Fast finite-time adaptive event-triggered output-feedback control for nonlinear uncertain systems

This article investigates an adaptive fast finite-time control problem for a class of nonlinear uncertain systems. First, to reduce the transmission load, an event-triggering mechanism is introduced into the channel from the controller to the actuator. Second, the observer is employed to estimate the unmeasurable state variables. Third, considering that the nonlinear functions of systems are completely unknown, neural networks are introduced to overcome the obstacles caused by unknown nonlinearities. Finally, an event-triggered adaptive fast finite-time output-feedback control strategy is proposed by means of the fast finite-time stability criterion and backstepping technique. The theoretical analysis illustrates that under the proposed control strategy, all signals in the closed-loop systems converge to a bounded domain within a finite time. Furthermore, the Zeno phenomenon can be avoided effectively. The main innovation is to design the adaptive controller from a new perspective. The validity of results is elaborated by numerical simulation.