# Structure Optimization and Control Design of Electronic Oxygen Regulator
###### Abstract  
The oxygen regulator is the core component of the aircraft life support system, which adjusts the flow and pressure of the breathing gas according to the pilot's breathing needs. In response to the problem that structural parameters are difficult to adjust and prone to jitter when the indirect oxygen regulator system is stable, a direct oxygen regulator is designed using a stepper motor to drive a lung-type flapper, replacing the diaphragm lever-type structure of the indirect oxygen regulator. Due to the nonlinearity and time-varying nature of the dynamic characteristics of oxygen regulators, a single-neuron PID control strategy based on online identification of RBF neural networks is proposed to improve the PID control performance. The RBF neural network is used to identify the Jacobian information of the controlled object, and the single-neuron PID controller completes the online adjustment of the controller parameters to realize the intelligent control of the system. Simulation experimental studies are conducted to verify the performance of the direct oxygen regulator. The result analysis verifies the excellence of the single-neuron PID control strategy based on online recognition of the RBF neural network to improve the system performance.  
electronic oxygen regulator; structural optimization; RBF neural network; single neuron; life support system +
Footnote †: journal: Applied sciences  
of the indirect oxygen regulator were analyzed by [PERSON] et al. [10]. Concerning the electronic control of oxygen regulators, [PERSON] et al. used switching control [11] and [PERSON] et al. applied fuzzy control to design a control law for an electronic oxygen regulator [12]. [PERSON] et al. used expert control to automatically adjust the PID parameters, which solved the deficiency of fixed control parameters [5]. [PERSON] et al. pointed out that disturbance suppression is a key issue in the control of oxygen regulators. Generalized predictive control (GPC) [13], automatic disturbance rejection control (ADRC) [14], and the adaptability has been applied to controllers to improve performance [15].  
In the control application of an oxygen regulator, there are unknown perturbations acting on the system such as gas source pressure changes and suction flow changes in the oxygen regulator, and it is difficult to achieve the desired control effect with a fixed parameter PID regulator. Therefore, the control method of a single-neuron PID is adopted, and an RBF neural network is introduced for online identification of system output to provide system dynamic information for the learning process of single-neuron PID.  
Through the study and analysis of an indirect oxygen regulator, it is found that the parameters of the indirect oxygen regulator are not easily adjustable, and they jointly affect the overall performance. It is necessary to optimize the structure of the indirect oxygen regulator by changing the parameters and proposing a direct oxygen regulator to solve the jitter problem caused by the diaphragm lever mechanism. An intelligent control method is applied to improve the breathing resistance and pressure fluctuation of the oxygen regulator. This paper designs a direct oxygen regulator structure based on the indirect oxygen regulator by merging the lung type valve mechanism and the electronically controlled residual pressure valve mechanism of the indirect electronic oxygen regulator, and optimizing it to a direct motor-driven valve mechanism to close or open the valve for oxygen supply and establish a mathematical model of respiratory pressure control. In this control system structure, the RBF neural network is used to identify the Jacobian information of the controlled object and the online adjustment of the controller parameters is completed by the single-neuron PID controller to realize the intelligent control of the system. The control performance of the structure's design and control method on pressure fluctuation and breathing resistance is verified through a simulation and experiment.