Distributed model predictive control for multiagent systems with improved consistency

This paper addresses an improved distributed model predictive control (DMPC) scheme for multiagentsystems with an attempt to improving its consistency. The deviation between what an agent is actually doing and whatits neighbors believe that agent is doing is penalized in the cost function of each agent. At each sampling instant thecompatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach,the performance cost is utilized as the Lyapunov function to prove closed-looped stability. The closed-loop stability isguaranteed if the weight matrix for deviation in the cost function are sufficiently large. The proposed distributed controlscheme is formulated as quadratic programming with quadratic constraints. A numerical example is given to illustrate theeffectiveness of the proposed scheme.