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Reliability-aware zonotopic tube-based model predictive control of a drinking water network

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A robust economic model predictive control approach that takes into account the reliability of actuators in a network is presented for the control of a drinking water network in the presence of uncertainties in the forecasted demands required for the predictive control design. The uncertain forecasted demand on the nominal MPC may make the optimization process intractable or, to a lesser extent, degrade the controller performance. Thus, the uncertainty on demand is taken into account and considered unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC is formulated to ensure robust constraint satisfaction, performance, stability as well as recursive feasibility through the formulation of an online tube-based MPC and an accompanying appropriate terminal set. Reliability is then modelled based on Bayesian networks, such that the resulting nonlinear function accommodated in the optimization setup is presented in a pseudo-linear form by means of a linear parameter varying representation, mitigating any additional computational expense thanks to the formulation as a quadratic optimization problem. With the inclusion of a reliability index to the economic dominant cost of the MPC, the network users’ requirements are met whilst ensuring improved reliability, therefore decreasing short and long term operational costs for water utility operators. Capabilities of the designed controller are demonstrated with simulated scenarios on the Barcelona drinking water network.
Rocznik
Strony
197--211
Opis fizyczny
Bibliogr. 30 poz., rys., tab., wykr.
Twórcy
  • Advanced Control Systems, Technical University of Catalonia (UPC), Rambla Sant Nebridi 22, 08222 Terrassa, Spain
  • Advanced Control Systems, Technical University of Catalonia (UPC), Rambla Sant Nebridi 22, 08222 Terrassa, Spain
autor
  • Advanced Control Systems, Technical University of Catalonia (UPC), Rambla Sant Nebridi 22, 08222 Terrassa, Spain
Bibliografia
  • [1] Bemporad, A. and Morari, M. (2007). Robust Model Predictive Control: A Survey, Springer, London, pp. 207–226.
  • [2] Cai, B., Lui, Y., Lui, K. and Chang, Y. (2020). Bayesian Networks for Reliability Engineering, Springer, Singapore.
  • [3] Cembrano, G., Quevedo, J., Puig, V., Pérez, R., Figueras i Jové, J., Verdejo, J., Escaler, I., Ramón, G., Barnet, G., Rodríguez, P. and Casas, M. (2011). PLIO: A generic tool for real-time operational predictive optimal control of water networks, Water Science and Technology: A Journal of the International Association on Water Pollution Research 64(2): 448–459.
  • [4] Chamseddine, A., Theilliol, D., Sadeghzadeh, I., Zhang, Y. and Weber, P. (2014). Optimal reliability design for over-actuated systems based on the MIT rule: Application to an octocopter helicopter testbed, Reliability Engineering & System Safety 132: 196–206.
  • [5] Grosso, J., Ocampo-Martínez, C., Puig, V., Limon, D. and Pereira, M. (2014). Economic MPC for the management of drinking water networks, European Control Conference (ECC), Strasbourg, France, pp. 790–795.
  • [6] Grosso, J., Velarde Rueda, P., Ocampo-Martinez, C., Maestre, J. and Puig, V. (2016). Stochastic model predictive control approaches applied to drinking water networks, Optimal Control Applications and Methods 38(4): 541–558.
  • [7] Haghifam, M.-R. (2015). Application of Bayesian networks in composite power system reliability assessment and reliability-based analysis, IET Generation, Transmission & Distribution 9(13): 1755–1764.
  • [8] Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, Berlin/Heidelberg.
  • [9] Karimi Pour, F., Puig, V. and Cembrano, G. (2019). Economic health-aware LPV-MPC based on system reliability assessment for water transport network, Energies 12(15): 3015.
  • [10] Khelassi, A., Theilliol, D. and Weber, P. (2010). Control design for over-actuated systems based on reliability indicators, UKACC International Conference on Control, Coventry, UK, pp. 1–6.
  • [11] Le, V., Stoica Maniu, C., Alamo, T., Camacho, E. and Dumur, D. (2013). Zonotopes: From Guaranteed State Estimation to Control, Wiley, Hoboken.
  • [12] Löfberg, J. (2003). Min-Max Approaches to Robust Model Predictive Control, PhD thesis, Linköping University, Linköping.
