Non-invasive ventilator single-level CPAP airway pressure self-adaptive control method

Abstract

The invention discloses a non-invasive ventilator single-level CPAP airway pressure self-adaptive control method, and relates to the technical field of ventilator control, the method comprises the following steps: S1, defining model parameters, and constructing a filter-ventilation hose aerodynamic model; s2, constructing a recursive least square estimator, and estimating the estimated values of the air resistance and the pressure difference; s3, inputting the estimated value to the input end and the fan end of the breathing machine to obtain a rotating speed signal of a turbofan, and adjusting the fan outlet pressure and the fan outlet flow by changing the rotating speed of the turbofan; and S4, based on the differential pressure estimation error, controlling and adjusting a pressure given signal of the recursive least square estimator in real time, and realizing full compensation of the internal differential pressure of the filter-ventilation hose. According to the invention, aiming at the nature of pressure change of the near-end airway, the whole control only relates to a filter-ventilation hose, and a patient model and parameters are not needed, so that the range of audiences is not limited.

Description

Technical Field

[0001] The present invention relates to the technical field of ventilator control, and in particular to a method for adaptively controlling single-level CPAP airway pressure of a non-invasive ventilator. Background Art

[0002] The purpose of the single-level continuous positive airway pressure (CPAP) mode of the ventilator is to use this positive pressure to help patients open their airways and breathe smoothly. This is very important for people with airway breathing disorders, especially for the treatment of obstructive sleep apnea syndrome. When the ventilator is in single-level CPAP mode, how to make the air pressure at the proximal end of the airway quickly track the constant pressure command at the blower end and maintain this constant pressure becomes the key to CPAP mode control. It has attracted widespread attention and research from scholars.

[0003] The CPAP control of the ventilator is faced with a wide range of patients, and the characteristics of the filter-hose system also change over time. In addition, the unknown influence of the patient's respiratory activity puts higher demands on the ventilator controller to accurately track the pressure setting and ensure the synchronization between the patient and the ventilator.

[0004] 1. The linear feedback controller introduced by the proximal pressure can only partially compensate for the pressure loss. Suboptimal performance described by overshoot and adjustment time is obtained. The controller parameters are fixed and cannot be changed during operation, resulting in poor robustness.

[0005] 2. Based on the linear feedback controller, variable gain control is introduced to reduce the overshoot of the patient's respiratory flow and avoid false triggering. This control still produces overshoot; at the same time, it is difficult to achieve control using the patient's respiratory flow.

[0006] 3. An adaptive feedback control method that adjusts controller parameters based on identifying the patient model. In actual engineering, it is difficult to accurately identify the patient model.

[0007] 4. Model predictive control requires precise patient model parameters, which is difficult to achieve in engineering practice.

[0008] 5. The iterative learning control method has good tracking performance. However, it is limited to repeated sequences of given points and initial conditions, and is greatly affected by the patient's spontaneous breathing activities, resulting in a significant drop in performance.

[0009] In summary, the existing control methods are difficult to fully exert their control effectiveness when faced with factors such as a large range of patients, time-varying filter-hose characteristics, and patient respiratory activity.

[0010] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0011] In view of the problems in the related art, the present invention proposes a method for adaptively controlling the airway pressure of a non-invasive ventilator with single-level CPAP to overcome the above-mentioned technical problems existing in the existing related art.

[0012] To this end, the specific technical solution adopted by the present invention is as follows:

[0013] A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP, the method comprising the following steps:

[0014] S1. Define model parameters and construct a pneumatic model of the filter-ventilation hose;

[0015] S2, constructing a recursive least squares estimator to estimate the estimated values ​​of air resistance and pressure difference;

[0016] S3, inputting the estimated value to the input end and the fan end of the ventilator to obtain a speed signal of the turbo fan, and adjusting the fan outlet pressure and the fan outlet flow rate by changing the turbo fan speed;

[0017] S4. Based on the pressure difference estimation error, the pressure setting signal of the recursive least squares estimator is controlled and adjusted in real time to achieve full compensation of the internal pressure difference of the filter-ventilation hose.

