Dynamic Event-Based Adaptive Finite-Time Tracking Control for Nonlinear Stochastic Systems Under State Constraints
针对非线性随机系统在非对称时变状态约束下的跟踪控制问题，提出一种基于动态事件触发机制的自适应有限时间控制算法，能直接约束状态变量并节省网络资源。
This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example.