Control of Nonlinear Heartbeat Models under Time- Delay-Switched Feedback Using Emotional Learning Control Arman Sargolzaei 1 , Kang K. Yen 1 , and MN. Abdelghani 2 1 Florida International University, Department of Electrical and Computer Engineering, Miami, Fl, USA Email: asarg001@fiu.edu 2 Department of Mathematics and Statistical Sciences, University of Alberta, Edmonton, Canada Abstract— In this paper, we adopt the Zeeman nonlinear heart model to discuss its stability and control its operation using emotional learning control (ELC). We also demonstrate the control of the heart model under threats of possible time delay introduced in the sensing loop. We compare the robustness of the ELC with other control methods such as the classical PID and the model predictive control (MPC) for the heart model under time delay attack. We have showed that ELC is more robust than the classical PID and the MPC. Index Terms— Emotional learning controller, TDS attack, nonlinear heartbeat model I. INTRODUCTION There are many design and analysis techniques developed for linear time-invariant (LTI) systems [1, 2]. However, for nonlinear systems which could possibly be time-varying, one needs different methodology. A major part of nonlinear control theory studies how to extend the well-known methods for liner systems to nonlinear systems. One of the most complex but robust nonlinear systems is the human heart. Electrocardiogram (ECG) records potential differences between two electrodes located on the skin at predetermined positions on the chest to measure the electrical activities in cardiac tissue. A solitary cycle of ECG consists of the activities of relaxation and contraction of the heart (called the heart pumping actions). One can identify an ECG signal by its P, Q, R, S, and T peaks, PR and ST segments, PR and QT intervals, and QRX complex. Characteristic information took out from ECG can be used to assess cardiac health and identify potential heart problems. For example, important information extracted from ECG recording is the time between successive R-peaks which is stated to as an RR-interval. The changeability of the series of RR-intervals, known as heart rate variability (HRV), is being used to measure heart functions: identifying patients risk for a cardiovascular failure [3], as “an indicator for mortality following myocardial infraction” [4], and as a measure of the contacts between different control mechanisms of physiology like respiratory sinus arrhythmia. The development of mathematical models of heartbeat (ECG) with appropriate PQRST peaks, QRX complex, (PR, ST) segments, (PR, QT) intervals and HRV spectra has been and continue to be the subject of wide investigations with varying degrees of successes. A good model of ECG will make available a valuable tool for analyzing the various physiological conditions effects on the outlines of the ECG and for the assessment of diagnostic ECG signal processing devices. The form of ECG signal is the result of the propagation of electrical activities in myocardium and HRV is the result of physiological and neurological controls. In 1972 Zeeman presented a set of nonlinear dynamical DOI: 01.IJRTET.10.2.38 © Association of Computer Electronics and Electrical Engineers, 2013 Int. J. on Recent Trends in Engineering and Technology, Vol. 10, No. 2, Jan 2014