Double-layer control method for respiration assisting system based on force-air pressure combined regulation and control

Abstract

The invention relates to a force-air pressure combined regulation-based breathing assistance system double-layer control method, which comprises the following steps of: firstly, collecting the breathing flow of a human body and the interaction force of the abdomen; secondly, the collected data are subjected to characterization processing, and the relation among the air supply pressure, the auxiliary force and the extra pressure acting on the lung is quantified; then, constructing a lung kinetic model, representing the influence of additional pressure on the lung, and performing parameter estimation by using a least square method to obtain estimated values of airway resistance R and lung elasticity E; further, a required pressure curve is designed based on the lung dynamic characteristics and the respiratory flow amplitude, and expected auxiliary force is deduced according to the pressure curve; then, dynamic auxiliary force planning is completed in combination with data of an interaction force sensor, and planning force and expected force of an actuator are obtained; finally, safe and accurate control over the breathing assisting system is completed through a proportional differential control method with a feed-forward compensation item. The method provided by the invention can accurately adapt to the breathing physiological needs of the user, and effectively solves the problems of low safety, poor comfort and the like of the current breathing auxiliary system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of respiratory assistance system control, in particular to a double-layer control method of a respiratory assistance system based on force-air pressure combined regulation. BACKGROUND

[0002] Respiratory assistance systems play an important role in clinical practice. Traditional respiratory assistance equipment mainly realizes ventilation support through air pressure regulation, but its control method is single and difficult to adapt to the complex mechanical property changes in the respiratory process, which may lead to problems such as asynchrony between man and machine. In addition, the existing control methods mostly use single-layer control strategies, which are insufficient in responding to the dynamic changes of respiratory mechanics parameters (such as airway resistance and lung compliance), thereby affecting the ventilation effect.

[0003] In recent years, force-air pressure combined regulation technology has gradually attracted attention. By combining airway pressure signals and respiratory mechanics feedback, the respiratory demand can be more accurately matched. However, the existing combined control methods still have defects such as regulation lag and insufficient stability, and lack of hierarchical collaborative optimization mechanism, making it difficult to achieve efficient control under dynamic working conditions. Therefore, a new control method that can hierarchically coordinate force and air pressure control and improve system response speed and adaptability is needed to optimize the performance of the respiratory assistance system and reduce clinical risks. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application proposes a double-layer control method of a respiratory assistance system based on force-air pressure combined regulation, which effectively solves the problems of low stability, poor precision, and asynchrony between man and machine in the control process of the respiratory assistance system, thereby realizing accurate adaptation to the respiratory physiological needs of the user.

[0005] The technical solution adopted by the present application to achieve the above-mentioned purposes is as follows:

[0006] A double-layer control method of a respiratory assistance system based on force-air pressure combined regulation, comprising the following steps:

[0007] 1) obtaining the respiratory flow rate of the human body and the interactive force of the abdomen, and performing characterization processing thereon;

[0008] 2) constructing a lung dynamics model and estimating the model parameters using the least squares method to obtain the estimated values of the airway resistance R and the lung elasticity E;

[0009] 3) constructing a respiratory assistance force curve based on the lung dynamics characteristics and the respiratory flow rate amplitude, and deriving the expected assistance force;

[0010] 4) using the data of the interactive force sensor to plan the dynamic assistance force, obtaining the planned force and the expected force of the actuator;

[0011] 5) Based on the actuator expected force, use the proportional-derivative control method with feed-forward compensation term to safely and accurately control the respiratory assistance system.

[0012] The step 1) comprises the following steps:

[0013] 1.1) Quantify the actuator gas supply pressure P of the abdomen based on the mechanical properties of the pneumatic pressing assembly sup and the relationship between the generated auxiliary force F a :

[0014] F a = α·P sup + μ

[0015] 0 ≤ P sup ≤ 20 kPa

[0016] Wherein, α, μ represents the force transmission gain coefficient;

[0017] 1.2) Construct a biomechanically-based force-pressure transmission model to establish a quantitative mapping relationship between the robot auxiliary force F a and the additional pressure P eXo exerted by the robot on the lung:

[0018]

[0019] Wherein, A is the equivalent area of the contact area of the auxiliary device, k th is the elastic coefficient of the chest, V0 is the initial chest volume, η is the pressure conduction efficiency of the chest tissue, C ab is the compliance coefficient of the diaphragm;

[0020] 1.3) Define the transmission coefficient Simplify the transmission model to a linear expression:

[0021] P exo = δ·F a .

