(484e) Multiple Model Predictive Control of the Cardiovascular System Using Vagal Nerve Stimulation

Vagal nerve stimulation (VNS) is currently under investigation for the treatment of various cardiovascular diseases that include heart failure, arrhythmia, and hypertension. In preclinical and clinical studies of VNS, stimulation parameters are heuristically determined in open-loop approach. But its therapeutic efficacy remains inconclusive, strongly suggesting the need for a closed-loop approach to optimize patient-specific stimulation parameters.

Several effective approaches have been reported for implementing closed loop control of VNS to determine optimal stimulation parameters in animal studies. However, most of them were designed as single-input-single-output systems. Our group previously developed a nonlinear model predictive control (NMPC) algorithm for VNS system. One of the challenges associated with this application of NMPC includes the development and validation of a predictive cardiac model to be used in NMPC. Another challenge involves the high computational cost of NMPC.

Multiple model predictive control (MMPC) has been widely used for control of nonlinear systems to reduce the online computational burden. Here, we propose a MMPC algorithm for automated regulation of heart rate and mean arterial pressure by optimally adjusting the amplitude and frequency of electrical pulses applied to three locations of the vagal nerve. The controller uses a predictive framework with multiple local models identified from our previously reported pulsatile rat cardiac model that emulates symptoms of hypertension in both rest and exercise states. The computational expense of the proposed method is verified with rigorous hardware-in-the-loop implementation, which provides an assessment of the efficacy of the algorithm for future implementation of our MMPC for pre-clinical and clinical studies.