SFC closed-loop heat dissipation system and method based on servo motor driver

Abstract

The invention discloses an SFC closed-loop heat dissipation system and method based on a servo motor driver, and belongs to the technical field of motor driving and heat management. Comprising a static frequency converter power module, an intelligent servo driver, a self-adaptive heat dissipation system, an independent power supply unit, a Hall current sensor and a high-linearity PT1000 platinum resistor temperature detection network, and the PT1000 platinum resistor temperature detection network is used for collecting real-time temperature signals of a core area of the intelligent servo driver. The Hall current sensor is used for collecting real-time working current parameters of a static frequency converter power module, a control module is integrated in the intelligent servo driver, and the control module constructs a temperature feedback and current feedback double-closed-loop control system based on the real-time working current parameters and real-time temperature signals. Therefore, the heat dissipation intensity of the self-adaptive heat dissipation system is adjusted. The servo motor driver has the advantages that the heat dissipation efficiency and the operation stability of the servo motor driver are remarkably improved, and the service life of equipment can be effectively prolonged.

Description

Technical Field

[0001] This invention belongs to the field of motor drive and thermal management technology, specifically relating to an SFC closed-loop heat dissipation system and method based on a servo motor driver. Background Technology

[0002] Static Frequency Converter (SFC) is a critical device for starting high-power motors. Its core power components (such as IGBTs and rectifier modules) generate a significant amount of heat during operation. If this heat accumulates and the junction temperature exceeds the safe threshold, it will directly lead to device performance degradation, system protection shutdown, or even permanent damage, seriously threatening the equipment's startup success rate and long-term operational reliability. Therefore, efficient and reliable heat dissipation control is a key technical aspect of SFC system design. Currently, common existing technical solutions for the heat dissipation requirements of static frequency converters (SFCs) mainly include: The first is passive cooling, where the SFC relies on heat sinks and natural air convection for heat dissipation, without fans or liquid cooling devices. Although this solution is low in cost, its heat dissipation capacity is limited and cannot meet the heat dissipation requirements of SFCs under high load and high temperature environments, nor can it be dynamically adjusted; The second is using an independent temperature-controlled fan, where the SFC uses an axial fan or centrifugal fan to force convection and remove the heat generated by heat-generating components such as IGBTs and rectifier modules inside the inverter. Although the independent temperature-controlled fan cooling method can actively dissipate heat, the temperature control system is independent of the motor drive and cannot obtain key status information of the drive's operation. It only relies on the current temperature feedback of the PT1000 platinum resistance thermometer, resulting in significant control lag. It cannot predict and adjust the heat dissipation intensity in advance based on key parameters reflecting the heat source status, such as the current and power of the drive.

[0003] In summary, existing technologies fail to fully utilize the powerful real-time computing capabilities, rich internal state information, and precise digital control capabilities of the servo motor driver, an intelligent control unit. How to deeply integrate the SFC's thermal control into the servo driver to build an intelligent closed-loop cooling system capable of anticipating heat source changes, responding quickly, and adjusting precisely has become an urgent technical problem to be solved. Summary of the Invention

[0004] The primary objective of this invention is to provide an SFC closed-loop heat dissipation system based on a servo motor driver. This system effectively overcomes the problems of high startup failure risk, serious energy waste, lag in temperature control, and impact on system reliability inherent in traditional heat dissipation solutions.

[0005] Another objective of this invention is to provide a closed-loop heat dissipation method for SFC based on a servo motor driver, which can ensure the full realization of the technical effects of the SFC closed-loop heat dissipation system.

[0006] The primary objective of this invention is to provide an SFC closed-loop cooling system based on a servo motor driver, comprising a static inverter power module, an intelligent servo driver, an adaptive cooling system, an independent power supply unit, a Hall current sensor, and a high-linearity PT1000 platinum resistance temperature detection network. The PT1000 platinum resistance temperature detection network is used to collect real-time temperature signals from the core area of ​​the intelligent servo driver. The Hall current sensor is used to collect real-time operating current parameters of the static inverter power module and feed back the data current to the intelligent servo driver. The intelligent servo driver performs closed-loop current monitoring. The intelligent servo driver integrates a control module, which constructs a dual closed-loop control system with temperature and current feedback based on the real-time operating current parameters and real-time temperature signals to adjust the heat dissipation intensity of the adaptive cooling system.

