Stability and feasibility of the complete hemodynamic and anesthetic regulatory problem - a multivariable predictive control study

Frederik Kussé
Persbericht

Zet pijn schaakmat met automatische toediening van medicatie

 

Tijd heelt alle wonden, het is een van de bekendste Nederlandse spreekwoorden. Het betekent dat we ons geen zorgen moeten maken over de pijn die we nu voelen aangezien die na verloop van tijd wel zal milderen. Klopt dit in de praktijk wel?

 

Heelt tijd alle wonden?

Tijdens een intensieve operatie willen we pijn zo snel mogelijk onder controle hebben. En wat met chronische pijn? Verdovingsmiddelen zijn in deze gevallen de enige realistische optie. Probleem opgelost zou je kunnen denken, maar is dat ook zo? Onderzoek toont aan dat verpleegkundigen pijn onderschatten en de helft van alle patiënten zegt last te hebben van pijn na een operatie. Verder blijft een overdosis door menselijke fout een reëel gevaar. Door het inschatten van pijn te baseren op objectieve metingen en een computer nauwkeurige dosissen te laten berekenen, kunnen we dit onnodig menselijk leed vermijden. Ook de gezondheidskosten gaan in dit geval naar omlaag omdat de gemiddelde hersteltijd korter wordt. Dit eindwerk focust enkel op verdoving tijdens een operatie, maar het ontwikkelde algoritme kan ook gebruikt worden voor bijvoorbeeld diabetespatiënten of tijdens hormoon- en kankerbehandelingen.

 

Automatische toediening van geneesmiddelen: meer dan verdoving alleen

Iedereen van ons heeft al medicatie moeten innemen. Van een verkoudheid in de winter tot een chronische aandoening, medicatie helpt ons te genezen of comfortabeler te leven. Het is echter wel belangrijk dat we de juiste dosis toegediend krijgen. Te weinig en het medicijn mist zijn werking. Te veel en deze overdosis kan nefaste bijwerkingen hebben. Om de juiste dosis te bepalen moet men niet alleen kijken naar de inname of toediening van de medicatie, maar ook naar de opname van het geneesmiddel door het lichaam. Jammer genoeg is deze opname niet enkel voor iedere patiënt verschillend, ook eenzelfde patiënt kan veranderen. Automatische toediening op basis van continue metingen is de enige optie om rekening te houden met deze variaties.

 

De verschillende onderdelen van verdoving tijdens een operatie

Bij verdoving denk je misschien alleen aan de afwezigheid van het voelen van pijn, maar dit is slechts één onderdeel van verdoving tijdens een operatie. Verdoving tijdens een operatie bestaat uit drie onderdelen: het verliezen van bewustzijn, de afwezigheid van het voelen van pijn en tijdelijke spierverlamming. Elk onderdeel vereist aparte medicatie en het is belangrijk dat alle componenten in orde zijn. Je wilt bijvoorbeeld niet dat je wakker wordt tijdens een operatie of dat je been ineens stuiptrekkingen krijgt. Ook kan het zijn dat je niet bij bewustzijn bent en volledig verlamd, maar dat je lichaam wel pijn voelt. Je bent je van niets bewust, maar na de operatie heb je er wel last van. Een bijkomend probleem is dat de verschillende medicijnen elkaar versterken of tegenwerken, afhankelijk van de patiënt. Dit is de reden dat het zelfs voor medisch personeel moeilijk is om een correcte verdoving toe te dienen.

 

Modelgebaseerde voorspellende regeling: van zelfrijdende wagens tot de toediening van medicatie

