Agent-based system for continuous control and its application to activated sludge process

• Autorzy: Pośpiech Jakub , Nocoń Witold , Stebel Krzysztof

Identyfikatory

DOI

10.24425/bpasts.2024.150114

• Języki publikacji: EN

Abstrakty

EN

This paper presents a concept of architecture and ontology layouts for the development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify the development of agent-based systems and improve their maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms and actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in the application of dissolved oxygen (DO) concentration control in a laboratory-activated sludge setup with a biological reactor. For that application, MAS incorporates agent-based controllers from the boundary-based predictive controllers (BBPC) family. Presented experiments prove the flexibility, resilience, and online reconfiguration ability of the proposed multiagent system.

Słowa kluczowe

EN

adaptive control; agent-based control; control of dissolved oxygen; model-based predictive control; multiagent systems; practical validation

PL

sterowanie adaptacyjne; sterowanie oparte na agentach; sterowanie tlenem rozpuszczonym; sterowanie predykcyjne oparte na modelach; systemy wieloagentowe; walidacja praktyczna

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 4
• Strony: art. no. e150114
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Pośpiech Jakub

• Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-2425-4799

autor

Nocoń Witold

• Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-4785-958X

autor

Stebel Krzysztof

• Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

• https://orcid.org/0000-0003-2912-6742

Bibliografia

• [1] S. Karnouskos, P. Leitao, L. Ribeiro, and A.W. Colombo, “Industrial Agents as a Key Enabler for Realizing Industrial Cyber-Physical Systems: Multiagent Systems Entering Industry 4.0,” IEEE Ind. Electron. Mag., vol. 14, no. 3, pp. 18–32, Sept. 2020, doi: 10.1109/MIE.2019.2962225.
• [2] L. Ding, Q. Han, X. Ge, and X. Zhang, “An Overview of Recent Advances in Event-Triggered Consensus of Multiagent Systems,” IEEE Trans. Cybern., vol. 48, no. 4, pp. 1110–1123, April 2018, doi: 10.1109/TCYB.2017.2771560.
• [3] D. Huang, Y. Chen, D. Meng, and P. Sun, “Adaptive Iterative Learning Control for High-Speed Train: A Multi-Agent Approach,” IEEE Trans. Syst., Man Cybern.-Syst., vol. 51, no. 7, pp. 4067–4077, July 2021, doi: 10.1109/TSMC.2019.2931289.
• [4] P. Qaderi-Baban, M.B. Menhaj, M. Dosaranian-Moghadam and A. Fakharian, “Intelligent multi-agent system for DC microgrid energy coordination control,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 4, pp. 741–748, Aug. 2019, doi: 10.24425/bpasts.2019.130183.
• [5] D. Seredyński et al., “Agent-based approach to the design of a multimodal interface for cyber-security event visualisation control,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1187–1205, Oct. 2020, doi: 10.24425/bpasts.2020.134662.
• [6] S. Bussmann and K. Schild, “An agent-based approach to the control of flexible production systems,” in ETFA 2001. 8th International Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.01TH8597), Antibes-Juan les Pins, France, 2001, vol.2, pp. 481–488, doi: 10.1109/ETFA.2001.997722.
• [7] T. Arai, Y. Aiyama, Y. Maeda, M. Sugi, and J. Ota, “Agile Assembly System by ‘Plug and Produce,” CIRP Annals, vol. 49, no. 1, pp. 1–4, Jan. 2000, doi: 10.1016/S0007-8506(07)62883-2.
• [8] M. Metzger and G. Polakow, “A Survey on Applications of Agent Technology in Industrial Process Control,” IEEE Trans. Ind. Inform., vol. 7, no. 4, pp. 570–581, Nov. 2011, doi: 10.1109/TII.2011.2166781.
• [9] A.J.N. van Breemen and T.J.A. de Vries, “Design and implementation of a room thermostat using an agent-based approach”, Control Eng. Practice, vol. 9, no. 3, pp. 233–248, 2001, doi: 10.1016/S0967-0661(00)00111-8.
• [10] M. Francisco, Y. Mezquita, S. Revollar, P. Vega, and Juan F. De Paz, “Multi-agent distributed model predictive control with fuzzy negotiation,” Expert Syst. Appl., vol. 129, pp. 68–83, Sept. 2019, doi: 10.1016/j.eswa.2019.03.056.
