Automatic control in biomedicine has attracted the attention of clinicians to mitigate the side effects resulting from drug overdoses administered to patients. To provide the most optimal and accurate results, the computer-controlled systems in biomedical engineering require more advanced tuning procedures that tackle patient variability and ensure the robustness of the control system. This has been enhanced over the past two decades through the replacement of standard PID controllers with fractional-order controllers. However, most of the developed fractional-order control methods address only the robustness with respect to gain variations. In this study, a novel fractional-order control algorithm that is robust to time constant variations is developed. The control algorithm is designed for second-order plus dead time systems. A graphical solution is chosen to solve the nonlinear system of equations for the proposed approach. Three biomedical applications are employed as case studies. The first one consists in the control of the bispectral index in general anesthesia, the second one refers to the blood glucose level control for diabetic patients, and finally, the third one tackles computerized control in chemotherapy. The closed-loop simulation results validate the efficiency of the tuning method according to the accepted values of the performance specifications in the scientific literature.

The integration of information technology and control engineering has significantly increased in clinical practice research, especially in the management of drug dosing for general anaesthesia. The main focus of the research is to achieve a personalised control of the Depth of Hypnosis based on a novel approach. The primary objective is to regulate the Bispectral Index by administering Propofol during the hypnotic phase of anaesthesia. A fractional order time delay model is proposed to replace the conventional PK-PD model. This model is estimated online during the induction phase. A fractional order Proportional-Integral-Derivative (PID) controller is then individually tuned for each patient. The closed loop simulation results demonstrate that the proposed controllers effectively minimise undershoot and achieve a short time to reach the desired time-to-target for the majority of the patients. The analysis indicates that the proposed controller is capable of maintaining the BIS signal within a safe range. Comparative analyses with similar research are provided to effectively contextualise this study within the existing literature. Disturbance rejection tests during the maintenance phase are performed to demonstrate the effectiveness of personalised control. The online adaptation that this research brings, compared to the previous attempts, allows for significantly improved results for the hypnosis phase.

The research field of clinical practice has experienced a substantial increase in the integration of information technology and control engineering, which includes the management of medication administration for general anaesthesia. The invasive nature of input signals is the reason why autotuning methods are not widely used in this research field. This study proposes a non-invasive method using small-amplitude sine tests to estimate patient parameters, which allows the design of a personalised controller using an autotuning principle. The primary objective is to regulate the Bispectral Index through the administration of Propofol during the maintenance phase of anaesthesia, using a personalised fractional-order PID. This work aims to demonstrate the effectiveness of personalised control, which is facilitated by the proposed sine-based method. The closed-loop simulation results demonstrate the efficiency of the proposed approach.

Little research has been carried out in terms of modeling and control of analgesia. However, emerging new technology and recent prototypes paved the way for several ideas on pain modeling for control. Recently, such an idea has been proposed for measuring the Depth of Analgesia (DoA). In this paper, that solution is further exploited towards obtaining a novel fractional-order model and dedicated controller for DoA. First, clinical data from patients undergoing general anesthesia are used to determine a commensurate fractional-order model of the skin impedance at each sampling period. Second, we provide a proof of concept indicating that fractional order changes due to variations in the infused opioid drug (Remifentanil). Third, a fractional-order model for DoA is developed correlating the changes in the pain index (as the output signal) and the Remifentanil infusion rate (as the input signal). Standard optimization routines are used to estimate the parameters. A database of 19 real patients is used. Lastly, a preliminary fractional-order controller is designed and tested in simulation for the 19 patients. The closed-loop simulation results correspond to the expected clinical outcomes.