[0018] Further, defining model parameters and constructing the aerodynamic model of the filter-ventilation hose includes the following steps:

[0019] S11, obtaining the proximal airway pressure of the filter-ventilation hose and the fan outlet pressure, and subtracting the proximal airway pressure from the fan outlet pressure as the pressure difference;

[0020] S12, obtaining the fan outlet flow rate and the air resistance of the filter-ventilation hose, and defining the pressure difference as the product of the fan outlet flow rate and the air resistance based on the aerodynamic theory;

[0021] S13. Define the proximal airway pressure, fan outlet pressure, fan outlet flow, air resistance and pressure difference as model parameters, and construct the aerodynamic model of the filter-ventilation hose.

[0022] Furthermore, the pressure difference is calculated as:

[0023] △p=p o -p w =q o r h

[0024] In the formula, Δp represents the pressure difference; p 0 Indicates the fan outlet pressure; p w Represents the proximal airway pressure; q 0 Indicates the fan outlet flow rate; r hIndicates the air resistance of the filter-vent hose.

[0025] Furthermore, a recursive least squares estimator is constructed to estimate the estimated values ​​of air resistance and pressure difference, including the following steps:

[0026] S21. Based on the robust adaptive control principle, the covariance matrix dynamic equation and the air resistance estimation state equation are established, and a recursive least squares estimator with a forgetting factor is constructed;

[0027] S22, calculating the product of the real-time estimated value of air resistance and the fan outlet flow rate to obtain a pressure difference estimated value;

[0028] S23, setting normalized parameters of the pressure difference, the pressure difference estimation value, the fan outlet flow rate, the air resistance and the real-time estimation value, and calculating the pressure difference estimation error between the pressure difference and the pressure difference estimation value.

[0029] Furthermore, the expression of the covariance matrix dynamic equation is:

[0030]

[0031] In the formula, represents the dynamic equation of the covariance matrix; β represents the forgetting factor that accelerates the calculation speed; P represents the covariance matrix; q 0 represents the fan outlet flow rate; m represents the normalization coefficient;

[0032] The expression of the state equation for air resistance estimation is:

[0033]

[0034] In the formula, represents the dynamic equation of the covariance matrix; Δp represents the true value of the pressure difference; Represents the real-time estimated value of air resistance; q 0 Indicates the fan outlet flow rate.

[0035] Furthermore, the expression of the recursive least squares estimator is:

[0036]

[0037] In the formula, represents the estimated value of the pressure difference; Δp represents the true value of the pressure difference; m represents the normalization coefficient; P represents the covariance matrix; q 0 Indicates the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose; Represents the real-time estimated value of air resistance; p 0 Indicates the fan outlet pressure; p w Represents the proximal airway pressure; q 0 Indicates the fan outlet flow rate.

[0038] Furthermore, the calculation formula of the pressure difference estimation error is:

[0039]

[0040] Z=θ *T φ

[0041]

[0042] Where ε represents the pressure difference estimation error; z represents the normalization parameter of the true value of the pressure difference; represents the normalization parameter of the pressure difference estimate; m represents the normalization coefficient; Δp represents the true value of the pressure difference; q 0 Indicates the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose; represents the average value of the real-time estimated value of air resistance; φ represents the parameter flow value of the fan outlet flow; θ * represents the true value of the parameter of air resistance; θ represents the parameter estimate of the real-time estimate of air resistance.

[0043] Furthermore, the estimated value is input to the input end and the fan end of the ventilator to obtain the speed signal of the turbo fan, and the fan outlet pressure and the fan outlet flow rate at the fan outlet are adjusted by changing the speed, including the following steps:

[0044] S31, obtaining a pressure difference estimation value and a real-time estimation value of air resistance, driving a turbine fan inside the ventilator to operate, and obtaining a speed signal of the turbine fan to obtain a turbine fan speed;

[0045] S32, establishing a mathematical relationship model between the turbine fan speed and the fan outlet pressure;

[0046] S33. Based on the mathematical relationship model, the fan outlet pressure and fan outlet flow rate of the turbo fan are linearly adjusted by changing the turbo fan speed.

