Method, device and equipment for dynamic speed regulation and control of solder paste stirring equipment

Abstract

The present application relates to the field of equipment control technology, and discloses a method, device and equipment for dynamic speed regulation and control of solder paste stirring equipment. The method includes: initializing a microprocessor control unit in a solder paste stirring system to obtain a system initialization parameter matrix; real-time acquisition and filtering of solder paste temperature and viscosity to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve; state space modeling and Kalman filtering calculation of the solder paste stirring system to obtain a system state estimation result; linear matrix inequality constraint construction and objective function optimization solution of the system state estimation result to obtain a first control gain matrix; based on the first control gain matrix, a stirring mode is selected and a feedforward-feedback composite control calculation is performed, and a stirring motor control input signal is output, thereby achieving high-precision control of the stirring speed and improving the reliability and robustness of the solder paste stirring system in complex industrial environments.

Description

Technical Field

[0001] The present application relates to the field of equipment control technology, and in particular to a method, device and equipment for dynamically adjusting and controlling the speed of solder paste stirring equipment. Background Art

[0002] Solder paste is a key material used in surface mount technology (SMT) in the electronics manufacturing industry. Its uniformity and fluidity directly impact soldering quality and the reliability of electronic products. Traditional solder paste mixing equipment often uses a fixed speed mode, unable to adjust in real time to changes in solder paste viscosity and ambient temperature fluctuations. This leads to problems such as uneven mixing and fluctuating solder paste quality. Solder paste viscosity changes significantly with ambient temperature fluctuations or after prolonged use. Traditional mixing equipment lacks the ability to sense and adapt to these changes, making it difficult to maintain stable solder paste quality. Furthermore, different types of solder paste have different viscosity characteristics and optimal mixing parameters. Existing mixing equipment typically uses fixed parameters set empirically, making it difficult to meet the flexible requirements of high-variety, small-batch production.

[0003] Existing solder paste mixing control systems commonly suffer from technical issues such as insufficient control accuracy, weak anti-interference capabilities, and poor adaptability. Stirring speed control is often affected by environmental noise and the nonlinear characteristics of the solder paste, resulting in deviations between the actual speed and the target speed. Furthermore, traditional PID controllers have difficulty adjusting parameters for solder pastes with large viscosity variations, making precise control difficult. Furthermore, the lack of real-time monitoring and analysis of the solder paste's state during the mixing process prevents dynamic adjustment of the control strategy based on the actual state of the solder paste. These issues lead to fluctuations in solder paste mixing quality, which in turn affects the consistency and reliability of the subsequent solder paste printing process. Summary of the Invention

[0004] The present application provides a method, device and equipment for dynamically regulating and controlling the speed of a solder paste stirring device, thereby achieving high-precision control of the stirring speed and improving the reliability and robustness of the solder paste stirring system in complex industrial environments.

[0005] A first aspect of the present application provides a method for dynamically adjusting and controlling the speed of a solder paste stirring device, the method comprising:

[0006] Initializing the microprocessor control unit in the solder paste stirring system to obtain a system initialization parameter matrix;

[0007] Based on the system initialization parameter matrix, the solder paste temperature and viscosity are collected and filtered in real time to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve;

[0008] Performing state space modeling and Kalman filter calculation on the solder paste stirring system according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve to obtain a system state estimation result;

[0009] Performing linear matrix inequality constraint construction and objective function optimization on the system state estimation result to obtain a first control gain matrix;

[0010] Based on the first control gain matrix, the stirring mode is selected and the feedforward-feedback composite control calculation is performed to output a stirring motor control input signal.

[0011] A second aspect of the present application provides a device for dynamically adjusting and controlling the speed of a solder paste stirring device, the device comprising:

[0012] An initialization module is used to initialize the microprocessor control unit in the solder paste stirring system to obtain a system initialization parameter matrix;

[0013] A filtering module is used to collect and filter the solder paste temperature and viscosity in real time based on the system initialization parameter matrix to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve;

[0014] a calculation module, configured to perform state space modeling and Kalman filter calculation on the solder paste stirring system according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve, to obtain a system state estimation result;

[0015] A solution module, configured to construct a linear matrix inequality constraint and perform an optimization solution on the system state estimation result to obtain a first control gain matrix;

[0016] The output module is used to select the stirring mode and perform feedforward-feedback composite control calculation based on the first control gain matrix, and output the stirring motor control input signal.

[0017] The third aspect of the present application provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned method for dynamically adjusting and controlling the speed of the solder paste stirring equipment.

[0018] Compared with the existing technology, the present application has the following beneficial effects: high-precision control of stirring speed is achieved through LMI controller design and linear matrix inequality constrained optimization; by adopting observer-based state estimation and recursive parameter identification technology, the system can automatically adjust the stirring force and control parameters according to the change of solder paste viscosity to adapt to different environmental conditions; the three stirring modes of constant speed, periodic speed change and adaptive are designed to achieve smooth switching through the cosine transition function, avoiding mechanical shock; the recursive least squares method is used to perform real-time fitting calculation of the solder paste viscosity-temperature characteristics, and the feedforward-feedback composite control strategy is used to accurately compensate for the nonlinear parameter dependence characteristics of the solder paste; the state estimation based on Kalman filtering and the continuously updated database form a closed-loop self-optimization mechanism; through precise control, the stability and consistency of the solder paste uniformity are ensured, and the yield of subsequent printing and welding processes is improved; the iterative LMI control method effectively suppresses external interference such as motor vibration, temperature changes and viscosity detection noise, thereby improving the reliability and robustness of the system in complex industrial environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] The structures, proportions, sizes, etc. depicted in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which the present invention can be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportional relationships, or adjustments in size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and objectives that can be achieved by the present invention.

[0021] Figure 1 1 is a flow chart of a method for dynamically adjusting and controlling the speed of a solder paste stirring device provided by an embodiment of the present invention;

[0022] Figure 2 This is a schematic block diagram of the structure of a device for dynamically adjusting and controlling the speed of a solder paste stirring device provided by an embodiment of the present invention;

[0023] Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0026] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should be further understood that the term "and / or" used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 In one embodiment of the present application, a method for dynamically adjusting and controlling the speed of a solder paste stirring device includes:

[0028] Step 100: Initialize the microprocessor control unit in the solder paste stirring system to obtain a system initialization parameter matrix;

[0029] It is understandable that the execution subject of the present application can be a speed dynamic adjustment and control device for solder paste mixing equipment, or a terminal or server, which is not limited here. The embodiment of the present application is described by taking the server as the execution subject as an example.

[0030] Specifically, a pre-set program in the microprocessor control unit is started to initialize the microprocessor, enabling it to execute subsequent control tasks based on input signals. During the startup program, the microprocessor clock frequency is set to ensure it operates at an appropriate frequency to meet real-time control requirements. The clock frequency is set based on the microprocessor's performance and system requirements to ensure the stability and accuracy of the entire control system. The non-volatile memory in the microprocessor control unit is read to retrieve stored solder paste data, including standard viscosity values ​​for different solder paste types, optimal stirring speed ranges, and control parameters for different solder pastes. This data constitutes a first solder paste parameter database. Simultaneously, connections are established between the temperature sensor and viscosity sensor to ensure stable signal transmission and real-time reflection of the solder paste's physical state. Sensor connection parameters are then obtained. After the connections are established, standard sample measurements are performed on the temperature sensor and viscosity sensor to ensure sensor accuracy and reliability. This measurement comparison yields a sensor calibration parameter matrix. Vector control technology is then configured for the variable frequency drive. The variable frequency drive controls the operating state of the stirring motor, and its parameters directly impact the system's dynamic performance. When configuring vector control technology, the VFD's rated power, frequency range, and accuracy parameters are set. These settings determine the motor's operating range, control accuracy, and response speed, ensuring accurate and efficient mixing under varying load conditions. During this configuration, the load variations of the mixing system are taken into account to ensure stable motor operation under various operating conditions. Based on the microprocessor's basic operating parameters, the primary solder paste parameter database, sensor connection parameters, the sensor calibration parameter matrix, and the VFD's setup parameters, the motor's rotor inertia, friction coefficient, damping ratio, and mechanical transmission ratio are calculated. These parameters directly impact the system's dynamic characteristics, such as stability and control accuracy during acceleration and deceleration, so comprehensive consideration is required. After completing these calculations, a set of system initialization parameter matrices is obtained.

[0031] Step 200: Perform real-time acquisition and filtering of solder paste temperature and viscosity based on the system initialization parameter matrix to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve;

[0032] Specifically, a temperature sensor collects the current solder paste temperature and performs median filtering. Median filtering effectively removes outliers and noise from the temperature data, making the temperature data smoother and more accurate. The filtered temperature data reflects the actual temperature state of the solder paste, avoiding control errors caused by data fluctuations. Similarly, a viscosity sensor collects the current viscosity data of the solder paste in real time. The collected viscosity data is subjected to exponential smoothing filtering, which stabilizes the current viscosity data by weighted averaging of past data, thereby suppressing the impact of data fluctuations and obtaining filtered viscosity data. Temperature compensation is calculated based on the filtered temperature and viscosity data. The difference between the reference temperature and the actual temperature is calculated and multiplied by the compensation coefficient to obtain a temperature compensation value, which is used to adjust the solder paste viscosity data so that the viscosity value reflects the actual fluidity of the solder paste at the current temperature. This compensation process eliminates the impact of temperature changes on viscosity, resulting in a standardized viscosity value. The filtered and compensated temperature and viscosity data are then vector-mapped to construct a solder paste state vector, which contains temperature and viscosity information and reflects the physical state and rheological properties of the solder paste. Based on filtered temperature and viscosity data, the relationship between solder paste viscosity and temperature is modeled. Recursive least squares fitting is performed using this method. This method is an effective real-time updating method that dynamically adjusts model parameters based on changing temperature and viscosity data, accurately fitting the solder paste's viscosity-temperature characteristic curve and describing how the paste's viscosity changes at different temperatures.

[0033] Step 300: Perform state space modeling and Kalman filter calculation on the solder paste stirring system according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve to obtain a system state estimation result;

[0034] It should be noted that based on the solder paste state vector, a state-space model for the solder paste stirring process is established, including stirring speed, motor torque, and solder paste flow state indicators. This model describes the relationship between stirring speed and solder paste flow state. This model describes how various physical quantities interact during the stirring process. Based on the solder paste viscosity-temperature characteristic curve, a polynomial expansion is performed on the system matrix in the basic state equation. The viscosity-temperature characteristic curve reveals how solder paste viscosity varies with temperature, a characteristic that directly affects flow behavior and viscosity control during the stirring process. By performing a polynomial expansion on the system matrix in the basic state equation, a set of expanded matrices representing the viscosity dependence is obtained. These expanded matrices translate the effect of temperature on solder paste viscosity into mathematical form, enabling the system model to more accurately reflect the fluidity changes of solder paste at different temperatures. Based on the expanded matrix set, the system matrix coefficients are identified using a recursive instrumental variable method to obtain the system identification results. The goal of system identification is to estimate the coefficients in the system matrix based on actual measurement data. Using the recursive instrumental variable method, the coefficients of the system matrix are continuously updated to better align with the actual process behavior, thereby obtaining the optimized model parameters. Based on the system identification results, a Kalman filter state observer is constructed. The Kalman filter is a method that uses a state-space model to recursively estimate the system state. It can effectively handle dynamic changes in the system state and noise interference. When constructing the Kalman filter, the initial state estimate and the initial error covariance matrix are set to obtain the state observer model. The initial value is derived from the system's initial state, while the initial error covariance matrix reflects the uncertainty of the system's initial state. The state observer model is then calculated in the prediction step. By using the state equation to forward-forward the system state, the Kalman filter calculates a priori state estimate and its error covariance matrix. The prior state estimate represents an estimate of the system state based on available information in the absence of actual measurements, while the error covariance matrix measures the uncertainty of the estimate. In the prediction step, the Kalman filter updates the state based on the actual measurement output. Specifically, the Kalman filter calculates the Kalman gain matrix for the prior estimate based on the actual measurement data. This gain matrix determines the weighting between the prior estimate and the actual measurement, thereby ensuring more accurate state updates. By updating the state, the prior estimate is adjusted to an estimate closer to the actual state, thereby improving the accuracy and stability of the system. After multiple iterations, the Kalman filter outputs an accurate estimate of the system state.

[0035] Step 400: constructing linear matrix inequality constraints and solving objective function optimization on the system state estimation result to obtain a first control gain matrix;

[0036] Specifically, control performance indicators are defined based on the system state estimation results. These indicators include requirements for system response speed, steady-state error, energy consumption, and system stability. When designing the controller, the target design parameter set is determined based on these indicators. This parameter set provides a clear direction and goal for controller adjustment, ensuring that the control process meets system performance requirements while ensuring system stability and efficiency. By defining the control performance indicators, the desired effect of system control is quantified and provides a target for subsequent optimization calculations. Based on the controller design target parameter set, a parameter-dependent Lyapunov function is constructed. The Lyapunov function is a mathematical tool used to analyze system stability. Its definition proves whether a system is stable under given control conditions. By constructing the Lyapunov function, a stability criterion function for the system is established. This function is used to evaluate whether the controller design parameters meet system stability requirements and provide a basis for subsequent optimization. The Lyapunov function not only analyzes system stability but also provides theoretical support for controller parameter selection, ensuring that the controller design meets the actual system requirements. Based on the system stability criterion function, positive definiteness constraints and stability constraints are constructed to ensure that the designed controller not only meets stability requirements but also avoids system instability or divergence in actual operation. These constraints combine system stability with control performance to obtain a basic linear matrix inequality constraint system. Auxiliary variables are introduced and matrix transformations are performed on this basic linear matrix inequality constraint system to obtain the first linear matrix inequality constraint system. The introduction of auxiliary variables simplifies the computation and reduces the complexity of the constraints, while matrix transformations further optimize the constraint system. This method transforms the originally complex linear matrix inequality into a more manageable form, improving solution efficiency and simplifying the optimization process. The first linear matrix inequality constraint system is combined with pole placement region constraints, adding inner and outer radius pole region restrictions. These constraints ensure that the system's pole distribution meets the desired stability requirements. Pole placement is an important component in control system design, determining the system's response characteristics, including its oscillation and decay rate. Pole region restrictions accurately control the system's dynamic performance, ensuring that the system operates within the stable range and avoiding instability caused by improper pole placement. A discrete-time state feedback objective function, consisting of both a state penalty and a control penalty, is constructed for a second linear matrix inequality constrained system. These two terms balance the control accuracy of the system state with the energy consumption of the control input, avoiding overcontrol or overdependence on a single control input, thereby achieving an optimal compromise in controller design. By quantifying the system state and control input, the discrete-time state feedback objective function enables the controller to approach optimal control performance within a limited time by continuously adjusting the state feedback.The optimal solution is gradually approached through the iterative LMI (Linear Matrix Inequality) algorithm, resulting in the first control gain matrix. The LMI algorithm is a mathematical tool used in control system optimization. By optimizing the objective function, the control gains are gradually adjusted until the optimal solution is found. During this process, the LMI algorithm solves the optimization problem with multiple constraints through continuous iteration and ultimately outputs an optimal control gain matrix.

