Fixed-Time Adaptive Neural Control for Constrained Stochastic Nonlinear Systems with Unknown Covariance Noise

In this paper, the fixed-time control problem for a class of constrained stochastic nonlinear systems subject to unknown covariance noise is considered. By utilizing the auxiliary system method, the input saturation of the system is solved. Then, the barrier Lyapunov function (BLF) is established to ensure constraint satisfaction. A novel fixed-time control strategy based on neural networks and adaptive control is proposed to handle the unknown covariance noise. Furthermore, a fixed-time stability analysis is employed to ensure that the closed-loop system can achieve semi-globally uniformly ultimately bounded (SGUUB) within a fixed time. Finally, the effectiveness of the proposed control scheme is demonstrated through a simulation example.