[0047] Furthermore, the mathematical relationship model between the turbine fan speed and the fan outlet pressure is expressed as:

[0048]

[0049] In the formula, p 0 Indicates the fan outlet pressure; p i represents the local atmospheric pressure; D represents the diameter of the turbine fan impeller; and n represents the speed of the turbine fan.

[0050] Furthermore, the ventilator includes a permanent magnet synchronous motor, a turbine fan, a filter-ventilation hose, a leakage hole and a breathing mask.

[0051] The beneficial effects of the present invention are:

[0052] 1. By targeting the nature of proximal airway pressure changes, the entire control only involves the filter-ventilation hose, and no patient model and parameters are required, so there is no audience range restriction; and the recursive least squares estimator estimates the air resistance in real time, avoiding control errors caused by changes in air resistance during operation; similarly, because the control only involves the filter-ventilation hose, the influence of the patient's spontaneous breathing activity on the control target is completely shielded, so that it has no effect on the performance of the control system.

[0053] 2. The present invention utilizes the simulink simulation results of the non-invasive ventilator single-level CPAP proximal pressure adaptive control, which shows that under this control, the system proximal pressure can quickly track the given pressure setting and remain unchanged; there is no overshoot, the adjustment time is adjustable, and the steady-state error is zero, which well realizes the non-invasive ventilator single-level CPAP proximal pressure adaptive control; further simulation shows that when the system instruction is changed to a dual-level pressure given signal, the proximal end also exhibits very good fast and difference-free tracking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 is a flow chart of a method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to an embodiment of the present invention;

[0056] Figure 2 is a structural schematic diagram of a non-invasive ventilator-patient ventilation system according to an embodiment of the present invention;

[0057] Figure 3 is a simulation block diagram of a single-level CPAP adaptive control system for a non-invasive ventilator according to an embodiment of the present invention;

[0058] Figure 4 is a simulation result of single-level CPAP adaptive control of a non-invasive ventilator according to an embodiment of the present invention;

[0059] Figure 5 is a schematic diagram of effects of different control methods according to an embodiment of the present invention;

[0060] Figure 6 1 is a simulation curve of patient respiratory flow under different control methods according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] According to an embodiment of the present invention, a method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP is provided.

[0062] The present invention aims to provide an adaptive control method for the single-level CPAP operation mode of a non-invasive ventilator, so as to overcome the influence of factors such as a wide range of patients, time-varying filter-hose characteristics, and patient breathing activities on CPAP treatment. The present invention solves the control problems encountered by the existing control methods, such as unadjustable controller parameters, unsatisfactory performance indicators, difficult identification of patient model parameters, given pressure of the ventilator system, and poor adaptability to the patient's spontaneous breathing activities.

[0063] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the method for adaptively controlling the airway pressure of a non-invasive ventilator single-level CPAP according to an embodiment of the present invention, the method comprises the following steps:

[0064] S1. Define model parameters and construct the aerodynamic model of the filter-ventilation hose.

[0065] In the description of the present invention, defining model parameters and constructing the aerodynamic model of the filter-ventilation hose includes the following steps:

[0066] S11. Obtain the proximal airway pressure of the filter-ventilation hose and the fan outlet pressure, and take the difference between the fan outlet pressure and the proximal airway pressure as the pressure difference.

[0067] In the description of the present invention, the calculation formula of the pressure difference is:

[0068] △p=p o -p w =q o r h

[0069] In the formula, Δp represents the pressure difference; p 0 Indicates the fan outlet pressure; p w Represents the proximal airway pressure; q 0 Indicates the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose.

