Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system

Design of an Efficient Controller for Arterial Oxygen Saturation in Neonatal Infants

A main problems for premature infants is respiratory distress syndrome (IRDS), also called neonatal respiratory distress syndrome, or respiratory distress syndrome of newborn. Due to IRDS, the infant requires intervention in the form of respiratory support to increase the inspired oxygen. Physicians must keep the range of the Arterial Oxygen Saturation (SpO_2) between 82 – 95% to help the premature infants to get oxygen enough while preventing other complications. If the blood oxygen saturation is more than 95% or less than 82%, the infant is at risk for retinopathy of prematurity. The control is analyzed using PI, PID, Model Predictive Controller (MPC), Robust control wit PID and Robust control with MPC to ensure stability and minimum settling time to reach the accuracy of output SpO_2 by applying the Fraction of Inspired Oxygen (FiO_2) as control action. MPC is an optimal control strategy based on numerical optimization by using a system model and optimizing at regular intervals. We can predict the future control inputs and future plant responses. An error model is created using the resulting ranges of system gains and time constant from [18]. The ?-synthesis controller is developed to control the oxygen percentage of inspired air and performance specifications are defined. The H_? method is used to determine the robust stability and robust performance are achieved with the system uncertainty that described by the error model. A comparison among a static proportional integral, proportional integral derivative, the model predictive controller, the robust controller with PID controller, and the robust controller with MPC found that the robust controller with MPC displays the best performance for a system with large ranges of model parameters. The results got from this dissertation are ; PI controller has large overshoots and large steady state error when using large values of K_I but when decreasing the values of K_I got good response with low overshoot and zero steady, PID controller has large overshoots and large steady state error when using large values of K_I and small values of K_p but when decreasing the values of K_I and increasing values of K_p got good response with low overshoot and zero steady, MPC controller has a zero steady state error and no peak overshoot and achieves SpO_2 to be a minimum settling time of 105 sec and zero steady state error, in robust control system based on the PID that showed the results of controller can guarantee stability and performance for whole range of model parameters and robust model predictive controller was analyzed, we did get the robust stability, nominal performance and robust performance. The robust controller is found to have a robust stability and performance, but with a low bandwidth frequency due to a conservative control design required to achieve robust stability with an extremely high level of model error. The main goal of the robust controller was analyzed for performance and stability. It was shown to be more nominally stable and have nominal performance and robust stability and performance. We showed that the result of controller can guarantee stability and performance for a whole range of model parameters. These results will help the respiratory infants to get alive in range of oxygen between 85% - 94%. It is very important to alleviate the workload of nurses in an intensive care unit when this controller is used to reduce the time and amount of harmful desaturation events

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

Investigation of the performance of an automatic arterial oxygen controller

Premature infants often require respiratory support with a varying concentration of the fraction of inspired oxygen (FiO2) to keep the arterial oxygen saturation (SpO2) within the desired range to avoid both hypoxemia and hyperoxemia. Currently, manual adjustment of FiO2 is the common practice in neonatal intensive care units (NICUs). The automation of this adjustment is a topic of interest. The research team, at University of Missouri-Columbia (UMC), has developed a novel automatic arterial oxygen saturation controller. In this study, a systematic approach has been developed to investigate both non-clinical and clinical performance of this device. The non-clinical investigation of the performance was performed using a neonatal respiratory model (hardware-in-the-loop test). A factorial experimental design was utilized to generate challenging model responses of SpO2, which were addressed by the controllers. With this study, we demonstrate the stability and ability of the adaptive PI-controller to improve oxygen saturation control over manual control by increasing the proportion of time where SpO2 of the neonatal respiratory model was within the desired range and by minimizing the variability of the SpO2. In addition, the controller ability to significantly reduce the number of hypoxemic events of the neonatal respiratory model was reported. Results of this investigation show the competence of the controller estimation system for estimating neonatal respiratory model parameters while the adaptive PI-controller was in use. Also, the functionality of the controller with no mechanical or communication failure was validated non-clinically before heading forward to the clinical trial. The clinical investigation of the performance was performed by conducting a clinical trial at the NICU of the MU Women's and Children's Hospital. The crossover design was used for the clinical trial to allow within-subject comparison and to eliminate interpatient variability. Two human subjects, with two different target ranges of SpO2, were enrolled in the study. The adaptive automatic PI-controller shows clinical feasibility to improve the maintenance of SpO2 within the intended range. With this study, we demonstrate the potential of the automatic controller to minimize the variability of SpO2. In addition, the controller shows the ability to reduce the bradycardia and the hypoxemia. Moreover, the hardware and software of the controller show an ability to transition from manual to automatic mode, and vice versa with no pronounced “bump” or step variation in the control signal, and stability and performance were not adversely affected during the transitions.Includes bibliographical reference

Fractional-Order PID Controllers for Temperature Control:A Review

Fractional-order proportional integral derivative (FOPID) controllers are becoming increasingly popular for various industrial applications due to the advantages they can offer. Among these applications, heating and temperature control systems are receiving significant attention, applying FOPID controllers to achieve better performance and robustness, more stability and flexibility, and faster response. Moreover, with several advantages of using FOPID controllers, the improvement in heating systems and temperature control systems is exceptional. Heating systems are characterized by external disturbance, model uncertainty, non-linearity, and control inaccuracy, which directly affect performance. Temperature control systems are used in industry, households, and many types of equipment. In this paper, fractional-order proportional integral derivative controllers are discussed in the context of controlling the temperature in ambulances, induction heating systems, control of bioreactors, and the improvement achieved by temperature control systems. Moreover, a comparison of conventional and FOPID controllers is also highlighted to show the improvement in production, quality, and accuracy that can be achieved by using such controllers. A composite analysis of the use of such controllers, especially for temperature control systems, is presented. In addition, some hidden and unhighlighted points concerning FOPID controllers are investigated thoroughly, including the most relevant publications

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Editoria

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools