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Tytuł artykułu

Degradation tolerant optimal control design for stochastic linear systems

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Safety-critical and mission-critical systems are often sensitive to functional degradation at the system or component level. Such degradation dynamics are often dependent on system usage (or control input), and may lead to significant losses and a potential system failure. Therefore, it becomes imperative to develop control designs that are able to ensure system stability and performance whilst mitigating the effects of incipient degradation by modulating the control input appropriately. In this context, this paper proposes a novel approach based on an optimal control theory framework wherein the degradation state of the system is considered in the augmented system model and estimated using sensor measurements. Further, it is incorporated within the optimal control paradigm leading to a control law that results in deceleration of the degradation rate at the cost of system performance whilst ensuring system stability. To that end, the speed of degradation and the state of the system in discrete time are considered to develop a linear quadratic tracker (LQT) and regulator (LQR) over a finite horizon in a mathematically rigorous manner. Simulation studies are performed to assess the proposed approach.
Rocznik
Strony
5--14
Opis fizyczny
Bibliogr. 24 poz., rys., wykr.
Twórcy
autor
  • CRAN, UMR 7039, CNRS, University of Lorraine, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France
  • CRAN, UMR 7039, CNRS, University of Lorraine, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France
  • CRAN, UMR 7039, CNRS, University of Lorraine, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France
Bibliografia
  • [1] Åström, K.J. and Wittenmark, B. (1995). Adaptive Control, Dover Publications, Mineola.
  • [2] Athans, M. (1971). The role and use of the stochastic linear-quadratic-Gaussian problem in control system design, IEEE Transactions on Automatic Control 16(6): 529-552.
  • [3] Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M. and Schröder, J. (2006). Diagnosis and Fault-Tolerant Control, Vol. 2, Springer, Berlin.
  • [4] Bole, B.M., Brown, D.W., Pei, H.-L., Goebel, K., Tang, L. and Vachtsevanos, G. (2010). Fault adaptive control of overactuated systems using prognostic estimation, Annual Conference of the PHM Society, Portland, USA, Vol. 2.
  • [5] Bressel, M., Hilairet, M., Hissel, D. and Bouamama, B.O. (2016). Extended Kalman filter for prognostic of proton exchange membrane fuel cell, Applied Energy 164: 220-227.
  • [6] Brown, D., Bole, B. and Vachtsevanos, G. (2010). A prognostics enhanced reconfigurable control architecture, 18th Mediterranean Conference on Control and Automation, MED’10, Marrakech, Morocco, pp. 1061-1066.
  • [7] Brown, D.W., Georgoulas, G., Bole, B., Pei, H.-L., Orchard, M., Tang, L., Saha, B., Saxena, A., Goebel, K. and Vachtsevanos, G. (2021). Prognostics enhanced reconfigurable control of electro-mechanical actuators, Annual Conference of the PHM Society, Nashville, USA.
  • [8] Durrant-Whyte, H.F. (2006). Introduction to estimation and the Kalman filter, Australian Centre for Field Robotics, Sydney, https://www.dynsyslab.org/archive/RecEst2010/www.idsc.ethz.ch/Courses/Archives/Recursive_Estimation/recursive_filtering_2010/EstimationNotes.pdf.
  • [9] Félix, M.S., Martinez, J.J., Bérenguer, C. and Tidriri, K. (2022). Degradation analysis in a controlled flexible drive train subject to torsional phenomena under different wind speed conditions, 2022 10th International Conference on Systems and Control (ICSC), Marseille, France, pp. 90-95.
  • [10] Hamdi, H., Rodrigues, M., Rabaoui, B. and Benhadji Braiek, N. (2021). A fault estimation and fault-tolerant control based sliding mode observer for LPV descriptor systems with time delay, International Journal of Applied Mathematics and Computer Science 31(2): 247-258, DOI: 10.34768/amcs-2021-0017.
  • [11] Jha, M.S., Bressel, M., Ould-Bouamama, B. and Dauphin-Tanguy, G. (2016). Particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond graph framework, Computers & Chemical Engineering 95: 216-230.
  • [12] Kanso, S., Jha, M.S., Galeotta, M. and Theilliol, D. (2022). Remaining useful life prediction with uncertainty quantification of liquid propulsion rocket engine combustion chamber, IFAC-PapersOnLine 55(6): 96-101.
  • [13] Kanso, S., Jha, M.S. and Theilliol, D. (2023). Degradation tolerant optimal control design for linear discrete-times systems, International Conference on Diagnostics of Processes and Systems, Chmielno, Poland, pp. 398-409.
  • [14] Knight, J.C. (2002). Safety critical systems: Challenges and directions, Proceedings of the 24th International Conference on Software Engineering, Orlando, USA, pp. 547-550.
  • [15] Lewis, F.L., Vrabie, D. and Syrmos, V.L. (2012). Optimal Control, Wiley, Hoboken.
  • [16] Lipiec, B., Mrugalski, M., Witczak, M. and Stetter, R. (2022). Towards a health-aware fault tolerant control of complex systems: A vehicle fleet case, International Journal of Applied Mathematics and Computer Science 32(4): 619-634, DOI: 10.34768/amcs-2022-0043.
  • [17] Noura, H., Theilliol, D., Ponsart, J.-C. and Chamseddine, A. (2009). Fault-Tolerant Control Systems: Design and Practical Applications, Springer, Berlin.
  • [18] Obando, D.R., Martinez, J.J. and Bérenguer, C. (2021). Deterioration estimation for predicting and controlling RUL of a friction drive system, ISA Transactions 113: 97-110.
  • [19] Pour, F.K., Theilliol, D., Puig, V. and Cembrano, G. (2021). Health-aware control design based on remaining useful life estimation for autonomous racing vehicle, ISA Transactions 113: 196-209.
  • [20] Rodriguez, D.J., Martinez, J.J. and Berenguer, C. (2018). An architecture for controlling the remaining useful lifetime of a friction drive system, IFAC-PapersOnLine 51(24): 861-866.
  • [21] Salazar, J.C., Weber, P., Nejjari, F., Sarrate, R. and Theilliol, D. (2017). System reliability aware model predictive control framework, Reliability Engineering and System Safety 167: 663-672.
  • [22] Söderström, T. (2002). Discrete-Time Stochastic Systems: Estimation and Control, Springer, Berlin.
  • [23] Stengel, R.F. (1986). Optimal Control and Estimation, Dover Publications, Mineola.
  • [24] Zhang, Z., Yang, Z., Liu, S., Chen, S. and Zhang, X. (2022). A multi-model based adaptive reconfiguration control scheme for an electro-hydraulic position servo system, International Journal of Applied Mathematics and Computer Science 32(2): 185-196, DOI: 10.34768/amcs-2022-0014.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-39920545-3c12-4431-8b43-bda3ea905138
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