Autonomous closed loop pressure control micro infusion system driven by magnetic levitation pump

Abstract

The application discloses a kind of magnetic levitation pump driven autonomous closed loop pressure control micro infusion systems, belong to micro fluid accurate delivery technical field.For the wear, pollution and pulsation problem caused by mechanical contact of existing micro pump, the application uses non-contact magnetic levitation pump as driving core, fundamentally eliminates physical friction, realizes the smooth delivery of pollution-free, low pulsation.More critically, the system integrates pressure sensor and controller to form closed loop feedback, can monitor pipeline pressure in real time and dynamically adjust the speed of pump, realize the accurate, autonomous stable control to output pressure, and can actively compensate the pressure fluctuation caused by pipeline blockage and other working condition changes.The application combines non-contact drive and closed loop pressure control, significantly improves the accuracy, stability and safety of micro infusion, and is suitable for biological medicine, semiconductor and other high-clean, high-precision application fields.

Description

Technical Field

[0001] This invention relates to the field of microfluidic precision delivery technology, specifically to a magnetically levitated pump-driven autonomous closed-loop pressure-controlled micro infusion system. Background Technology

[0002] In cutting-edge fields such as modern biomedicine, semiconductor manufacturing, and chemical analysis, increasingly stringent requirements are being placed on the high-precision and high-stability delivery of liquids at the micro-liter and even nano-liter levels. Currently, mainstream technologies for achieving such micro-volume delivery generally employ traditional micro-pumps such as peristaltic pumps, plunger pumps, or diaphragm pumps as the drive core, and regulate output through open-loop control or simple closed-loop control based on flow sensors. These solutions, to a certain extent, meet basic micro-volume delivery needs and form the basis of existing technologies.

[0003] However, as application scenarios increasingly demand higher purity, stability, and control precision in the delivery process, the existing technical solutions have gradually revealed their inherent limitations. Traditional micropumps, due to their mechanical contact drive principle, inevitably suffer from mechanical wear, particulate matter generation, and shear forces on the fluid during long-term operation. This is extremely detrimental to the delivery of high-purity reagents or bioactive macromolecules. More critically, most of these systems lack the ability to directly and actively sense and adjust the actual output state at the end of the pipeline. When encountering common but unpredictable disturbances such as partial pipeline blockage, fluid viscosity drift due to temperature changes, or fluctuations in downstream load, the system cannot compensate in time, often causing the actual output pressure or flow rate to deviate from the preset target, thus seriously affecting the yield of the final product, the accuracy of experimental results, or the safety of the treatment process. Therefore, how to achieve high-precision, robust, and autonomous stable control of the delivery process, especially the key process parameter of pressure, while ensuring the purity and lack of contamination of the fluid, has become a substantial technical problem urgently needing to be solved in this field. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a micro infusion system with autonomous closed-loop pressure control driven by a magnetic levitation pump, which solves the problem of achieving high-precision and robust autonomous stable control of the delivery process, especially the key process parameter of pressure, while ensuring the purity and lack of contamination of the fluid.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system, comprising:

[0008] A magnetic levitation pump assembly, the magnetic levitation pump assembly including an impeller and an array of electromagnetic coils for levitation and driving the impeller;

[0009] A sensor is installed downstream of the infusion line;

[0010] and a controller, the controller comprising:

[0011] Fluid characteristic sensing module: The fluid characteristic sensing module controls the electromagnetic coil array to apply a preset excitation signal to the impeller, and collects the response parameters of the electromagnetic coil array to calculate the physical characteristics of the fluid in the pump body;

[0012] A feedforward control module is configured to generate a feedforward control signal for driving the impeller rotation based on the physical characteristics calculated by the fluid characteristic sensing module and the preset infusion target.

[0013] Feedback compensation module: The feedback compensation module is configured to receive feedback signals from the downstream sensor during the rotation of the impeller, and to compensate and adjust the control driving the impeller based on the feedback signals.

[0014] As a preferred embodiment of the autonomous closed-loop pressure control micro infusion system driven by the magnetic levitation pump described in this invention, the electromagnetic coil array is configured to switch between a sensing operating mode for acquiring the response parameters and a driving operating mode for driving the impeller to rotate in response to a mode switching command from the controller. In the sensing operating mode, the electrical signal energy applied to the electromagnetic coil array is lower than the driving threshold, while in the driving operating mode, the electrical signal energy applied to the electromagnetic coil array is higher than the driving threshold.

[0015] As a preferred embodiment of the magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, the downstream sensor includes an integrated sensing module having: a first sensing unit for identifying discrete abnormal events by monitoring discontinuous changes in a physical property of the fluid, and a second sensing unit configured to continuously generate measurement signals in response to changes in a process parameter of the fluid, wherein the output of the first sensing unit is used to trigger a safety interruption, and the output of the second sensing unit constitutes the feedback signal.

[0016] As a preferred embodiment of the magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, wherein: the first sensing unit includes an energy transmitter and an energy receiver, which are arranged on both sides of the infusion fluid passage; the first sensing unit identifies the discrete abnormal event by analyzing the change in a propagation parameter of an energy beam emitted by the energy transmitter, penetrating the fluid, and being received by the energy receiver; the second sensing unit includes: a flexible diaphragm exposed to the fluid pressure; and a transducer coupled to the flexible diaphragm, the transducer being configured to convert the mechanical response of the flexible diaphragm into a measurement signal.

