A neural dynamic event-triggered mechanism for adaptive sliding mode control of nonlinear networked Markovian jump systems

This paper addresses the adaptive sliding mode control problem for a class of nonlinear networked Markovian jump systems based on event-triggered observers. First, a neural network-based event-triggered mechanism is proposed to integrate with the designed observer. Second, a novel integral sliding surface function is designed, and the corresponding sliding mode dynamics and error dynamics are derived. Third, to address the nonlinear disturbances and malicious attacks within the system, an adaptive compensator is proposed to ensure system security. Furthermore, an observer-based event-triggered sliding mode controller is designed to ensure finite-time convergence to the predefined sliding mode surface. Fourth, based on the determined transition rates, it is proven that the sliding mode dynamics exhibit stochastic stability with a prescribed H∞ performance level in terms of linear matrix inequality method, and it is also shown that Zeno behavior is excluded. Finally, the effectiveness of the control scheme is verified by simulation using a single-link manipulator model.