CN115645735A - Multi-target physiological control method, system and device for artificial heart pump and storage medium - Google Patents
Multi-target physiological control method, system and device for artificial heart pump and storage medium Download PDFInfo
- Publication number
- CN115645735A CN115645735A CN202211040001.XA CN202211040001A CN115645735A CN 115645735 A CN115645735 A CN 115645735A CN 202211040001 A CN202211040001 A CN 202211040001A CN 115645735 A CN115645735 A CN 115645735A
- Authority
- CN
- China
- Prior art keywords
- pump
- blood flow
- artificial heart
- heart pump
- sliding mode
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Abstract
The invention discloses a multi-target physiological control method, a system, a device and a storage medium for an artificial heart pump. The method comprises the following steps: establishing a coupling model of a cardiovascular circulation system and an artificial heart pump; optimizing parameters of the sliding mode controller by adopting a genetic particle swarm algorithm; inputting the heart rate, the average arterial pressure, the minimum pump flow, the body activity level and the clinical condition as input quantities into a variable domain fuzzy controller, and outputting an optimal target value of the blood flow; and inputting the optimal target value of the blood flow and the blood flow into a blood flow self-adaptive PI feedback controller for calculation, outputting a signal, inputting the signal into an optimized sliding mode controller, and controlling the rotating speed of a motor of the artificial heart pump through the sliding mode controller to control the dynamic change of the blood flow. The complicated control system of the artificial heart pump is simplified through a layering idea, each layer plays its role, the rotating speed of the pump can be dynamically adjusted according to the state of a patient, and the life quality of the patient is further improved.
Description
Technical Field
The invention belongs to the technical field of biomedical engineering, and relates to a multi-target physiological control method, a multi-target physiological control device and a storage medium for an artificial heart pump.
Background
Heart failure is a common problem in the medical field today, and heart transplantation is the best solution to heart failure, but the number of transplantable hearts per year is very small, and the matching influence is also large. Since the advent, the artificial heart pump has been successfully used as a bridge for heart transplantation, and has become an effective way for prolonging the life of a plurality of heart failure patients. In the treatment process of heart failure, maintaining the normal physiological characteristics of heart failure patients as much as possible is the key to the success of treatment. The development of control systems that can be adjusted to the metabolic needs of the body is called physiological control. The goal of artificial cardiac pump control is to provide adequate cardiac output while maintaining adequate pressure perfusion and to effectively improve biocompatibility, and researchers have therefore proposed new methods of physiological control. To achieve this goal, a great deal of research and development has been carried out by domestic and foreign scholars, such as feedback control based on velocity modulation, control based on flow, aortic pressure feedback, feedback control based on heart rate modulation, and the like. In recent years, with the rapid development of cardiovascular and biomedical engineering, more and more artificial heart pumps adopt more advanced bionic modes, and higher requirements are put forward on the performance of a physiological control method.
Currently, most physiological control methods are designed by adding a sensor to obtain hemodynamic information, for example, CN114588532A discloses an artificial heart intelligent control method based on biomedical engineering, which specifically comprises the following steps: the method comprises the following steps: acquiring a signal, namely acquiring a pump body state signal and a human body physiological signal through an internal acquisition module and an external sensing module; step two: signal preprocessing and data extraction, wherein the pump body state signal and the human body physiological signal are subjected to differential amplification and filtering correction, the difference amplification and the filtering correction are carried out, the calculation result is obtained, and meanwhile, a normal threshold interval of the human body physiological signal stored in a microcomputer storage module and a pump body state signal in the normal threshold interval are extracted; step three: judging a threshold value, namely judging whether the calculation result is within a normal threshold value interval, if so, jumping to the step four, otherwise, jumping to the step six; step four: judging the time length, namely judging whether the calculation result is in a normal time length interval, if so, jumping to the fifth step, otherwise, jumping to the sixth step; step five: linear control, performing constant current control on the artificial heart pump body based on the pump body state signal in the normal threshold range; step six: and nonlinear control, namely performing dynamic nonlinear control on the artificial heart pump body based on a deep learning model. The presence of sensors complicates the control system and increases the risk of infection. On the other hand, factors influencing the physiological control performance of the artificial heart pump are many, and most of the current research only aims at single variable and stable state control, which may not achieve optimal control because of neglecting the influence of other factors and dynamic changes. Further, the overall control of the respective factors and the final control object, i.e., the rotational speed of the pump motor, are interrelated, and this should be taken into consideration when designing the control system.
In conclusion, how to provide a control method of an efficient bionic artificial heart pump is one of the problems to be solved in the field of artificial heart pumps.
Disclosure of Invention
Aiming at the defects and actual requirements of the prior art, the invention provides a multi-target physiological control method, a system, a device and a storage medium for an artificial heart pump.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for multi-target physiological control of an artificial heart pump, the method comprising:
establishing a coupling model of a cardiovascular circulation system and an artificial heart pump;
optimizing parameters of the sliding mode controller by adopting a genetic particle swarm algorithm;
inputting the heart rate, the average arterial pressure, the minimum pump flow, the body activity level and the clinical condition as input quantities into a variable domain fuzzy controller, and outputting an optimal target value of the blood flow;
the aortic pressure and the blood flow volume are respectively calculated by using an aortic pressure sliding mode observer and a blood flow volume sliding mode observer, the optimal target value of the blood flow volume and the blood flow volume are input into a blood flow volume self-adaptive PI feedback controller for calculation, signals are output and input into an optimized sliding mode controller, the motor rotation speed of the artificial heart pump is controlled by the sliding mode controller, and the dynamic change of the blood flow volume is controlled.
