Adaptive Tracking Control for Stochastic Nonlinear Systems via State‐Based Triggering Mechanism

To alleviate the communication burden arising from the transmission of state information from sensors to the controller, a state‐based event‐triggered adaptive neural network (NN) control scheme with adjustable thresholds is proposed for stochastic nonlinear systems (SNSs). Specifically, by embedding hyperbolic functions into the triggering condition, an event‐triggered mechanism with thresholds that are dynamically adjusted according to the system states is developed, thereby improving the flexibility of the triggering intervals. To rigorously address the error terms introduced by event triggering, a new lemma is established to support the subsequent analysis. To cope with the difficulty arising from the non‐differentiability of the virtual control laws in event‐triggered backstepping design, a first‐order filter and a new error‐compensation scheme are further introduced to ensure the smooth implementation of the recursive design, handle the filtering errors induced by event triggering, and facilitate the closed‐loop stability analysis. Simulation examples and comparative results demonstrate the effectiveness and advantages of the proposed method.