[0017] As a preferred embodiment of the autonomous closed-loop pressure control micro infusion system driven by the magnetic levitation pump described in this invention, the preset excitation signal is a subthreshold excitation signal, the energy of which is controlled below the driving threshold that is insufficient to cause the impeller to generate macroscopic infusion drive, and the energy is sufficient to excite a dynamic response between the impeller and the fluid that can be detected by the electromagnetic coil array and characterize the physical properties of the fluid.

[0018] As a preferred embodiment of the magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, the response parameters of the electromagnetic coil array include: a first electrical parameter obtained by analyzing the amplitude relationship between the excitation signal and the electrical response signal of the electromagnetic coil array, used to characterize the fluid viscous damping effect; and a second electrical parameter obtained by analyzing the phase relationship between the excitation signal and the electrical response signal, used to characterize the fluid inertial load effect.

[0019] As a preferred embodiment of the magnetic levitation pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, the controller calculates the physical properties of the fluid by performing calculations on the first and second electrical parameters measured in real time in a pre-established correlation model that defines the quantitative relationship between electrical parameters and fluid physical properties. The correlation model associates the change of the first electrical parameter with the change of fluid viscosity and the change of the second electrical parameter with the change of fluid density.

[0020] As a preferred embodiment of the magnetic levitation pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, the feedforward control module is configured to generate the feedforward control signal by using a pre-established predictive control model that maps the fluid physical properties and the preset infusion target to a set of specific electromagnetic drive parameters, and to perform predictive open-loop drive of the impeller rotation before the compensation adjustment is intervened.

[0021] As a preferred embodiment of the magnetic levitation pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, the feedback compensation module is configured such that: the controller calculates the deviation between the real-time feedback signal of the downstream sensor and the expected target value determined by the feedforward control signal, and uses the deviation to generate a correction signal through an adaptive algorithm to continuously fine-tune the feedforward control driving the impeller in order to compensate for disturbance factors that the prediction model fails to cover.

[0022] As a preferred embodiment of the magnetic levitation pump-driven autonomous closed-loop pressure control micro infusion system of the present invention, the adaptive algorithm is configured to continuously evaluate the response performance of the system under compensation and adjustment, and dynamically adjust its internal key control parameters based on the evaluation results, thereby enabling the compensation and adjustment to self-optimize and adapt to the gradual changes in the dynamic characteristics of the system.

[0023] The beneficial effects of this invention are as follows: This invention utilizes a non-contact magnetic levitation pump, solving the problems of equipment wear and particulate contamination caused by physical friction, enabling extremely stable and clean delivery of high-purity liquids. More importantly, this invention equips this advanced pump with a complete autonomous closed-loop control system. This system not only monitors the actual pressure at the end of the pipeline in real time, but also accurately senses the viscosity and density of the liquid before infusion. Based on this comprehensive real-time information, the system can pre-calculate the optimal drive command, thereby achieving rapid and smooth startup. During infusion, in the event of sudden situations such as pipeline blockage or load changes, the system can immediately perform proactive compensation and adjustment, always precisely stabilizing the pressure at the preset target value. This advanced control strategy combining real-time sensing, predictive control, and dynamic compensation allows this invention to achieve a qualitative leap in the accuracy, stability, and robustness in handling complex operating conditions of infusion, fully meeting the stringent requirements of high-end fields such as biomedicine and semiconductors. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 Flowchart of a micro infusion system with autonomous closed-loop pressure control driven by a magnetic levitation pump.

[0026] Figure 2 This is a flowchart of the online self-sensing process for fluid physical properties.

[0027] Figure 3Flowchart for generating composite control signals.

[0028] Figure 4 This is a functional diagram of the downstream integrated sensing module. Detailed Implementation

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0032] Example 1

[0033] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system, comprising:

[0034] A magnetic levitation pump assembly, comprising an impeller and an array of electromagnetic coils for levitation and driving the impeller, as detailed below:

[0035] The electromagnetic coil array is configured to switch between a sensing operating mode for acquiring response parameters and a driving operating mode for driving the impeller to rotate in response to a mode switching command from the controller. In the sensing operating mode, the electrical signal energy applied to the electromagnetic coil array is lower than the driving threshold, while in the driving operating mode, the electrical signal energy applied to the electromagnetic coil array is higher than the driving threshold.

[0036] The controller's internal firmware defines two core operating modes: sensing mode and drive mode. The controller can switch the electromagnetic coil array between these two modes quickly and seamlessly by issuing mode switching commands based on the system's current task.

[0037] In a preferred embodiment of the present invention, the core execution component is a specially designed magnetic levitation pump assembly, the hardware of which mainly includes an impeller, an electromagnetic coil array, and a core controller.

[0038] Impeller: It is the only moving part in the pump. In this embodiment, it is made of samarium cobalt permanent magnet material by injection molding process to ensure its corrosion resistance and high magnetic energy product in complex fluid environments.

[0039] Electromagnetic coil array: This is the sole actuator for achieving contactless levitation and rotational drive of the impeller. This array is precisely integrated inside the pump casing and consists of multiple independent coils wound with high-conductivity oxygen-free copper. In this embodiment, a Texas Instruments DRV8301 three-phase brushless DC motor driver is used for high-frequency pulse width modulation control.