[0070] S12. Obtain the fan outlet flow rate and the air resistance of the filter-ventilation hose. Based on the aerodynamic theory, define the pressure difference as the product of the fan outlet flow rate and the air resistance.

[0071] S13. Define the proximal airway pressure, fan outlet pressure, fan outlet flow, air resistance and pressure difference as model parameters, and construct the aerodynamic model of the filter-ventilation hose.

[0072] S2. Construct a recursive least squares estimator to estimate the estimated values ​​of air resistance and pressure difference.

[0073] In the description of the present invention, constructing a recursive least squares estimator to estimate the estimated value of air resistance and pressure difference includes the following steps:

[0074] S21. Based on the robust adaptive control principle, the covariance matrix dynamic equation and the air resistance estimation state equation are established, and a recursive least squares estimator with forgetting factor is constructed.

[0075] In the description of the present invention, the expression of the covariance matrix dynamic equation is:

[0076]

[0077] In the formula, represents the dynamic equation of the covariance matrix; β represents the forgetting factor that accelerates the calculation speed; P represents the covariance matrix; q 0 represents the fan outlet flow rate; m represents the normalization coefficient;

[0078] The expression of the state equation for air resistance estimation is:

[0079]

[0080] In the formula, represents the dynamic equation of the covariance matrix; Δp represents the true value of the pressure difference; Represents the real-time estimated value of air resistance; q 0 Indicates the fan outlet flow rate.

[0081] In the description of the present invention, the expression of the recursive least squares estimator is:

[0082]

[0083] In the formula, represents the estimated value of the pressure difference; Δp represents the true value of the pressure difference; m represents the normalization coefficient; P represents the covariance matrix; q 0 Indicates the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose; Represents the real-time estimated value of air resistance; p 0 Indicates the fan outlet pressure; p w Represents the proximal airway pressure; q 0 Indicates the fan outlet flow rate.

[0084] S22. Calculate the product of the real-time estimated value of the air resistance and the fan outlet flow rate to obtain an estimated value of the pressure difference.

[0085] In the description of the present invention, the calculation formula of the pressure difference estimation error is:

[0086]

[0087] Z=θ *T φ

[0088]

[0089] Where ε represents the pressure difference estimation error; z represents the normalization parameter of the true value of the pressure difference; represents the normalization parameter of the pressure difference estimate; m represents the normalization coefficient; Δp represents the true value of the pressure difference; q 0 It indicates the fan outlet flow rate; rh indicates the air resistance of the filter-ventilation hose; represents the average value of the real-time estimated value of air resistance; φ represents the parameter flow value of the fan outlet flow; θ * represents the true value of the parameter of air resistance; θ represents the parameter estimate of the real-time estimate of air resistance.

[0090] S23, setting normalized parameters of the pressure difference, the pressure difference estimation value, the fan outlet flow rate, the air resistance and the real-time estimation value, and calculating the pressure difference estimation error between the pressure difference and the pressure difference estimation value.

[0091] S3. Input the estimated value to the input end and the fan end of the ventilator to obtain the speed signal of the turbo fan, and adjust the fan outlet pressure and the fan outlet flow by changing the turbo fan speed.

[0092] In the description of the present invention, the estimated value is input to the input end and the fan end of the ventilator to obtain the speed signal of the turbo fan, and the fan outlet pressure and the fan outlet flow rate at the fan outlet are adjusted by changing the speed, including the following steps:

[0093] S31, obtaining a pressure difference estimation value and a real-time estimation value of air resistance, driving the turbine fan inside the ventilator to operate, and obtaining a speed signal of the turbine fan to obtain a speed of the turbine fan.

[0094] S32. Establish a mathematical relationship model between the turbine fan speed and the fan outlet pressure.

[0095] In the description of the present invention, the mathematical relationship model between the turbine fan speed and the fan outlet pressure is expressed as:

[0096]

[0097] In the formula, p 0 represents the fan outlet pressure; pi represents the local atmospheric pressure; D represents the turbine fan impeller diameter; and n represents the turbine fan speed.

