Design and Optimization of Dual-Chamber Pacemakers Using Hunting Harris-Manta Optimization Algorithm (HMOA) for Enhanced PIDA Control

Abstract

The design and implementation of a dual-chamber pacemaker system, leveraging the Hunting Harris-Manta Optimization Algorithm (HHMOA) to improve proportional-Integral-Deriavitive-Acceleration (PIDA) control. Dual-chamber pacemakers are critical for regulating atrial and ventricular synchronization, necessitating precise and adaptive control mechanisms. The proposed HHMOA synergistically combines the strengths of Manta Ray Foraging Optimization (MRFO) and Harris Hawks Optimization (HHO) to create a robust hybrid algorithm that fine-tunes the PIDA controller parameters for optimal performance. The HHMOA optimizes key performance metrics, including steady-state error, settling time,energy consumption,rise time, andpeak overshoot. Using MATLAB/Simulink as the simulation platform, the pacemaker system is modelled and the controller’s performance under various physiological conditions is evaluated. The hybrid algorithm facilitates a significant reduction in heart rate regulation errors, enhances response times, improves stability, and lowers power consumption, which extends the pacemaker’s lifespan and ensures reliable heart rhythm control. Through extensive simulations, the HHMOA demonstrates superior performance over conventional optimization methods, showcasing significant improvements in error minimization, response speed, and energy efficiency. This research highlights the potential of hybrid optimization algorithms in advancing medical device control systems, particularly in the precise and reliable operation of dual-chamber pacemakers.