Welk algoritme wordt nu gebruikt om de verdoving te regelen? Wel, laten we daarvoor beginnen bij het schaakspel. Jij en je tegenstander doen om de beurt zetten, reagerend op elkaars zetten, in de hoop op het einde de andere koning te kunnen slaan. Tijdens het spel probeer je te voorspellen wat de andere zal doen. Op basis hiervan werk je een strategie uit om het spel zo snel mogelijk te winnen. Je gaat dus op basis van een model (de spelregels en kennis over je tegenstander) voorspellen wat er gaat gebeuren. Deze informatie gebruik je om je zetten zo aan te passen (te regelen) dat je het spel wint. Dit is exact wat modelgebaseerde voorspellende regeling doet (zie figuur). Op basis van metingen (bloeddruk, elektrische activiteit doorheen de spieren, …) en een wiskundig model van de patiënt ga je proberen een voorspelling te doen. De regelaar berekent dan de optimale ‘zet’, de dosis die moet worden toegediend. Deze dosis wordt vervolgens gebruikt om de volgende zetten te berekenen. De eerste zet wordt pas uitgevoerd zodra de regelaar een strategie berekend heeft die leidt tot een goede verdoving. Hierna herhaalt het volledige proces zich: meting, voorspelling, optimale dosis, enzovoort. Modelgebaseerde voorspellende regeling is een techniek die al met succes wordt toegepast in zelfrijdende wagens en in dit eindwerk voor het eerst op de volledig verdoving tijdens een operatie.

image 471

Figuur: Modelgebaseerde voorspellende regelaar voor verdoving

 

Hoe veilig is dit algoritme?

Hoe zeker zijn we dat we altijd het schaakspel gaan winnen? Het grote voordeel van modelgebaseerde voorspellende regeling is het voorspellende karakter. Indien het algoritme geen strategie vindt om een goede verdoving te bereiken, wordt er een foutmelding gegeven en kan het medisch personeel ingrijpen. De regelaar kan bovendien rekening houden met veiligheidsbeperkingen, bijvoorbeeld een maximale dosis. In dit eindwerk werd bewezen dat het algoritme veilig is in ‘normale’ omstandigheden, zoals beschreven in andere medische onderzoeken over verdoving. Verder werd een test ontwikkeld waarmee onmiddellijk kan worden nagegaan of het algoritme veilig is in andere omstandigheden of voor andere toepassingen.

 

Conclusie

Automatische toediening van medicatie heeft het potentieel de doeltreffendheid van deze medicatie te verbeteren en gezondheidskosten te verlagen. Het stelt ons in staat risico’s te verminderen en de toediening comfortabeler te maken. In dit eindwerk is aangetoond dat dit voor verdoving mogelijk is. Het ontwikkelde algoritme garandeert de veiligheid van de patiënt en kan ook voor andere toepassingen gebruikt worden.

Bibliografie

Bibliografie:

[1] D. Copot and C. Ionescu, "Models for Nociception Stimulation and Memory Effects in Awake and Aware Healthy Individuals," IEEE Transactions on Biomedical Engineering, vol. 66, no. 3, pp. 718-726, March 2019.

[2] R. Hamill-Ruth and M. Marohn, "Evaluation of Pain in the Critically Ill Patient," Critical Care Clinics, vol. 15, no. 1, pp. 35-54, 1999.

[3] K. Puntillo, D. Stannard, C. Miaskowski, K. Kehrle and S. Gleeson, "Use of a Pain Assessment and Intervention Notation (P.A.I.N) tool in critical Care Nursing Practice: Nurses’ Evaluations," Heart and Lung: the journal of critical care, vol. 31, no. 4, pp. 303-314, 2002.

[4] N. Desbiens, A. Wu, S. Broste, N. Wenger, A. Connors, J. Lynn, Y. Yasui, R.S. Philips and W. Fulkerson, "Pain and Satisfaction with Pain Control in seriously ill hospitalized Adults: findings from the SUPPORT research investigations. For the SUPPORT investigators. Study to understand Prognoses and Preferences for Outcomes and Risks of Treatment," Critical Care Medicine, vol. 24, no.12, pp. 1956-1961, 1996.

[5] P. McArdle, "Intravenous Analgesia,"Critical Care Clinics, vol. 15, no. 1, pp. 89-104, 1999.

[6] M. Neckebroek, T. De Smet and M. Struys, "Automated drug delivery in anesthesia," Curr Anesthesiol Rep, vol. 3, pp. 18-26, 2013.

[7] T. Schnider, C. Minto, S. Shafer, P. Gampus, C. Andresen, D. Goodale and E. Youngs, "The influence of age on Propofol pharmacodynamics," Anaesthesiology, vol. 90, pp. 1502-1516, 1999.

[8] D. Copot, R. De Keyser and C. Ionescu, "Drug Interaction Between Propofol and Remifentanil in Individualised Drug Delivery Systems," 9th IFAC Symposium on Biological and Medical Systems, The International Federation of Automatic Control, Berlin, Germany, Aug.31 – Sept 2, 2015.