[0037] Step 500 : Selecting a stirring mode and performing feedforward-feedback composite control calculation based on a first control gain matrix, and outputting a stirring motor control input signal.

[0038] Specifically, first, based on the solder paste viscosity and temperature data in the solder paste state vector, the stirring mode library is queried and matched. The stirring mode library contains multiple stirring modes, each optimized for different solder paste characteristics and process requirements, including constant speed stirring mode, periodic variable speed stirring mode, and adaptive stirring mode. The constant speed stirring mode is suitable for solder pastes with stable viscosity and minimal temperature fluctuations, providing uniform stirring. The periodic variable speed stirring mode uses sinusoidal speed fluctuations and is suitable for solder pastes whose viscosity gradually changes over time or require periodic perturbations to prevent material sedimentation or adhesion. The adaptive stirring mode is an advanced mode that dynamically adjusts the stirring speed based on real-time viscosity and temperature feedback, making it suitable for complex process environments or variable solder paste characteristics. After the system selects the optimal stirring mode based on the current solder paste characteristics, a specific stirring mode type is determined. Based on the selected stirring mode type, a reference velocity trajectory is calculated and generated. This process adopts different velocity generation strategies depending on the mode type. In constant-speed stirring mode, a fixed stirring speed is set. This speed value is derived from the optimal parameters in the stirring mode library to ensure optimal stirring performance given the current solder paste characteristics. In periodic variable-speed stirring mode, a sinusoidal velocity trajectory is generated. This velocity variation creates periodic flow disturbances, enhancing stirring performance and preventing material adhesion and agglomeration. In adaptive stirring mode, the stirring speed is dynamically adjusted by real-time calculation of the viscosity change rate and temperature trends, ensuring that the stirring process remains optimal. A lookup table interpolation calculation is performed on the first control gain matrix to generate the real-time control gain matrix. During dynamic control, changes in the control gain matrix significantly impact the system's response speed and stability. Through lookup table interpolation, the control gain parameters are dynamically adjusted based on the current solder paste state and control requirements, ensuring that the controller remains optimal. To facilitate switching between different stirring modes, a cosine transition function is designed to ensure smooth transitions in the control output signal during mode switching, avoiding system instability or sudden changes caused by mode switching. This smooth transition design not only improves control system stability but also effectively reduces mechanical shock to the motor, extending the life of the equipment. The feedforward and feedback control components are calculated based on the stirring reference velocity trajectory, the system state estimation results, and the real-time control gain matrix. The feedforward control component, based on the desired velocity trajectory, can predict system requirements in advance and apply corresponding control signals, reducing system response lag. The feedback control component, based on the actual system state, dynamically adjusts the control signal by comparing the error between the actual output and the desired output, thereby achieving precise error correction. By summing the feedforward and feedback control components, a complete control input signal is obtained, combining the advantages of fast response speed and high accuracy. The digital control signal is converted to an analog signal using a D / A (digital / analog) converter.During this process, a current limiting protection function is added to prevent motor overload or control signal violations in extreme situations. By setting the control signal update period, the system appropriately adjusts the control signal refresh rate under different operating conditions to ensure real-time and stable control signals. After all control logic processing is completed, the final stirring motor control input signal is output, driving the motor to perform stirring operations according to the optimal speed trajectory.

[0039] The motor encoder measures the actual stirring speed to obtain the current actual motor speed data. In solder paste mixing equipment, the motor encoder serves as a speed sensor, accurately feeding back the motor's rotational speed and providing high-resolution speed data. The difference between the actual stirring speed data and a pre-set reference stirring speed trajectory is calculated to analyze the system's speed tracking performance. By calculating the difference between the actual and reference speeds, the speed error is calculated, reflecting the system's current control accuracy and revealing the controller's response characteristics under different operating conditions. The speed error is statistically analyzed, including metrics such as the mean, variance, and peak value. These statistical analysis results form the speed control performance evaluation results, which accurately determine whether the current controller's performance meets expectations. Based on the speed control performance evaluation results, the system model parameters are identified online using the recursive least squares method. The recursive least squares method is an algorithm that updates the system model in real time. By continuously incorporating new measurement data, the parameters of the system's state-space model are dynamically adjusted to better adapt to the dynamic characteristics of the actual system. During this process, updating the system model parameters involves more than simple numerical adjustments. Instead, the model parameters are smoothly updated through a weighted calculation of historical and current data, ensuring system model stability and convergence. Using online identification methods, the system maintains control model accuracy under varying solder paste viscosities, temperature variations, or fluctuations in stirring load. Based on the updated system state-space model, the linear matrix inequality (LMI) optimization problem is resolved to obtain an updated control gain matrix. The LMI optimization algorithm solves a complex set of mathematical inequalities to find control gain parameters that optimize system performance. Compared to the initial control gain matrix, the updated control gain matrix better reflects the optimal control strategy for the system under the current state. The LMI optimization process not only considers system stability constraints but also multiple control objectives, such as control input amplitude limits and response time requirements. Through this multi-objective optimization calculation, the updated control gain matrix achieves more efficient stirring control while ensuring stable system operation. The updated control gain matrix is ​​then fused with the first control gain matrix through exponential smoothing to produce the second control gain matrix. The exponential smoothing fusion algorithm sets a smoothing coefficient and combines the old and new gain matrices according to a certain weight, making the control gain change process smoother and avoiding system instability caused by sudden changes in control parameters. The solder paste parameter database is updated and supplemented based on operational performance data. Data on solder paste temperature and viscosity changes is continuously collected and analyzed, and compared with the standard parameters in the first solder paste parameter database.When significant deviations are detected between actual data and the parameters in the database, the database is updated through data fitting and statistical analysis. This includes information such as viscosity-temperature curves for different solder paste types, optimal stirring speed ranges, and control parameters. This dynamic database update mechanism enables the system to adapt to changes in solder paste batches or types, maintaining high control accuracy and stirring quality over the long term. After a period of operation, a second solder paste parameter database is generated, containing not only the original standard parameters but also actual operating data that has undergone multiple corrections and supplements.

[0040] In the embodiment of the present application, high-precision control of the stirring speed is achieved through LMI controller design and linear matrix inequality constrained optimization; by adopting observer-based state estimation and recursive parameter identification technology, the system can automatically adjust the stirring force and control parameters according to the change of solder paste viscosity to adapt to different environmental conditions; the three stirring modes of constant speed, periodic speed change and adaptive are designed to achieve smooth switching through the cosine transition function, avoiding mechanical shock; the recursive least squares method is used to perform real-time fitting calculation of the solder paste viscosity-temperature characteristics, and the feedforward-feedback composite control strategy is used to accurately compensate for the nonlinear parameter dependence characteristics of the solder paste; the state estimation based on Kalman filtering and the continuously updated database form a closed-loop self-optimization mechanism; through precise control, the stability and consistency of the solder paste uniformity are ensured, and the yield of subsequent printing and welding processes is improved; the iterative LMI control method effectively suppresses external interference such as motor vibration, temperature changes and viscosity detection noise, thereby improving the reliability and robustness of the system in complex industrial environments.