[0040] Pulse width modulation (PWM) is a highly effective technique that uses the digital output of a microprocessor to control analog circuits. Its core idea is to use a constant-amplitude electrical signal (e.g., equal to the DC bus voltage) to switch rapidly on and off, effectively simulating a continuously changing, adjustable-energy analog signal.

[0041] Working principle: Within each fixed, extremely short modulation cycle (e.g., 50 microseconds, corresponding to a frequency of 20kHz), the driver outputs not a continuous voltage, but a square wave pulse. By precisely controlling the duration of the high level (on state) within this cycle, we can control the average voltage or energy of the equivalent output within that cycle.

[0042] Core Controller: The controller of this pump assembly, in this embodiment, is a high-performance microcontroller based on STMicroelectronics' STM32H7 series. Its internal firmware is the core for realizing dual-mode switching.

[0043] 2. Dual-mode switching and working principle of the core controller

[0044] In this embodiment, the controller for the magnetic levitation pump assembly is a high-performance microcontroller based on STMicroelectronics' STM32H7 series. The STM32H7 series high-performance microcontroller possesses powerful floating-point arithmetic capabilities and rich peripheral interfaces, enabling it to process complex sensor signals and control algorithms in real-time and in parallel, meeting the requirements of our invention.

[0045] Based on this, the core innovation of the controller lies in the fact that its internal firmware defines two switchable core operating modes: sensing operating mode and driving operating mode.

[0046] 1. Sensing Working Mode: In this mode, the controller generates an electrical signal with energy below the drive threshold.

[0047] Numerical scenario example:

[0048] In a preferred embodiment, the signal is set to a standard sinusoidal voltage signal with a frequency of 20 kHz, the peak value of which is ( (5V)

[0049] When performing power and energy-related calculations, the effective value of the signal (RMS) needs to be used. For a standard sine wave, the conversion relationship between the effective value and the peak value is as follows: Therefore, the effective voltage value used in this calculation is:

[0050]

[0051] Assuming the equivalent resistance of the electromagnetic coil array is 10 ohms and the signal duration is 10 ms, then the total probe energy injected into the system is... Approximately:

[0052] (millijoules).

[0053] Functional role: The main role of the electromagnetic coil array is as a highly sensitive dynamic inductive sensor, used to acquire response parameters to obtain information.

[0054] 2. Drive working mode:

[0055] After completing fluid characteristic sensing and generating feedforward control signals, the system immediately switches to this mode to start or continue the infusion task. In this mode, the controller generates a set of electrical signals with energy higher than the drive threshold.

[0056] Numerical scenario example: Through experimental calibration, the driving threshold for the typical working fluid designed in this system is determined. The range is roughly between 150 mJ (for pure water) and 800 mJ (for high-concentration drug solutions). Obviously, the detection energy of 12.5 mJ in the aforementioned sensing mode is more than an order of magnitude smaller than the lowest driving threshold, thus technically ensuring the reliability of mode differentiation.

[0057] Functional role: In this mode, the electromagnetic coil array transforms into the stator of a high-efficiency brushless DC motor. The DRV8301 driver applies a high-energy, precisely timed drive signal to the coil array according to the target speed. The resulting powerful rotating magnetic field can stably suspend the impeller and drive it to rotate at high speed to output power.

[0058] A sensor is installed downstream of the infusion line, as follows:

[0059] The downstream sensor includes an integrated sensing module having: a first sensing unit for identifying discrete abnormal events by monitoring discontinuous changes in a physical property of the fluid, and a second sensing unit configured to continuously generate measurement signals in response to changes in a process parameter of the fluid, wherein the output of the first sensing unit is used to trigger a safety interruption, and the output of the second sensing unit constitutes a feedback signal.

[0060] The first sensing unit includes an energy transmitter and an energy receiver, which are arranged on both sides of the infusion fluid passage. The first sensing unit identifies discrete abnormal events by analyzing the change in a propagation parameter of an energy beam emitted by the energy transmitter, penetrating the fluid, and being received by the energy receiver. The second sensing unit includes a flexible diaphragm exposed to fluid pressure and a transducer coupled to the flexible diaphragm, which is configured to convert the mechanical response of the flexible diaphragm into a measurement signal.

[0061] Overall function and structure of the module:

[0062] The downstream sensor used in this invention is designed as an integrated sensing module, rather than a simple series connection of multiple independent sensors. At the core of this module is a single injection-molded housing made of medical-grade polycarbonate, with a precision-machined fluid channel running through its interior. This integrated design aims to minimize dead volume in the flow path, prevent liquid residue and cross-contamination, and ensure that all sensing signals originate from the same point, thereby improving data synchronization and accuracy.

[0063] Functionally, this module is divided into two collaborative units that together constitute the core sensing component of the system, responsible for safety monitoring and process feedback:

[0064] First sensing unit (safety monitoring function): Dedicated to system safety monitoring. Its function is to continuously monitor the fluid to identify discrete abnormal events in the fluid. Once such an event is detected, it immediately generates a high-priority hardware interrupt signal to trigger an emergency safety interrupt of the system to ensure the safety of downstream devices or applications.

[0065] The second sensing unit (process control function) provides continuous and accurate measurement of process parameters. It continuously quantifies a key process parameter of the fluid (pressure in this embodiment) and converts the measurement result into an electrical signal in real time. This signal constitutes the feedback signal of the entire closed-loop control system, providing a data basis for the controller to make fine adjustments.

[0066] In this invention, discrete anomalies refer to independent entities that suddenly appear in a continuous fluid and whose physical properties differ significantly from those of the main fluid. Their common physical characteristic is that their acoustic impedance, optical refractive index, and other properties differ greatly from those of the main fluid. Therefore, when they flow through the monitoring area, they cause drastic and abrupt changes in the monitored physical signal. In the application scenario of this embodiment, such events mainly include, but are not limited to, bubbles, particulate contaminants, or blood clots.

[0067] The specific structure and working principle of the sensing unit

[0068] In this embodiment, the first sensing unit is implemented as an ultrasonic bubble detection unit. Its working principle is to establish a transverse ultrasonic detection field in the fluid channel.

[0069] Structure setup: A 2MHz piezoelectric ceramic transducer (as energy transmitter) and an identical transducer (as energy receiver) are respectively installed in grooves on opposite sides of the module housing, ensuring precise alignment between the two, with their connection line passing perpendicularly through the fluid channel.

[0070] Workflow and Principle Explanation:

[0071] Transmission and Propagation: Driven by a high-frequency voltage, the transmitter continuously emits a focused ultrasonic beam (i.e., an energy beam) into the fluid. As this ultrasonic beam propagates through the fluid, several of its physical properties are profoundly affected by the fluid medium. These physical properties, describing the state of the beam after it penetrates the medium, are collectively referred to as wave propagation parameters, such as beam intensity, arrival time, and phase change.

[0072] Parameter Selection and Measurement: In this embodiment, to achieve the fastest and most reliable detection of discrete anomalies, the system is configured to focus on analyzing one of the most intuitive propagation parameters: beam strength. Electrically, this strength is directly reflected in the amplitude of the electrical signal received by the receiver.

[0073] Anomaly Detection Mechanism: The physical basis for this selection is that liquids are excellent mediums for sound wave propagation. During normal infusion, the ultrasonic beam can penetrate the liquid with minimal energy loss, thus maintaining the amplitude of the received signal at a stable and relatively high reference level. However, when a bubble flows through the detection field, due to the significant acoustic impedance mismatch at the gas-liquid interface, the interface acts like a mirror, reflecting and scattering most of the sound wave energy, with only a very small amount of energy able to propagate forward and reach the receiver.

[0074] Judgment and Output: This physical effect causes a sharp and significant attenuation in the amplitude of the received signal. Therefore, the control circuit continuously monitors the amplitude of the received signal and compares it with a preset normal reference value. Once a sharp drop in amplitude exceeding a threshold is detected, it is determined that a discrete abnormal event has been identified, and an interrupt signal is immediately issued.

[0075] The specific implementation of the second sensing unit—a microcomputer-controlled pressure sensing structure:

[0076] In this embodiment, the second sensing unit is implemented as a microelectromechanical system (MEMS) piezoresistive pressure sensor chip. Its core is a micro-mechanical structure with extremely high sensitivity to pressure changes, namely a flexible diaphragm.

[0077] Structure: The microelectromechanical system (MEMS) chip is directly integrated onto the inner wall of the fluid channel. At its core is an extremely thin, square, flexible diaphragm made of single-crystal silicon using a precision etching process, with one side of the diaphragm directly exposed to the fluid. At the edge region where stress is most concentrated on the diaphragm, a set of piezoresistive resistors forming a Wheatstone bridge are integrated using semiconductor technology; these resistors are the transducer elements integrated with the diaphragm.

[0078] Workflow:

[0079] Pressure action: The static pressure of the fluid acts directly and uniformly on the surface of the flexible diaphragm, causing it to produce a small mechanical deformation proportional to the magnitude of the pressure.

[0080] Electromechanical conversion: The deformation of the diaphragm (i.e., mechanical response) will cause stress on the piezoresistive strip integrated on it, thereby changing its resistance value.

[0081] Signal Output: The Wheatstone bridge, acting as a transducer, converts and amplifies this minute resistance change into a clear, stable differential voltage signal that is linearly related to the pressure. This voltage signal, after conditioning by subsequent circuitry, forms a continuous pressure measurement signal usable by the main controller.

[0082] And a controller, which is configured as follows:

[0083] The electromagnetic coil array is controlled to apply a preset excitation signal to the impeller, and the response parameters of the electromagnetic coil array are collected to calculate the physical properties of the fluid in the pump body, as follows:

[0084] The preset excitation signal is a subthreshold excitation signal, whose energy is controlled below the driving threshold that is insufficient to cause the impeller to generate macroscopic fluid delivery drive. The energy is sufficient to excite a dynamic response between the impeller and the fluid that can be detected by the electromagnetic coil array and characterize the fluid physical properties.

[0085] The response parameters of the electromagnetic coil array include: a first electrical parameter, obtained by analyzing the amplitude relationship between the excitation signal and the electrical response signal of the electromagnetic coil array, used to characterize the fluid viscous damping effect; and a second electrical parameter, obtained by analyzing the phase relationship between the excitation signal and the electrical response signal, used to characterize the fluid inertial load effect.

[0086] The controller calculates the physical properties of the fluid by performing calculations on the first and second electrical parameters measured in real time in a pre-established correlation model that defines the quantitative relationship between the electrical parameters and the fluid physical properties. The correlation model associates the change of the first electrical parameter with the change of fluid viscosity and the change of the second electrical parameter with the change of fluid density.

