# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Anaesthesia
In this study [82], the authors designed a linear MPC to control intravenous anaesthesia based on a single input (rate of propofol infusion) and single output (bispectral index). The effectiveness of the developed LMPC in tracking the BIS reference, while also accommodating for restrictions, disturbances, and noise in the measured variables, was investigated. Taking into account closed-loop time delays, the performance of this method in comparison to that of a traditional proportional-integral -derivative (PID) controller was evaluated. The results of the simulations demonstrated that the suggested linear MPC is superior to the standard PID controller in such applications. Figure 9 shows an NMPC control loop architecture for anaesthetic applications. This NMPC attains and maintains the desired level of patient bispectral index (BIS). Anaesthetic dosage is administered to the patient by the control loop as follows. Initially, the state observer estimates the future state vector of the system (level of anaesthesia of the patient). The NMPC controller takes this state vector estimate as an input and computes the optimal anaesthetic dosage to be administered to the patient. In the subsequent time steps, the state observer advances the state vector estimate to the next horizon, which is followed  
by the controller to compute and deliver optimal dosages to the patient at those discrete time instants [83]. Researchers [17] also attempted closed-loop modeling and control of volatile anaesthesia through a traditional online MPC as well as a multi-parametric MPC. A Kalman filter was employed to determine the dynamic status of anaesthesia on the basis of end-tidal concentrations measured by an online estimator integrated in the closed loop. The authors demonstrated simulations for anaesthetic induction as well as disturbances arising during the course of anaesthesia, along with online parameter estimation of an explicit MPC controller architecture [17]. In a similar work, [PERSON] et al. [84] attempted automated anaesthesia using the BIS signal obtained from the electroencephalogram (EEG) to control the infusion of the hypnotic drug propofol during surgery in order to prevent adverse effects and reduce post-operative recovery duration. The authors found that the low quality of the EEG data caused interruptions in the BIS signals generated during the operation. Hence, they proposed a fault-tolerant MPC architecture to prevent faulty propofol dosages leading to intraoperative patient arousal due to breaks in the BIS signals received by the MPC. A patient undergoing ambulatory surgery must obtain the appropriate dose of anaesthesia medicines to reduce the likelihood of adverse responses after release. In order to create more effective anaesthesia, a hypnotic control system was developed by [PERSON] et al. [85]. The authors measured EEG-derived BIS as an indicator of the patient's hypnotic state. Their system primarily consisted of three components: (1) an MPC-based feedback controller with a time-delay-handling functionality; (2) a function to estimate MPC parameters; and (3) a function to prevent intraoperative risks such as the overinfusion of the anaesthetic drug and/or patient agitation due to underdosage. Computer-controlled infusion systems play a vital role in maintaining the positive balance in such situations, wherein medicine and engineering work together to achieve the optimal results [86]. Medical specialists can concentrate on high-value jobs, while computer-based drug delivery manages regular tasks such as anaesthesia and haemodynamic maintenance. Patient safety is the driving force behind the wider implementation of automation in clinical anaesthetic delivery.