[0022] The step 2) comprises the following steps:

[0023] 2.1) Based on the first-order spring-damper model, construct the lung dynamics model, and obtain through the pressure balance equation:

[0024] P mus + P exo = RQ M + EV E

[0025] Wherein, P mus is the lung pressure generated by the respiratory muscle group, Q M , V Erespectively represent the average airflow velocity and minute ventilation, R is the airway resistance, and E is the lung elasticity;

[0026] 2.2) Estimate the model parameters using the least squares method:

[0027] [RE] T = (H T H) -1 H T y

[0028]

[0029] y = [delta * F a (1) … delta * F a (N)] T

[0030] wherein the upper index A represents the respiratory monitoring parameters under robot assistance, B represents the respiratory monitoring parameters under autonomous breathing, N represents the amount of data collected, H and y represent intermediate variables.

[0031] The step 3) comprises the following steps:

[0032] 3.1) Construct a respiratory assistance force curve:

[0033]

[0034] wherein P mus (theta) is the respiratory muscle pressure at time theta, the pressure amplitude Q0 is the initial flow amplitude, the phase difference theta is the respiratory phase;

[0035] 3.2) Derive the expected assistance force F a (theta) based on the respiratory assistance force curve:

[0036]

[0037] The step 4) is specifically:

[0038]

[0039] wherein F plan (theta) is the planned force, is the actuator expected force, F int (theta) is the sensor interaction force, B and K are respectively the damping coefficient and the stiffness coefficient, which can be set according to the assistance demand of the user, and alpha is the force transmission gain coefficient.

[0040] The step 5) is specifically:

[0041] The control function of the proportional differential control method with a feedforward compensation term is constructed, and the function is used to control the expected actuator supply pressure P sup The control function is:

[0042]

[0043] Wherein, I(θ) is the control current, k1 is the current-flow conversion coefficient, C f is the flow coefficient, P in is the gas source pressure, k P , k D are control parameters, which can be freely adjusted according to the user's auxiliary demand.

[0044] The present application has the following advantages and benefits:

[0045] The present application provides a double-layer control method of a breathing auxiliary system based on force-air pressure combined regulation, which can realize dynamic force regulation of the respiratory system, complete double constraint of interactive force and supply air pressure, automatically adjust the auxiliary force and air pressure through real-time monitoring of the interactive force and supply air pressure, and ensure the accuracy of the breathing auxiliary process. The present method greatly improves the human-machine adaptability of the system and ensures the safety of the breathing auxiliary process. The high human-machine adaptability enables the breathing auxiliary robot to better integrate into the daily life of the user, thereby providing long-term, stable and safe breathing support. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is the flowchart of the method of the present application;

[0047] Figure 2 is the lung dynamics model diagram of the present application;

[0048] Figure 3 is the control effect tracking diagram of the method of the present application; DETAILED DESCRIPTION

[0049] The present application will be further described in detail below in combination with the drawings and examples.

[0050] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below in combination with the drawings. In the following description, a lot of specific details are set forth in order to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the spirit of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0052] As shown is a method flow chart of the application; Figure 1 As shown is a method flow chart of the application;

[0053] A breathing assistance system control method based on force-air pressure combined regulation, comprising the following parts:

[0054] The application relates to a breathing assistance system double-layer control method based on force-air pressure combined regulation. sup , auxiliary force F a and the relationship among the additional pressure P exo acting on the lungs; then, a lung dynamics model is constructed to represent the influence of the additional pressure on the lungs, and least square method is used for parameter estimation to obtain the estimated values of airway resistance R and lung elasticity E; further, based on the lung dynamics characteristics and the breathing flow amplitude, a required breathing assistance force curve is designed, and the expected auxiliary force is derived according to the breathing assistance force curve; then, the dynamic auxiliary force planning is completed by combining the data of the interactive force sensor to obtain the planned force and the expected force of the actuator; finally, the safety and precision control of the breathing assistance system is completed through the proportional-derivative control method with a feedforward compensation term. The method proposed in the application can accurately adapt to the respiratory physiological needs of the user, and effectively solve the problems of low safety and poor comfort of the current breathing assistance system.

[0055] The data representation processing method is as follows:

[0056] Firstly, the mechanical characteristics of the pneumatic pressing assembly are determined to quantify the relationship between the actuator air supply pressure P sup of the abdomen and the generated auxiliary force F a .

[0057] F a = alpha * P sup + mu (0 <= P sup <= 20kPa)

[0058] Wherein, alpha, mu represent force transmission gain coefficient, can be obtained by calibration. This method has three advantages: without complex mechanical modeling, the implementation process is simplified, and the calculation efficiency is high. Without complex mechanical modeling means avoiding the tedious theoretical analysis and mathematical derivation of the internal structure and working principle of the pneumatic pressing assembly, reducing the errors and uncertainties that may occur in the modeling process. The simplified implementation process makes the experimental operation more convenient, and the required data can be obtained faster. And high calculation efficiency ensures that the results can be quickly obtained when dealing with a large amount of experimental data, improving the efficiency of the research.

[0059] Then, a biomechanics-based force-pressure transmission model is constructed to establish the quantitative mapping relationship between the robot-assisted force F a and the additional pressure P exo exerted on the lung by the robot.

[0060]

[0061] Wherein, A is the equivalent area of the contact area of the auxiliary device, k th is the elastic coefficient of the chest, representing the size of the elastic restoring force generated by the chest when the volume changes. V0 is the initial chest volume, and eta is the pressure transmission efficiency of the chest tissue. C ab is the compliance coefficient of the diaphragm, reflecting the ability of the diaphragm to deform under pressure.

[0062] Finally, let the transmission coefficient be

[0063] P exo = delta * F a

[0064] Further, in order to characterize the influence of airway additional pressure on the lung, a lung dynamics model is constructed. The least square method is used for parameter estimation, and the estimated values of airway resistance R and lung elasticity E are obtained.

[0065] Figure 2 The lung dynamics model of the present application is shown in the figure

[0066] The lung dynamics model adopts a first-order spring-damper model, and according to the pressure balance equation, we can get:

[0067] P mus + P exo = RQ M + EV E

[0068] Wherein, P mus is the lung pressure generated by the respiratory muscle group, Q M , V EThe average airflow velocity and minute ventilation, respectively, can be measured by the respiratory flow sensor, R is the airway resistance, and E is the lung elasticity.

[0069] The airway resistance R and lung elasticity E can be obtained by parameter estimation, as follows:

[0070] [RE] T = (H T H) -1 H Y y

[0071]

[0072] y = [δ·F a (1) … δ·F a (N)] T

[0073] Further, according to the lung dynamics and the respiratory flow amplitude, a required respiratory assistance force curve is designed, and the respiratory assistance force curve is generated as follows:

[0074]

[0075] wherein the pressure amplitude is the maximum muscle pressure required to generate a flow with an amplitude of Q0; the phase difference reflects the advance of the muscle pressure waveform relative to the flow waveform. θ is the respiratory phase, representing the progress of the respiratory phase.

[0076] The dynamic assistance force planning method is as follows:

[0077] First, the expected assistance force

[0078]

[0079] Then, combined with the data of the interaction force sensor, the planning force F plan and the actuator expected force

[0080]

[0081] wherein F int is obtained by the interaction force sensor, the damping term suppresses the rapid change of the assistance force, and the stiffness term K·F a (θ) maintains the compliance characteristics of the system, thereby balancing safety and assistance effect. The damping term provides a reverse damping force when the assistance force changes too fast, slows down the change speed of the assistance force, and avoids sudden impact on the patient. The stiffness term K·F aThe (θ) makes the system able to automatically adjust the rigidity according to the size of the interaction force when the auxiliary force is applied, appropriately increases the rigidity of the system when the interaction force is large, and guarantees the auxiliary effect; and reduces the rigidity when the interaction force is small, and improves the comfort of the patient. The force feedback mechanism can effectively prevent the discomfort of the patient caused by the excessive auxiliary force, while maintaining the necessary auxiliary effect.

[0082] In order to realize the accurate tracking control of the desired pressure P sup , a proportional-derivative control method with a feedforward compensation term is proposed, and the control function is as follows:

[0083]

[0084] Wherein, I is the control current, k1 is the current-flow conversion coefficient, determined by the pressure valve, C f is the flow coefficient, and P in is the gas source pressure (constant value).

[0085] The nonlinear feedforward control term is introduced in the formula, wherein the nonlinear model formula of the flow-pressure difference of the pneumatic system is used in the square root operation, the physical characteristic compensation of the pipeline supply pressure P sup is realized, so as to enhance the open-loop dynamic response capability. In the actual pneumatic execution system, the supply pressure of the pipeline will be affected by various factors, such as the fluctuation of the gas source pressure, the change of the pipeline resistance, etc. The nonlinear feedforward control term can adjust the control current in advance according to the change of these factors, so that the system can respond to the change of the desired pressure more quickly, and improve the dynamic performance of the system. By adjusting the proportional gain k P and the differential gain k D , the system can track the desired pressure quickly while ensuring the closed-loop stability.

[0086] In order to further illustrate the beneficial effects of the method of the present application, a control effect tracking test is carried out. The subject maintains a breathing rate of 30 times per minute, and the control system continuously runs for 30 seconds. During this period, all state variables are synchronously sampled and recorded.

[0087] Figure 3 The control effect tracking graph of the method of the present application is shown in FIG.

