Open Access

Multimode approach using Reinforcement Learning and Digital Twin for operating mode management

,  and   
Feb 28, 2025

Cite
Download Cover

Kamach, O., Piétrac, L., Niel, É., 2006. Multi-model approach to discrete events systems: Application to operating mode management. Math. Comput. Simul., 70(5–6), 394–407. DOI: 10.1016/j.matcom.2005.11.008. Search in Google Scholar

Ghosh, S., Samanta, G., De La Sen, M., 2021. Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy. Computation, 9(5), 59. DOI: 10.3390/computation9050059. Search in Google Scholar

Elqabli, Z., Chater, Y., Kamach, O., 2022. Operation modes scheduling: a formal framework for identification of the compatible state, 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), IEEE, Tanger, 1-6. DOI: 10.1109/LOGISTIQUA 55056.2022.9938021. Search in Google Scholar

Kamach, O., Chafik, S., Piétrac, L., 2002. Representation of a reactive system with different models. Proc. IEEE Int. Conf. Syst. Man Cybern.,4, 263–267. DOI: 10.1109/icsmc.2002.1173293. Search in Google Scholar

Kamach, O., Chafik, S., Piétrac, L., Niel, É., 2005. Supervisory uniqueness for operating mode systems. IFAC, 16(1). DOI: 10.3182/20050703-6-cz-1902.01443. Search in Google Scholar

Phan, L.T.X., Chakraborty, S., Lee, I., 2009. Timing analysis of mixed time/event-triggered multi-mode systems. Proc. - Real-Time Syst. Symp., 271–280. DOI: 10.1109/RTSS.2009.24. Search in Google Scholar

Phan, L.T.X., Lee, I., Sokolsky, O., 2010. Compositional analysis of multi-mode systems. Proc. - Euromicro Conf. Real-Time Syst., 197–206. DOI: 10.1109/ECRTS.2010.35. Search in Google Scholar

Faraut, G., Piétrac, L., Niel, É., 2009. Formal approach to multimodal control design: Application to mode switching. IEEE Trans. Ind. Informatics, 5(4), 443–453. DOI: 10.1109/TII.2009.2028135. Search in Google Scholar

Faraut, G., Piétrac, L., Niel, É., 2008. Identification of incompatible states in mode switching. IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, 121–128. DOI: 10.1109/ETFA.2008.4638382. Search in Google Scholar

Kamach, O., Niel, É., Piétrac, L., 2007. Repulsive / Attractive Discrete State Space Sets for Switching Management. Studies in Informatics and Control, 16(1), 83. Search in Google Scholar

El ghadouali, A., Kamach, O., Amami, B., 2012. Static approach for switching between different operating modes. 2nd Int. Conf. Commun. Comput. Control Appl. CCCA. DOI: 10.1109/CCCA.2012.6417878. Search in Google Scholar

Kamach, O., 2004. Approche multi-modèle pour les systèmes à événements discrets: application à la gestion des modes de fonctionnement.” Lyon, INSA, France. Search in Google Scholar

Goossens, J., Richard, P., 2013. Partitioned scheduling of multimode multiprocessor real-time systems with temporal isolation. ACM Int. Conf. Proceeding Ser., 297–305. DOI: 10.1145/2516821.2516822. Search in Google Scholar

Nandola, N.N., Bhartiya, S., 2008. A multiple model approach for predictive control of nonlinear hybrid systems. J. Process Control, 18(2), 131–148. DOI: 10.1016/j.jprocont.2007.07.003. Search in Google Scholar

Abdallah, I., Gehin, L., Ould Bouamama B., 2018. Event driven Hybrid Bond Graph for Hybrid Renewable Energy Systems part I: Modelling and operating mode management. Int. J. Hydrogen Energy, 22088–22107. DOI: 10.1016/j.ijhydene.2017.10.144. Search in Google Scholar

El ghadouali, A., Kamach, O., Amami, B., 2013. Safe switching of discrete events systems: Application to operating mode management. in Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM), IEEE, 1–7. Search in Google Scholar

