Event-triggered Adaptive Tracking Control for Stochastic Nonlinear Systems With State Constraints

This article studies the problem of dynamic-surface-based event-triggered adaptive tracking control for a class of strictly-feedback stochastic nonlinear systems with state constraints. Firstly, radial basis function neural networks (RBFNNs) are used to approximate unknown nonlinear continuous functions, and barrier Lyapunov functions (BLFs) are used to address state constraint problems. Then, the dynamic surface control (DSC) scheme is applied to solve the “explosion of complexity” issue, and error compensation signals are added to reduce the error caused by the filter to achieve a more effective control performance and optimize the algorithm. In addition, this research also considers the case of systems with relative threshold event-triggered mechanisms to save communication resources, and the existence of the lower bound of the minimum inter-event time is proved to exclude the Zeno behavior. Meanwhile, an adaptive tracking controller with the backstepping control strategy is designed, so that all signals in the closed-loop system are bounded and the tracking error converges to a small residual set of the origin in probability. Finally, the simulation examples are given to demonstrate the effectiveness of the control method.