[0087] Step 1: Stimulus and Detection

[0088] The fluid characteristic sensing module controls the electromagnetic coil array to operate in sensing mode, and generates a standard sinusoidal voltage signal with a frequency of f=20kHz and a peak value of 5V through its built-in digital signal generator and pulse width modulation module. It is used as a preset excitation signal and applied to the electromagnetic coil array, and the duration of the signal is strictly controlled to be only 10 milliseconds.

[0089] The pulse width modulation (PWM) module is a standard hardware peripheral in modern digital controllers used for precise control of analog output. Its basic principle is to switch digital signals at extremely high frequencies and precisely control the ratio of high-level (on) to low-level (off) time (i.e., duty cycle) within a cycle. This, after filtering by external power circuits (such as an H-bridge) and inductive loads (such as electromagnetic coils), yields an equivalent analog voltage or current with precise amplitude and waveform. In this embodiment, it is through precise programming and real-time modulation of the PWM module's duty cycle that the controller is able to generate the required subthreshold sinusoidal voltage signal.

[0090] The signal is applied to the electromagnetic coil array via an H-bridge drive circuit connected to the array, and its duration is strictly controlled to be only 10 milliseconds. The total energy, determined by the signal's amplitude, frequency, waveform, and duration, is precisely controlled at a subthreshold level. It is sufficient to induce minute, controlled high-frequency mechanical vibrations in the impeller, but far from enough to produce the macroscopic rotation required for infusion. The fundamental purpose of this excitation signal is not for driving, but for detection.

[0091] Step 2: Response Acquisition and Parameter Decoding

[0092] Applying excitation signal Simultaneously, the controller acquires the electrical response signal, i.e. the actual current signal, flowing through the electromagnetic coil array in real time via a high-speed synchronous sampling circuit. The digital signal processing unit inside the controller immediately... and Real-time cross-correlation calculations are performed to accurately resolve two independent key electrical parameters that characterize the fluid's physical properties.

[0093] First electrical parameter (admittance magnitude) ): By analyzing the excitation voltage With response current The magnitude relationship between them is obtained, and its calculation formula is as follows:

[0094] .

[0095] in, It is the effective value of the measured response current signal. It is the effective value of the applied excitation voltage signal. This parameter directly and quantitatively characterizes the viscous damping effect produced by the fluid on the impeller vibration.

[0096] In this invention, the viscous damping effect refers to the fact that the high-frequency vibration of the impeller is essentially a continuous relative shear motion between its surface and the fluid. The viscosity of the fluid determines the rate of energy dissipation during this process. High-viscosity fluids lead to faster energy dissipation, which manifests in electrical systems as increased equivalent resistance or reduced energy transfer efficiency, thereby affecting the primary electrical parameters.

[0097] Second electrical parameter (phase angle) ): By analyzing the excitation voltage With response current The phase relationship between them is obtained, and its calculation formula is as follows:

[0098] .

[0099] in, It is the time delay of the current signal relative to the voltage signal, measured through cross-correlation calculations. This parameter directly and quantitatively characterizes the inertial load effect jointly generated by the fluid and the impeller.

[0100] In this invention, the inertial load effect refers to the fact that the high-frequency vibration of the impeller is a continuous cyclic process of forward and reverse acceleration. The density of the fluid determines the magnitude of the added mass. High-density fluid increases the total inertia of the system, causing a greater time delay (phase lag) in the system's response to the driving force. This change in time response characteristics directly affects the second electrical parameter.

[0101] At this point, the controller has obtained a two-dimensional electrical coordinate system. , This constitutes the only electrical characterization of the current fluid state within the pump body.

[0102] Step 3: Model Calculation and Characteristic Determination

[0103] Next, the controller needs to convert this abstract electrical coordinate ( , This is translated into specific physical properties (viscosity and density). This is accomplished through a pre-established correlation model.

[0104] In this embodiment, the association model is specifically a two-dimensional lookup table stored in the controller's internal non-volatile memory. The process of building this lookup table is as follows: In a laboratory environment, the system is calibrated using a series of standard fluids with known precise viscosity and density, and their corresponding unique electrical coordinates are measured. , Thus, a domain of physical properties (viscosity) is established. ,density ) to electrical parameter domain ( , A high-precision mapping database.

[0105] During the real-time operation of the system, the controller will transmit the electrical parameters that have just been measured in real time ( , Using this as input, a fast retrieval and calculation are performed in this two-dimensional lookup table. The core of its algorithm can be represented as:

[0106] ( , )=BilinearInterpolate(LUT, , ).

[0107] Here, LUT represents a pre-established two-dimensional lookup table, and BilinearInterpolate represents the bilinear interpolation algorithm performed on the lookup table to perform accurate calculations between discrete data points in the lookup table, thereby solving for high-precision fluid viscosity. and fluid density .

[0108] Further explanation of the bilinear interpolation algorithm:

[0109] The goal of this algorithm is to accurately calculate the value at any point located between grid points in a lookup table composed of discrete, gridded calibration data points. In this embodiment, its execution process is conceptually clear and step-by-step:

[0110] Positioning grid cells: The controller first determines the grid cells based on the input real-time electrical coordinates ( , In a two-dimensional lookup table, the smallest rectangular grid cell containing the coordinate point is located. This cell is defined by four adjacent, known calibration data points (the four corner points).

[0111] First linear interpolation: The algorithm first interpolates along a coordinate axis (e.g., the admittance magnitude). (Axis), perform a linear interpolation calculation on each of the two opposite sides formed by these four corner points to obtain two new intermediate points.