[0098] S33. Based on a mathematical relationship model, the fan outlet pressure and fan outlet flow rate of the turbo fan are linearly adjusted by changing the turbo fan speed.

[0099] S4. Based on the pressure difference estimation error, the pressure setting signal of the recursive least squares estimator is controlled and adjusted in real time to achieve full compensation of the internal pressure difference of the filter-ventilation hose.

[0100] In the description of the present invention, the ventilator includes a permanent magnet synchronous motor, a turbo blower, a filter-ventilation hose, a leakage hole and a breathing mask.

[0101] like Figure 2 As shown in the figure, the non-invasive ventilator-patient ventilation system consists of a turbine blower driven by a permanent magnet synchronous motor, a filter-hose airway, a leak hole, a breathing mask, and a patient's lungs. The working process is: the permanent magnet synchronous motor drives the turbine blower to rotate according to the pressure given signal, compresses the outside air and sends it into the filter-hose airway, where a leak hole is provided. The main airway is connected to the patient's breathing mask to provide air to enter the patient's lungs to complete the inhalation process. When exhaling, the patient's exhaled waste gas is discharged from the system through the leak hole. Figure 2 The definitions of variables and parameters are shown in Table 1.

[0102] Table 1: Non-invasive ventilator-patient ventilation system variables and parameter definitions

[0103]

[0104] The present invention describes the structural principle of a non-invasive ventilator-patient ventilation system. In view of the existing control methods, the control scheme of the present invention focuses on the filter-ventilation hose (FH) that causes the proximal pressure change. Due to the existence of FH, the proximal pressure p w With the fan outlet pressure p 0 There is a pressure difference Δp=p o -p w .

[0105] According to aerodynamics, this pressure difference can also be expressed as the fan outlet flow rate q 0 Air resistance r of FH h The product of is expressed as Δp = q o r h During ventilation, the air resistance of FH is h is unknown.

[0106] According to the robust adaptive control principle, this scheme designs a recursive least squares estimator for Δp ​​and obtains the air resistance r h The real-time estimate of

[0107] Introduce it into the input end and superimpose it with the fan end pressure setting to obtain the speed signal required by the fan turbine. Change the speed to change the pressure p at the fan outlet. 0 , flow rate q 0 At the same time, using the error The estimator parameters are controlled and changed in real time to achieve full compensation of the pressure difference Δp.

[0108] Theory shows that this process is exponentially asymptotic, thus smoothly achieving the rapid tracking and fixed value adjustment of the proximal pressure of the single-level CPAP non-invasive ventilator.

[0109] The principles, formula derivation and steps of the above control algorithm are described as follows.

[0110] The problem solved in this paper is that since the air resistance of FH is unknown, the pressure difference compensator parameters between the pressure at the outlet of the turbo blower and the pressure at the proximal end of the airway cannot be determined, and full compensation cannot be achieved. It is difficult to achieve a perfect control effect.

[0111] The present invention adopts a self-correcting adaptive control law and uses a recursive least squares estimation method with a forgetting factor to estimate r h , while compensating the pressure difference. And assume that the pressure difference estimate is Air resistance h The estimated value is Fan outlet flow q 0 .

[0112] To derive the standard formula, assume that the true value of the pressure difference is: z = Δp = p o -p w =r h q o , the pressure difference is estimated to be:

[0113] Let φ = q o ,θ * =r h (For r h The true value of , but the value is unknown), (For r h estimated value).

[0114] The pressure difference estimation error is:

[0115] That is, z = θ *T φ, Error function:

[0116] Where m is the normalization coefficient.

[0117] 1. Establish the optimal cost function

[0118]

[0119] The above formula consists of two parts. The integral part is the penalty function for the error in the range [0, t]; the rest is the penalty function for the initial value estimate. The goal is to find the parameter estimate θ corresponding to the minimum value of the cost function, so that θ→θ * .