In the invention, based on the idea of layered control, the control process of the artificial heart pump is divided into three layers: a motor speed control layer, a heart pump flow feedback control layer and a multi-target physiological control layer. The multi-target physiological control layer takes heart rate, average arterial pressure, minimum pump flow, body activity level and clinical conditions as input, the target value of the heart pump flow feedback control layer as output, a variable universe fuzzy controller is designed, and the optimal flow target value considering the self state and clinical conditions of a patient is obtained; a flow self-adaptive PI feedback regulation mechanism is introduced into the heart pump flow feedback control layer to stabilize the blood flow at an optimal target value; the motor speed control layer adopts a motor double-closed-loop sliding mode control method based on a genetic particle swarm algorithm, so that the rotating speed of the pump can quickly respond to provide stable blood flow output, the complicated control process of the artificial heart pump is simplified through a layering thought, each layer has the full function, the common work meets the requirement of blood perfusion of a heart failure patient, the rotating speed of the pump can be dynamically adjusted according to the state of the patient, and the life quality of the patient is further improved.
According to the invention, a genetic particle swarm (GAPSO) algorithm-based motor sliding mode control method for the artificial heart pump is adopted, the rotating speed performance of the motor for the artificial heart pump is improved by introducing the nonsingular terminal sliding mode controller, and the performance parameters of the sliding mode controller are optimized by utilizing the genetic particle swarm algorithm, so that the rotating speed of the motor is better matched with the dynamic change of blood flow.
In the invention, an electrical network model of a cardiovascular circulation system is established by combining an electrical network equivalent theory, and an artificial heart pump is connected to an aorta from a left ventricle in the cardiovascular electrical network system to form a cardiovascular circulation system and artificial heart pump coupling model.
Preferably, the state equation of the coupling model of the cardiovascular circulation system and the artificial heart pump has the following overall format:
where ω (t) represents the speed of the artificial heart pump, b is a constant vector, and r (x) is a ramp function that characterizes the flow at the valve, the atrial pressure x when the heart is in filling phase 2 Greater than the pressure x of the heart chamber 1 The atrial to ventricular flow after mitral valve opening is r (x) 2 -x 1 )/R M Rest of the time, closed, in the same way, ventricular pressure x during ejection 1 Will continue to increase until the aortic pressure x is exceeded 4 With the aortic valve open, the flow into the aorta is r (x) 1 -x 4 )/R A 。
Preferably, the torque T of the artificial heart pump e The expression is as follows:
wherein u represents an initial phase angle; lambda 1 Representing the initial phase angle of the current of the suspension winding; I.C. A d 、I q Respectively representing the components of the torque winding current on d and q axes; ω represents the rotor speed; l is d 、L q Respectively representing components of the equivalent inductance on d and q axes; u. of d 、u q Respectively representing components of the equivalent voltage on d and q axes; r represents the equivalent resistance on the torque winding; j represents moment of inertia; f represents a load force; t is m Representing the load torque; n represents the number of coil turns of the torque winding; p represents the torque winding pole pair number; psi F Representing the flux linkage magnitude of the permanent magnets and torque windings.
Preferably, the sliding mode control sliding mode controller comprises two parts, namely a sliding mode surface design part and a control law design part.
In the invention, a nonsingular sliding mode surface is selected as the sliding mode surface of the motor sliding mode controller for the artificial heart pump, and the formula is as follows:
wherein β >0, p and q (p > q) are positive odd numbers, and 1 is composed of P/q <2.
The time at which the control system reaches the equilibrium state from any initial state can now be expressed as:
for the control law, the invention adopts the index approaching law with better performance at present, namely the control law
Wherein
k and epsilon are two key performance parameters of the control law, the larger k is, the systemThe switching plane can be approached more quickly, and the occurrence of system buffeting can be improved by reducing epsilon.
The control system of the bearingless permanent magnet motor for the artificial heart pump is a second-order nonlinear dynamic system, and the general expression of the system is as follows:
wherein X = [ X ] 1 ,x 2 ] T Representing a system state variable; u represents a control input; g (t) represents external interference.
By combining equation (1) and equation (5), we can obtain:
the given amount of the rotating speed of the bearingless permanent magnet motor rotor is assumed to be omega * If the actual rotational speed is ω, the error state variable of the rotational speed can be listed as ω
By combining the formula (1) and the formula (7), the method can be obtained
In summary, the rotor speed error system equation of state can be expressed as:
preferably, the physical activity level is set by the patient according to the state of the patient, and is divided into three states of rest (lightness), moderate (walking) and vigorous activity (going upstairs).
