Periodic Event-Triggered Output-Feedback Control of Stochastic Nonlinear Systems With Flexible Tracking Performance

This study considers the periodic event-triggered prescribed tracking problem for stochastic nonlinear systems, whose output is available only at sampling time. With the limited sampled data of output, a state observer via neural-network approximation is constructed to estimate the unmeasurable states, and then a novel event-triggered mechanism is designed by monitoring the estimated states at sampling time to avoid the continuous communication. The negative deviation effects between the event-triggered controller and the continuous controller are eliminated by introducing two intermediate sampling deviation terms. Moreover, a performance function is introduced to achieve more flexible tracking performance. This function represents different performance behaviors and addresses the issue of redesigning controllers. By determining an allowable sampling period, it is proven that all states of the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error satisfies a flexible prescribed performance. Finally, two examples verify the effectiveness.