Immune PID controller based on differential evolution algorithm for heart rate regulation

This paper presents different structures of immune proportional-integral-derivative (PID) control system, to regulate the heart rate. It is based on Yanagihara, Noma, and Irisawa (YNI) model that represent the mathematical model of the heart. Three structure designs have been proposed to emphasize the optimality in the control process. In this work differential evolution (DE) algorithm is considered to optimize the controller parameters. The performance of the proposed three controllers has been compared to traditional PID methods. The comparison results show best improvement when using the proposed structure-III with 0% maximum overshoot, a reduction of 98.9%, 96.8% and 30.8% in rising time, settling time, and steady state error respectively.

[1]  Xin-hua Liu,et al.  Development of a GA-Fuzzy-Immune PID Controller with Incomplete Derivation for Robot Dexterous Hand , 2014, TheScientificWorldJournal.

[2]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.

[3]  Jiao Li,et al.  Development and Prospect of the Artificial Immune System , 2003 .

[4]  Wei Wang,et al.  A New Immune PID Controller in Material-Level Control , 2007, Third International Conference on Natural Computation (ICNC 2007).

[5]  F. Ghorbel,et al.  Differential hysteresis modeling of a shape memory alloy wire actuator , 2005, IEEE/ASME Transactions on Mechatronics.

[6]  Intan Zaurah Mat Darus,et al.  PID controller tuning using evolutionary algorithms , 2012 .

[7]  Mohamed Esmail Karar,et al.  Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers , 2018 .

[8]  Gholamreza Attarodi,et al.  Design and processing of a novel algorithm using Anfis for new generation of cardiac pacemakers , 2017, 2017 Computing in Cardiology (CinC).

[9]  Vitaliy Feoktistov.  Differential Evolution: In Search of Solutions , 2006 .

[10]  James P. Keener,et al.  Mathematical physiology , 1998 .

[11]  Ahmed Elakkary,et al.  PID parameters optimization using ant-colony algorithm for human heart control , 2017, 2017 23rd International Conference on Automation and Computing (ICAC).

[12]  A. Noma,et al.  Reconstruction of sino-atrial node pacemaker potential based on the voltage clamp experiments. , 1980, The Japanese journal of physiology.

[13]  R. Malathi,et al.  Design of Advanced Control Strategies for Cardiovascular System , 2018 .

[14]  K. P. Anderson,et al.  Sudden Cardiac Death Unresponsive to Implantable Defibrillator Therapy: An Urgent Target for Clinicians, Industry and Government , 2005, Journal of Interventional Cardiac Electrophysiology.

[15]  Shivaram P. Arunachalam,et al.  Intelligent fractional-order PID (FOPID) heart rate controller for cardiac pacemaker , 2016, 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT).

[16]  Amira S. Ashour,et al.  An optimized Mamdani FPD controller design of cardiac pacemaker , 2019, Health Inf. Sci. Syst..

[17]  G. W. Beeler,et al.  Reconstruction of the action potential of ventricular myocardial fibres , 1977, The Journal of physiology.

[18]  D. Noble,et al.  Reconstruction of the electrical activity of cardiac Purkinje fibres. , 1975, The Journal of physiology.

[19]  Paul Yager,et al.  Healthcare innovation point-of-care technologies conference , 2014 .

[20]  MengChu Zhou,et al.  Optimal Single-Pulse for Pacemakers Based on a Sinoatrial Model , 2013, IEEE/ASME Transactions on Mechatronics.

[21]  D. Noble.  A modification of the Hodgkin—Huxley equations applicable to Purkinje fibre action and pacemaker potentials , 1962, The Journal of physiology.