[0041] In a specific embodiment, the process of executing step 100 may specifically include the following steps:

[0042] Starting and executing a pre-set program of the microprocessor control unit in the solder paste stirring system, setting the clock frequency of the microprocessor control unit, and obtaining basic operating parameters of the microprocessor;

[0043] Reading solder paste type data stored in a non-volatile memory to obtain a first solder paste parameter database including standard viscosity values, optimal stirring speed ranges, and control parameters of different types of solder paste;

[0044] Establish connections between the temperature sensor and the viscosity sensor to obtain sensor connection parameters, and perform standard sample measurements and comparisons on the temperature sensor and the viscosity sensor to obtain a sensor calibration parameter matrix;

[0045] Configure the variable frequency drive with vector control technology, set the rated power, frequency range and accuracy parameters, and obtain the variable frequency drive setting parameters;

[0046] Based on the basic operating parameters of the microprocessor, the first solder paste parameter database, the sensor connection parameters, the sensor calibration parameter matrix and the variable frequency drive setting parameters, the motor rotor inertia, friction coefficient, damping ratio and mechanical transmission ratio are calculated and integrated to construct the system initialization parameter matrix.

[0047] Specifically, during the system startup phase, the entire control system is started by executing the pre-set program of the microprocessor control unit. During the microprocessor startup process, its clock frequency is set. Clock frequency The setting needs to consider the system's response time requirements and the complexity of the processing task. For example, in scenarios that require high-frequency data acquisition and complex control operations, the clock frequency should be as high as possible to ensure calculation speed and response accuracy. After the setting is completed, the basic parameters of the microprocessor operation are generated, including computing power. , clock cycle and data transfer rates After the microprocessor is initialized, the solder paste type data stored in the non-volatile memory is read. This data includes the standard viscosity values ​​of different types of solder paste. , optimal stirring speed range and related control parameters When reading this data, load it into memory to build the first solder paste parameter database .in Indicates different types of solder paste, = The total number of solder paste types. These parameters will directly affect the system's decision logic when selecting the stirring mode and setting the stirring speed. At the same time, establish connections with the temperature sensor and viscosity sensor. Sensor connection parameters Including the communication protocol, interface type and signal transmission rate of the sensor. After the connection is established, in order to ensure the accuracy of the sensor measurement data, the temperature sensor and viscosity sensor are measured and compared with the standard sample to obtain the sensor calibration parameter matrix The calibration parameter matrix is ​​used to correct the systematic error of the sensor. For example, the offset calibration formula of the temperature sensor is expressed as:

[0048]

[0049] in is the calibrated temperature value, is the raw measurement value of the sensor, Is the temperature calibration coefficient matrix. After the sensor part is configured, the vector control technology is configured for the variable frequency drive. Set the rated power of the variable frequency drive , frequency range and control accuracy The core of vector control technology is to achieve precise control of motor speed and torque by controlling the motor's magnetic field and torque current. In actual calculation, the frequency converter setting parameters Represented as a vector:

[0050]

[0051] After all basic parameters are configured, the basic parameters are run based on the microprocessor. , the first solder paste parameter database , sensor connection parameters , sensor calibration parameter matrix And variable frequency drive setting parameters , calculate the dynamic characteristics of the motor, including the rotor inertia , friction coefficient , damping ratio and mechanical transmission ratio For example, in the motor control process, the rotor inertia Calculated by the following formula:

[0052]

[0053] in is the rated power of the motor, is the frequency of the driver, is the mechanical transmission ratio. The system integrates these key parameters to construct the system initialization parameter matrix :

[0054]

[0055] In a specific embodiment, the process of executing step 200 may specifically include the following steps:

[0056] The current temperature of the solder paste is collected and median filtered through the temperature sensor to obtain filtered temperature data;

[0057] The viscosity sensor collects the current viscosity of the solder paste and performs exponential smoothing filtering to obtain filtered viscosity data;

[0058] Based on the filtered temperature data and the filtered viscosity data, a compensation coefficient product operation is performed on the temperature reference value and the actual temperature difference to obtain a temperature-compensated standardized viscosity value;

[0059] Perform vector mapping on the filtered temperature data, filtered viscosity data, temperature deviation value, and viscosity deviation value to obtain a solder paste state vector;

[0060] Based on the filtered temperature data and filtered viscosity data, the relationship between solder paste viscosity and temperature is fitted using a recursive least squares method to obtain a solder paste viscosity-temperature characteristic curve.

[0061] Specifically, the current temperature of the solder paste is collected in real time through a temperature sensor, and the collected temperature data is processed using a median filter algorithm. Median filtering is a nonlinear signal processing method that can effectively remove outliers and retain the true trend of data changes. Assume that at time The temperature data sequence collected at each moment is , temperature data after median filtering It is expressed by the formula:

[0062]

[0063] in is the filter window size, Represents the temperature data at the current moment and before, and Median represents the median of these data. Median filtering effectively filters out the sudden noise in the data and obtains smooth temperature data. At the same time, the current viscosity data of the solder paste is obtained through the viscosity sensor. To ensure the smoothness and response speed of the viscosity data, the exponential smoothing filter algorithm is used to process the viscosity data. The exponential smoothing filter can make the viscosity data smooth while maintaining a fast response speed based on the weighted calculation of historical data and current data. The filtered viscosity data The calculation formula is:

[0064]

[0065] in is the original viscosity data at the current moment, is the filtered viscosity data at the previous moment, is the smoothing coefficient, and its value range is Smoothing coefficient The larger it is, the more sensitive the system is to new data and the faster it responds, but the worse the smoothing effect is. On the contrary, The smaller the value, the smoother the data, but the system response will be slower. Exponential smoothing filtering effectively suppresses random fluctuations in viscosity data and ensures data stability. After obtaining smooth temperature and viscosity data, temperature compensation calculation is performed to eliminate the influence of ambient temperature changes on viscosity measurement. Standardized viscosity value after temperature compensation The calculation formula is:

[0066]

[0067] in is the temperature reference value, Is the temperature compensation coefficient, which indicates the sensitivity of temperature change to viscosity. Through temperature compensation, the system can always maintain the consistency of viscosity data under different temperature conditions. Based on the filtered temperature data, viscosity data, temperature deviation value and viscosity deviation value, vector mapping is performed to obtain the solder paste state vector , which is expressed as:

[0068]

[0069] This state vector comprehensively reflects the current solder paste's physical properties and temperature and viscosity deviations, providing the necessary foundational data for establishing the state-space model. The viscosity-temperature characteristic curve is fitted using the recursive least squares (RLS) method to obtain the solder paste viscosity-temperature characteristic curve. The RLS algorithm continuously updates the fitting model parameters by minimizing the sum of squared errors, enabling the model to dynamically adapt to changes in temperature and viscosity data. The fitting model is expressed as:

[0070]

[0071] in These are the coefficients of the fitting polynomial. These coefficients are dynamically adjusted through the recursive least squares method so that the model can be recalculated and updated each time new temperature and viscosity data are input to maintain the fitting accuracy of the model.

[0072] In a specific embodiment, the process of executing step 300 may specifically include the following steps:

[0073] Based on the solder paste state vector, a state space model is established for the solder paste stirring process, including stirring speed, motor torque, and solder paste flow state indicators, and the basic state equation of the solder paste stirring system is obtained.

[0074] According to the solder paste viscosity-temperature characteristic curve, the system matrix in the basic state equation is expanded polynomially to obtain the expansion matrix group that characterizes the viscosity dependence relationship;

[0075] Based on the expanded matrix group, the system matrix coefficients are identified by recursive instrumental variable method to obtain the system identification results;

[0076] According to the system identification results, a Kalman filter state observer is constructed, the initial state estimation value and the initial error covariance matrix are set, and the state observer model is obtained;

[0077] Perform prediction step calculations on the state observer model, use the state equation to forward-calculate the system state, and obtain the state prior estimate and its error covariance matrix;

[0078] For the state prior estimation value, the Kalman gain matrix is ​​calculated according to the actual measurement output and the state is updated to obtain the system state estimation result.

[0079] Specifically, based on the solder paste state vector, a state space model including stirring speed, motor torque and solder paste flow state index is established for the solder paste stirring process. Including stirring speed , motor torque , solder paste viscosity and temperature Based on these state quantities, a state space model of the solder paste stirring system is constructed, and the dynamic behavior of the system is expressed in the form of a state equation. The basic state equation is expressed as:

[0080]

[0081]

[0082] in is the system state vector, containing State variables, is the control input vector, usually the drive signal of the motor, is the system state matrix, is the control input matrix, and represent process noise and measurement noise respectively, is the measurement output vector, is the measurement matrix. After obtaining the basic state equation, since the viscosity of solder paste is significantly affected by temperature, according to the solder paste viscosity-temperature characteristic curve, the system matrix in the state equation is expanded polynomially to accurately describe the temperature dependence of viscosity. Assuming viscosity and temperature The relationship is expressed in polynomial form as:

[0083]

[0084] in are the polynomial coefficients to be determined, by replacing the system matrix Perform polynomial expansion to obtain an expansion matrix group that characterizes the viscosity dependence :

[0085]

[0086] in, is the matrix coefficient corresponding to the viscosity-temperature relationship, is the temperature data measured by the current system. Through this polynomial expansion, the system matrix dynamically reflects the rheological characteristics of the solder paste under different temperature conditions. Based on the expanded matrix group, the system matrix coefficients are identified using the recursive instrumental variable method to ensure that the model coefficients always remain optimal during actual operation. The system identification uses the recursive instrumental variable method to estimate the coefficients. By continuously introducing new measurement data, the coefficients of the system matrix are dynamically updated, so that the model can accurately reflect the actual behavior of the solder paste mixing system. The core of the recursive instrumental variable method is to calculate the system matrix coefficients by minimizing the prediction error. The optimal estimate of , its update formula is expressed as:

[0087]

[0088] in is the gain matrix, is the actual measured output, is the model prediction output, which is updated recursively The coefficients enable the system model to maintain a high prediction accuracy under complex working conditions. After the system identification is completed, the state observer of the Kalman filter is constructed based on the updated system model. The Kalman filter can make the best estimate of the system state when the system is disturbed by noise and set the initial value of the state estimate. and the initial error covariance matrix , we get the state observer model. Error covariance matrix Dynamically adjusted through the prediction and update process, its initialization formula is:

[0089]

[0090] in represents the mathematical expectation, is the predicted value of the initial state. Through this initialization process, the Kalman filter reasonably estimates the uncertainty of the system state. After the state observer model is constructed, the state equation is used to forward-calculate the system state through the prediction step calculation to obtain the state prior estimate. and its error covariance matrix :

[0091]

[0092]

[0093] in is the process noise covariance matrix. The prediction step enables the system to estimate the state of the next moment through forward calculation and provides the basis for subsequent state updates. After obtaining the state prior estimate, the Kalman gain matrix To update the state, the calculation formula of Kalman gain is:

[0094]

[0095] in Is the measurement noise covariance matrix. By calculating the Kalman gain, the system dynamically adjusts the weight distribution between the predicted value and the actual measured value. Based on the Kalman gain matrix, the system updates the state estimate and the error covariance matrix :

[0096]

[0097]

[0098] in is the unit matrix. Through this prediction-update iterative process, the Kalman filter can continuously optimize the system state estimation, so that the system can maintain a high estimation accuracy when subjected to external disturbances.

[0099] In a specific embodiment, the process of executing step 400 may specifically include the following steps:

[0100] Define the control performance index based on the system state estimation results to obtain the controller design target parameter set;

[0101] Based on the controller design target parameter set, the parameter-dependent Lyapunov function is constructed to obtain the system stability criterion function;

[0102] According to the system stability criterion function, positive definiteness constraints and stability constraints are constructed to obtain the basic linear matrix inequality constraint group;

[0103] Auxiliary variables are introduced and matrix transformation is performed on the basic linear matrix inequality constraint group to obtain the first linear matrix inequality constraint system;

[0104] The first linear matrix inequality constraint system is combined with the pole placement region constraint condition, and the pole region restrictions of the inner radius and the outer radius are added to obtain the second linear matrix inequality constraint system;

[0105] A discrete-time state feedback objective function including state penalty term and control penalty term is constructed for the second linear matrix inequality constraint system. The optimal solution is gradually approached through the iterative LMI algorithm to obtain the first control gain matrix.

[0106] Specifically, based on the system state estimation results, control performance indicators are defined, and the target parameter set of the controller design is determined by these performance indicators. These performance indicators include the system response speed, steady-state error, damping ratio, overshoot, and energy consumption. In order to quantify these control objectives, a performance indicator function is defined , which integrates the trade-off between the system's dynamic response characteristics and control energy, and its expression is:

[0107]

[0108] in is the system state vector, is the control input, is the state weight matrix, is the control weight matrix. and The selection of determines the sensitivity of the control system to state deviation and control energy, which directly affects the target parameter set of the controller design. These parameter sets are dynamically adjusted through the optimization process to achieve the optimization of the system control performance. After determining the target parameter set of the controller design, the parameter-dependent Lyapunov function is constructed based on these parameters to obtain the system stability criterion function. Lyapunov function is a positive definite function whose derivative is a semi-negative definite function, and the global asymptotic stability of the system is proved by the Lyapunov stability criterion. For linear systems, its Lyapunov function is chosen to be a quadratic function:

[0109]

[0110] in is a positive definite matrix, by choosing the appropriate So that the system meets the stability requirements. According to the Lyapunov stability condition, the system is stable when the following inequality is satisfied:

[0111]

[0112] To convert this condition into LMI form, the system matrix Perform parameter dependency processing so that The matrix can dynamically adapt to the state changes of the system. The obtained system stability criterion function can provide a strict mathematical guarantee for the stability of the system and provide a theoretical basis for the subsequent LMI constraint construction. According to the system stability criterion function, the positive definiteness constraint and stability constraint of the system are constructed to obtain the basic linear matrix inequality constraint group. The positive definiteness constraint guarantee matrix is positive definite, that is The stability constraint ensures the stability of the system by constructing an LMI inequality, which is specifically expressed as:

[0113]

