Improving Performance and Safety in Mechanical Ventilation: A Robust Control Approach for Airway Pressure and Patient Flow
DOI:
https://doi.org/10.12928/biste.v7i4.14283Keywords:
Mechanical Ventilation, Active Disturbance Rejection Control (ADRC), Airway Pressure Control, Patient Flow Regulation, Respiratory System DynamicsAbstract
The mechanical ventilation system demand precise and highly responsive control for airway pressure (Paw) and the patient flow (Qpat) as system nonlinearities and time varying disturbances for example the changes in lung mechanics or patient effort it compromise patient safety and treatment efficacy. This study addresses to critical challenge of the robust regulation by comparing two advanced nonlinear control strategies which ar: the Nonlinear PID (NPID) controller and the Active Disturbance Rejection Control with Nonlinear PD structure (ADRC-NPD) controller. The research utilizes a state space model of the respiratory system that developed and simulated in the MATLAB/Simulink for rigorously test controller performance under abrupt changes in the desired pressure setpoint (Pset). The model incorporate clinically relevant lung mechanics that including fixed values for the airway resistance (Rl) and lung compliance (Cl) to represent specific patient scenario. Performance is assessed using key metrics are rise time, overshoot/undershoot, settling time and tracking error. The ADRC-NPD controller consistently demonstrated superior performance that attributed to it Extended State Observer (ESO) for real time estimation and compensation of total system disturbances. Specifically the ADRC-NPD achieved a significantly faster rise time reach to (0.174s vs. 0.38s) and minimal undershoot (-0.3025% vs. -16.573%) compared to the NPID controller that indicating exceptional tracking fidelity and stability crucial for patient well being. The findings strongly that suggest that the ADRC-NPD provides a more robust and clinically viable control solution. Future work will focus on the real-time clinical simulation and hardware in the-loop implementation to validate these results under dynamic and patient specific conditions.
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