[0007] In a specific embodiment of the present invention, the control module is equipped with a feedforward-feedback composite control architecture. The feedforward channel uses the real-time operating current I of the motor as the input quantity, and the feedback channel uses the temperature signal T collected by the PT1000 platinum resistance temperature detection network as the feedback quantity. The control module realizes the orderly switching of the system operating state through SFC.

[0008] In another specific embodiment of the present invention, a current-heat generation mathematical model is provided in the feedforward channel. This mathematical model calculates the motor heating power in real time based on the motor thermal conversion coefficient k. The motor thermal conversion coefficient k is calibrated by fitting the temperature rise rate under constant current. The feedback channel uses an incremental digital PID algorithm to process the temperature deviation signal, and the control module is provided with a parameter self-tuning module to realize online optimization of PID control parameters.

[0009] In another specific embodiment of the present invention, the parameter self-tuning module comprises a fuzzy adaptive tuning PID controller consisting of a two-dimensional fuzzy controller and a PID controller. The two-dimensional fuzzy controller divides the fuzzy language E and EC of the error e and the error change rate ec into 7 linguistic variable levels, and outputs the variable ΔK. P ΔK I and ΔK D Fuzzy linguistic variable K P ′、K I ′ and K D It is also divided into 7 levels of language variables; the fuzzy adaptive tuning PID controller adjusts the PID parameters according to the preset fuzzy control rules, which are based on the operator's practical experience and E, EC and K. P ′、K I ′ and K DThe logical relationship between ' is established; the fuzzy adaptive tuning PID controller uses the area centroid method to complete the declarative processing of the output quantity. By calculating the centroid of the area enclosed by the fuzzy set membership function curve and the horizontal coordinate, the horizontal coordinate value corresponding to the centroid is selected as the representative value of the fuzzy set, and thus ΔK is obtained. P ΔK I ΔK D The clear values ​​are then used to obtain a lookup table of clear values ​​for the PID adjustment parameters.

[0010] In another specific embodiment of the present invention, the calculation of the control quantity of the feedforward channel further includes establishing a heat generation prediction model.

[0011]

[0012] Where k represents the motor thermal conversion system, and ζ represents the inertia compensation coefficient.

[0013] In another specific embodiment of the present invention, the control module is provided with a state machine, which is divided into three main operating states: standby state, adjustment state, and protection state. The state machine switches states based on the comparison results of the detected temperature signal and current signal with preset thresholds.

[0014] The state transition logic of the state machine is as follows:

[0015] When the detection temperature T < T sct At -5℃, the system enters standby mode and shuts down the adaptive cooling system;

[0016] When the temperature deviation TT text When the temperature is ≤5℃, the system enters the regulation state, and outputs the superimposed feedforward control quantity and the feedback control quantity, U. t =αU f +(1-α)U b The heat dissipation power is dynamically adjusted, and α is the weighting coefficient of the feedforward control.

[0017] When the detection temperature T > T max When the current I exceeds the safety threshold, the system enters a protection state, activates the maximum heat dissipation power of the adaptive cooling system, and triggers an alarm.

[0018] In a further specific embodiment of the present invention, the parameter self-tuning module employs a relay feedback method, specifically including: injecting a step disturbance signal during the system startup phase; and recording the system oscillation periods Tu and K. p =0.6K u T i =0.5T u T d =0.125T uAmplitude Au; calculate initial PID parameters according to the Ziegler-Nichols formula; set online adjustment factor β = 1 - e (-t / τ) This enables dynamic adjustment of PID parameters.

[0019] In a further specific embodiment of the present invention, it includes:

[0020] The signal acquisition module includes a Hall current sensor and a PT1000 platinum resistance temperature sensor. The Hall current sensor has a measurement range of 0-50A and an accuracy of ±0.5%. The PT1000 platinum resistance temperature sensor has a temperature measurement range of -50 to 150℃ and an accuracy of ±0.3℃.

[0021] The control decision module includes a microprocessor unit with a built-in control algorithm and a state machine management unit for implementing SFC running state switching.

[0022] The actuator includes a brushless DC fan with a PWM speed regulation range of 10% to 100% and a speed regulation range of 0 to 2500 r / min; the microprocessor unit also includes: a fault diagnosis submodule in the intelligent driver, which monitors the health status in real time; a data storage submodule, which records historical operating data; and a communication interface that supports the Modbus-RTU protocol.