[0120] Among them, τ is the integral variable, t is the current time, e -βt is the exponential decay factor at time t, e -β(t-τ) is the exponential decay factor at time t-τ (characterizing the decay in the time [0, t]), Q 0 is the weight matrix in the cost function.

[0121] in, (positive definite symmetric matrix); β is the forgetting factor that accelerates the calculation speed.

[0122] 2. Find the gradient of the function in formula (1) with respect to θ, and set it to zero, and we can get:

[0123]

[0124] Right now

[0125]

[0126] Obtained from this

[0127] in

[0128] 3. To avoid matrix inversion operations, the differential equation can be used recursively to calculate P(t).

[0129] Due to PP -1 =I, where P -1 is the inverse matrix of the matrix P.

[0130] Taking the derivative of both ends of the above equation with respect to time, we get

[0131]

[0132] but

[0133]

[0134] Substituting the above equation into equation (*), we can get

[0135]

[0136] Simplified

[0137] Similarly, the derivative of (2) with respect to time, taking into account εm 2 = z - θ T φ, we can get the optimal step length path direction, that is,

[0138]

[0139] 4. Parameter estimation error Among them, r h is a constant. From formula (4), we can get:

[0140]

[0141] Formula (3) and (4) are the recursive least squares estimation algorithms with forgetting factor to solve this problem.

[0142] The present invention uses the turbine fan speed n and the fan outlet pressure p o Relationship

[0143]

[0144] In the formula, p i is the local atmospheric pressure; D is the diameter of the turbine fan impeller. The above formula shows that the turbine fan outlet pressure and the turbine fan speed are linear functions. Changing the turbine fan speed n can linearly change the turbine fan outlet pressure p 0 .

[0145] In the problem solved by the present invention, all variables are scalars, so formulas (3) and (4) can be written as formulas (7) and (8). (The definitions of relevant variables have been given above)

[0146] Estimator covariance matrix dynamic equation

[0147]

[0148] Air resistance h Estimation of the state equation

[0149]

[0150] Then by The estimated value of Δp can be obtained. According to the definition of Δp, the pressure difference recursive least squares estimator can be expressed as

[0151]

[0152] Formula (9) is the pressure difference recursive least squares estimator. It can be seen that its input is the turbine fan outlet flow rate q o , turbo blower outlet pressure p o , ventilator proximal airway pressure p wThe output of the estimator is fed back to the system input and algebraically summed with the system given value, thus forming a feedback compensator. This achieves full compensation for Δp ​​and achieves perfect control effect.

[0153] The above solution is now further described in conjunction with specific embodiments.

[0154] The structure of the single-level CPAP adaptive control system of the non-invasive ventilator is as follows Figure 3 shown. Figure 3 In the system, the forward channel is composed of a blower, a filter-ventilation hose-patient airway (FHP); ​​the feedback channel is a closed loop formed by the output of the FHP pressure difference recursive least squares estimator (Estimator) back to the system input end and superimposed with the system pressure setting, which compensates for the changes caused by the pressure difference in real time and ensures that the proximal pressure is constant. The SPBE signal simulates the patient's spontaneous breathing and adds white noise interference.

[0155] In the simulation system, the corresponding module equations are as follows:

[0156] 1. According to the principles of fluid mechanics, Figure 2 , and see p l , p 0 (see Table 1 for definitions) as independent variables, the algebraic equations for the remaining pressure and flow of the system are (including the description of the FHP link):

[0157]

[0158] 2. Lung state equation for patients with spontaneous breathing:

[0159]

[0160] in, The item represents the impact of the patient's spontaneous breathing.

[0161] 3. Dynamic equations of permanent magnet synchronous motor

[0162]

[0163] Where, ζ represents the motor damping ratio; ω n Indicates the natural oscillation frequency of the motor.

[0164] 4. Δp estimator algorithm

[0165] Estimator covariance matrix dynamic equation:

[0166]

[0167] Air resistance h Estimate the state equation:

[0168]

[0169] Air resistance h Estimated error state equation:

[0170]

[0171] Where P is the covariance matrix; m(>0) represents the normalized attenuation parameter.

