Economic MPC using a Cyclic Horizon with Application to Networked Control Systems
For control theorists and practitioners, this work extends economic MPC theory by introducing a cyclic horizon that reduces computational burden while maintaining guarantees, but the contribution is incremental over existing economic MPC frameworks.
The paper analyzes an economic MPC scheme with a cyclic horizon, providing performance guarantees and establishing convergence to an optimal subset under dissipativity, with asymptotic stability conditions. Results are illustrated on a Networked Control Systems example.
In this paper, we analyze an economic model predictive control scheme with terminal region and cost, where the system is optimally operated in a certain subset of the state space. The predictive controller operates with a cyclic horizon, which means that starting from an initial length, the horizon is reduced by one at each time step before it is restored to its maximum length again after one cycle. We give performance guarantees for the closed loop, and under a suitable dissipativity condition, establish convergence to the optimal subset. Moreover, we present conditions under which asymptotic stability of the optimal subset can be guaranteed. The results are illustrated in a practical example from the context of Networked Control Systems, which initially motivated the development of the theory presented in this paper.