[0023] In yet another specific embodiment of the present invention, the adaptive heat dissipation system includes: a permanent magnet synchronous motor for driving a cooling fan; a heat dissipation duct with an axial air intake and radial air exhaust structure; and temperature monitoring points arranged at the stator windings and bearings of the permanent magnet synchronous motor. When the air intake is axial, the airflow enters the heat dissipation duct along the axial direction of the motor shaft and can directly pass through the core heat-generating areas of the motor stator and rotor. The air intake path is short and uniform. When the air exhaust is radial, the airflow is discharged perpendicular to the shaft direction, forming a 90-degree angle with the air intake direction, quickly carrying away the heat in the core area and preventing hot air backflow.

[0024] Another objective of this invention is achieved as follows: a closed-loop heat dissipation method for SFC based on a servo motor driver, comprising the following steps:

[0025] Step S1: Construct a feedforward-feedback composite control architecture;

[0026] Step S2, establish the current-heat generation mathematical model Q=k×I 2 , where k is the thermal conversion coefficient of the motor calibrated experimentally;

[0027] Step S3: The temperature deviation signal is processed using an incremental digital PID algorithm, and a parameter self-tuning module is set to achieve online optimization of control parameters;

[0028] Step S4: Design a three-state operation mechanism based on the state machine's state transition logic:

[0029] Step S5: The orderly switching of the operating state is achieved through SFC. The control process includes: system initialization, calibration of Hall current sensor and PT1000 platinum resistance temperature sensor, synchronous acquisition of current signal I and temperature signal T, real-time calculation of heat generation power Q by feedforward module, execution of PID calculation by feedback module and output of PWM duty cycle by control quantity integration module.

[0030] This invention, employing the aforementioned structure, predicts and adjusts the heat dissipation intensity of the SFC closed-loop cooling system in advance based on key parameters reflecting the heat source state, such as the driver's current and power. This constructs a dual closed-loop control system with both temperature and current feedback, effectively overcoming key problems of traditional cooling solutions, such as high startup failure risk, significant energy waste, lag in temperature control systems, and impact on system reliability. By using feedforward prediction to avoid heat dissipation lag and feedback correction to ensure control accuracy, combined with intelligent state switching and parameter self-tuning technology, this invention effectively solves the problems of lag response, insufficient control accuracy, and poor adaptability in traditional cooling systems. It significantly improves the heat dissipation efficiency and operational stability of the servo motor driver, effectively extending equipment lifespan and greatly reducing the risk of overheating failures. Attached Figure Description

[0031] Figure 1 This is a diagram of the SFC closed-loop heat dissipation system architecture for the servo motor driver described in this invention.

[0032] Figure 2 This is a block diagram illustrating the principle of the fuzzy adaptive PID controller described in this invention.

[0033] Figure 3 This is the triangular membership function diagram described in this invention. Detailed Implementation

[0034] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the description of the embodiments is not a limitation on the technical solution. Any formal but not substantive changes made based on the concept of the present invention should be considered within the scope of protection of the present invention.

[0035] In the following description, all directional (or orientational) concepts involving up, down, left, right, front, and back refer to the position of the figure being described, and are intended to facilitate public understanding. Therefore, they should not be construed as a special limitation on the technical solution provided by this invention.

[0036] This invention relates to an SFC closed-loop heat dissipation system based on a servo motor driver, such as... Figure 1The system includes an independent power supply unit, an adaptive cooling system, a Hall current sensor, an intelligent servo drive, a static inverter power module, and a high-linearity PT1000 platinum resistance temperature detection network mounted on it. The PT1000 platinum resistance temperature detection network, with the PT1000 platinum resistance temperature sensor at its core, is used to collect real-time temperature signals from the core area of ​​the intelligent servo drive. The Hall current sensor collects real-time operating current parameters from the static inverter power module and feeds back current data to the intelligent servo drive. The intelligent servo drive then performs closed-loop current monitoring. An integrated control module within the intelligent servo drive constructs a dual closed-loop control system based on the real-time operating current parameters and real-time temperature signals, adjusting the heat dissipation intensity of the adaptive cooling system.

[0037] Specifically, the control module is equipped with a feedforward-feedback composite control architecture. The feedforward channel uses the real-time operating current I of the motor as the input, and the feedback channel uses the temperature signal T collected by the PT1000 platinum resistance temperature detection network as the feedback. The control module realizes the orderly switching of the system's operating state through SFC (Sequential Function Chart).

[0038] Furthermore, the feedforward channel incorporates a current-heat generation mathematical model. This model calculates the motor's heating power in real time based on the motor's thermal conversion coefficient k, which is calibrated through a temperature rise rate fitting experiment under constant current. The feedback channel employs an incremental digital PID algorithm to process the temperature deviation signal, and the control module includes a parameter self-tuning module for online optimization of PID control parameters. Specifically, the PID parameter self-tuning module uses a relay feedback method. During system startup, a step disturbance signal is injected, the system oscillation period Tu and amplitude Au are recorded, and the initial PID parameters K are calculated according to the Ziegler-Nichols formula. p =0.6K u T i =0.5T u T d =0.125T u Set the online adjustment factor β = 1 - e (-t / τ) Furthermore, by adjusting factors online, the system achieves dynamic parameter optimization and dynamic adjustment of PID parameters, thereby further enhancing its adaptability to different operating conditions.

[0039] Furthermore, the present invention also includes: a signal acquisition module, comprising a Hall current sensor and a PT1000 platinum resistance temperature sensor; the Hall current sensor has a measurement range of 0-50A and an accuracy of ±0.5%; the PT1000 platinum resistance temperature sensor has a temperature measurement range of -50 to 150℃ and an accuracy of ±0.3℃; a control decision module, comprising a microprocessor unit with a built-in control algorithm (PID algorithm) and a state machine management unit for implementing SFC operating state switching; and an actuator, comprising a brushless DC fan with a PWM speed regulation range of 10% to 100% and a speed adjustment range of 0 to 2500 r / min. The microprocessor unit further includes: a fault diagnosis submodule within the intelligent driver, which monitors the health status in real time; a data storage submodule, which records historical operating data; and a communication interface supporting the Modbus-RTU protocol.

[0040] Furthermore, the adaptive cooling system includes: a permanent magnet synchronous motor for driving the cooling fan, with a rated power of 5.5kW, balancing power output and cooling costs; a cooling duct with an axial intake and radial exhaust structure; and temperature monitoring points located on the stator windings and bearings of the permanent magnet synchronous motor. During axial intake, the airflow enters the cooling duct along the axis of the motor shaft, directly passing through the core heat-generating areas of the stator and rotor, resulting in a short and uniform intake path. During radial exhaust, the airflow exits perpendicular to the shaft direction, forming a 90-degree angle with the intake direction, quickly carrying away heat from the core areas and preventing hot air recirculation. This design maximizes the contact area between the duct and the motor's heat-generating components, resulting in low wind resistance, high cooling efficiency, and suitability for the heat output of a 5.5kW motor, ensuring long-term stable operation.

[0041] This invention obtains key parameters such as the current of the static frequency converter using a LEM LAH 50-P Hall current sensor, and combines this with temperature measurement data from a PT1000 platinum resistance thermometer. Based on key parameters reflecting the heat source status, such as the current and power of the intelligent servo drive, it predicts and adjusts the heat dissipation intensity of the SFC closed-loop cooling system in advance, thereby constructing a dual closed-loop control system with temperature and current feedback. The SFC closed-loop cooling method of this invention includes the following steps.

[0042] Step S1: Construct a feedforward-feedback composite control architecture. The feedforward channel uses the real-time operating current I of the motor as the input, and the feedback channel uses the temperature signal T collected by the PT1000 temperature sensor as the feedback. The system realizes the orderly switching of the operating state through SFC, thus constructing a complete closed-loop control system.

[0043] Step S2 involves a feedforward control algorithm, establishing a current-heat generation mathematical model Q = k × I. 2, where k is the motor thermal conversion coefficient calibrated through experiments. This model calculates the motor's heat generation power in real time and provides feedforward control for the heat dissipation system.

[0044] The calorific value prediction model is as follows:

[0045] (k: thermal conversion coefficient, ζ: inertia compensation coefficient)

[0046] Parameters were calibrated experimentally.

[0047] Measure the temperature rise rate under constant current and fit k;

[0048] Apply a constant current I and record the slope of the temperature rise curve.

[0049] calculate (Ignoring heat dissipation)

[0050] The temperature hysteresis is recorded when the step current changes, and ζ is fitted.

