Finite‐time adaptive event‐triggered control for nonstrict‐feedback stochastic nonlinear systems with full state time‐varying constraints

Abstract

Summary This article is concerned with the finite‐time adaptive event‐triggered control for stochastic nonstrict‐feedback nonlinear systems, which are under asymmetric full‐state time‐varying constraints. By utilizing the hyperbolic tangent function as a nonlinear mapping approach, the system with time‐varying constraints is transformed into an unconstrained stochastic nonlinear system. To reduce the communication burden and energy consumption, adaptive event‐triggered control (ETC) is extended to the converted stochastic nonstrict‐feedback system combining the dynamic surface control (DSC) technology and the characteristic of Gaussian function. The designed controller can make the system semi‐globally finite‐time stable in probability (SGFSP), which ensures a fast convergence speed. Moreover, to approximate the unknown nonlinear functions, radial basis function neural networks (RBF NNs) are adopted. The proposed control scheme can be confirmed in the fact that all signals in the closed‐loop system are semi‐globally uniformly bounded in probability, and the tracking error converges rapidly to a small neighborhood of zero in finite time. Two simulation results are given to demonstrate the effectiveness of the proposed controller.