[9] G. Dumont, "Feedback control for clinicians," Journal of Clinical Monitoring and Computing, vol. 23, pp. 435-454, 2014.

[10] A. Absalom, R. De Keyser and M. Struys, "Closed loop anaesthesia: are we getting close to finding the holy grail?" Anesthesia and Analgesia, vol. 112, pp. 516-518, 2011.

[11] C. Ionescu, J.T. Machado, R. De Keyser, J. Decruyenaere and M.M.R.F. Struys, "Nonlinear dynamics of the patient’s response to drug effect during general anesthesia," Communications in Nonlinear Science and Numerical Simulation, vol. 20, no. 3, pp. 914-926, 2015.

[12] C. Rocha, T. Mendonca and M. Silva, "Individualizing propofol dosage: a multivariate linear model approach," Journal of Clinical Monitoring and Computing, vol. 28, no. 6, pp. 525-536, 2014.

[13] D. Copot and C. Ionescu, “Drug delivery system for general anesthesia: where are we?” Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), vol. -, pp. 2452-2457, 2014.

[14] F. Padula, C. Ionescu, N. Latronico, M. Paltenghi, A. Visioli and G. Vivacqua, "Optimized PID control of depth of hypnosis in anesthesia," ComputerMethods and Programs in Biomedicine, vol. 144, pp. 21-35, 2017.

[15] L. Kovacs, “Linear parameter varying (LPV) based robust control of type I diabetes driven for real patient data," Knowledge based Systems, vol. 122, pp. 199-213, 2017.

[16] D. Drexler, L. Kovacs, J. Sapi, I. Harmati and Z. Benyo, “Model-based analysis and synthesis of tumor growth under angiogenic inhibition: a case study," IFAC Proceedings, vol. 44, no. 1, pp. 3753-3759, 2011.

[17] B. Kiss, J. Sapi and L. Kovacs, “Imaging method for model-based control of tumor diseases," Proceedings of the IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY), vol. -, pp. 271-275, 2013.

[18] J. K. Popovic, D. Spasic, J. Tovic, J. Kolarovic, R. Malti, I. M. Mitic, S. Pilipovic and T. Atanackovic, “Fractional model for pharmacokinetics of high dose methotrexate in children with acute lymphoblastic leukaemia," Commun Nonlinear Sci Nummer Simulat, vol. 22, pp. 451-471, 2015.

[19] A. Churilov, A. Medvedev and A. Shepeljavyi, “Mathematical model of non basal testosterone regulation in the male by pulse modulated feedback," Automatica, vol. 45, pp. 78-85, 2009.

[20] S. Bibian, C.R. Ries, M. Huzmezan and G.A. Dumont, “Introduction to Automated Drug Delivery in Clinical Anesthesia," European Journal of Control, vol. 11, pp. 535-557, 2005.

[21] C. M. Ionescu, D. Copot, M. Neckebroek and C. I. Muresan, "Anesthesia regulation: Towards completing the picture," 2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Cluj Napoca, 2018, pp. 1-6.

[22] C. Ionescu, I. Nascu and R. De Keyser, “Lessons learned from closed loop control in engineering: towards a multivariable approach regulating depth of anesthesia," J. Clin Monit and Comp, vol. 62, pp. 537-546, 2014.

[23] M.R.F. Struys, H. Vereecke, A. Moerman, EW. Jensen, D. Verhaeghen, N. De Neve, F. Dumortier and E. Mortier, "Ability of the bispectral index, autoregressie modelling with exogenous input-derived auditory evoked potentials and predicted propofol concentrations to measure patient responsiveness during anaesthesia," Anesthesiology, vol. 99, pp. 802-812, 2003.

[24] N. Liu, T. Chazot, A. Genty, A. Landais, A. Restoux, K. McGee, P.A. Laloe, B. Trillat, L. Barvais and M. Fischler, "Closed-loop coadministration of propofol and remifentanil guided by bispectral index: a randomized multicentre study," Anesthesia and Analgesia, vol. 112, no. 3, pp. 546-557, 2011.