Preferably, the clinical condition is graded into 5, with grade 1 being very poor cardiac function, grade 2 being poor cardiac function, grade 3 being general cardiac function, grade 4 being less impaired cardiac function, and grade 5 being slightly impaired (or better restored) cardiac function.
Preferably, fuzzy system rules in the variable domain fuzzy controller are based on knowledge of heart failure and artificial heart pump operation, defined according to a general circulatory regulation system, and grouped according to their input variables and their interaction with a physiological control system.
Preferably, the fuzzy system rule is as shown in table 1.
TABLE 1
HR is heart rate, MAP is mean arterial pressure, PP is patient clinical condition, AL is patient activity level, MF is minimum blood flow for artificial heart pump.
Preferably, the method further comprises controlling the artificial heart pump heart rate using a heart rate adaptive control strategy.
Preferably, the heart rate adaptive control strategy adopts a non-parametric model adaptive control theory.
Preferably, the multi-target physiological control method for the artificial heart pump further comprises the step of controlling the aortic pressure difference by adopting a pulse-assisted control algorithm.
Preferably, the beat assist control algorithm comprises:
A. designing a sliding mode controller by taking the aortic pressure as a variable, and setting a feedback regulation mechanism: introducing two mutually-converted aortic pressure high-low reference values, and stabilizing the aortic pressure difference in a high-low reference value range by making the aortic pressure jump between two limit values;
B. and setting a target value of the average aortic pressure according to the actual condition of the heart failure patient, designing a sliding mode controller according to the target value, and controlling the average aortic pressure.
According to the invention, a heart rate self-adaptive control strategy and a pulsation auxiliary control algorithm are adopted, so that on one hand, the parameters of the heart pump can be dynamically adjusted along with the change of the state of a human body, and the dynamic bionic performance of the heart pump in control is improved; on the other hand, the pressure difference of the aorta can be increased, and the bionic pulsation of the blood is improved.
Preferably, the motor of the artificial heart pump is a bearingless permanent magnet motor.
Preferably, the method further comprises comparing the minimum left ventricular pressure x 1min The left ventricle pressure feedback mechanism is set as the criterion for judging the occurrence of suction.
In the invention, a prevention suction regulation mechanism is added, so that the suction phenomenon of the artificial heart pump can be prevented.
In a second aspect, the invention provides an artificial heart pump multi-target physiological control system, which is based on a cardiovascular circulation system and a coupling model of the artificial heart pump, and comprises a multi-target physiological control module, a heart pump flow feedback control module and a motor speed control module.
The multi-target physiological control module is used for executing the following steps:
the heart rate, the average arterial pressure, the minimum pump flow, the body activity level and the clinical condition are used as input quantities, input into a variable domain fuzzy controller and output an optimal target value of the blood flow.
The heart pump flow feedback control module is used for executing the following steps:
respectively calculating aortic pressure and blood flow by using an aortic pressure and blood flow sliding-mode observer, inputting the optimal target value of blood flow and blood flow into a blood flow self-adaptive PI feedback controller for calculation, and outputting signals;
the motor speed control module is used for executing the steps.
And inputting a signal output by the self-adaptive PI feedback controller into a sliding mode controller, controlling the motor rotating speed of the artificial heart pump through the sliding mode controller, and controlling the dynamic change of the blood flow, wherein the sliding mode controller is optimized through a genetic particle swarm optimization.
Preferably, the state equation of the coupling model of the cardiovascular system and the artificial heart pump has the following overall format:
preferably, the torque T of the artificial heart pump e The expression is as follows:
preferably, the multi-target physiological control module further comprises a heart rate adaptive control strategy for controlling the heart rate of the artificial heart pump.
Preferably, the heart rate adaptive control strategy adopts a non-parametric model adaptive control theory.
Preferably, the multi-target physiologic control module further comprises controlling the aortic pressure difference using a pulsatile assist control algorithm.
Preferably, the beat assist control algorithm comprises:
A. designing a sliding mode controller by taking aortic pressure as a variable, and setting a feedback regulation mechanism: two mutually-converted aortic pressure high-low reference values are introduced, and the aortic pressure is made to jump between two limit values, so that the aortic pressure difference is stabilized in a high-low reference value range;
B. and setting a target value of the average aortic pressure according to the actual condition of the heart failure patient, designing a sliding mode controller according to the target value, and controlling the average aortic pressure.
Preferably, the motor of the artificial heart pump is a bearingless permanent magnet motor.
Preferably, the heart pump flow feedback control module further comprises a minimum left ventricular pressure x 1min < 1 as a criterion for occurrence of aspiration, a left ventricular pressure feedback mechanism is set.
In a third aspect, the invention provides an artificial heart pump multi-target physiological control device, which comprises a system monitoring interface, a computer, a controller, an artificial heart pump, a flow meter, a pressure sensor and a resistance regulator, wherein the computer executes the artificial heart pump multi-target physiological control method in the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon instructions, which, when executed on a computer, cause the computer to execute the method for multi-objective physiologic control of an artificial cardiac pump according to any one of the first aspect.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the multi-target physiological control method for the artificial heart pump, a complex control system of the artificial heart pump is simplified through a layering idea, each layer has the full function, and the blood perfusion requirements of heart failure patients are met through the common work; the multi-target physiological control layer comprehensively considers five performance indexes of blood flow, aortic pressure, heart rate, body activity level and clinical conditions, and can dynamically adjust the pump rotating speed according to the state of a patient so as to improve the life quality of the patient;
(2) According to the multi-target physiological control method for the artificial heart pump, a heart rate self-adaptive control strategy and a pulsation auxiliary control algorithm are added, the blood flow can be dynamically adjusted along with the change of the heart rate, the aortic pressure difference can be increased, the bionic pulsation of blood is improved, and in addition, the control method also prevents the suction phenomenon of the artificial heart pump;
(3) The invention adopts a pump motor sliding mode control method based on a GAPSO algorithm in motor control, and optimizes the performance parameters of the controller on the basis of improving sliding mode control, so that the rotating speed of the motor is better matched with the dynamic change of the blood flow of the artificial heart pump.
Drawings
FIG. 1 is a block diagram of a control system for a multi-objective physiological control method of an artificial heart pump according to the present invention;
FIG. 2 is a diagram of a model of the coupling between the cardiovascular circulatory system and the artificial heart pump;
FIG. 3 is a flow chart of the genetic particle swarm algorithm of the present invention;
FIG. 4 is a flow chart of the blood flow feedback control of the present invention;
FIG. 5A is a graph of the membership function of aortic pressure in the multi-objective variable domain fuzzy controller of the present invention;
FIG. 5B is a graph of the membership function of the central rate of the multi-objective variable domain fuzzy controller of the present invention;
FIG. 5C is a graph of the membership function of the activity level of the body in the multi-objective variable domain fuzzy controller of the present invention;
FIG. 6A is a diagram of aortic pressure under the control method of the present invention;
FIG. 6B is a graph showing the blood flow rate under the control method of the present invention;
FIG. 7A is a graph showing comparison results of aortic pressure dynamics of four physiologic control methods for an artificial heart pump, including the control method of the present invention;
FIG. 7B is a graph showing comparative results of blood flow dynamics of four physiologic control methods for artificial heart pumps, including the control method of the present invention.
Detailed Description
To further illustrate the technical means adopted by the present invention and the effects thereof, the present invention is further described below with reference to the embodiments and the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention.
The examples do not show the specific techniques or conditions, according to the technical or conditions described in the literature in the field, or according to the product specifications. The reagents or apparatus used are conventional products commercially available from normal sources, not indicated by the manufacturer.
In the embodiments of the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
In the specific embodiment of the invention, a multi-target physiological control method for an artificial heart pump is provided, and a control system block diagram is shown in fig. 1.
Based on the idea of layered control, the invention divides a control system into three layers: a motor speed control layer, a heart pump flow feedback control layer and a multi-target physiological control layer. And the multi-target physiological control layer positioned on the third layer adopts a variable domain fuzzy algorithm to comprehensively consider parameter indexes such as Heart Rate (HR), mean Arterial Pressure (MAP), minimum pump flow, physical activity level, clinical conditions and the like, and finally outputs target blood flow to the second layer for adjusting the blood flow of the artificial heart pump. In addition, a heart rate self-adaptive controller and a pulsation auxiliary controller are additionally arranged on the third layer, so that on one hand, the parameters of the heart pump can be dynamically adjusted along with the change of the human body state, and the dynamic bionic performance of the heart pump in control is improved; on the other hand, the pressure difference of the aorta can be increased, and the bionic pulsation of the blood is improved. The second layer of heart pump flow feedback control layer has the function of meeting the blood flow perfusion requirement of the heart failure patient body by ensuring the blood flow, wherein the blood flow feedback controller processes the optimal target value of the patient blood flow obtained by the third layer and the estimated value of the blood flow obtained by the blood flow sliding mode observer, and outputs signals to the sliding mode controller of the first layer of motor, and the blood flow is adjusted by controlling the rotating speed of the motor of the artificial heart pump, so that the blood flow can meet the requirement at any time. The first layer is a motor speed control layer which ensures the dynamic performance of the motor rotating speed. In the control system, a bearing-free permanent magnet sheet motor sliding mode control method for the artificial heart pump based on the GAPSO algorithm is adopted, the rotating speed performance of the motor for the artificial heart pump is improved by introducing the nonsingular terminal sliding mode controller, and the performance parameters of the sliding mode controller are optimized by utilizing the genetic particle swarm algorithm, so that the rotating speed of the motor is better matched with the dynamic change of the blood flow volume.
Example 1
The embodiment provides a multi-target physiological control method for an artificial heart pump.
Firstly, a cardiovascular circulation system and artificial heart pump coupling model is established.
And establishing an electrical network model of the cardiovascular circulatory system by combining an electrical network equivalent theory. Then, according to the practical clinical application of the artificial heart pump, the artificial heart pump is connected to the aorta from the left ventricle in the electrical network system of the cardiovascular system, and a model for coupling the cardiovascular system and the artificial heart pump is formed, as shown in fig. 2, and the model is the basis of method verification.
