Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review

Abstract

The reliable and effective automation of biomedical therapies is the need of the hour for medical professionals. A model predictive controller (MPC) has the ability to handle complex and dynamic systems involving multiple inputs/outputs, such as biomedical systems. This article firstly presents a literature review of MPCs followed by a survey of research reporting the MPC-enabled automation of some biomedical therapies. The review of MPCs includes their evolution, architectures, methodologies, advantages, limitations, categories and implementation software. The review of biomedical conditions (and the applications of MPC in some of the associated therapies) includes type 1 diabetes (including artificial pancreas), anaesthesia, fibromyalgia, HIV, oncolytic viral treatment (for cancer) and hyperthermia (for cancer). Closed-loop and hybrid cyber-physical healthcare systems involving MPC-led automated anaesthesia have been discussed in relatively greater detail. This study finds that much more research attention is required in the MPC-led automation of biomedical therapies to reduce the workload of medical personnel. In particular, many more investigations are required to explore the MPC-based automation of hyperthermia (cancer) and fibromyalgia therapies.

Keywords

• model predictive controller
• biomedical therapy automation
• diabetes
• anaesthesia
• artificial pancreas
• hyperthermia