Dynamic event-triggered control design for a saturated nonlinear system based on NN disturbance observer

In the paper, a dynamic event-triggered mechanism based-control strategy is proposed for a class of strict feedback nonlinear systems with input saturation and external disturbances. Then, a novel disturbance observer (DO) is designed to simultaneously estimate the approximation errors of the neural network (NN) and the external disturbances. In particular, the proposed DO does not require neural weight information, which results in higher precision than those of methods involving weight information. Next, an auxiliary system is designed to deal with the input saturation. Furthermore, to reduce the frequency of controller updates, the logarithmic dynamic event-triggered mechanism (LDETM) is designed. By incorporating a control input transmission mechanism into trigger conditions, it effectively mitigates input saturation. Finally, simulations are performed to validate the effectiveness and superiority of the proposed control strategy.