The lower half of the model of FIG. 2 runs from left to right, x 2 To x 3 The paths of (1) respectively represent the left atrium, left ventricle, aorta, and the uppermost part passes through R S The path from right to left back to the left atrium then represents the entire individual-pulmonary cycle; x is the number of 6 The path followed represents the connection of the artificial heart pump from the left ventricle to the aorta. Wherein: c R And Ct denotes left atrial and left ventricular compliance, the vessel wall being elastic, the compliance being indicative of the degree to which the vessel volume changes with changes in blood pressure; diode D M And D A Representing the mitral valve and aortic valve, respectively, with conduction and cutoff representing valve opening and closing, respectively; r M And R A Respectively mitral valve flow resistance and aortic valve flow resistance; l is a radical of an alcohol C Representing the inertia of blood, R C And C S Peripheral resistance and arterial compliance, respectively. The time-variant of the model is the volume characteristic of the left ventricle, reflected by a time-varying capacitance value C (t), which is the inverse of the ventricular elastic function E (t). The model of the present invention assumes that the right atrium, right ventricle and pulmonary circulation system are normal, i.e. the artificial heart pump has a negligible effect on these parts. Therefore, the first and second electrodes are formed on the substrate,the cardiovascular system in this simple model is divided into 5 sections (x in table 1) 1 To x 5 ) And forms a 6-order coupling model with the artificial heart pump, and 6 state variables and related physiological meanings in the model are listed in table 2.
TABLE 2
By writing kirchhoff's theorem on each node in the cardiovascular circulatory system electric network, the state equation of the cardiovascular circulatory system and artificial heart pump coupling model is deduced to have the following overall format:
where ω (t) represents the rotational speed of the artificial heart pump and b is a constant vector. r (x) is a ramp function that is used to characterize the blood flow at the valve, the atrial pressure x when the heart is in filling phase 2 Greater than the pressure x of the heart chamber 1 The flow rate from the atrium to the ventricle after mitral valve opening is r (x) 2 -x 1 )/R M And is turned off at the rest of time. By analogy, ventricular pressure x when in the ejection phase 1 Will continue to increase until the aortic pressure x is exceeded 4 With the aortic valve open, the flow into the aorta is r (x) 1 -x 4 )/R A 。
And secondly, establishing a mathematical model of the torque of the artificial heart pump.
According to the working principle of the bearingless permanent magnet motor for the artificial heart pump and the Maxwell equation, the torque T can be deduced e Expression (i) of
Wherein u represents an initial phase angle; lambda [ alpha ] 1 Representing the initial phase angle of the current of the suspension winding; i is d 、I q Respectively representing the components of the torque winding current on d and q axes; ω represents the rotor speed; l is d 、L q Respectively representing components of the equivalent inductance on d and q axes; u. of d 、u q Respectively representing the components of the equivalent voltage on d and q axes; r represents the equivalent resistance on the torque winding; j represents moment of inertia; f represents a load force; t is a unit of m Representing the load torque; n represents the number of coil turns of the torque winding; p represents the torque winding pole pair number; psi F Representing the flux linkage magnitude of the permanent magnets and torque windings.
Thirdly, designing a sliding mode controller aiming at the motor speed control layer in the figure 1.
The sliding mode control comprises two parts of sliding mode surface design and control law design:
the nonsingular sliding mode surface is selected as the sliding mode surface of the motor sliding mode controller for the artificial heart pump, and the formula is as follows:
wherein β >0, p and q (p > q) are positive odd numbers, and 1 is composed of P/q <2.
The time at which the control system reaches the equilibrium state from any initial state can now be expressed as:
for the control law, the invention adopts the index approaching law with better performance at present, namely the control law
Wherein
k and epsilon are two key performance parameters of a control law, the system can approach a switching surface more quickly if k is larger, and the occurrence of system buffeting can be improved if epsilon is reduced.
The control system of the bearingless permanent magnet motor for the artificial heart pump is a second-order nonlinear dynamic system, and the general expression of the control system is as follows:
wherein X = [ X ] 1 ,x 2 ] T Representing a system state variable; u represents a control input; g (t) represents external interference.
By combining formula (1) and formula (5), the method can be obtained
Suppose the given amount of rotational speed of the bearingless permanent magnet motor rotor is ω * And the actual rotating speed quantity is omega, the rotating speed error state variable can be listed as:
the simultaneous formula (1) and formula (7) can be obtained
In summary, the rotor speed error system equation of state can be expressed as
Although the sliding mode control has a remarkable effect on the control of the bearingless permanent magnet motor, the sliding mode control system has the defect of instability and the like caused by high-frequency vibration, and the main reason is that the parameter setting in the sliding mode function is improper. Aiming at the problem, the invention applies a genetic particle swarm optimization (GAPSO) algorithm to parameter optimization of the sliding mode controller, and as shown in FIG. 3, the optimization of the controller parameters can be completed through the steps of FIG. 3.
