Model Predictive Control for Discrete and Continuous Timed Petri Nets  

作　　者：Marwa Taleb Edouard Leclercq Dimitri Lefebvre 

机构地区：[1]Laboratory GREAH, University of Le Havre

出　　处：《International Journal of Automation and computing》2018年第1期25-38,共14页国际自动化与计算杂志（英文版）

基　　金：supported by the region Haute-Normandie Project(Nos.CPER-SER-DDSMRI 2013,2014 and CPER-SER-SEL 2015)

摘　　要：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.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.

关 键 词：Model predictive control timed continuous Petri net （TCPN） T-timed discrete Petri net （T-TDPN） fluidification optimization constant control. 

分 类 号：TP301.1[自动化与计算机技术—计算机系统结构]