Predictive Control Over a Dynamical Token Bucket Network

Abstract

In systems subject to communication constraints, carefully scheduling the transmission of updated control values can greatly improve the trade-off between communication effort and control performance. In this letter, we consider a dynamical communication network together with a predictive controller that has explicit knowledge thereof. In the usual fashion of rollout strategies in networked control, the controller both schedules transmissions and computes the corresponding control values. Using tools from model predictive control, stability of the considered setup for nonlinear, constrained plants is established. The special case of linear plants is investigated in more detail. Furthermore, strict performance improvement over a feasible baseline control is established in case that the plant is additionally unconstrained. By means of a numerical example, effectiveness of the considered approach is demonstrated.