[25] G. Dumont, A. Martinez and M. Ansermino, "Robust control of depth of anaesthesia," international Journal of Adaptive Control Signal Processing, vol. 23, pp. 435-454, 2009.

[26] M. Denai, D. A. Linkens, A. J. Asbury, A. D. MacLeod and W. M. Gray, "Self-tuning PID control of atracurium-induced muscle relaxation in surgical patients," IEEE Proceedings, vol. 137, pp. 261-272, 1990.

[27] T. Mendonça, H. M. Lic, P. Lago and S. Esteves, "Hipocrates: a robust system for the control of neuromuscular blockade," Journal Clinical Monitoring and Computing, vol. 18, pp. 265-273, 2004.

[28] C. M. Ionescu , R. D. Keyser, B. C. Torrico, T. D. Smet, M. M. Struys and J. E. Normey-Rico, "Robust Predictive Control Strategy Applied for Propofol Dosing Using BIS as a Controlled Variable During Anesthesia," IEEE Transactions on Biomedical Engineering, vol. 55, no. 9, pp. 2161-2170, Sept. 2008.

[29] K. Soltesz, J-O. Hahn, T. Hägglund, G. Dumont and M. Ansermino, "Individualized closed-loop control of propofol anesthesia: a preliminary study," Biomed Signal Processing Control, in print 2013.

[30] C.S. Nunes, T. Mendonca, L. Antunes, D.A. Ferreira, F. Lobo and F. Amorim, "Modelling drugs‘ pharmacodynamic interaction during general anaesthesia: the choice of pharmacokinetic model," Model Control Biomed Syst, vol. 6, no. 1, pp. 447-452, 2006.

[31] M. Da Silva, T. Mendonca and T. Wigren, "Nonlinear adaptive control of neuromuscular blockade in anaesthesia," 50th IEEE conference on decision and control, joint with European conference control, Orlando, FL, 2011, pp. 47-52.

[32] T. De Smet, M.M. Struys, S. Greenwald, E.P. Mortier and S.L. Shafer, "Estimation of optimal modelling weights for a Bayesian-based closed-loop system for propofol administration using the bispectral index as a controlled variable: a simulation study," Anesth Analg, vol. 105, no. 6, pp. 1629-38, 2007.

[33] M.M. Struys, T. De Smet, S. Greenwald, A.R. Absalom, S. Bingé and E.P. Mortier, "Performance evaluation of two published closed-loop control systems using bispectral index monitoring: a simulation study," Anesthesiology, vol. 100, no. 3, pp. 640-647, 2004.

[34] I. Nascu, C.M. Ionescu and R. De Keyser, "Evaluation of three protocols for automatic DOA regulation using propofol and remifentanil," The 9th IEEE international conference on control and automation 2011, Santiago, Chile, 2011.

[35] P. Dua and E. Pistikopoulos, "Modelling and control of drug delivery systems," Comput Chem Eng , vol. 29, pp. 2290-6, 2005.

[36] I. Nascu, A. Krieger, C.M. Ionescu and E. Pistikopoulous, "Advanced model-based control studies for the induction and maintenance of intravenous anesthesia," IEEE Trans. Biomed. Eng., vol. 62, pp. 832-841, 2015.

[37] X. Jin, C. Kim and G. Dumont, “A semi-adaptive control approach to closed-loop medication infusion," Int J Adapt Control and Signal Proce, vol. 31, pp. 240-354, 2017.

[38] C. Ionescu, D. Copot and R. De Keyser, “Anesthesiologist in the loop and predictive algorithm to maintain hypnosis while mimicking surgical disturbance," IFAC World Congress, Toulouse, France, July 2017, pp. 15080-15085, 2017.

[39] A.R. Absalom, v. Mani, T. De Smet and M.M. Struys, "Pharmacokinetic models for propofol – defining and illuminating the devil in the detail," Br J Anaesth, vol. 103, pp. 26-37, 2009.

[40] D. Takizawa, K. Nishikawa, E. Sato, H. Hiraoka, K. Yamamoto, S. Saito, R. Horiuchi and F. Goto, “A dopamine infusion decreases propofol concentration during epidural blockade under general anesthesia," Canadian Anesth J, vol. 52, pp. 463-466, 2005.