[0088] As can be seen from the graph, according to the breathing phase, the method accurately realizes the generation of the corresponding lung auxiliary force. During the system operation, the interaction force F int is always lower than the target auxiliary force; when the dynamic auxiliary force planning is carried out, if F intWhen the rate of change of the desired pressure is too large, the planning ability will decrease significantly to ensure safety. At the same time, by adjusting the parameters B and K, the elasticity of the actuator structure can be adjusted to provide personalized assistance for patients. When tracking the desired supply pressure, the tracking error of the actuator supply pressure relative to the desired pressure is -0.86±1.7kPa (mean±standard deviation), and the response time is less than 50ms, which demonstrates high-precision pressure tracking performance.

[0089] In summary, it can be shown that the method realizes hierarchical mapping and closed-loop control from the respiratory assistance force curve to the actuator control by reasonably designing the force-pressure conversion relationship. The dynamic planning and adjustment mechanism of the outer loop of the assistance force effectively guarantees the safety and comfort in the respiratory assistance process, while the accurate control of the supply pressure in the inner loop realizes the target assistance effect.

Claims

1. A double-layer control method of a breathing assistance system based on force-air pressure combined regulation, characterized in that, The method comprises the following steps: 1) obtaining the respiratory flow and the interactive force of the abdomen of a human body and performing characterization processing thereon; 2) constructing a lung dynamics model and estimating the model parameters using a least square method to obtain estimated values of airway resistance R and lung elasticity E; 3) constructing a respiratory assistance force curve based on the lung dynamics characteristics and the respiratory flow amplitude and deriving an expected assistance force; 4) performing dynamic assistance force planning using data of the interactive force sensor to obtain a planned force and an actuator expected force; 5) performing safe and accurate control of the respiratory assistance system using a proportional-derivative control method with a feedforward compensation term based on the actuator expected force.

2. The double-layer control method of a breathing assistance system based on force-air pressure combined regulation according to claim 1, characterized in that, The step 1) comprises the following steps: 1.1) Quantification of the actuator supply air pressure P of the abdomen based on the mechanical characteristics of the pneumatic pressing assembly sup and the resulting assistive force F a : F a = a · P sup + μ 0 < P sup ≤ 20 kPa Wherein, α and μ represent force transmission gain coefficients; 1.2) Constructing a biomechanics-based force-pressure transfer model to establish a quantitative mapping relationship between the robot-applied force F a and the additional pressure P exerted on the lung: exo ​ where A is the equivalent area of the contact area of the auxiliary device, k th is the elastic coefficient of the thoracic cavity, V0is the initial thoracic cavity volume, η is the pressure transmission efficiency of the chest tissue, C ab is the compliance coefficient of the diaphragm; 1.3) Defining the transfer coefficient Simplifying the transfer model to a linear expression: P exo = δ · F a .

3. The double-layer control method of a breathing assistance system based on force-air pressure combined regulation according to claim 1, characterized in that, The step 2) comprises the following steps: 2.1) constructing a lung dynamics model based on a first-order spring-damper model and obtaining a pressure balance equation: P mus +P exo = RQ M + EV E where P mus is the lung pressure generated by the respiratory muscle group, Q M , V E represent the mean airflow velocity and minute ventilation, respectively, R is the airway resistance, and E is the lung elastance. 2.2) estimating the model parameters using a least square method: [RE] T = (H T H) -1 H T y y = [δ · F a (1) … δ·F a (N)] T Wherein, the upper index A represents a respiratory monitoring parameter under robot assistance, B represents a respiratory monitoring parameter under autonomous respiration, N represents the amount of collected data, and H and y represent intermediate variables.

4. The double-layer control method of the breathing assistance system based on force-air pressure combined regulation according to claim 1, characterized in that, The step 3) comprises the following steps: 3.1) constructing a respiratory assistance force curve: wherein P mus (θ) is the respiratory muscle pressure at time θ, pressure amplitude Q0is the initial flow amplitude, phase difference θ is the respiratory phase; 3.2) Deriving the desired assistance force F based on the breathing assistance force curve a (θ):

5. The double-layer control method of a breathing assistance system based on force-air pressure combined regulation according to claim 1, characterized in that, The step 4) is specifically: where F plan (θ) is the planning force, is the actuator desired force, F int (θ) is the interaction force of the sensor, B and K are damping and stiffness coefficients, respectively, which can be set according to the user's assistance demand, and α is the force transmission gain coefficient.

6. The double-layer control method of a breathing assistance system based on force-air pressure combined regulation according to claim 1, characterized in that, The step 5) is specifically: The control function of the proportional-differential control method with a feed-forward compensation term is constructed, and the function is used to control the desired actuator supply pressure P sup (θ) is performed, and the control function is: Wherein, I(θ) is control current, k1 is current-flow conversion coefficient, C f is flow coefficient, P in is gas source pressure, k P , k D is control parameter, which can be freely adjusted according to the auxiliary demand of the user.

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