• [11] Y.N. Guo, J. Cheng, D. Gong, and J. Zhang, “ANovel Multi-agent Based Complex Process Control System and Its Application,” in Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, pp. 319–330. doi: 10.1007/978-3-540-37256-1_39.
• [12] S. Videau, C. Bernon, P. Glize, and J.L. Uribelarrea, “Controlling Bioprocesses Using Cooperative Self-organizing Agents,” in Advances on Practical Applications of Agents and Multiagent Systems, Berlin, Germany, 2011, pp. 141–150. doi: 10.1007/978-3-642-19875-5_19.
• [13] S. Karnouskos and P. Leitão, “Key Contributing Factors to the Acceptance of Agents in Industrial Environments,” IEEE Trans. Ind. Inform., vol. 13, no. 2, pp. 696–703, Apr. 2017, doi: 10.1109/TII.2016.2607148.
• [14] V. Mařík and J. Lažanský, “Industrial applications of agent technologies”, Control Eng. Practice, vol. 15, no. 11, pp. 1364–1380, 2007, doi: 10.1016/j.conengprac.2006.10.001.
• [15] G. Polaków, “JADE environment performance evaluation for agent-based continuous process control algorithm,” in 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, 2016, pp. 571–576, doi: 10.1109/MMAR.2016.7575199.
• [16] G. Polaków, P. Laszczyk, and M. Metzger, “Agent-based approach to model-based dynamically reconfigurable control algorithm,” in 2015 20th International Conference on Process Control (PC), Strbske Pleso, Slovakia, 2015, pp. 375–380, doi: 10.1109/PC.2015.7169992.
• [17] D. Choiński, W. Nocoń, and M. Metzger, “Multi-Agent System for Hierarchical Control with Self-organising Database,” in Agent and Multi-Agent Systems: Technologies and Applications-KES-AMSTA 2007, Wroclaw, Poland, 2007, pp. 655–664. doi: 10.1007/978-3-540-72830-6_68.
• [18] M. Senik and D. Choiński, “Distributed control systems integration and management with an ontology-based multi-agent system,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no 5, pp. 613–620, Oct. 2018, doi: 10.24425/bpas.2018.124277.
• [19] L. Ribeiro, S. Karnouskos, P. Leitão, J. Barbosa, and M. Hochwallner, “Performance Assessment Of The Integration Between Industrial Agents And Low-Level Automation Functions,” in 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, Portugal, 2018, pp. 121–126, doi: 10.1109/INDIN.2018.8471927.
• [20] G. Tchobanoglous, F.L. Burton, and H.D. Stensel, Wastewater Engineering: Treatment and Reuse. New York, NY, USA: McGraw-Hill, 2003.
• [21] P. Łaszczyk, “Predictive functional control of dissolved oxygen with online estimation of oxygene uptake rate.” in Proceedings of the 20th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 2015, pp. 602–607. doi: 10.1109/MMAR.2015.7283943.
• [22] R. Piotrowski, H. Sawicki, and K. Żuk, “Novel hierarchical nonlinear control algorithm to improve dissolved oxygen control in biological WWTP,” J. Process Control, vol 105, pp. 78–87, Sep. 2021, doi: 10.1016/j.jprocont.2021.07.009.
• [23] R. Piotrowski, M.A. Brdys, K. Konarczak, K. Duzinkiewicz, and W. Chotkowski, “Hierarchical dissolved oxygen control for activated sludge processes,” Control Eng. Practice, vol. 16, no. 1, pp. 114–131, Jan. 2008, doi: 10.1016/j.conengprac.2007.04.005.
• [24] K. Stebel, J. Pospiech, W. Nocon, J. Czeczot and P. Skupin, “Boundary-Based Predictive Controller and its Application to Control of Dissolved Oxygen Concentration in Activated Sludge Bioreactor,” IEEE Trans. Ind. Electron., vol. 69, no. 10, pp. 10541–10551, Oct. 2022, doi: 10.1109/TIE.2021.3123629.
• [25] M. Sànchez,U. Cortés, J. Lafuente, I.R. Roda and M. Poch, “DAIDEPUR: an integrated and distributed architecture for wastewater treatment plants supervision,” Artif. Intell. Eng., vol. 10, no. 3, pp. 275–285, Aug. 1996, doi: 10.1016/0954-1810(96)00004-0.
• [26] J. Pospiech, “Multi-Agent System for Closed Loop Model-Based Control of Dissolved Oxygen Concentration,” in 2021 25th International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, 2021, pp. 145–149, doi: 10.1109/MMAR49549.2021.9528445.
• [27] M. Czyżniewski, R. Łangowski, and R. Piotrowski, “Respiration rate estimation using non-linear observers in application to wastewater treatment plant,” J. Process Control, vol 124, pp. 70–82, Apr. 2023, doi: 10.1016/j.jprocont.2023.02.008.