Robust fractional order PI control for cardiac output stabilisation

Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

[1]  Julio E. Normey-Rico,et al.  Robust Predictive Control Strategy Applied for Propofol Dosing Using BIS as a Controlled Variable During Anesthesia , 2008, IEEE Transactions on Biomedical Engineering.

[2]  Sigurd Skogestad,et al.  Simple analytic rules for model reduction and PID controller tuning , 2003 .

[3]  Derek A. Linkens,et al.  A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic , 2005, Eng. Appl. Artif. Intell..

[4]  João Miranda Lemos,et al.  Drug Delivery for Neuromuscular Blockade With Supervised Multimodel Adaptive Control , 2009, IEEE Transactions on Control Systems Technology.

[5]  Robin De Keyser,et al.  A novel auto-tuning method for fractional order PI/PD controllers. , 2016, ISA transactions.

[6]  Clara-Mihaela Ionescu,et al.  The role of fractional calculus in modeling biological phenomena: A review , 2017, Commun. Nonlinear Sci. Numer. Simul..

[7]  Igor Podlubny,et al.  Fractional-order systems and PI/sup /spl lambda//D/sup /spl mu//-controllers , 1999 .

[8]  Dinesh Vyas,et al.  Nanorobotic Applications in Medicine: Current Proposals and Designs. , 2014, American journal of robotic surgery.

[9]  Jovan Popović,et al.  Fractional model for pharmacokinetics of high dose methotrexate in children with acute lymphoblastic leukaemia , 2015, Commun. Nonlinear Sci. Numer. Simul..

[10]  Antonio Visioli,et al.  Inversion-based propofol dosing for intravenous induction of hypnosis , 2016, Commun. Nonlinear Sci. Numer. Simul..

[11]  Tore Hägglund,et al.  Automatic tuning of simple regulators with specifications on phase and amplitude margins , 1984, Autom..

[12]  K. Åström,et al.  Revisiting the Ziegler-Nichols step response method for PID control , 2004 .

[13]  Robain De Keyser,et al.  Fractional order control of unstable processes: the magnetic levitation study case , 2015 .

[14]  Kevin L. Moore,et al.  Relay feedback tuning of robust PID controllers with iso-damping property , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Naira Hovakimyan,et al.  Neural Network Adaptive Output Feedback Control for Intensive Care Unit Sedation and Intraoperative Anesthesia , 2007, IEEE Transactions on Neural Networks.

[16]  Guy A. Dumont,et al.  A semi-adaptive control approach to closed-loop medication infusion , 2017 .

[17]  B. Wayne Bequette,et al.  Hemodynamic Control using Direct Model Reference Adaptive Control ­ Experimental Results , 2005, Eur. J. Control.

[18]  YangQuan Chen,et al.  Tuning and auto-tuning of fractional order controllers for industry applications , 2008 .

[19]  Levente Kovcs,et al.  Linear parameter varying (LPV) based robust control of type-I diabetes driven for real patient data , 2017 .

[20]  José António Tenreiro Machado,et al.  Nonlinear dynamics of the patient's response to drug effect during general anesthesia , 2015, Commun. Nonlinear Sci. Numer. Simul..

[21]  Alexander Medvedev,et al.  Mathematical model of non-basal testosterone regulation in the male by pulse modulated feedback , 2009, Autom..

[22]  Sean Mackey,et al.  Multivariate Analysis of Chronic Pain Patients Undergoing Lidocaine Infusions: Increasing Pain Severity and Advancing Age Predict Likelihood of Clinically Meaningful Analgesia , 2007, The Clinical journal of pain.

[23]  Clara M. Ionescu,et al.  Optimized PID control of depth of hypnosis in anesthesia , 2017, Comput. Methods Programs Biomed..