Citation: Parihar, S.; Shah, P.; Sekhar, R.; Lagoo J. Model Predictive Control and Its Role in Biomedical Therapeutic Automation—A Brief Review. Appl. Syst. Innov. 2022, 5, 118. https://doi.org/10.3390/asi5060118 Academic Editor: Juan A. Gómez-Pulido Received: 20 October 2022 Accepted: 21 November 2022 Published: 24 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review Sushma Parihar 1 , Pritesh Shah 1, * , Ravi Sekhar 1 and Jui Lagoo 2 1 Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU), Pune 412115, India 2 Symbiosis Medical College for Women (SMCW), Symbiosis International (Deemed University) (SIU), Pune 412115, India * Correspondence: pritesh.shah@sitpune.edu.in 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; fibromyalgia; HIV; cancer; cyber-physical healthcare 1. Introduction The idea of automated disease control is not new. Researchers have worked on it since the creation of the first analytical models between 1960 and the early 1970s [1]. These researchers’ goal was to explore newer and more effective mathematical models to aid medical diagnostic procedures and therapeutic regimens. By nature, physiological systems are innately nonlinear and time-varying, making them difficult to predict. Therefore, some clinical researchers feel that a formal model-based approach is not suitable for effective application in medication and that open-loop control architecture is the way ahead in medicine administration. In spite of the fact that the assignment of mathematical model variables to human test subjects appears ambitious, the establishment of feedback loop for testing and controlling therapy is not only possible but also feasible. MPCs are a prominent control solution that has been employed in therapeutic automation. The main goal of this study is to firstly review the background, evolution, methodol- ogy and salient features of MPCs followed by a review of MPC-led therapeutic automation in some important medical conditions. The motivation behind this study is to promote more research in this important field primarily because of two reasons—1. easing the routine workload of medical personnel and 2. the capability of MPCs to effectively deal with biomedical therapeutic closed-loop conditions. The first part of the literature search included the basic MPC domain keywords of ‘model predictive control’, ‘model predictive controller’, ‘mpc’, ‘mpc control’, ‘mpc controller’, ‘mpc architecture’ and ‘mpc model’ Appl. Syst. Innov. 2022, 5, 118. https://doi.org/10.3390/asi5060118 https://www.mdpi.com/journal/asi