[0114] in Is a preset positive definite matrix used to adjust the stability margin of the system. In this process, these constraints are converted into the standard form of LMI to ensure that they can be processed by the LMI solution algorithm. The basic linear matrix inequality constraint group is subjected to auxiliary variable introduction and matrix transformation to obtain the first linear matrix inequality constraint system. In the LMI solution process, the auxiliary variables The introduction of can effectively reduce the complexity of calculation. ( is the control gain matrix to be solved), converting the original nonlinear constraints into linear form, so that the LMI solver can handle complex optimization problems. After matrix transformation and variable substitution, the basic LMI constraint group is further simplified to:

[0115]

[0116] This matrix form ensures the stability and controllability of the system and provides a basis for the subsequent LMI solution. After completing the construction of the first LMI constraint system, it is combined with the pole configuration region constraint condition, and the pole region restrictions of the inner radius and outer radius are added to obtain the second LMI constraint system. The pole configuration region can effectively control the dynamic response characteristics of the system, such as the attenuation rate and oscillation characteristics, by setting the allowable distribution range of the poles on the complex plane. In LMI, pole configuration is achieved by adding the following form of region restrictions:

[0117]

[0118] in Represents the set extreme point area, for example, by setting the inner and outer radii, the extreme points are limited to a circular or elliptical area, thereby ensuring the response speed and stability of the system. Based on the second LMI constraint system, a discrete-time state feedback objective function including state penalty terms and control penalty terms is constructed. The optimal solution is gradually approached through the iterative LMI algorithm to obtain the first control gain matrix. The objective function form is:

[0119]

[0120] Where tr represents the trace of the matrix, is a positive definite matrix in the Lyapunov function, is the auxiliary variable matrix, and They are the weight matrices of state and control respectively. Through the LMI iterative algorithm, the and Matrix, so that the objective function Minimize and finally calculate the optimal control gain matrix .

[0121] In a specific embodiment, the process of executing step 500 may specifically include the following steps:

[0122] According to the solder paste viscosity and temperature data in the solder paste state vector, the stirring mode library is queried and matched, and the stirring mode that best suits the current solder paste characteristics is selected from the constant speed stirring mode, periodic variable speed stirring mode, and adaptive stirring mode to obtain the stirring working mode type;

[0123] Based on the mixing working mode type, the reference speed trajectory is generated and calculated, including the fixed speed value in constant speed mode, the sinusoidal fluctuation speed in periodic speed mode, and the dynamic adjustment speed in adaptive mode, to obtain the mixing reference speed trajectory;

[0124] Performing a lookup table interpolation calculation on the first control gain matrix to obtain a real-time control gain matrix, and designing a cosine transition function according to the stirring mode switching process to calculate a smooth transition control output signal;

[0125] Based on the stirring reference speed trajectory, system state estimation results and real-time control gain matrix, the feedforward control component and feedback control component are calculated and accumulated to obtain the complete control input signal;

[0126] The complete control input signal is converted into analog form by a D / A converter, a current limiting protection function is added, and the control signal update cycle is set to obtain the stirring motor control input signal.

[0127] Specifically, based on the viscosity of the solder paste state vector and temperature Data, query and match the preset stirring mode library. The stirring mode library includes three main modes: constant speed stirring mode, periodic variable speed stirring mode and adaptive stirring mode. Each mode is suitable for different solder paste characteristics and process requirements. For example, when the solder paste viscosity is high and the temperature is stable, the constant speed stirring mode provides a uniform stirring effect; when the solder paste viscosity changes drastically with temperature, the periodic variable speed stirring mode disturbs the solder paste through sinusoidal fluctuations to prevent material precipitation; and in a complex dynamic environment, the adaptive stirring mode adjusts the speed in real time to adapt to the dynamic viscosity changes of the solder paste. By setting the characteristic range of viscosity and temperature and , establish a mode selection function :

[0128]

[0129] in Indicates constant speed mode, Indicates the periodic speed change mode, Indicates adaptive mode. Through this logical judgment, the stirring mode is automatically selected to obtain the stirring mode type that best suits the current solder paste characteristics. Based on the stirring mode type, the corresponding reference speed trajectory is generated. In constant speed mode, the reference speed trajectory is a fixed value. , ensuring that the stirring motor runs at a constant speed, which is suitable for solder paste types with little viscosity change. For the periodic speed change mode, the system generates a sinusoidal speed trajectory , the expression is:

[0130]

[0131] in is the base speed, is the fluctuation amplitude, is the fluctuation frequency. By adjusting these parameters, the periodic change of speed is achieved, generating a disturbance suitable for the stirring effect. In the adaptive mode, the reference speed trajectory is generated by the dynamic model, which is specifically expressed as:

[0132]

[0133] in The model parameters are obtained by recursive least squares fitting based on historical data. This adaptive trajectory can respond to changes in solder paste viscosity and temperature in real time to achieve dynamic speed control. After generating the reference speed trajectory, the first control gain matrix Perform lookup table interpolation calculation to obtain the real-time control gain matrix The lookup table interpolation method dynamically adjusts the control gain by performing linear interpolation or polynomial interpolation based on the current solder paste state vector in the preset control gain matrix set. For example, assuming the control gain matrix is ​​viscosity and temperature The interpolation calculation formula is:

[0134]

[0135] in and It is the interpolation base point. In this way, the control gain is dynamically adjusted with the change of solder paste characteristics to maintain the response accuracy and stability of the system. In the process of switching the stirring mode, in order to ensure the smooth transition of the speed control signal, a cosine transition function is designed. :

[0136]

[0137] in is the transition time window. Through this cosine function, the control signal in the old mode is converted to Control signals for smooth transition to new modes , and finally get the transition control output signal:

[0138]

[0139] This smooth transition method effectively avoids system instability caused by sudden changes in control signals during mode switching, improving the safety of the mixing process and the service life of the equipment. Based on the mixing reference speed trajectory, system state estimation results and real-time control gain matrix, the feedforward control component is calculated. and feedback control component , and perform cumulative synthesis to obtain the complete control input signal The feedforward control component is calculated based on the dynamic rate of change of the desired velocity trajectory and is used to pre-eliminate the lag in the system's dynamic response. The formula is:

[0140]

[0141] The feedback control component is obtained by calculating the error between the state estimation result and the target speed, and is used to correct the system state in real time. The formula is:

[0142]

[0143] The final complete control input signal is:

[0144]

[0145] The feedforward-feedback composite control strategy can not only ensure the response speed of the system in different modes, but also maintain a stable control effect under dynamically changing working conditions. Perform digital-to-analog conversion to generate analog signals for driving the stirring motor. During the signal output process, add current limiting protection function by setting the maximum current threshold , to prevent the motor from being damaged by overload in extreme cases. Set the update cycle of the control signal , ensuring that the control signal is refreshed at an appropriate frequency so that the stirring process can respond to changes quickly while maintaining sufficient stability and safety.

[0146] In a specific embodiment, the method for dynamically adjusting and controlling the speed of the solder paste stirring device further includes the following steps:

[0147] The actual stirring speed is measured by the motor encoder to obtain the actual stirring speed data;

[0148] The difference between the actual stirring speed data and the stirring reference speed trajectory is calculated and statistical characteristics are analyzed to obtain the speed control performance evaluation results;

[0149] Based on the speed control performance evaluation results, the system model parameters are identified online using the recursive least squares method to obtain an updated system state space model.

