Uniform-performance-constrained fixed-time neuro-control for stochastic nonlinear systems under dynamic event triggering

Abstract

This article studies the implementation of practical fixed-time control in stochastic nonlinear systems, implementing event-triggered communication between the controller and the actuator. Firstly, to accomplish the problem of uniform tracking error performance constraints, the improved performance function is investigated, which is combined with the asymmetric barrier Lyapunov function to achieve fast convergence speed and steady state accuracy. Secondly, the practical fixed-time stability is applied in the stochastic nonlinear closed-loop system, which fuses fixed-time command filtering and improved filtering error compensation mechanisms to avoid computational explosion issue. Furthermore, in order to relieve the communication load on the controller and actuator, adjustable trigger thresholds are designed, based on which dynamic event triggering mechanisms are presented for stochastic nonlinear systems. Additionally, the uncertain system behavior is estimated using RBF neuro-networks and the designed controller avoids the singularity problem. Finally, the proposed controller verifies that the system error converges to zero in a fixed time under the Lyapunov stability theory, and that the system output is within the preset boundaries, realizing boundedness of all signals. The superiority of the control method is further demonstrated by three simulation studies including two practical examples.