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Lecturer(s)
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Černohorský Josef, doc. Ing. Ph.D.
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Hlava Jaroslav, doc. Dr. Ing.
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Course content
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Lectures: 1) Input/output and state-space models of systems, deepening and extending previous knowledge; the concept of in-silico testing; analysis of the insulin-glucose system dynamics model by Chiara Dalla Man, used for in-silico testing of artificial pancreas algorithms. 2) Technical implementation of an artificial pancreas (AP), historical overview of the development of this technology; computational core, sensors, and actuators; application of digital PID controllers as the AP control algorithm. 3) Principle of feedforward disturbance compensation and its application in artificial pancreas-type devices. 4) Principle of model predictive control (MPC) and its use in artificial pancreas devices as well as in other biomedical applications. 5) Target-controlled infusion (TCI) as a feedforward control strategy; commonly used models (Marsh, Schnider, Eleveld, and others) and their characteristics; the Shafer-Gregg algorithm. 6) Control methods used for automatic control of the depth of anesthesia, reasons for the failure of earlier attempts to introduce this technology into clinical practice (e.g., Sedasys), and future perspectives. 7) Feedback control algorithms for automation of perioperative hemodynamic optimization. 8) Actuators using electromagnetic field DC motors, voice coil a solenoid actuators and their control. 9) Position, speed and acceleration sensors, torque sensors. 10) Brushless motors, principles of electronic commutation, application of brushless motors in medical devices 11) AC drives induction motor, scalar open-loop control 2) Synchronous servodrives linear and rotational, control of synchronous servodrives 3) Solid state actuators and other miniature drives in medical applications 14) Motion control systems, their basic functionalities Laboratories and seminars: 1) Advanced use of the MATLAB/Simulink simulation environment, including the implementation of a simplified nonlinear model of insulin-glucose system dynamics based on the model by Chiara Dalla Man, and correct specification of model initial conditions. 2) Linearization of the nonlinear model and evaluation of the validity range of the linearized approximation. Design and simulation of a digital PID controller for an Artificial Pancreas. 3) Extension of the PID controller with feedforward compensation based on glucose intake from meals. Assessment of the benefits and limitations of this approach under real-world conditions, where the glucose intake can only be estimated approximately. 4) Design and simulation of an Artificial Pancreas using model predictive control (MPC) principles. 5) Implementation of pharmacokinetic and pharmacodynamic models of propofol, including a Hill-type function mapping the effect-site concentration to the bispectral index (BIS). 6) Analysis and implementation of the Shafer-Gregg algorithm for target-controlled infusion (TCI) and comparison with an alternative approach based on quadratic programming. 7) Design and simulation of algorithms for automatic control of the depth of anesthesia. 8) Demonstration of DC motor control, energy balance, calculation of motor power output and efficiency 9) Drive dimensioning specification of the semestral assignment 10) Demonstration of brushless Maxon Epos drives 11) Parameter setting of drive control algorithms, demonstration of cascade feedback control and feedforward control structures. 12) Modelling of general electrical machine, its torque, speed and position control. 13) Demonstration of induction motor control using a frequency converter 14) Parameterisation of a frequency converter
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing), Laboratory work
- Contacts hours
- 56 hours per semester
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Learning outcomes
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The aim of the course is to deepen knowledge in the field of automatic control in two basic ways. In the first part of the course, students should acquire basic knowledge about the methods of automatic control and the possibilities for their biomedical applications. The second main theme of the course is the issue of control actuators, especially electric drives. This area is important in relation to the actuators of medical devices. Moreover, it is an essential prerequisite for more advanced courses in robotics as electric actuators and control systems are one of the most important components of more complex units of robots and manipulators.
In the field of cybernetics, students gain an overview of the rapidly evolving applications of cybernetic principles in biomedicine. Knowledge of fundamental principles is assumed from undergraduate studies, and emphasis is placed on a detailed understanding of specific applications. Graduates of the course understand how devices based on feedforward control operate, such as Target Controlled Infusion (TCI), and how devices based on a simple feedback control loop work, e.g., an Artificial Pancreas (AP). They also understand complex approaches that combine multiple control loops, such as those used in the automation of perioperative hemodynamic optimisation. Emphasis is placed not only on applications that are already in routine clinical use, but also on those that are in advanced stages of research and whose implementation in practice is realistically expected in the near future. In the field of electrical engineering, students also acquire theoretical foundations and practical skills in designing and implementing electric drives and their control systems. They become familiar with their operating principles as well as with programming methods. This area is important both in relation to actuators of medical devices and as essential preparation for more advanced courses in medical robotics, as electric servo drives and their control systems are crucial components of all more complex medical robotic systems and manipulators.
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Prerequisites
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Condition of registration: none
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Assessment methods and criteria
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Combined examination
Activity on the seminars and successful passing the tests are required for getting a credit. Examination is of the written and /or oral form (s). Understanding of the lectured topics is required.
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Recommended literature
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C. Dalla Man, R. A. Rizza and C. Cobelli. Meal Simulation Model of the Glucose-Insulin System, in IEEE Transactions on Biomedical Engineering, vol. 54, no. 10, pp. 1740-1749, Oct. 2007. 2007.
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Coeckelenbergh, S., Boelefahr, S., Alexander, B. et al. Closed-loop anesthesia: foundations and applications in contemporary perioperative medicine. J Clin Monit Comput 38, 487?504 (2024). 2024.
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Hemmerling, Thomas M. ; Jeffries, Sean D. Robotic Anesthesia: A Vision for 2050. Anesthesia & Analgesia 138(2):p 239-251, February 2024. 2024.
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Munachiso Nwokolo, Roman Hovorka. The Artificial Pancreas and Type 1 Diabetes, The Journal of Clinical Endocrinology & Metabolism, Volume 108, Issue 7, July 2023, pp. 1614?1623. 2023.
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Schütz A, Rami-Merhar B, Schütz-Fuhrmann I, et al. Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes. Journal of Diabetes Science and Technology. 2024;19(4):1060-1067. 2025.
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Struys, M. M. R. F., De Smet, T., Glen, J. B., Vereecke, H. E. M., Absalom, A. R., & Schnider, T. W. The History of Target-Controlled Infusion. Anesthesia and Analgesia, 122(1), 56-69. 2016.
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Voženílek Petr. Elektromechanické měniče. Praha, 2005. ISBN 978-80-01-03137-7.
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