[0112] Second linear interpolation: Subsequently, the algorithm performs interpolation along another coordinate axis (e.g., phase angle). (Axis), and perform another linear interpolation calculation on the two new intermediate points obtained in the previous step.

[0113] Results: The final result of this second linear interpolation calculation is the fluid viscosity that precisely corresponds to the input real-time electrical coordinates. and fluid density .

[0114] It should be understood that the association model is not limited to the form of a two-dimensional lookup table. In other embodiments, the model can also be implemented as a trained neural network model, or a mathematical function based on multiple regression analysis, which can also realize the relationship between electrical parameters and physical characteristics.

[0115] A specific numerical calculation and scenario example:

[0116] To illustrate more specifically, let's assume that in a particular scenario of a preferred embodiment of the present invention, the fluid within the pump body is a medical nutrient solution. The entire calculation process is as follows:

[0117] 1. Quantification of incentives and responses:

[0118] The controller applies a standard sinusoidal excitation signal with a peak value of 5V, frequency It is 20kHz.

[0119] The effective value of the response current was measured after high-speed sampling and calculation. It is 0.25A, and the time delay relative to the excitation voltage for 10.42μs.

[0120] 2. Calculation of electrical parameters:

[0121] The first step is to calculate the effective value of the excitation voltage. :

[0122] According to the conversion relationship of standard sine waves, = ,therefore:

[0123] .

[0124] The second step is to calculate the first electrical parameter (admittance modulus) according to the formula. ):

[0125] .

[0126] The third step is to calculate the second electrical parameter (phase angle) according to formula 2. ):

[0127] .

[0128] 3. Calculation of physical properties:

[0129] The controller takes the precisely calculated electrical coordinates (0.071S, -75°) as input and substitutes them into the algorithm (i.e., bilinear interpolation algorithm).

[0130] Assuming that after lookup table and interpolation operations, the final calculated real-time physical properties of the nutrient solution are: =1.2m (mPascals second) =1010 kg / m³ (kg / m³).

[0131] Based on physical characteristics and preset infusion targets, a feedforward control signal is generated to drive the impeller rotation.

[0132] In addition, during the impeller rotation process, feedback signals from downstream sensors are received, and the control of the drive impeller is compensated and adjusted based on the feedback signals, as follows:

[0133] Feedforward control module: It uses a pre-established predictive control model that maps fluid physical properties and preset infusion targets to a set of specific electromagnetic drive parameters to generate feedforward control signals, and performs predictive open-loop drive of impeller rotation before compensation and regulation intervention.

[0134] The feedback compensation module is configured such that the controller calculates the deviation between the real-time feedback signal from the downstream sensor and the desired target value determined by the feedforward control signal, and uses the deviation to generate a correction signal through an adaptive algorithm to continuously fine-tune the feedforward control of the impeller to compensate for disturbances that the prediction model fails to cover.

[0135] The adaptive algorithm is configured to continuously evaluate the system's response performance under compensation regulation and dynamically adjust its internal key control parameters based on the evaluation results, thereby enabling the compensation regulation to self-optimize and adapt to the gradual changes in the system's dynamic characteristics.

[0136] Predictive open-loop drive

[0137] In this invention, the preset infusion target is a quantitative control set point that the user sets for the infusion system and needs to achieve precisely. It can be a target pressure, a target flow rate, or a specific dosage program.

[0138] The controller executes subsequent control algorithms based on the specific infusion goals selected and set by the user.

[0139] When the user sets a preset infusion target (e.g., target pressure is...), After the pressure reaches 100 kPa, the feedforward control module is activated first. It acquires the fluid viscosity in real time. and density Along with target pressure These are input together into a pre-established high-fidelity fluid dynamics state-space model. The functional relationship of this model can be expressed as:

[0140] = ( , , ).

[0141] in, This represents the predictive control model. and These are the calculated target speed and target torque, respectively. The controller then uses field-oriented control and space vector pulse width modulation algorithms to convert these physical targets into feedforward control signals, which directly drive the electromagnetic coil array, achieving fast and stable predictive open-loop drive.

[0142] In this embodiment, the conversion process is achieved through a high-performance motor control algorithm widely used in the field—field-oriented control. The core idea of ​​this algorithm is to decompose the stator current vector of the AC motor into two mutually perpendicular DC components for independent control, thereby achieving precise decoupling control of the motor torque and flux linkage, as simple and efficient as controlling a DC motor. Its specific execution flow in this invention is as follows:

[0143] Target current generation: The controller first calculates the target torque Based on the motor's torque constant, it is directly converted into a quadrature-axis current reference value. (This component directly generates torque). Meanwhile, to achieve maximum efficiency, the direct-axis current reference value is... (This component generates magnetic flux) is set to zero.

[0144] Current closed-loop control: The controller measures the actual three-phase current flowing through the electromagnetic coil array in real time using Hall sensors or sensorless algorithms, and converts it into the actual quadrature-axis current in real time through Clarke transform and Parker transform. and direct-axis current .

[0145] Voltage vector calculation: The deviations of two independent proportional-integral (PI) current controllers from the reference current and the actual current, respectively. - and - Perform calculations and output the required quadrature-axis voltage. and direct axis voltage .