[41] D. Copot, F. Kussé, Ma. Ghita, Mi. Ghita, M. Neckebroek and A. Maxim, “Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia,” 15th IEEE International Conference on Control & Automation, 16-19 July, Edinburgh, Scotland, accepted for publication.

[42] C.C. Palerm and B.W. Bequette, “Hemodynamic control using direct model reference adaptive control – experimental results," European Journal of Control, vol. 11, no. 6, pp. 558-571, 2005.

[43] B. Geerts, L. Aarts and J. Jansen, "Methods in pharmacology: measurement of cardiac output," Br J Clin Pharmacol, vol. 71, no. 3, pp. 316-330, Mar 2011.

[44] W. Bradshaw, "The importance of mean arterial pressure as a patient assessment tool: in haemodialysis and acute care, " Australian nursing journal, vol. 20, no. 2, pp. 26-39, 2012.

[45] C. Ionescu, R. Hodrea and R. De Keyser, “Variable time-delay estimation for anesthesia control during intensive care," IEEE Transactions on biomedical engineering, vol. 58, no. 2, pp. 363-369, 2011.

[46] Q. Hui, W. Haddad, V. Chellaboina and T. Hayakawa, “Adaptive control of mammillary drug delivery systems with actuator amplitude constraints and system time delays," European Journal of Control, vol. 11, pp. 586-600, 2005.

[47] F. Padula, C. Ionescu, N. Latronico, M. Paltenghi, A. Visioli and G. Vivacqua, "Inversion-based propofol dosing for intravenous induction of hypnosis," Communications in Nonlinear Science and Numerical Simulation, vol. 39, pp. 481-494, 2016.

[48] D. Copot, A. Chevalier, C. M. Ionescu and R. De Keyser, "A two compartment fractional derivative model for Propofol diffusion in anesthesia," 2013 IEEE International Conference on Control Applications (CCA),Hyderabad, 2013, pp. 264-269.

[49] C. Minto, T. Schnider, T.G. Short, K.M. Gregg, A. Gentilini and S.L. Shafer, "Response surface model for anesthetic drug interactions," Anesthesiology, vol. 92, pp. 1603-1616, 2000.

[50] T.W. Schnider, C.F Minto, P.L. Gambus, C. Andresen, D.B. Goodale, S.L. Shafer and E.J. Youngs, “The Influence of Method of Administration and Covariates on the Pharmacokinetics of Propofol in Adult Volunteers," Anesthesiology, vol. 88, no. 5, pp. 1170-1182, 1998.

[51] C.F. Minto, T.W. Schnider, T.D. Egan, E. Youngs, H.J.M. Lemmens, P. L. Gambus, V. Billard, J. F. Hoke, K.H.P. More, D. J. Hermann, K.T. Muir, J.W. Mandema and S.L. Shafer, “Influence of Age and Gender on the Pharmacokinetics and Pharmacodynamics of Remifentanil: I. Model Development," Anesthesiology, vol. 86, no. 1, pp. 10-23, 1997.

[52] C.F. Minto, M. White, N. Morton and G.N. Kenny, "Pharmacokinetics and pharmacodynamics of remifentanil, II model application," Anesthesiology, vol. 86, pp. 24-33, 1997.

[53] E. Gepts, K. Jonckheer, V. Maes, W. Sonck and F. Camu, "Disposition kinetics of propofol during alfentanil anesthesia," Anaesthesia, vol. 43, pp. 8-13.

[54] S. Shafer and J. Varvel, "Pharmacokinetics, pharmacodynamics and rational opioid selection," Anesthesiology, vol. 74, pp. 53-63.

[55] T. Kirkpatrick, I. Cockshott, E. Douglas and W. Nimmo, "Pharmacokinetics of propofol (diprivan) in elderly patients," Anesthesia, vol. 60, pp. 146-150.

[56] C. Rocha, T. Mendonça and M.E Silva, “Modelling neuromuscular blockade: a stochastic approach based on clinical data," Mathematical and Computer Modelling of Dynamical Systems, vol. 19, no. 6, pp. 540-556, 2013.

[57] K. Leslie and T.G. Short, "Low bispectral index values and death: the unresolved causality dilemma," Anesth. Analg, vol. 113, pp. 660-663, 2011.

