Modeling And Simulation Of Complex Medical Cyber-physical System

Medical Cyber-Physical Systems（MCPS）is a kind of Cyber-Physical Systems（CPS）for modern medicine,which are used to prevent and detect various diseases.Each MCPS has embedded control devices and an independent network system.It uses physical space as the physical basis,provides sensing information to cyberspace through sensing devices,and receives the control information from cyberspace to control physical devices.Considering the particularity of medical applications,the design of MCPS needs to consider more characteristics,such as higher requirements for security,and real-time and network delay,which needs to be fully verified before the system is put into use.By establishing the system model for early verification,the problems existing in the system can be found and corrected in time and effectively,so that developers can design and shape the system more efficiently.Therefore,the research on MCPS modeling and simulation has certain application value and theoretical significance.In this thesis,a kind of complex MCPS-automatic anesthesia system is modeled and simulated.The first complexity of the model is reflected in the need to ensure the system meets the security requirements while constructing the system component.To solve this problem,the system is formally reasoned by using various modeling forms,and the complex system is hierarchically organized into multiple abstraction layers to manage the design complexity and provide verifiability.Specifically,the timed automaton model in UPPAAL is used to verify the security of the system.The functional requirements are refined by the Architecture Analysis and Design Language（AADL）,and the reasoning method of hypothesis guarantee is used to analyze the key requirements of infusion control software.Finally,the component behavior is modeled on Stateflow.By combining these modeling methods,we can analyze the component functions and ensure the maintenance of key safety performance.The reliability of the model is verified by simulation on MATLAB-Simulink.Another complexity of the model is the uncertainty of the Medical closed-loop control system caused by differences among patients.To solve this problem,a model-based closed-loop adaptive control architecture was proposed based on the concept of MCPS.Bispectral Index（BIS）signal was used as the control variable to control the depth of hypnosis during anesthesia.The system architecture was combined with the pharmacokinetic/pharmacodynamic model of the patient,and the calculated results were used as the feedback signal of the PID controller to correct the uncertainty of the model.In the control scheme,parameter adjustment is performed offline using the genetic algorithm to optimize the performance indicators of the patient data set.The infusion rate of anesthetic drugs can be automatically adjusted to keep the depth of anesthesia at a stable target value.By adding noise blocks,the robustness of the proposed method was tested.The effectiveness of the method is verified on the real patient data set that can represent a wide range of people.Simulation results show that the proposed method can reach the target value stably in a specified time and has good interference rejection.