[0150] Based on the updated system state space model, the linear matrix inequality optimization problem is re-solved to obtain an updated control gain matrix;

[0151] Performing exponential smoothing fusion calculation on the updated control gain matrix and the first control gain matrix to obtain a second control gain matrix;

[0152] Based on the system operation effect data, the solder paste characteristic parameters in the first solder paste parameter database are updated and supplemented to obtain a second solder paste parameter database.

[0153] Specifically, the actual stirring speed is measured in real time through the motor encoder to obtain the actual stirring speed data of the current system The motor encoder can provide high-precision speed feedback signals. Its output data is usually a discrete pulse signal, which can be accurately converted to motor speed by counting the pulse counts per unit time. In order to eliminate noise interference during the measurement process, the measurement data is processed by a low-pass filter to obtain smooth actual speed data. The actual speed data is compared with the preset stirring reference speed trajectory. Perform difference calculation to evaluate the speed tracking performance of the current control system. Speed ​​Error Calculated by the following formula:

[0154]

[0155] Perform statistical analysis on the speed error, including the mean of the error ,variance And the autocorrelation characteristics of the error ,in:

[0156]

[0157]

[0158]

[0159] These statistical characteristics can quantitatively describe the dynamic characteristics of system control accuracy, stability, and error. Based on the speed control performance evaluation results, in order to ensure that the system model is consistent with the actual operating state, the model parameters are identified online and the recursive least squares method is used to dynamically update the model. The recursive least squares method dynamically adjusts the parameter matrix in the system state space model by minimizing the prediction error. 、 and . Assume that the state space model of the system is:

[0160]

[0161]

[0162] in is the state vector, is the control input, is the system output, and are process noise and measurement noise respectively. In the recursive least squares method, the update formula of the model parameters is:

[0163]

[0164]

[0165]

[0166] in is the parameter vector to be estimated, including 、 、 The coefficients in is the gain matrix, is the covariance matrix is the regression vector, is the forgetting factor, which is used to adjust the influence weight of historical data. Through the recursive calculation of the recursive least squares method, the parameters of the system model can be dynamically adjusted every time new measurement data arrives, so that the system model always maintains a high-precision fit to the actual system behavior. After obtaining the updated system state space model, the linear matrix inequality (LMI) optimization problem is re-solved based on the new model parameters to obtain the updated control gain matrix The goal of LMI optimization is to ensure the stability and control performance of the system by solving the following LMI constraints:

[0167]

[0168]

[0169] in is the Lyapunov function matrix, is the state weight matrix, is the auxiliary variable matrix. By iteratively solving the LMI problem, the new optimal control gain matrix is ​​obtained In order to maintain a smooth change of the control gain, the updated control gain matrix With the original first control gain matrix Perform exponential smoothing fusion calculation to obtain the second control gain matrix The exponential smoothing fusion calculation formula is:

[0170]

[0171] in is the smoothing coefficient, which is used to adjust the fusion ratio of the new and old control gains. When it is larger, the system can adapt to model changes faster, but the response is more radical; when When it is smaller, the control gain changes more smoothly, but the system adaptation speed will be reduced. , achieving a smooth transition of the control system during dynamic changes, ensuring the stability and control accuracy of the stirring process. Based on the system operation effect data, the actual solder paste viscosity, temperature and control response characteristics are compared and analyzed with the solder paste characteristic parameters in the first solder paste parameter database. For parameters with significant differences, recursive least squares fitting or statistical regression methods are used to update and supplement them to generate a second solder paste parameter database.

[0172] The above describes the method for dynamically adjusting and controlling the speed of the solder paste stirring device in the embodiment of the present application. The following describes the device 10 for dynamically adjusting and controlling the speed of the solder paste stirring device in the embodiment of the present application. Figure 2 In one embodiment of the present application, a device 10 for dynamically adjusting and controlling the speed of a solder paste stirring device includes:

[0173] Initialization module 11, used to initialize the microprocessor control unit in the solder paste stirring system to obtain a system initialization parameter matrix;

[0174] The filtering module 12 is used to collect and filter the solder paste temperature and viscosity in real time based on the system initialization parameter matrix to obtain the solder paste state vector and the solder paste viscosity-temperature characteristic curve;

[0175] A calculation module 13 is used to perform state space modeling and Kalman filter calculation on the solder paste stirring system according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve to obtain a system state estimation result;

[0176] A solving module 14 is used to construct a linear matrix inequality constraint and solve an objective function optimization for the system state estimation result to obtain a first control gain matrix;

[0177] The output module 15 is used to select the stirring mode and perform feedforward-feedback composite control calculation based on the first control gain matrix, and output the stirring motor control input signal.

[0178] Through the collaborative efforts of the aforementioned components, high-precision control of the stirring speed is achieved through LMI controller design and linear matrix inequality constrained optimization. Observer-based state estimation and recursive parameter identification techniques are used to automatically adjust the stirring force and control parameters according to changes in solder paste viscosity to adapt to different environmental conditions. The three stirring modes designed, namely constant speed, periodic speed change, and adaptive speed, are smoothly switched through a cosine transition function to avoid mechanical shock. The recursive least squares method performs real-time fitting calculations on the solder paste viscosity-temperature characteristics, and combined with a feedforward-feedback composite control strategy, accurately compensates for the solder paste's nonlinear parameter dependence. Kalman filtering-based state estimation and a continuously updated database form a closed-loop self-optimization mechanism. Precise control ensures stable and consistent solder paste uniformity, improving the yield of subsequent printing and soldering processes. The iterative LMI control method effectively suppresses external interference such as motor vibration, temperature changes, and viscosity detection noise, improving the reliability and robustness of the system in complex industrial environments.

[0179] See also Figure 3 , Figure 3 This is a schematic block diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected via a device bus 303, wherein the memory 302 may include a non-volatile storage medium and an internal memory.

[0180] The non-volatile storage medium can store a computer program. The computer program includes program instructions. When the program instructions are executed by the processor 301, the processor 301 can execute any of the above-mentioned methods for dynamically adjusting and controlling the speed of the solder paste stirring device.

[0181] The processor 301 is used to provide computing and control capabilities to support the operation of the entire electronic device 300 .

[0182] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned methods for dynamically adjusting and controlling the speed of the solder paste stirring equipment.

[0183] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device 300 involved in the solution of the present application. The specific electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0184] It should be understood that the processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0185] It should be noted that, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the electronic device 300 described above can refer to the corresponding process of the speed dynamic adjustment and control method of the aforementioned solder paste stirring equipment, and will not be repeated here.

[0186] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0188] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dynamically adjusting and controlling the speed of a solder paste stirring device, characterized in that: The method comprises: Initialize the microprocessor control unit in the solder paste stirring system to obtain a system initialization parameter matrix; specifically include: starting and executing a preset program of the microprocessor control unit in the solder paste stirring system, setting the clock frequency of the microprocessor control unit, and obtaining basic operating parameters of the microprocessor; reading the solder paste type data stored in the non-volatile memory to obtain a first solder paste parameter database containing standard viscosity values, optimal stirring speed ranges and control parameters of different types of solder paste; connecting and establishing a temperature sensor and a viscosity sensor to obtain sensor connection parameters, and performing standard sample measurement and comparison on the temperature sensor and the viscosity sensor to obtain a sensor calibration parameter matrix; configuring the variable frequency drive with vector control technology, setting the rated power, frequency range and accuracy parameters, and obtaining variable frequency drive setting parameters; based on the basic operating parameters of the microprocessor, the first solder paste parameter database, the sensor connection parameters, the sensor calibration parameter matrix and the variable frequency drive setting parameters, calculating and integrating the motor rotor inertia, friction coefficient, damping ratio and mechanical transmission ratio to construct a system initialization parameter matrix; Based on the system initialization parameter matrix, the solder paste temperature and viscosity are collected and filtered in real time to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve; Performing state space modeling and Kalman filter calculation on the solder paste stirring system according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve to obtain a system state estimation result; Performing linear matrix inequality constraint construction and objective function optimization on the system state estimation result to obtain a first control gain matrix; Based on the first control gain matrix, the stirring mode is selected and the feedforward-feedback composite control calculation is performed to output a stirring motor control input signal.

