Fuzzy control method for ventilation pressure of anesthesia machine

Abstract

A fuzzy control method for ventilation pressure of an anesthesia machine comprises the following steps of, in a pressure control mode, calculating a pressure value at the current flow rate, wherein Pis the pressure; using the Kalman estimation algorithm to estimate the pressure at the next moment of the current pressure value; substituting the estimated pressure value into an error system to calculate a control error amount, and performing PID control on the control error amount to convert a control pressure required at the next moment; converting the control pressure into a standard controlflow by using the formula P=F*R; and inputting the converted control flow in a flow PID proportional controller for flow feedback control.

Description

technical field [0001] The invention relates to a high-performance and high-precision pressure control ventilation mode for realizing anesthesia machines and ventilators, and belongs to the technical field of medical equipment manufacturing. Background technique [0002] In the control of anesthesia machines and ventilators, pressure-controlled ventilation is required for some special patients. There are two known pressure control methods: [0003] The pressure PID control is used to directly control the pressure of the patient. The advantage of this method is that the algorithm is simple to implement, and the PID pressure control realized by using a high-performance processor can be used for most patients with special diseases. The disadvantage is that for patients with different lesions, their own R and C values ​​are different. Using the traditional pressure control method to control the entire system based solely on the monitored pressure cannot well adapt to pressure c...

AI Technical Summary

Problems solved by technology

The disadvantage is that in the design process, the discourse gear and the membership relationship of the membership function in the fuzzy control rules have a relatively large impact on the control algorithm, and the gears and membership relationships are too dense. The calculation time of the control algorithm is too long, and expensive DSP is required. It can be realized quickly, if the gear position and subordinate relationship are too sparse, and the rule coverage is not enough, some control values ​​under certain R and C values ​​will not be covered, resulting in overshooting during the ventilation process, especially in very few special cases There will be shock phenomenon, which will increase the ventilation burden of the patient under these special R and C values, and cause complications