Model predictive control for discrete and continuous timed Petri nets

The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net (TCPN) and T-timed discrete Petri net (T-TDPN) from an initial state to a desired one. Model predictive control (MPC) is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.