  • [13] Mayne, D., Seron, M. and Raković, S.V. (2005). Robust model predictive control of constrained linear system with bounded disturbances, Automatica 41(2): 219–224.
  • [14] Mejdi, S., Messaoud, A. and Ben Abdennour, R. (2020). Fault tolerant multicontrollers for nonlinear systems: A real validation on a chemical process, International Journal of Applied Mathematics and Computer Science 30(1): 61–74, DOI: 10.34768/amcs-2020-0005.
  • [15] Müller, M., Angeli, D. and Allgöwer, F. (2013). Economic model predictive control with self-tuning terminal cost, European Journal of Control 19(5): 408–416.
  • [16] Philippe, W. and Lionel, J. (2006). Complex system reliability modelling with dynamic object oriented Bayesian networks, IET Generation, Transmission and Distribution 91(2): 149–162.
  • [17] Pour, F.K., Puig, V. and Cembrano, G. (2018). Health-aware LPV-MPC based on system reliability assessment for drinking water networks, IEEE Conference on Control Technology and Applications (CCTA), Copenhagen, Denmark, pp. 187–192.
  • [18] Puig, V., Escobet, T., Sarrate, R. and Quevedo, J. (2015). Fault diagnosis and fault tolerant control in critical infrastructure systems, in E. Kyriakides and M. Polycarpou (Eds), Intelligent Monitoring, Control, and Security of Critical Infrastructure Systems, Studies in Computational Intelligence, Vol. 565, Springer, Berlin/Heidelberg.
  • [19] Raković, S. V., Kerrigan, E., Kouramas, K. and Mayne, D. (2005). Invariant approximations of the minimal robust positively invariant set, IEEE Transactions on Automatic Control 50(3): 406–410.
  • [20] Rausand, M. and Hoyland, A. (2004). System Reliability Theory, 2nd Edn., Wiley, Hoboken.
  • [21] Ray, A. and Caplin, J. (2000). Life extending control of aircraft: Trade-off between flight performance and structural durability, The Aeronautical Journal 104(1039): 397–408.
  • [22] Salazar, J.C., Sanjuan, A., Nejjari, F. and Sarrate, R. (2020). Health-aware and fault-tolerant control of an octorotor UAV system based on actuator reliability, International Journal of Applied Mathematics and Computer Science 30(1): 47–59, DOI: 10.34768/amcs-2020-0004.
  • [23] Salazar, J., Weber, P., Nejjari, F., Sarrate, R. and Theilliol, D. (2017). System reliability aware model predictive control framework, Reliability Engineering & System Safety 167(2): 663–672.
  • [24] Sanchez-Sardi, H., Escobet, T., Puig, V. and Odgaard, P. (2018). Health-aware model predictive control of wind turbines using fatigue prognosis, International Journal of Adaptive Control and Signal Processing 32(4): 614–627.
  • [25] Toro, R., Ocampo-Martínez, C., Logist, F., Impe, J.V. and Puig, V. (2011). Tuning of predictive controllers for drinking water networked systems, IFAC Proceedings Volumes 44(1): 14507–14512.
  • [26] Velarde, P., Maestre, J.M., Ocampo-Martinez, C. and Bordons, C. (2016). Application of robust model predictive control to a renewable hydrogen-based microgrid, European Control Conference (ECC), Aalborg, Denmark, pp. 1209–1214.
  • [27] Wang, Y., Alamo, T., Puig, V. and Cembrano, G. (2018). Economic model predictive control with nonlinear constraint relaxation for the operational management of water distribution networks, Energies 11(4): 1–20.
  • [28] Wang, Y., Puig, V. and Cembrano, G. (2017). Non-linear economic model predictive control of water distribution networks, Journal of Process Control 56: 23–34.
  • [29] Zagórowska, M., Wu, O., Ottewill, J., Reble, M. and Thornhill, N. (2020). A survey of models of degradation for control applications, Annual Reviews in Control 50: 150–173.
  • [30] Zeller, M. and Montrone, F. (2018). Combination of component fault trees and Markov chains to analyze complex, software-controlled systems, 3rd International Conference on System Reliability and Safety (ICSRS), Barcelona, Spain, pp. 13–20.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-734c5364-4aaa-4b13-92fb-b690f6b29c89
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