[58] C.M. Ionescu, "Modelling Drug Effect Using Fractional Calculus," Fractional Calculus, Ed. Roy Abi Zeid Daou & Xavier Moreau, Nova Science Publishers, 2015, pp. 243-258. Print.

[59] C.M. Ionescu, D. Copot and R. De Keyser, "Three compartmental models for propofol diffusion during general anesthesia," Discontinuity, Nonlinearity and Complexity, vol. 2, no. 4, pp. 357-368, 2013.

[60] J.J. Trujillo, "Fractional models: sub and super-diffusives, and undifferentiable solutions," Innovation in Engineering Computational Technology,pp. 371-401, 2006.

[61] I. Petras and R. L. Magin, "Simulation of drug uptake in a two compartmental fractional model for a biological system," J. Commun Nonlinear Sci Nummer Simulat, vol. 16, pp. 4588-4595, 2011.

[62] M. Weiss, “Comparison of distributed and compartmental models of drug disposition: assessment of tissue uptake kinetics," Journal of Pharmacokinetics and Pharmacodynamics, vol. 43, pp. 505-512, 2016.

[63] R. De Keyser, C. Ionescu, C. Festila, "A one-step procedure for frequency response estimation based on switch-mode transfer function analyser," Proc. IEEE-CDC Conf on Decision and Control joint with ECC – European Control Conference, December 12-15, Orlando, Florida, pp. 1189-1194.

[64] S. Milne, G. Kenny and S. Schraag, "Propofol sparing effect of remifentanil using closed loop anaesthesia," British Journal of Anaesthesia, vol. 90, no. 5, pp. 623-629, 2003.

[65] D. Copot and C. M. Ionescu, “A fractional order impedance individualised model of nociceptor stimulation," 9th IFAC Symposium on Robust Control Design ROCOND, Florianópolis, 2018, pp. 416-421.

[66] J.C. Scott and D.R. Stanski, "Decreased fentanyl and alfentanil dose requirements with age. A simultaneous pharmacokinetic and pharmacodynamic evaluation," J. Pharmacol Exp Ther, vol. 240, pp. 159-166, 1987.

[67] N. Yang, M.Z. Zuo, Y. Yue, Y. Wang, Y. Shi and X.N. Zhang, “Comparison of C50 for Propofol-Remifentanil Target Controlled Infusion and Bispectral Index at Loss of Consciousness and Response to painful Stimulus in Elderly and Young Patients," Chinese Medical Journal, vol. 128, pp. 1994-1999, 2015.

[68] T.W. Bouillon, J. Bruhn, L. Radulescu, C. Andresen, T.J. Shafer, C. Cohane and S.L Shafer, “Pharmacodynamic interaction between Propofol and Remifentanil regarding hypnosis, tolerance of laryngoscopy, bispectral index, and electroencephalographic approximate entropy," Anesthesiology, vol. 100, no. 6, pp. 1353-1372, 2004.

[69] M.M. Silva, T. Wigren and T. Mendonça, “Nonlinear identification of a minimal neuromuscular blockade model in anesthesia," IEEE Trans Control System Technologies, vol. 20, pp. 181-188, 2012.

[70] H. Alonso, T. Mendonça and P. Rocha, “A hybrid method for parameter estimation and its application to biomedical systems," Computer Methods and Programs in Biomedicine, vol. 89, pp. 112-122, 2008.

[71] C.N. Sessler, M.S. Gosnell, M.J. Grap, G.M. Brophy, P.V. O’Neal, K.A. Keane, E.P. Tesore and R.K. Elswick, "The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients ," Am J Respir Crit Care Med, vol. 166, pp. 1338-1344, 2002.

[72] Zh. Zhusubaliyev, A. Medvedev and M. Silva, “Bifurcation Analysis of PID Controlled Neuromuscular Blockade in Closed-loop Anesthesia," Journal of Process Control, Vol. 25, pp. 152-163, 2015.

[73] J.F. Standing, G.B. Hammer, W.J. Sam and D.R. Drover, “Pharmacokinetic-pharmacodynamic modelling of the hypotensive effect of Remifentanil in infants undergoing cranioplasty," Pediatric Anesthesia, vol. 20, no. 1, pp. 7-18, 2010.

[74] A.M. Kirit and S. Rajaguru, "Adopting pade approximation for first order plus dead time models for blending process," International Journal of Engineering & Technology, vol. 7, pp. 2800-2805, 2018.