[0146] Inverse coordinate transformation and space vector pulse width modulation generation: The controller then uses inverse Park transformation to generate the calculated voltage vector ( , The system transforms from the DC coordinate system back to the two-phase AC stationary coordinate system. This final voltage vector is then fed into the space vector pulse width modulation module, which ultimately calculates the precise duty cycle of the six pulse width modulation signals driving the three-phase H-bridge circuit.

[0147] Through the complete algorithm process based on field-oriented control described above, the controller successfully and efficiently converts a macroscopic physical target (speed and torque) into a set of directly executable microscopic electrical signal parameters, thereby achieving precise drive of the impeller.

[0148] 2. Real-time feedback and compensation

[0149] After the feedforward control quickly stabilizes the system near the target pressure, the feedback compensation module seamlessly intervenes to perform fine adjustments. In this embodiment, this module is a proportional-integral-derivative controller.

[0150] Deviation calculation: The module continuously calculates real-time pressure. ( ) and expected target value The formula for the deviation between them is:

[0151] E( )= - ( ).

[0152] Correction signal generation and execution: The module generates and executes the signal based on the deviation E ( A correction signal is calculated using the core algorithm. ( Its core algorithm is shown in the following formula:

[0153] ( )= +Ki* ( ) + .

[0154] in, This is a key control parameter of the proportional-integral-derivative (PID) controller. This correction signal, as a fine-tuning variable, is superimposed on the feedforward control signal to form the final drive signal, compensating for disturbances not covered by the predictive model.

[0155] These three parameters correspond to the proportional, integral, and differential parts of the algorithm, respectively, and their functions and roles are as follows:

[0156] proportional gain This parameter determines the strength of the controller's response to the current deviation. Multiply by the current deviation This constitutes the main driving force of the correction signal. The larger the value, the stronger the controller's attempt to correct the deviation and the faster the response speed; however, excessively large values... This could lead to system overshoot or even instability.

[0157] Integral gain This parameter determines the strength of the controller's response to historical cumulative deviations. Multiplying the deviation E(t) by the integral over time constitutes the steady-state error elimination term of the corrected signal. Its core task is to eliminate those small but long-term static deviations. Even if the current deviation is small, as long as it persists, the integral term will continue to accumulate until the deviation is eventually completely eliminated.

[0158] Differential gain This parameter determines the strength of the controller's response to the rate of change of deviation. Multiplying the deviation E(t) by the derivative with respect to time (i.e., the rate of change) constitutes the prediction and damping term of the correction signal. Its core task is to predict the future trend of the deviation and react in advance. If the deviation is decreasing rapidly, the derivative term will have a counteracting effect to suppress overshoot and oscillations, increasing the stability of the system.

[0159] 3. Self-optimization and self-adaptation

[0160] To address the gradual changes in the dynamic characteristics of the system (such as pipeline aging), this invention introduces an adaptive algorithm that enables the proportional-integral-derivative controller to self-optimize.

[0161] Continuous performance evaluation: The controller runs an internal performance evaluation module that continuously monitors and quantifies key performance indicators of the system's response, such as overshoot and settling time.

[0162] Dynamic parameter tuning: Based on these evaluation results, the controller dynamically and online adjusts the key control parameters (proportional gain) of the proportional-integral-derivative controller through a built-in expert rule base. Integral gain Differential gain The purpose of this expert rule base is to weigh and adjust control parameters based on different performance deviations. Its adjustment logic can be exemplified as follows:

[0163] / / Rule A: Suppress overshoot

[0164] IF (overshoot > threshold overshoot) THEN integral gain ( = Integral gain ( )*(1- )

[0165] ENDIF

[0166] / / Rule B: Speed ​​up response

[0167] IF (Stabilization time > Threshold stability) THEN proportional gain ( ) = Proportional Gain ( )*(1+ )

[0168] ENDIF

[0169] In this algorithm, threshold overshoot and threshold stability are preset performance warning lines based on application requirements, used to trigger different adjustment actions. α and β are preset adjustment step sizes used to control the speed and stability of the self-optimization process. These parameters were all determined by our technical personnel during the system design phase based on the application scenario.

[0170] A specific numerical calculation and scenario example:

[0171] Continuing with the previous scenario, the controller has obtained the fluid physical properties, and the user sets the target pressure. =100kPa.

[0172] Feedforward calculation: The controller will =1.2m =1010kg / m³, Substituting these three values ​​(=100kPa) into the predictive control model represented by the formula... The model calculations determine the optimal target rotational speed required to achieve the objective. =3000rpm, target torque =0.5mN m. The controller then generates the corresponding space vector pulse width modulation signal for driving.

[0173] Feedback compensation: Assuming that under feedforward drive, due to slight temperature changes in the pipeline, the real-time pressure feedback from the downstream sensor... ( The pressure stabilized at 99.5 kPa. The controller calculated the deviation E according to the formula. =100kPa 99.5 kPa = 0.5 kPa. The proportional-integral-derivative controller calculates a positive correction signal according to the formula. ( This signal will slightly increase the energy of the final drive signal, causing a slight increase in rotational speed, thereby precisely pulling the pressure back to 100.0 kPa.

[0174] Adaptive Adjustment: Assuming the user subsequently adjusts the target pressure to 150 kPa, the system response generates a 3% overshoot. Since 3% is greater than the preset overshoot threshold (e.g., 2%), Algorithm 2 is triggered, automatically adjusting one or more control gains (such as integral gain). According to the preset step size Make a slight downward adjustment to ensure a smoother response next time.