Azzabi, O., Ben Njima, C., Messaoud, H., 2017a. Modeling a system with hybrid automata and multi - Models, Int. Conf. Control. Autom. Diagnosis, ICCAD, 87–90. DOI: 10.1109/CADIAG.2017.8075636. Azzabi, O., Ben Njima, C., Messaoud, H., 2017b. New approach of diagnosis with hybrid automata, Int. Conf. Control. Autom. Diagnosis (ICCAD), 298–302. DOI: 10.1109/CADIAG.2017.8075674. Search in Google Scholar

Azzabi, O., Ben Njima, C., Messaoud, H., 2016. Diagnosis of a dynamic hybrid system by hybrid timed automata. Int. Conf. Control. Decis. Inf. Technol (CoDIT), 618–623. DOI: 10.1109/CoDIT.2016.7593633. Search in Google Scholar

Yang, Z., Aoki, T., Tan, Y., 2019. Modeling the required indoor temperature change by hybrid automata for detecting thermal problems. Proc. IEEE Pacific Rim Int. Symp. Dependable Comput. PRDC, vol. 2018-December, 135–144. DOI: 10.1109/PRDC.2018.00024. Search in Google Scholar

Abdallah, I., Gehin, L., Ould Bouamama B., 2017. On-line robust graphical diagnoser for hybrid dynamical systems. Eng. Appl. Artif. Intell., 69, 36–49. DOI: 10.1016/j.engappai.2017.12.002. Search in Google Scholar

Mrabet, W., Ladhari, T., 2013. Towards a multi_agent system for manufacturing reconfiguration, 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), 1-6, IEEE. Search in Google Scholar

Dou, C.X., Liu, B., 2014. Hierarchical management and control based on MAS for distribution grid via intelligent mode switching. Int. J. Electr. Power Energy Syst., 54, 352–366. DOI: 10.1016/j.ijepes.2013.07.029. Search in Google Scholar

Borangiu, T., Rəileanu, S., Berger, T., Trentesaux, D., 2015. Switching mode control strategy in manufacturing execution systems. Int. J. Prod. Res., 53(7), 1950–1963. DOI: 10.1080/00207543.2014.935825. Search in Google Scholar

Yu, J., Dou, C., Li, X., 2016. MAS-Based Energy Management Strategies for a Hybrid Energy Generation System. IEEE Trans. Ind. Electron., 63(6), 3756–3764. DOI: 10.1109/TIE.2016.2524411. Search in Google Scholar

Dou, C.X., Wang, W.Q., Hao, D.W., Bin Li, X., 2015. MAS-based solution to energy management strategy of distributed generation system. Int. J. Electr. Power Energy Syst., 69, 354–366. DOI: 10.1016/j.ijepes. 2015.01.026. Search in Google Scholar

An, Y., Wu, N., 2018. Scheduling of crude oil operations for minimizing the usage of simultaneously-charging-and-feeding mode, 15th IEEE Int. Conf. Networking, Sens. Control (ICNSC), 1–6. DOI: 10.1109/ICNSC. 2018.8361347. Search in Google Scholar

An, Y., Wu, N.Q., Hon, C.T., Li, Z.W., 2017. Scheduling of crude oil operations in refinery without sufficient charging tanks using petri nets. Appl. Sci., 7(6). DOI: 10.3390/app7060564. Search in Google Scholar

Outafraout, K., Nait-Sidi-Moh, A., 2017. Modeling and simulation of a multimodal transportation system based on hybrid Petri nets, 14th International Multi-Conference on Systems, Signals & Devices (SSD), IEEE, 413–418. Search in Google Scholar

Ge, Y., Zhu, F., Ling, X., Liu, Q., 2019. Safe Q-Learning Method Based on Constrained Markov Decision Processes. IEEE Access, 7, 165007–165017. DOI: 10.1109/ACCESS.2019.2952651. Search in Google Scholar

Oroojlooy, A., Hajinezhad, D., 2022. A review of cooperative multi-agent deep reinforcement learning. Appl. Intell., 1–81. DOI: 10.1007/s10489-022-04105-y. Search in Google Scholar

Yang, S., Xu, Z., 2022. Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing. Int. J. Prod. Res., 60(16), 4936–4953. DOI: 10.1080/00207543.2021.1943037. Search in Google Scholar

Li, X., Chen, G., Wu, G., Sun, Z., Chen, G., 2023. Research on multi-Agent D2D Communication Resource Allocation Algorithm Based on A2C. Electron.,12(2). DOI: 10.3390/electronics12020360. Search in Google Scholar