Fourthly, a flow adaptive PI feedback regulation mechanism is designed for the heart pump flow feedback control layer in fig. 1, the detailed flow is shown in fig. 4, and as can be seen from fig. 4, in the heart pump flow feedback control layer, an aortic pressure and blood flow sliding mode observer is firstly designed, the detection principle is that when the motor rotates, a corresponding relation exists among the rotating speed, the current, the flow and the pressure difference, and whether the working state of the artificial heart pump provides enough physiological perfusion can be estimated through the observation of the cardiovascular circulation system and the artificial heart pump coupling model on the blood flow. Secondly, designing a heart pump blood flow self-adaptive PI feedback controller, calculating the optimal target value of the heart failure patient obtained by the third layer and the estimated value of the blood flow obtained by the blood flow sliding-mode observer, and outputting a signal to the sliding-mode controller of the first layer of the motor, so as to control the dynamic change of the blood flow by controlling the rotating speed.
Meanwhile, as can be seen from fig. 1, a preventive pumping regulation mechanism is also designed in the heart pump flow feedback control layer, and the design principle of the mechanism is as follows: according to clinical trials, it has been shown that when left ventricular pressure approaches 0mmHg, meaning that aspiration may occur, the present invention will minimize left ventricular pressure x for greater safety 1min Less than 1 is used as the judgment standard for suction, a left ventricle pressure feedback mechanism is set in the control system, and x is avoided 1min The case of < 1 occurs, thereby preventing aspiration. The above-described preventive pumping regulation mechanism was written into the control system via MATLAB.
And fifthly, designing a multi-target variable universe fuzzy controller aiming at the multi-target physiological control layer in the figure 1.
A variable universe fuzzy control algorithm is adopted to process the multi-target physiological control layer, firstly, target variables need to be designed, and the multi-target physiological control of the invention totally sets 5 target variables. The input amount of the controller is 5 variables of Heart Rate (HR), mean Arterial Pressure (MAP), minimum pump flow, body activity level (PP) and clinical condition, and the output amount of the controller is the optimal target value of blood flow. Of the input variables, the first 3 input variables can be estimated by an observer, and the last 2 input variables are special and need to be set manually. For example, the physical activity level needs the setting of the patient according to the state of the patient, the setting is divided into three states of rest (lightness), moderate (walking) and violent activity (going upstairs), and the setting of the variable can be completed by rotating the control panel button at the later stage. The clinical condition is divided into 5 levels, and it is necessary to determine which level the patient is in based on data according to the clinical condition and recovery condition of the patient. For example, a rating of 1 indicates very poor cardiac function, which means that the artificial heart pump is more assisted. A rating of 5 indicates less impaired (or better recovery) of cardiac function, which means that the artificial heart pump will be slightly functional.
Fig. 5A-5C show the membership functions of variables in the variable universe fuzzy controller, and this embodiment shows only the membership functions of aortic pressure (fig. 5A), heart rate (fig. 5B) and physical activity level (fig. 5C) due to space limitations. Fuzzy system rules are defined based on knowledge of heart failure and artificial heart pump operation, according to the general circulatory regulatory system, and grouped according to their input variables and their interaction with the physiological control system. In the construction process of the fuzzy rule, the blood circulation system, the reflux of the artificial heart pump, the pulsation, the hemodynamic condition, the heart rate dynamic change, expert advice and the like are comprehensively considered, the optimization of the rule follows a heuristic method, and all the rules are mutually connected. If the input and hemodynamic variables are not consistent, the fuzzy controller will take safety action, e.g., when the patient forgets to switch the motion conditions back to rest after stopping, the fuzzy rules will work, will stay the control system in the rest setting and send a reminder. And 3, summarizing fuzzy rules used in the variable universe fuzzy controller, and writing the fuzzy rules into a control system in an m file form through MATLAB (matrix laboratory), so that the fuzzy control on the multiple variable universes of the artificial heart pump can be realized, and the optimal blood flow target value most suitable for the heart failure patient is obtained.
TABLE 3
The steps of the multi-target physiological control method of the artificial heart pump are described above, and the validity of the method is verified. The control method and the fuzzy rule are written into a cardiovascular and artificial heart pump coupling system model in the form of S-function in the embodiment. After parameters are set, the FIS file written with fuzzy rules is operated to obtain the optimal target value of blood flow, then the coupling model of the cardiovascular system and the artificial heart pump is operated to obtain the hemodynamic condition of the artificial heart pump, and the most important performance indexes of the hemodynamic condition are selected, namely aortic pressure (Pao) and blood flow (Q) a ) The results of the analysis are shown in fig. 6A and fig. 6B, after the heart-around is inserted into the artificial heart pump of the control method of the present invention, the aortic pressure range is 88-119 mmHg, the average aortic pressure can be basically maintained near 100mmHg, the peak value of aortic blood flow can reach 277L/min, the aortic stroke output SV is about 65.82mL, and the cardiac output CO is about 4.94L/min, which are all in the normal physiological range, which indicates that the artificial heart pump of the control method of the present invention can assist the damaged heart to obtain sufficient blood perfusion for the human body.
