Swarm optimization approach to design PID controller for artificially ventilated human respiratory system

A novel PID controller for pressure control of artificial ventilator using optimal rule based fuzzy inference system with RCTO algorithm

In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60-80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80-90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.

An optimal internal model proportional integral controller to improve pressure tracking profile of artificial ventilator

Mechanical ventilator (MV) is a breathing support medical equipment used during medical surgery or in an intensive care unit (ICU) for critical patients to reduce the breathing work of the patient under ventilation. In this work, a piston-driven mechanical ventilator is studied. Such types of ventilators are generally used as anaesthesia ventilators with volume-controlled modes. A modern ventilator can operate in different modes, such as volume control, pressure control, or both. Pressure-controlled mode may be better suited for some cases, such as patients undergoing laparoscopic surgery, as compared to volume-controlled mode because it may increase compliance during pneumoperitoneum, enhance oxygenation, and lower the stress response postoperatively. The pressure profile of a PCV must exactly track the pressure profile of a patient under ventilation to avoid ventilator-induced diaphragmatic dysfunction. Therefore, an optimal internal model control base proportional integral (IMC-PI) controller is proposed. The optimum parameter of the filter component of the IMC-PI controller is found by an enhanced class topper optimization (ECTO) algorithm. The performance of the proposed controller is analyzed in different scenarios and compared with existing results. Graphical Abstract Optimal internal model proportional integral controller for human respiratory ventilator.

A novel Human Conception Optimizer for solving optimization problems

Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.

Cascaded robust control of mechanical ventilator using fractional order sliding mode control

A mechanical ventilator is an important medical equipment that assists patients who have breathing difficulties. In recent times a huge percentage of COVID-19 infected patients suffered from respiratory system failure. In order to ensure the abundant availability of mechanical ventilators during COVID-19 pandemic, most of the manufacturers around the globe utilized open source designs. Patients safety is of utmost importance while using mechanical ventilators for assisting them in breathing. Closed loop feedback control system plays vital role in ensuring the stability and reliability of dynamical systems such as mechanical ventilators. Ideal characteristics of mechanical ventilators include safety of patients, reliability, quick and smooth air pressure buildup and release.Unfortunately most of the open source designs and mechanical ventilator units with classical control loops cannot achieve the above mentioned ideal characteristics under system uncertainties. This article proposes a cascaded approach to formulate robust control system for regulating the states of ventilator unit using blower model reduction techniques. Model reduction allows to cascade the blower dynamics in the main controller design for airway pressure. The proposed controller is derived based on both integer and non integer calculus and the stability of the closed loop is ensured using Lyapunov theorems. The effectiveness of the proposed control method is demonstrated using extensive numerical simulations.

Fuzzy PID Control for Respiratory Systems

This paper presents the implementation of a fuzzy proportional integral derivative (FPID) control design to track the airway pressure during the mechanical ventilation process. A respiratory system is modeled as a combination of a blower-hose-patient system and a single compartmental lung system with nonlinear lung compliance. For comparison purposes, the classical PID controller is also designed and simulated on the same system. According to the proposed control strategy, the ventilator will provide airway flow that maintains the peak pressure below critical levels when there are unknown parameters of the patient's hose leak and patient breathing effort. Results show that FPID is a better controller in the sense of quicker response, lower overshoot, and smaller tracking error. This provides valuable insight for the application of the proposed controller.

Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.