Experiential Assistance Control for a Class of Networked Stochastic Systems: A Multi-Conditions Adaptive Event-Triggered Approach

This paper studies the experiential assistance control problem for a class of networked Markov jump systems by using multi-conditions adaptive event-triggered approach. First, a novel multi-conditions adaptive event-triggered scheme is proposed to greatly reduce the data transmission rate and save network resources more effectively. Second, the experience-based assistance strategy is introduced to design a human-machine hybrid resilient controller, which can effectively reduce the impact of denial of service attacks and spoofing attacks. By combining these techniques with mode-dependentH∞performance index, we derive the desired controller solution conditions. In the end, simulation examples of DC-DC buck converter circuit model are presented to test the effectiveness, availability and advantages of the presented control method.