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Abstrakty
In this study, an observer-based adaptive fuzzy controller for prescribing drug dosage in cancer treatment is presented. In the controller design, it is supposed that only the tumor cells and the concentration of Interleukin-2 (IL-2) are measurable. After defining new state variables for the system, a state observer is employed to estimate the unmeasurable state when the unknown dynamic functions of the system are approximated by the fuzzy systems. The stability of the closed-loop system is demonstrated using the Lyapunov theory. Simulation results show the good performance of the observer-based controller taking into account the unknown system dynamics.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1137--1148
Opis fizyczny
Bibliogr. 37 poz., rys., tab., wykr.
Twórcy
- Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran
autor
- Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran
Bibliografia
- [1] Teles FF, Lemos JM. Cancer therapy optimization based on multiple model adaptive control. Biomed Signal Process Control 2019;48:255–64. https://doi.org/10.1016/j.bspc.2018.09.016.
- [2] Zouari F. Neural network based adaptive back-stepping dynamic surface control of drug dosage regimens in cancer treatment. Neurocomputing 2019;366:248–63. https://doi.org/10.1016/j.neucom.2019.07.096.
- [3] Mohite UL, Patel HG. Optimization assisted Kalman filter for cancer chemotherapy dosage estimation. Artif Intell Med 2021;119:102152. https://doi.org/10.1016/j.artmed.2021.102152.
- [4] Chareyron S, Alamir M. Model-free feedback dosing for a mixed cancer therapy. Biotechnol Prog 2009;25(3):690–700. https://doi.org/10.1002/btpr.114.
- [5] Alamir M. Robust feedback design for combined therapy of cancer. Optim Control Appl Methods 2014;35(1):77–88. https://doi.org/10.1002/oca.2057.
- [6] Nazari M, Babaei N, Nazari M. Nonlinear SDRE based adaptive fuzzy control approach for age-specific drug delivery in mixed chemotherapy and immunotherapy. Biomed Signal Process Control 2021;68:102687. https://doi.org/10.1016/j.bspc.2021.102687.
- [7] Mannelli G, Gallo O. Cancer stem cells hypothesis and stem cells in head and neck cancers. Cancer Treat Rev 2012;38(5):515–39. https://doi.org/10.1016/j.ctrv.2011.11.007.
- [8] Ledzewicz U, Naghnaeian M, Schättler H. An optimal control approach to cancer treatment under immunological activity. Applicationes Mahematicae 2011;38:17–31. https://doi.org/10.4064/am38-1-2.
- [9] Itik M, Salamci MU, Banks SP. Optimal control of drug therapy in cancer treatment. Nonlinear Anal: Theory Methods Appl 2009;71(12):1473–86. https://doi.org/10.1016/j.na.2009.01.214.
- [10] Lobato FS, Machado VS, Steffen V. Determination of an optimal control strategy for drug administration in tumor treatment using multi-objective optimization differential evolution. Comput Methods Programs Biomed 2015;131:51–61. https://doi.org/10.1016/j.cmpb.2016.04.004.
- [11] Wu X, Liu Q, Zhang K, Cheng M, Xin X. Optimal switching control for drug therapy process in cancer chemotherapy. Eur J Control 2018;42:49–58. https://doi.org/10.1016/j.ejcon.2018.02.004.
- [12] Rihan FA, Lakshmanan S, Maurer H. Optimal control of tumour-immune model with time-delay and immunechemotherapy. Appl Math Comput 2019;353:147–65. https://doi.org/10.1016/j.amc.2019.02.002.
- [13] Babaei N, Salamci MU. Mixed therapy in cancer treatment for personalized drug administration using model reference adaptive control. Eur J Control 2019;50:117–37. https://doi.org/10.1016/j.ejcon.2019.03.001.
- [14] Chien TL, Chen CC, Huang CJ. Feedback linearization control and its application to MIMO cancer immunotherapy. IEEE Trans Control Syst Technol 2010;18(4):953–61. https://doi.org/10.1109/TCST.2009.2029089.
- [15] Czako B, Sápi J, Kovács L. Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method. In: 21st International Conference on Intelligent Engineering Systems (INES). https://doi.org/10.1109/INES.2017.8118569.
- [16] Babaei N, Salamci MU. Personalized drug administration for cancer treatment using Model Reference Adaptive Control. J Theor Biol 2015;371:24–44. https://doi.org/10.1016/j.jtbi.2015.01.038.
- [17] Ho HF, Wong YK, Rad AB, Lo WL. State observer based indirect adaptive fuzzy tracking control. Simul Model Pract Theory 2005;13(7):646–63. https://doi.org/10.1016/j.simpat.2005.02.003.