There are three working modes in clinical application at present: the control method of the invention belongs to extension and improvement of the co-pulsation auxiliary mode. In order to further highlight the advantages of the physiological control method, the four methods, namely the constant-current auxiliary method, the conventional co-pulsation auxiliary method, the counterpulsation auxiliary method and the control method, are contrasted and analyzed respectively. The method is added into the built cardiovascular and artificial heart pump coupling system model through MATLAB/Simulink respectively, the same parameters and environment are set, the aortic pressure and blood flow results of each method are drawn on the same graph, and the data tables of each method are summarized to carry out quantitative analysis on the results, as shown in fig. 7A and 7B. The comparison result shows that the method designed by the invention has certain advantages on the aortic pressure and the blood flow, the four methods can reach the normal aortic pressure range on the aortic pressure, and the constant-current auxiliary mode and the counterpulsation auxiliary mode have even higher aortic pressure (110-120 mmHg), because the speed of the constant-current auxiliary mode is always fixed at the highest rotating speed, and the counterpulsation auxiliary mode is that the phase is different by one-half cycle, the artificial heart pump can also pump blood in the diastole of the damaged heart in the mode; however, the two modes have a fatal defect that the active pulse pressure difference is small, the pulse pressure difference of the constant-current auxiliary mode is 14mmHg, the counterpulsation auxiliary mode is only 10mmHg, and the normal value is 30-40 mmHg, which can cause the blood pulsation performance to be greatly reduced, and the recovery of the damaged heart is greatly influenced.
In conclusion, the multi-target physiological control method for the artificial heart pump simplifies the complicated control system of the artificial heart pump through a layering idea, each layer plays its role, and the complex control system and each layer work together to meet the requirement of blood perfusion of heart failure patients; the multi-target physiological control layer comprehensively considers five performance indexes of blood flow, aortic pressure, heart rate, body activity level and clinical conditions, and can dynamically adjust the pump rotating speed according to the state of a patient so as to improve the life quality of the patient.
The applicant states that the present invention is illustrated in detail by the above examples, but the present invention is not limited to the above detailed methods, i.e. it is not meant that the present invention must rely on the above detailed methods for its implementation. It should be understood by those skilled in the art that any modification of the present invention, equivalent substitutions of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific modes, etc., are within the scope and disclosure of the present invention.
Claims (10)
1. A multi-target physiological control method for an artificial heart pump, the method comprising:
establishing a coupling model of a cardiovascular circulation system and an artificial heart pump;
optimizing parameters of the sliding mode controller by adopting a genetic particle swarm algorithm;
inputting the heart rate, the average arterial pressure, the minimum pump flow, the body activity level and the clinical condition as input quantities into a variable domain fuzzy controller, and outputting an optimal target value of the blood flow;
the aortic pressure and the blood flow are respectively calculated by using an aortic pressure sliding mode observer and a blood flow sliding mode observer, the optimal target value and the blood flow are input into a blood flow self-adaptive PI feedback controller for calculation, signals are output and input into an optimized sliding mode controller, the rotating speed of a motor of the artificial heart pump is controlled by the sliding mode controller, and the dynamic change of the blood flow is controlled.
2. The multi-objective physiological control method for an artificial heart pump according to claim 1, further comprising controlling the heart rate of the artificial heart pump by using a heart rate adaptive control strategy;
preferably, the heart rate adaptive control strategy adopts a nonparametric model adaptive control theory;
preferably, the multi-target physiological control method for the artificial heart pump further comprises the step of controlling the aortic pressure difference by adopting a pulsation auxiliary control algorithm;
preferably, the beat assist control algorithm comprises:
A. designing a sliding mode controller by taking the aortic pressure as a variable, and setting a feedback regulation mechanism: introducing two interconverted aortic pressure high-low reference values, and stabilizing the aortic pressure difference in a high-low reference value range by making the aortic pressure jump between two limit values;
B. and setting a target value of the average aortic pressure according to the actual condition of the heart failure patient, designing a sliding mode controller according to the target value, and controlling the average aortic pressure.
3. The method for multi-objective physiological control of an artificial heart pump according to claim 1 or 2, wherein the motor of the artificial heart pump is a bearingless permanent magnet motor.
4. The multi-objective physiologic control method for an artificial cardiac pump according to any one of claims 1-3, further comprising assigning a minimum left ventricular pressure x 1min < 1 as the judgment standard for occurrence of pumping, the left ventricular pressure feedback mechanism is set.
5. The multi-target physiological control system of the artificial heart pump is characterized in that the system is based on a cardiovascular circulation system and a coupling model of the artificial heart pump, and the system comprises a multi-target physiological control module, a heart pump flow feedback control module and a motor speed control module;
the multi-target physiological control module is used for executing the following steps:
inputting the heart rate, the average arterial pressure, the minimum pump flow, the body activity level and the clinical condition as input quantities into a variable domain fuzzy controller, and outputting an optimal target value of the blood flow;
the heart pump flow feedback control module is used for executing the following steps:
respectively calculating aortic pressure and blood flow by using an aortic pressure and blood flow sliding-mode observer, inputting the optimal target value of the blood flow and the blood flow into a blood flow self-adaptive PI feedback controller for calculation, and outputting signals;
the motor speed control module is used for executing the following steps:
and inputting a signal output by the self-adaptive PI feedback controller into a sliding mode controller, controlling the rotating speed of a motor of the artificial heart pump through the sliding mode controller, and controlling the dynamic change of the blood flow, wherein the sliding mode controller is optimized through a genetic particle swarm optimization.
