Distributed Aperiodic Model Predictive Control for perturbed multi-agent systems

Abstract: In this master thesis we propose an aperiodic formulation of Model Predictive Control for distributed agents with additive bounded disturbances. In this control method, each agent solves an optimal control problem only when certain control performances are not guaranteed according to several triggering rules. This may lead not only to the dramatic reduction of energy expenditures but also to the alleviation of communication loads among them. The problem will be considered to be general and practical; it handles the non-linearity of the each agent which is perturbed by additive bounded disturbances, where the triggering rule is derived from several robust stability criterion. The triggering rule will be addressed for event-based control and self-triggered control, which are the two main different aperiodic control approaches. Finally some simulation results verify our proposal.