Neural Networks Based Learning Control for Stochastic Nonlinear Systems with Unknown Dead-Zone and State Constraints

This paper proposes an adaptive learning algorithm for stochastic nonlinear systems with dead-zone characteristics and state constraints. First, the approximation capability of neural networks is utilized to model the unknown nonlinear dynamics, and an adaptive neural network controller is designed based on the backstepping control technique. Second, a dynamic event-triggering mechanism is developed, where the triggering condition dynamically adjusts over time based on system information and internal variables. Meanwhile, the auxiliary system is utilized to counteract the dead-zone nonlinearity, and the state constraints are handled by designing a barrier Lyapunov function. The proposed event-triggered dynamic surface control strategy not only ensures the semi-global uniform ultimate boundedness of the stochastic nonlinear system but also effectively eliminates the Zeno behavior. Finally, simulation experiments demonstrate the feasibility of the proposed learning strategy.