Observer-based event-triggered sliding mode control for uncertain descriptor systems with a neural-network event-triggering sampling scheme

This paper investigates the problem of the observer-based event-triggered sliding mode control for uncertain descriptor systems with exogenous disturbance via a new neural-network event-triggering strategy. Firstly, a new neural-network event-triggering communication scheme is proposed in order to decide whether sampled data is to be transmitted for the observer. Secondly, the existence conditions and the stability conditions of the sliding mode dynamics are presented in terms of linear matrix inequalities. An adaptive event-triggered sliding mode controller is designed to ensure the retainability to the sliding surface from beginning almost surely. Thirdly, an effective strategy is developed to avoid the Zeno behavior without decomposing the descriptor system, and the positive lower-bound of the inter-execution time intervals is provided. The existing method can not directly get the positive lower-bound. Thus, the parameters used for scaling corresponding inequalities are introduced to solve the problem. Finally, simulation results are presented to illustrate the effectiveness of the proposed method and are superior to the existing event triggering strategies in the literature. (C) 2019 Published by Elsevier B.V.