Exact tracking of nonlinear systems under triggering state signals

This paper concentrates on the event-triggered adaptive asymptotic tracking control problem for a class of nonlinear systems. By using the neural network, the unknown nonlinearities existing in the systems are skilfully addressed. To save communication resources, a state-related event-triggered mechanism (ETM) is devised that only when the triggering condition is violated, the system states are sampled and transmitted to the controllers to update parameters. Because the incorporation of the state-related ETM has caused the state signals to be sporadic, a non-smooth analysis is performed. It is strictly proved that the tracking error asymptotically converges to the origin and the communication burden from the sensor-to-controller (S-C) channels is reduced. Meanwhile, the lower bound of the inter-execution interval is proved to be a constant. Finally, a simulation example confirms the benefits of the developed scheme.