[0172] The simulation results are as follows Figure 4 shown.

[0173] White noise interference is added into the simulation to test the anti-interference ability of the system. Figure 4 The dashed line in the top image indicates the 5 cmH 2 O CPAP continuous positive pressure is given; the solid line shows the effect of tracking and maintaining the constant value of the proximal pressure within 6 seconds. Figure 4 The solid line in the middle image represents the airway flow, indicating that the patient is breathing smoothly on his own. Figure 4 The solid line in the bottom image simulates the opening and closing pressure of the lungs when the patient breathes spontaneously in the presence of noise. It can be seen that this adaptive control can keep the proximal pressure constant, ensuring that CPAP treatment is not affected by the patient's spontaneous breathing, and the patient's spontaneous breathing is smooth during the control process. At the same time, there is no overshoot in the dynamic process, and the adjustment time is adjustable; the steady-state error is close to zero.

[0174] Figure 5 The simulation comparison of this control method with feedforward + feedback control and feedforward + feedback + PID control methods is shown.

[0175] Figure 5 In the equation, s represents 5 cmH 2 Pressure setting signal p of O s ; The solid line represents the proximal pressure tracking signal p during adaptive control w ; The dotted line indicates the p when feedforward + feedback + PID control w Response signal; the thin dotted line indicates p during feedforward + feedback control w Response signal. Figure 5 It shows that the adaptive control can complete the transition process within 5 seconds and perfectly track the 5cmH 2 Pressure setting signal p of O s When other control methods are used, they cannot track the pressure given signal p at all. s .

[0176] Figure 6 The patient's respiratory flow response signals under the above three controls are shown.

[0177] Figure 6It shows that only when this adaptive control is used, the patient's respiratory flow does not overshoot, so there will be no synchronous mis-triggering of the ventilator and the patient.

[0178] In summary, with the help of the above technical solution of the present invention, by targeting the nature of the proximal airway pressure change, the entire control only involves the filter-ventilation hose, and no patient model and parameters are required, so there is no audience range restriction; and the recursive least squares estimator estimates the air resistance in real time, avoiding the control error caused by the change of air resistance during operation; similarly, since the control only involves the filter-ventilation hose, the influence of the patient's spontaneous breathing activity on the control target is completely shielded, so that it has no effect on the performance of the control system. The present invention uses the simulink simulation results of the non-invasive ventilator single-level CPAP proximal pressure adaptive control to show that under this control, the system proximal pressure can quickly track the pressure constant given and remain unchanged; there is no overshoot, the adjustment time is adjustable, and the steady-state error is zero, which well realizes the non-invasive ventilator single-level CPAP proximal pressure adaptive control; further simulation shows that when the system instruction is changed to a dual-level pressure given signal, the proximal end also shows very good fast and difference-free tracking performance.

[0179] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP, characterized in that: The method comprises the following steps: S1. Define model parameters and construct a pneumatic model of the filter-ventilation hose; S2, constructing a recursive least squares estimator to estimate the estimated values ​​of air resistance and pressure difference; S3, inputting the estimated value to the input end and the fan end of the ventilator to obtain a speed signal of the turbo fan, and adjusting the fan outlet pressure and the fan outlet flow rate by changing the turbo fan speed; S4. Based on the pressure difference estimation error, the pressure setting signal of the recursive least squares estimator is controlled and adjusted in real time to achieve full compensation of the internal pressure difference of the filter-ventilation hose.

2. The method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 1, characterized in that: Defining model parameters and constructing the aerodynamic model of the filter-ventilation hose comprises the following steps: S11, obtaining the proximal airway pressure of the filter-ventilation hose and the fan outlet pressure, and subtracting the proximal airway pressure from the fan outlet pressure as the pressure difference; S12, obtaining the fan outlet flow rate and the air resistance of the filter-ventilation hose, and defining the pressure difference as the product of the fan outlet flow rate and the air resistance based on the aerodynamic theory; S13. Define the proximal airway pressure, fan outlet pressure, fan outlet flow, air resistance and pressure difference as model parameters, and construct the aerodynamic model of the filter-ventilation hose.

3. A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 2, characterized in that: The pressure difference is calculated as: Δp=p o -p w =q o r h In the formula, Δp represents the pressure difference; p0 represents the fan outlet pressure; p w Indicates proximal airway pressure; q0 represents the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose.

4. The method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 1, characterized in that: The method of constructing a recursive least squares estimator to estimate the estimated values ​​of air resistance and pressure difference comprises the following steps: S21. Based on the robust adaptive control principle, the covariance matrix dynamic equation and the air resistance estimation state equation are established, and a recursive least squares estimator with a forgetting factor is constructed; S22, calculating the product of the real-time estimated value of air resistance and the fan outlet flow rate to obtain a pressure difference estimated value; S23, setting normalized parameters of the pressure difference, the pressure difference estimation value, the fan outlet flow rate, the air resistance and the real-time estimation value, and calculating the pressure difference estimation error between the pressure difference and the pressure difference estimation value.

5. A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 4, characterized in that: The expression of the covariance matrix dynamic equation is: In the formula, represents the covariance matrix dynamic equation; β represents the forgetting factor that accelerates the calculation speed; P represents the covariance matrix; q0 represents the fan outlet flow rate; m represents the normalization coefficient; The expression of the air resistance estimation state equation is: In the formula, represents the covariance matrix dynamic equation; Δp represents the true value of the pressure difference; Indicates the real-time estimate of air resistance; q0 represents the fan outlet flow rate.

6. A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 4, characterized in that: The expression of the recursive least squares estimator is: In the formula, represents the estimated value of differential pressure; Δp represents the true value of the pressure difference; m represents the normalization coefficient; P represents the covariance matrix; q0 represents the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose; Indicates the real-time estimate of air resistance; p0 represents the fan outlet pressure; p w Indicates proximal airway pressure; q0 represents the fan outlet flow rate.

7. A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 4, characterized in that: The calculation formula of the pressure difference estimation error is: z=θ *T f Where ε represents the pressure difference estimation error; z represents the normalization parameter of the true value of the pressure difference; A normalization parameter representing the pressure difference estimate; m represents the normalization coefficient; Δp represents the true value of the pressure difference; q0 represents the fan outlet flow rate; r h Indicates the air resistance of the filter-vent hose; represents the average value of the real-time estimate of air resistance; φ represents the parameter flow value of the fan outlet flow; θ * The true value of the parameter representing the air resistance; θ represents a parameter estimate of a real-time estimate of air resistance.

8. The method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 1, characterized in that: The step of inputting the estimated value to the input end and the fan end of the ventilator to obtain a speed signal of the turbo fan, and adjusting the fan outlet pressure and the fan outlet flow rate at the fan outlet by changing the speed comprises the following steps: S31, obtaining a pressure difference estimation value and a real-time estimation value of air resistance, driving a turbine fan inside the ventilator to operate, and obtaining a speed signal of the turbine fan to obtain a turbine fan speed; S32, establishing a mathematical relationship model between the turbine fan speed and the fan outlet pressure; S33. Based on the mathematical relationship model, the fan outlet pressure and fan outlet flow rate of the turbo fan are linearly adjusted by changing the turbo fan speed.

9. A method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 8, characterized in that: The mathematical relationship model between the turbine fan speed and the fan outlet pressure is expressed as follows: Where, p0 represents the fan outlet pressure; p i Indicates the local atmospheric pressure; D represents the diameter of the turbine fan impeller; n is the turbine fan speed.

10. The method for adaptively controlling airway pressure of a non-invasive ventilator single-level CPAP according to claim 1, characterized in that: The ventilator comprises a permanent magnet synchronous motor, a turbine fan, a filter-ventilation hose, a leakage hole and a breathing mask.

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