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We consider a two-level discrete-time control framework with real-timeconstraints where a central controller issues setpoints to be implemented bylocal controllers. The local controllers implement the setpoints with someapproximation and advertize a prediction of their constraints to the centralcontroller. The local controllers might not be able to implement the setpointexactly, due to prediction errors or because the central controller convexifiesthe problem for tractability. In this paper, we propose to compensate for thesemismatches at the level of the local controller by using a variant of the errordiffusion algorithm. We give conditions under which the minimal (convex)invariant set for the accumulated-error dynamics is bounded, and give acomputational method to construct this set. This can be used to compute a boundon the accumulated error and hence establish convergence of the average errorto zero. We illustrate the approach in the context of real-time control ofelectrical grids.

Real-Time Minimization of Average Error in the Presence of Uncertainty and Convexification of Feasible Sets