# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 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, evolutionary, methodology 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'individually and in conjunction with'review' and'survey'. The second part of the literature search included the MPC domain keywords in conjunction with application-specific keywords, such as 'biomedical', 'therapy', 'biomedical therapy', 'automation', 'biomedical therapy automation', 'therapeutic automation', 'automated disease control', 'diabetes', 'blood glucose level', 'BGL', 'anaesthesia', 'artificial pancreas', 'cancer', 'hyperthermia', 'fibromyalgiagia', 'HIV', 'AIDS', 'cyber physical healthcare' and 'cyber physical health system'. For the historical review of MPCs and MPC algorithms, landmark papers published from the 1960s to 1990s were considered. Papers published in the past two decades were primarily considered for the review of various MPC architectures, advantages, limitations and software. Although no specific time frame was decided upon to gather literature dealing with MPC in the selected biomedical conditions and therapies, more papers were found from the recent twenty years as compared to the previous decades. All papers were sourced from Scopus, Sciencedirect and Web of Science databases. MPC refers to a group of control methods that are employed in single-input single-output (SISO) and multi-input multi-output (MIMO) systems. An MPC is capable of managing MIMO systems that share complex system information between their inputs and outputs [2]. Because of the complexity of these exchanges, it is extremely difficult to construct MIMO systems using standard controllers such as proportional-integral-derivative (PID) controllers. An MPC, on the other hand, is capable of controlling all of the outputs while allowing online process input-output exchanges simultaneously. An MPC is also capable of dealing with contradicting restrictions. The handling of constraints is important because breaking them could result in unintended consequences. An MPC has the ability to preview data and react accordingly. If the MPC controller is aware of set point changes in advance, it will be able to better react to those changes and increase its overall performance. An MPC fulfills the requirement of a dynamic process model to reduce the discrepancy between the expected and actual plant outputs. The earliest MPCs have been in use in process industries since the 1980s. MPCs can be applied to both basic and complex processes.