[75] C. Yu, R. J. Roy, H. Kaufman and B. W. Bequette, "Multiple-model adaptive predictive control of mean arterial pressure and cardiac output," IEEE Transactions on Biomedical Engineering, vol. 39, no. 8, pp. 765-778, 1992.

[76] S. H. Lehnigk, "On the Hurwitz Matrix, " Zeitschrift für Angewandte Mathematik und Physik, vol. 21, no. 3, pp. 498-500, 1970.

[77] J.B. Rawlings and D.Q Mayne, Model predictive control: Theory and design, Madison, Wisconsin: Nob Hill Pub. 2009.

[78] F. Allgöwer, R. Findeisen and Z.K. Nagy, "Nonlinear model predictive control: From theory to application," J. Chin. Inst. Chem. Engrs., vol. 35, pp. 299-315, 2004.

[79] R. Findeisen and F. Allgöwer. "An introduction to nonlinear model predictive control," 21st Benelux Meeting on Systems and Control, 2002, pp. 1-23.

[80] H. Chen and F. Allgöwer. "A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability," Automatica, vol. 34, no. 10, pp. 1205-1218, 1998.

[81] H. Michalska and D. Q. Mayne, "Robust receding horizon control of constrained nonlinear systems," IEEE Transactions on Automatic Control, vol. 38, no. 11, pp. 1623-1633, Nov. 1993.

[82] E.J. Haseltine and J.B. Rawlings , "Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation," Ind. Eng. Chem. Res., vol. 44, no. 8, pp. 2451-2460, 2005.

[83] S. Yu, M. Reble, H. Chen and F. Allgöwer, "Inherent robustness properties of quasi-infinite horizon nonlinear model predictive control," Automatica, vol. 50, no. 9, pp. 2269-2280, 2014.

[84] "nlmpcmove," https://nl.mathworks.com/help/mpc/ref/nlmpc.nlmpcmove.html. Accessed: 11-05-2019.

[85] L. Grüne and J. Pannek, Nonlinear Model Predictive Control. Springer, Cham, 2017.

[86] "Configure Optimization Solver for Nonlinear MPC," https://nl.mathworks.com/help/mpc/ug/configure-optimization-solver for-nonlinear-mpc.html. Accessed: 11-05-2019.

[87] "Local vs. Global Optima," https://nl.mathworks.com/help/optim/ug/local vs-global-optima.html. Accessed: 11-05-2019.

[88] G.V. Reklaitis, A. Ravindran and K.M. Ragsdell, Engineering optimization: Methods and applications, New York: Wiley, 1983.

[89] S.J. Wright, "Efficient convex optimization for linear MPC," in Handbook of Model Predictive Control. Birkhäuser, Cham, pp. 287-303, 2019.

[90] M. Neckebroek, C.-M. Ionescu, K. van Amsterdam, T. De Smet, P. De Baets, J. Decruyenaere, R. De Keyser and M. Struys. "A comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: an observational, open-label pilot study," Journal of Clinical Monitoring and Computing, 2019.

[91] T. Mendonça, J. M. Lemos, H. Magalhaes, P. Rocha and S. Esteves, "Drug Delivery for Neuromuscular Blockade With Supervised Multimodel Adaptive Control," IEEE Transactions on Control Systems Technology, vol. 17, no. 6, pp. 1237-1244, Nov. 2009.

[92] M.M. Struys, M.J. Coppens, N. De Neve, E.P. Mortier, A.G. Doufas, J.F. Van Bocxlaer and S.L. Shafer, "Influence of administration rate on propofol plasma-effect site equilibration," Anesthesiology, vol. 107, pp. 386-96, 2007.

[93] K. Masui, M. Kira, T. Kazama, S. Hagihira, E.P. Mortier and M.M.R.F. Struys, "Early phase pharmacokinetics but not pharmacodynamics are influenced by propofol infusion rate," Anesthesiology, vol. 111, pp. 805-817, 2001.

Universiteit of Hogeschool
Master of Science in Electromechanical Engineering: Control Engineering and Automation
Publicatiejaar
2019
Promotor(en)
prof. dr. ir. Clara-Mihaela Ionescu
Kernwoorden
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