Adaptive fractional-order blood glucose regulator based on high-order sliding mode observer.

Type I diabetes is described by the destruction of the insulin-producing beta-cells in the pancreas. Hence, exogenous insulin administration is necessary for Type I diabetes patients. In this study, to estimate the states that are not directly available from the Bergman minimal model a high-order sliding mode observer is proposed. Then fractional calculus is combined with sliding mode control (SMC) for blood glucose regulation to create more robustness performance and make more degree of freedom and flexibility for the proposed method. Then an adaptive fractional-order SMC is proposed. The adaptive SMC protect controller against disturbance and uncertainties while the fractional calculus provides robust performance. Numerical simulation verifies that the proposed controllers have better performance in the presence of disturbance and uncertainties without chattering.

[1]  Igor Podlubny,et al.  Mittag-Leffler stability of fractional order nonlinear dynamic systems , 2009, Autom..

[2]  Mohamed Darouach,et al.  Static output feedback ℋ∞ control for a fractional-order glucose-insulin system , 2013 .

[3]  Manuel A. Duarte-Mermoud,et al.  Using general quadratic Lyapunov functions to prove Lyapunov uniform stability for fractional order systems , 2015, Commun. Nonlinear Sci. Numer. Simul..

[4]  F. Mainardi,et al.  Recent history of fractional calculus , 2011 .

[5]  YangQuan Chen,et al.  Fractional-order exponential switching technique to enhance sliding mode control , 2017 .

[6]  Paul Bogdan,et al.  Reducing risk of closed loop control of blood glucose in artificial pancreas using fractional calculus , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Masoud Goharimanesh,et al.  Fractional Order PID Controller for Diabetes Patients , 2015 .

[8]  Sara Dadras,et al.  Control of a novel class of fractional-order chaotic systems via adaptive sliding mode control approach ☆ , 2013 .

[9]  Patrick Lanusse,et al.  Fractional Order Controller Design for A Flexible Link Manipulator Robot , 2013 .

[10]  Mohammad Hassan Khooban,et al.  Design of Optimal Self-Regulation Mamdani-Type Fuzzy Inference Controller for Type I Diabetes Mellitus , 2014 .

[11]  Driss Boutat,et al.  Observer Design for a Nonlinear Minimal Model of Glucose Disappearance and Insulin Kinetics , 2014, BIOSIGNALS.

[12]  Leonid M. Fridman,et al.  Second-order sliding-mode observer for mechanical systems , 2005, IEEE Transactions on Automatic Control.

[13]  Y. Chen,et al.  Adaptive fractional-order switching-type control method design for 3D fractional-order nonlinear systems , 2015 .

[14]  Le Yi Wang,et al.  Observer-Based State Feedback for Enhanced Insulin Control of Type ‘I’ Diabetic Patients , 2011, The open biomedical engineering journal.

[15]  L. Fridman,et al.  Higher‐order sliding‐mode observer for state estimation and input reconstruction in nonlinear systems , 2008 .

[16]  Leonid M. Fridman,et al.  Finite-time state observation for non-linear uncertain systems via higher-order sliding modes , 2009, Int. J. Control.

[17]  Phyo Phyo San,et al.  Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes. , 2016, ISA transactions.

[18]  Yuri B. Shtessel,et al.  High-order sliding-mode control for blood glucose: Practical relative degree approach , 2013 .

[19]  Reza Ghaderi,et al.  Fractional order control of a coupled tank , 2010 .

[20]  Ahmad Reza Vali,et al.  Back Stepping Sliding Mode Control of Blood Glucose for Type I Diabetes , 2014 .

[21]  Hadi Delavari,et al.  A novel fractional adaptive active sliding mode controller for synchronization of non-identical chaotic systems with disturbance and uncertainty , 2017 .

[22]  Javad Akbari Torkestani,et al.  A learning automata-based blood glucose regulation mechanism in type 2 diabetes , 2014 .

[23]  Duarte Valério,et al.  NINTEGER: A NON-INTEGER CONTROL TOOLBOX FOR MATLAB , 2004 .

[24]  Weihua Deng,et al.  Remarks on fractional derivatives , 2007, Appl. Math. Comput..

[25]  D. Baleanu,et al.  Control of an uncertain fractional-order Liu system via fuzzy fractional-order sliding mode control , 2012 .

[26]  Z. Benyo,et al.  Design of Luenberger Observer for Glucose-Insulin Control via Mathematica , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Yangquan Chen,et al.  Computers and Mathematics with Applications Stability of Fractional-order Nonlinear Dynamic Systems: Lyapunov Direct Method and Generalized Mittag–leffler Stability , 2022 .

[28]  Muhammad Ilyas,et al.  Super twisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients , 2017, Biomed. Signal Process. Control..

[29]  G. P. Rangaiah,et al.  Review and Analysis of Blood Glucose (BG) Models for Type 1 Diabetic Patients , 2011 .

[30]  Zaki Nossair,et al.  Evaluation of Using a Recurrent Neural Network (RNN) and a Fuzzy Logic Controller (FLC) In Closed Loop System to Regulate Blood Glucose for Type-1 Diabetic Patients , 2012 .

[31]  Radhakant Padhi,et al.  Optimal blood glucose regulation of diabetic patients using single network adaptive critics , 2011 .

[32]  Shouming Zhong,et al.  Fractional-order sliding mode based extremum seeking control of a class of nonlinear systems , 2014, Autom..

[33]  Fabian León-Vargas,et al.  Postprandial blood glucose control using a hybrid adaptive PD controller with insulin-on-board limitation , 2013, Biomed. Signal Process. Control..

[34]  Patricio Colmegna,et al.  Reducing Risks in Type 1 Diabetes Using ${\cal H}_\infty$ Control , 2014, IEEE Transactions on Biomedical Engineering.

[35]  Manuel A. Duarte-Mermoud,et al.  Lyapunov functions for fractional order systems , 2014, Commun. Nonlinear Sci. Numer. Simul..

[36]  Edgar N. Sánchez,et al.  Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients , 2012, J. Frankl. Inst..

[37]  Y. Batmani.  Blood glucose concentration control for type 1 diabetic patients: a non-linear suboptimal approach. , 2017, IET systems biology.

[38]  Sara Dadras,et al.  Energy-saving control strategy for lighting system based on multivariate extremum seeking with Newton algorithm ☆ , 2017 .