- [18] Zouari F, Ibeas A, Boulkroune A, Cao J, Arefi MM. Neuro-adaptive tracking control of non-integer order systems with Input Nonlinearities and time-varying Output Constraints. Inf Sci 2019;485:170–99. https://doi.org/10.1016/j.ins.2019.01.078.
- [19] Zouari F, Ibeas A, Boulkroune A, Cao J, Arefi MM. Neural network controller design for fractional-order systems with input nonlinearities and asymmetric time-varying Pseudo-state constraints. Chaos Solitons Fractals 2021;144:110742. https://doi.org/10.1016/j.chaos.2021.110742.
- [20] Wang S, Na J, Ren X. RISE-based asymptotic prescribed performance tracking control of nonlinear servo mechanisms. IEEE Trans on Syst, Man, and Cybernetics: Systems 2018;48(12):2359–70. https://doi.org/10.1109/TSMC.2017.2769683.
- [21] Azimi MM, Koofigar HR. Adaptive fuzzy back-stepping controller design for uncertain under-actuated robotic systems. Nonlinear Dyn 2014;79:1457–68. https://doi.org/10.1007/s11071-014-1753-y.
- [22] Shao Cheng Tong, Li Q, Chai T. Fuzzy adaptive control for a class of nonlinear systems. Fuzzy Sets Syst 1999;101(1):31–9.
- [23] Sepasi Sh, Kalat AA, Seyedabadi M. An adaptive back-stepping control for blood glucose regulation in type 1diabetes. Biomed Signal Process Control 2021;66:102498. https://doi.org/10.1016/j.bspc.2018.07.016.
- [24] Shamloo NF, Kalat AA, Chisci L. Indirect adaptive fuzzy control of nonlinear descriptor systems. Eur J Control 2020;51:30–8. https://doi.org/10.1016/j.ejcon.2019.06.007.
- [25] Wang CH, Liu HL, Lin TC. Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems. IEEE Trans Fuzzy Syst 2002;10(1):39–49. https://doi.org/10.1109/91.983277.
- [26] Chen GM, Wang WY, Lee TT, Tao CW. Observer-based direct adaptive fuzzy-neural control for anti-lock braking systems. Int J Fuzzy Syst 2006;8:208–18. https://doi.org/10.30000/ijfs.200612.0005.
- [27] Tong SC, Li YM, Shi P. Observer-based adaptive fuzzy backstepping output feedback control of uncertain MIMO pure-feedback nonlinear systems. IEEE Trans Fuzzy Syst 2012;20(4):771–85. https://doi.org/10.1109/TFUZZ.2012.2183604.
- [28] Gao YF, Sun XM, Wen C, Wang W. Observer-based adaptive NN control for a class of uncertain nonlinear systems with non-symmetric input saturation. IEEE Trans Neural Networks Learn Syst 2017;28(7):1520–30. https://doi.org/10.1109/TNNLS.2016.2529843.
- [29] Shi W. Observer-based adaptive fuzzy prescribed performance control for feedback linearizable MIMO nonlinear systems with unknown control direction. Neurocomputing 2019;368:99–113. https://doi.org/10.1016/j.neucom.2019.08.066.
- [30] Izadbakhsh A, Kalat AA, Khorashadizadeh S. Observer-based adaptive control for HIV infection therapy using the Baskakov operator. Biomed Signal Process Control 2021;65. https://doi.org/10.1016/j.bspc.2020.102343 102343.
- [31] Léchappé V, Cirrincione M, Han QL. Approximation of the disturbance dynamics by Extended State Observer Using an Artificial Delay. IFAC-Papers Online 2020;53(2):4768–73.
- [32] Wang S, Ren X, Na J, Zeng T. Extended-state-observer-based funnel control for nonlinear servomechanisms with prescribed tracking performance. IEEE Trans on Automation Sci And Eng 2017;14(1):98–108. https://doi.org/10.1109/TASE.2016.2618010.
- [33] Kirschner D, Panetta JC. Modeling immunotherapy of the tumor-immune interaction. J Math Biol 1998;37(3):235–52. https://doi.org/10.1007/s002850050127.
- [34] Zadeh L. Fuzzy sets. Inform Control 1965;8(3):338–53. https://doi.org/10.1016/S0019-9958(65)90241-X.
- [35] Wang LX. A course in fuzzy systems and control. Englewood Cliffs: Prentice Hall press; 1996.
- [36] Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1985;SMC-15(1):116–32.
- [37] Slotine JE, Li W. Applied nonlinear control. Englewood Cliffs: Prentice Hall; 1991.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f627ee4b-c561-4578-8a2e-091ca75d4249