2. The method for dynamically adjusting and controlling the speed of solder paste stirring equipment according to claim 1, characterized in that: The solder paste temperature and viscosity are collected and filtered in real time based on the system initialization parameter matrix to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve, including: The current temperature of the solder paste is collected and median-filtered by the temperature sensor to obtain filtered temperature data; The viscosity sensor collects the current viscosity of the solder paste and performs exponential smoothing filtering to obtain filtered viscosity data; Based on the filtered temperature data and the filtered viscosity data, performing a compensation coefficient product operation on the temperature reference value and the actual temperature difference to obtain a temperature-compensated normalized viscosity value; Performing vector mapping on the filtered temperature data, the filtered viscosity data, the temperature deviation value, and the viscosity deviation value to obtain a solder paste state vector; Based on the filtered temperature data and the filtered viscosity data, a recursive least squares fitting calculation is performed on the relationship between solder paste viscosity and temperature to obtain a solder paste viscosity-temperature characteristic curve.

3. The method for dynamically adjusting and controlling the speed of solder paste stirring equipment according to claim 1, characterized in that: The state space modeling and Kalman filter calculation of the solder paste stirring system are performed according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve to obtain a system state estimation result, including: Based on the solder paste state vector, a state space model including stirring speed, motor torque and solder paste flow state index is established for the solder paste stirring process to obtain the basic state equation of the solder paste stirring system; According to the solder paste viscosity-temperature characteristic curve, a polynomial expansion process is performed on the system matrix in the basic state equation to obtain an expansion matrix group representing the viscosity dependence relationship; Based on the expanded matrix group, the system matrix coefficients are identified using a recursive instrumental variable method to obtain a system identification result; According to the system identification results, a Kalman filter state observer is constructed, and an initial state estimation value and an initial error covariance matrix are set to obtain a state observer model; Performing prediction step calculation on the state observer model, using the state equation to forward-calculate the system state, and obtaining a priori estimate of the state and its error covariance matrix; For the state prior estimation value, the Kalman gain matrix is ​​calculated according to the actual measurement output and the state is updated to obtain the system state estimation result.

4. The method for dynamically adjusting and controlling the speed of solder paste stirring equipment according to claim 1, characterized in that: The linear matrix inequality constraint construction and objective function optimization solution of the system state estimation result are performed to obtain a first control gain matrix, including: Defining control performance indicators for the system state estimation results to obtain a controller design target parameter set; A parameter-dependent Lyapunov function is constructed based on the controller design target parameter set to obtain a system stability criterion function; According to the system stability criterion function, constructing positive definiteness constraints and stability constraints, and obtaining a basic linear matrix inequality constraint group; Performing auxiliary variable introduction and matrix transformation on the basic linear matrix inequality constraint group to obtain a first linear matrix inequality constraint system; Combining the first linear matrix inequality constraint system with the pole placement region constraint condition, adding pole region restrictions of inner radius and outer radius, to obtain a second linear matrix inequality constraint system; A discrete-time state feedback objective function including a state penalty term and a control penalty term is constructed for the second linear matrix inequality constraint system, and the optimal solution is gradually approached through an iterative LMI algorithm to obtain a first control gain matrix.

5. The method for dynamically adjusting and controlling the speed of solder paste stirring equipment according to claim 1, characterized in that: The selecting of the stirring mode and the feedforward-feedback composite control calculation based on the first control gain matrix, and outputting the stirring motor control input signal, include: According to the solder paste viscosity and temperature data in the solder paste state vector, a stirring mode library is queried and matched, and a stirring mode that best suits the current solder paste characteristics is selected from a constant speed stirring mode, a periodic variable speed stirring mode, and an adaptive stirring mode to obtain a stirring working mode type; Based on the stirring working mode type, a reference speed trajectory is generated and calculated, including a fixed speed value in constant speed mode, a sinusoidal fluctuation speed in periodic speed mode, and a dynamically adjusted speed in adaptive mode, to obtain a stirring reference speed trajectory; Performing a lookup table interpolation calculation on the first control gain matrix to obtain a real-time control gain matrix, and designing a cosine transition function according to the stirring mode switching process to calculate a smooth transition control output signal; Based on the stirring reference speed trajectory, the system state estimation result and the real-time control gain matrix, a feedforward control component and a feedback control component are calculated and accumulated to obtain a complete control input signal; The complete control input signal is converted into digital-to-analog by a D / A converter, a current limiting protection function is added, and a control signal update cycle is set to obtain a stirring motor control input signal.

6. The method for dynamically adjusting and controlling the speed of solder paste stirring equipment according to claim 1, characterized in that: The method for dynamically adjusting and controlling the speed of the solder paste stirring device further includes: The actual stirring speed is measured by the motor encoder to obtain the actual stirring speed data; Performing difference calculation and statistical characteristic analysis on the actual stirring speed data and the stirring reference speed trajectory to obtain a speed control performance evaluation result; Based on the speed control performance evaluation result, the system model parameters are identified online using a recursive least squares method to obtain an updated system state space model; Resolving the linear matrix inequality optimization problem based on the updated system state space model to obtain an updated control gain matrix; Performing exponential smoothing fusion calculation on the updated control gain matrix and the first control gain matrix to obtain a second control gain matrix; Based on the system operation effect data, the solder paste characteristic parameters in the first solder paste parameter database are updated and supplemented to obtain a second solder paste parameter database.

7. A device for dynamically adjusting and controlling the speed of solder paste stirring equipment, characterized in that: Used to execute the method for dynamically adjusting and controlling the speed of a solder paste stirring device according to any one of claims 1 to 6, the device for dynamically adjusting and controlling the speed of a solder paste stirring device comprising: An initialization module is used to initialize the microprocessor control unit in the solder paste stirring system to obtain a system initialization parameter matrix; A filtering module is used to collect and filter the solder paste temperature and viscosity in real time based on the system initialization parameter matrix to obtain a solder paste state vector and a solder paste viscosity-temperature characteristic curve; a calculation module, configured to perform state space modeling and Kalman filter calculation on the solder paste stirring system according to the solder paste state vector and the solder paste viscosity-temperature characteristic curve, to obtain a system state estimation result; A solution module, configured to construct a linear matrix inequality constraint and perform an optimization solution on the system state estimation result to obtain a first control gain matrix; The output module is used to select the stirring mode and perform feedforward-feedback composite control calculation based on the first control gain matrix, and output the stirring motor control input signal.

8. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the electronic device to execute the method for dynamically adjusting and controlling the speed of the solder paste stirring device according to any one of claims 1 to 6.

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