[0175] In summary, this invention utilizes a non-contact magnetic levitation pump to solve the problems of equipment wear and particulate contamination caused by physical friction, enabling extremely stable and clean delivery of high-purity liquids. More importantly, this invention equips this advanced pump with a complete autonomous closed-loop control system. This system not only monitors the actual pressure at the end of the pipeline in real time but also accurately senses the viscosity and density of the liquid before infusion. Based on this comprehensive real-time information, the system can pre-calculate the optimal drive command, thereby achieving rapid and smooth startup. During infusion, in the event of sudden situations such as pipeline blockage or load changes, the system can immediately perform proactive compensation and adjustment, always maintaining the pressure precisely at the preset target value. This advanced control strategy combining real-time sensing, predictive control, and dynamic compensation allows this invention to achieve a qualitative leap in the accuracy, stability, and robustness in handling complex operating conditions of infusion, fully meeting the stringent requirements of high-end fields such as biomedicine and semiconductors.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A micro infusion system with autonomous closed-loop pressure control driven by a magnetically levitated pump, characterized in that, include: A magnetic levitation pump assembly, the magnetic levitation pump assembly including an impeller and an array of electromagnetic coils for levitation and driving the impeller; The electromagnetic coil array is configured to switch between a sensing operating mode for acquiring response parameters and a driving operating mode for driving the impeller to rotate in response to a mode switching command from the controller. In the sensing operating mode, the electrical signal energy applied to the electromagnetic coil array is lower than the driving threshold, while in the driving operating mode, the electrical signal energy applied to the electromagnetic coil array is higher than the driving threshold. A sensor is installed downstream of the infusion line; and a controller, the controller comprising: Fluid characteristic sensing module: The fluid characteristic sensing module controls the electromagnetic coil array to apply a preset excitation signal to the impeller, and collects the response parameters of the electromagnetic coil array to calculate the physical characteristics of the fluid in the pump body; A feedforward control module is configured to generate a feedforward control signal for driving the impeller rotation based on the physical characteristics calculated by the fluid characteristic sensing module and the preset infusion target. Feedback compensation module: The feedback compensation module is configured to receive feedback signals from the downstream sensor during the rotation of the impeller, and to compensate and adjust the control driving the impeller based on the feedback signals.

2. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 1, characterized in that, The downstream sensor includes an integrated sensing module having: a first sensing unit for identifying discrete anomalies by monitoring discontinuous changes in a physical property of the fluid, and a second sensing unit configured to continuously generate a measurement signal in response to a change in a process parameter of the fluid, wherein the output of the first sensing unit is used to trigger a safety interruption, and the output of the second sensing unit constitutes the feedback signal.

3. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 2, characterized in that, The first sensing unit includes an energy transmitter and an energy receiver, which are arranged on both sides of the infusion fluid passage. The first sensing unit identifies the discrete abnormal events by analyzing the change in a propagation parameter of an energy beam emitted by the energy transmitter, penetrating the fluid, and being received by the energy receiver. The second sensing unit includes: a flexible diaphragm exposed to the fluid pressure; and a transducer element coupled to the flexible diaphragm, the transducer element being configured to convert the mechanical response of the flexible diaphragm into a measurement signal.

4. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 1, characterized in that, The preset excitation signal is a subthreshold excitation signal, whose energy is controlled below the driving threshold that is insufficient to cause the impeller to generate macroscopic fluid delivery drive. The energy is sufficient to excite a dynamic response between the impeller and the fluid that can be detected by the electromagnetic coil array and characterize the physical properties of the fluid.

5. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 1, characterized in that, The response parameters of the electromagnetic coil array include: a first electrical parameter, obtained by analyzing the amplitude relationship between the excitation signal and the electrical response signal of the electromagnetic coil array, used to characterize the fluid viscous damping effect; and a second electrical parameter, obtained by analyzing the phase relationship between the excitation signal and the electrical response signal, used to characterize the fluid inertial load effect.

6. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 5, characterized in that, The controller calculates the physical properties of the fluid by performing calculations on the first and second electrical parameters measured in real time in a pre-established correlation model that defines the quantitative relationship between electrical parameters and fluid physical properties. The correlation model associates the change of the first electrical parameter with the change of fluid viscosity and the change of the second electrical parameter with the change of fluid density.

7. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 1, characterized in that, The feedforward control module is configured to generate the feedforward control signal by using a pre-established predictive control model that maps the fluid physical properties and the preset infusion target to a set of specific electromagnetic drive parameters, and to perform predictive open-loop drive of the impeller rotation before the compensation adjustment is intervened.

8. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 1, characterized in that, The feedback compensation module is configured such that: the controller calculates the deviation between the real-time feedback signal of the downstream sensor and the desired target value determined by the feedforward control signal, and uses the deviation to generate a correction signal through an adaptive algorithm to continuously fine-tune the feedforward control driving the impeller in order to compensate for disturbance factors that the prediction model fails to cover.

9. The magnetically levitated pump-driven autonomous closed-loop pressure control micro infusion system according to claim 8, characterized in that, The adaptive algorithm is configured to continuously evaluate the response performance of the system under compensation and adjustment, and dynamically adjust its internal key control parameters based on the evaluation results, thereby enabling the compensation and adjustment to self-optimize and adapt to the gradual changes in the dynamic characteristics of the system.

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