6. The artificial cardiac pump multi-target physiological control system of claim 5, wherein the multi-target physiological control module further comprises a heart rate adaptive control strategy for controlling the heart rate of the artificial cardiac pump;
preferably, the heart rate adaptive control strategy adopts a non-parametric model adaptive control theory;
preferably, the multi-target physiological control module further comprises a pulse auxiliary control algorithm for controlling the aortic pressure difference;
preferably, the beat assist control algorithm comprises:
A. designing a sliding mode controller by taking the aortic pressure as a variable, and setting a feedback regulation mechanism: introducing two interconverted aortic pressure high-low reference values, and stabilizing the aortic pressure difference in a high-low reference value range by making the aortic pressure jump between two limit values;
B. and setting a target value of the average aortic pressure according to the actual condition of the heart failure patient, designing a sliding mode controller according to the target value, and controlling the average aortic pressure.
7. The artificial cardiac pump multi-target physiological control system according to claim 5 or 6, wherein the motor of the artificial cardiac pump is a bearingless permanent magnet motor.
8. The multi-target physiologic control system for an artificial cardiac pump according to any one of claims 5-7, wherein the cardiac pump flow feedback control module further comprises a minimum left ventricular pressure x 1min < 1 as a criterion for occurrence of aspiration, a left ventricular pressure feedback mechanism is set.
9. A multi-target physiological control device for an artificial heart pump is characterized by comprising a system monitoring interface, a computer, a controller, the artificial heart pump, a flow meter, a pressure sensor and a resistance regulator;
the computer executes the multi-target physiological control method of the artificial heart pump according to any one of claims 1-4.
10. A computer-readable storage medium having instructions stored thereon, which when executed on a computer, cause the computer to perform the method for multi-objective physiologic control of an artificial cardiac pump of any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211040001.XA CN115645735A (en) | 2022-08-29 | 2022-08-29 | Multi-target physiological control method, system and device for artificial heart pump and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211040001.XA CN115645735A (en) | 2022-08-29 | 2022-08-29 | Multi-target physiological control method, system and device for artificial heart pump and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115645735A true CN115645735A (en) | 2023-01-31 |
Family
ID=85024451
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211040001.XA Pending CN115645735A (en) | 2022-08-29 | 2022-08-29 | Multi-target physiological control method, system and device for artificial heart pump and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115645735A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115995291A (en) * | 2023-03-22 | 2023-04-21 | 安徽通灵仿生科技有限公司 | Control system and method for interventional ventricular catheter pump |
-
2022
- 2022-08-29 CN CN202211040001.XA patent/CN115645735A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115995291A (en) * | 2023-03-22 | 2023-04-21 | 安徽通灵仿生科技有限公司 | Control system and method for interventional ventricular catheter pump |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AlOmari et al. | Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review | |
| US11045640B2 (en) | Biomedical apparatus with a pressure-regulated blood pump | |
| US8747293B2 (en) | Rotational pump and methods for controlling rotational pumps | |
| Choi et al. | A sensorless approach to control of a turbodynamic left ventricular assist system | |
| Fetanat et al. | A physiological control system for an implantable heart pump that accommodates for interpatient and intrapatient variations | |
| Arndt et al. | Physiological control of a rotary blood pump with selectable therapeutic options: control of pulsatility gradient | |
| Petrou et al. | A novel multi-objective physiological control system for rotary left ventricular assist devices | |
| Ferreira et al. | A control system for rotary blood pumps based on suction detection | |
| Bozkurt et al. | Enhancement of arterial pressure pulsatility by controlling continuous-flow left ventricular assist device flow rate in mock circulatory system | |
| CN115645735A (en) | Multi-target physiological control method, system and device for artificial heart pump and storage medium | |
| Maslen et al. | Feedback control applications in artificial hearts | |
| CN113476737B (en) | Method, device and storage medium for improving bionic pulsatility of blood flow of artificial heart pump | |
| Petukhov et al. | A novel control method for rotary blood pumps as left ventricular assist device utilizing aortic valve state detection | |
| Ketelhut et al. | Iterative learning control of a left ventricular assist device | |
| Schrodel et al. | A physiological control strategy for continuous-flow left ventricular assist devices: The power ratio controller | |
| Baloa et al. | Control of rotary heart assist devices | |
| Ferreira et al. | A rule-based controller based on suction detection for rotary blood pumps | |
| Choi | Modeling and control of left ventricular assist system | |
| Boston et al. | Intelligent control design for heart assist devices | |
| Wu et al. | Modeling, estimation and control of cardiovascular systems with a left ventricular assist device | |
| Wu et al. | ADRC or adaptive controller–A simulation study on artificial blood pump | |
| Chen et al. | Physiological control of left ventricular assist devices based on gradient of flow | |
| CN116077825A (en) | Artificial heart intelligent physiological control system | |
| CN116421877A (en) | Control device, ventricular assist system and self-adaptive control method | |
| Giridharan et al. | Achieving physiologic perfusion with ventricular assist devices: Comparison of control strategies |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |