# Structure Optimization and Control Design of Electronic Oxygen Regulator
## 3 Control Method Design
### Controller Design  
According to the oxygen regulation principle of the direct oxygen regulator, the electronic control device drives the stepper motor to open or close the oxygen supply valve of the pulmonary mechanism according to parameters such as cockpit height, acceleration, and product input and output pressure collected by the sensor group. The goal of the controller design is, on the one hand, to regulate \(u\) to keep the output pressure \(y\) close to a given reference input; on the other hand, to regulate \(u\) to counteract the effects of various disturbances \(w\). In both cases, the smaller the control error \(e=r-y\), the smaller the suction resistance and pressure fluctuations. During the operation of the oxygen regulator, the output gas pressure was affected by various disturbances such as pilot suction flow, change in gas source pressure, a disturbance force acting on the flapper, change in motor parameters, etc. The structural parameters of the whole control system are time-varying and non-linear, and the PID regulator with fixed parameters cannot get the ideal control quality. A single-neuron PID control method is used, and an RBF neural network is introduced for online identification of the system output to provide system dynamic information for the learning process of the single-neuron PID [17]. The control system structure is shown in Figure 4. In Figure 4, SNPID is the single-neuron PID controller and RBFNNI is the RBF neural network discriminator. An adaptive single-neuron PID control method based on dynamic RBF neural network identification is proposed in the reference paper [18], where a dynamic neural network identifier identifies the system model online, obtains online tuning information of the PID parameters, and the single-neuron controller realizes the self-tuning of the controller parameters. The system has the advantages of high adaptability and robustness compared with the traditional RBF neural network-based PID control method. The single-neuron PID controller based on RBF neural network recognition consists of a single-neuron PID controller and a dynamic RBF neural network recognizer. The dynamic RBF neural network is used to model the system, obtain a predictive mathematical model of the system, replace the actual output of the object with the predicted output of the model, and then design the single-neuron PID controller. The network recognition structure adopts a series-parallel structure to ensure the stability of the system. The single-neuron PID controller is a controller that combines PID control laws and neural networks [19], and the implicit layer contains three units that are proportional (P),  
Figure 4: Structure of a single-neuron PID control system based on RBF identification. integral (I), and differential (D), and the structure of this controller is shown in Figure 5. Adaptivity is achieved through the adjustment of single-neuron weighting coefficients and the commonly used learning rules are the unsupervised Hebb learning rule, supervised Delta learning rule, and supervised Hebb learning rule [19]. In this paper, the controller performs online rectification of the weights based on the gradient descent method and the Jacobian information provided by the RBFNNI. The control and learning algorithms are as follows:  
Let the system control error be \(e(k)\) = \(r(k)-y(k)\). Take the neuron input as:  
\[\left\{ \begin{aligned} x_{c1}(k)=e(k)-e(k-1)\\ x_{c2}(k)=e(k)\\ x_{c3}(k)=e(k)-2e(k-1)+e(k-2)\end{aligned} \right. \tag{13}\]  
Neuronal outputs are:  
\[u(k)=u(k-1)+K{\sum\limits_{i=1}^{3}{v_{i}(k)x_{ci}(k)}} \tag{14}\]  
where \(v_{i}(k)\) is the weight coefficient of a single-neuron network, and \(K\) > 0 is the scale factor of single neuron.