Distributed event-trigger model predictive control for stochastic disturbance systems

Abstract

This paper investigates the event-triggered distributed model predictive control (DMPC) problem for multi-agent systems subject to stochastic disturbance. A novel event-triggering mechanism which involves the neighbors’ information and stochastic disturbance to achieve a trade-off between resource usage and model authenticity. In this framework, the stochastic disturbance is established and the distributed event-triggering algorithm is designed, thus achieving coordination control. To lower the number of triggers, a variable considering effects of cost function is introduced to design a event-triggering condition, meanwhile to closer realistic environment, a random function is added to the system’s state as the stochastic disturbance and we show that this event-triggered algorithm can still reduce computing resources and achieve stability when the system contains uncertain disturbances. Finally, numerical simulations are given to illustrate the effectiveness of the proposed control strategy.