Neuroadaptive Tracking Control for Uncertain Stochastic Pure Feedback Systems with Prescribed Performance

In this paper, our focus is on prescribed performance control (PPC), a method that specifically targets a class of uncertain stochastic nonlinear systems exhibiting a pure feedback form. Firstly, we employ the mean value theorem to decouple the pure feedback form inherent in the system. In addition, the neural network and dynamic surfance techniques are exploited to identify the unknown dynamics. And utilizing the backstepping technique, a novel neuroadaptive control algorithm is developed to achieve the desired performance while tracking the reference signal. The proposed adaptive PPC scheme guarantees that all closed-loop signals maintain uniform ultimate boundedness in probability. Furthermore, the proposed scheme ensures tight tracking of the reference signal, thus attaining prescribed performance control. Lastly, simulation results are presented to demonstrate the practical feasibility of our approach.