approach is proposed for a class of stochastic nonlinear strictfeedback systems in this paper. This approach simplifies the backstepping design and overcomes the problem of 'explosion of complexity' inherent in the backstepping method. The Lyapunov Stability analysis given in this paper shows that the control law can guarantee the solution of the closed-loop system uniformly ultimate boundedness (UUB) in probability. The simulation example is given to illustrate the effectiveness of the proposed control system.
['Zifu Li', 'Tieshan Li', 'Xiaori Gao']
Adaptive NN dynamic surface control for stochastic nonlinear strict-feedback systems