[0051] Step S3 involves processing the temperature deviation signal using an incremental digital PID algorithm and setting up a parameter self-tuning module to achieve online optimization of control parameters, specifically including:

[0052] Step S31, variable fuzzification: Based on system requirements and the current application status of fuzzy PID algorithms, a two-dimensional fuzzy controller and a PID controller are selected to form a fuzzy adaptive tuning PID controller, such as... Figure 2 As shown.

[0053] Based on the accuracy requirements and system characteristics, the two-dimensional fuzzy controller divides the fuzzy language E and EC of the error e and the error change rate ec into 7 levels of linguistic variables, and outputs the variable ΔK. P ΔK I and ΔK D Fuzzy linguistic variables using K P ′、K I ′ and K D ′ represents K P ′、K I ′ and K D It is also divided into 7 levels of language variables, then there are

[0054] E,EC,K′ P ,K I ′,K′ D ={NB,NM,NS,Z,PS,PM,PB}

[0055] Where NB, NM, NS, Z, PS, PM, and PB represent the fuzzy language terms of negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively, and their corresponding fuzzy universes are defined as follows:

[0056] E = EC = {-3, -2, -1, 0, 1, 2, 3}

[0057] K P ′=K I ′=K D ′={-3,-2,-1,0,1,2,3}

[0058] Step S32: Select the membership function. Considering factors such as ease of calculation, the triangular membership function was chosen. The fuzzy inference system editor in Matlab is used to export the curves of each membership function, as shown below. Figure 3 As shown.

[0059] Step S33: The fuzzy adaptive tuning PID controller adjusts the PID parameters according to a preset fuzzy control rule, which is based on the operator's practical experience and the relationship between E, EC, and K. P ′、K I ′ and K D The logical relationship between ' and ' is established.

[0060] Furthermore, in a fuzzy adaptive PID controller, the main function of the proportional element is to improve the system response speed. When the deviation e is small, K... P A smaller value should be chosen to prevent overshoot and allow the system to stabilize as quickly as possible; at the same time, the effect of the deviation change rate ec on K should be considered. P The effect of K is that when e and ec have the same sign, it indicates that the output tends to deviate from the stable value. In this case, K should be increased appropriately. P Conversely, K should be appropriately reduced. P K P The parameter adjustment rules are as follows:

[0061]

[0062] The integral term is mainly used to eliminate steady-state error. When the absolute value of the deviation e is large, K... I Use zero or a small value to avoid oscillations after system overshoot; when the absolute value of the deviation e is small, the integral element becomes effective, K I The value of K should be increased to meet the steady-state requirements of the system's integral and to eliminate steady-state error as soon as possible. I The parameter adjustment rules are as follows:

[0063]

[0064] The primary function of the differential element is to improve the dynamic performance of the system. In the initial stage of a control process where the deviation is large, K... D The value should not be too large, ideally zero or a small value; when the deviation is small, considering the system's anti-interference capability and response speed, K should be...D The value is appropriate. K D The parameter adjustment rules are as follows:

[0065]

[0066] Step S34, output declarification: The fuzzy adaptive tuning PID controller uses the area centroid method to declaratively process the output. By calculating the centroid of the area enclosed by the fuzzy set membership function curve and the horizontal coordinate, the horizontal coordinate value corresponding to the centroid is selected as the representative value of the fuzzy set, thereby obtaining ΔK. P ΔK I ΔK D The clear values ​​are then used to obtain a lookup table of clear values ​​for the PID tuning parameters. The calculation expression for this method is:

[0067]

[0068] In the formula: A(u) – membership function of the fuzzy set; U – universe of discourse; u * —Abscissa of the centroid of the area.

[0069] Therefore, for ΔK P ΔK I ΔK D By performing clear calculations and continuously adjusting the values ​​of E and EC in the fundamental universe, the corresponding ΔK can be obtained. P ΔK I ΔK D The clear values ​​can then be used to obtain a lookup table of clear values ​​for the PID adjustment parameters.

[0070] Step S4, State Machine Design: The control module incorporates a state machine. This state machine switches states based on the comparison between detected temperature and current signals and preset thresholds. The state machine design provides end-to-end assurance for stable system operation, dividing the system into three core states: standby, adjustment, and protection, based on preset temperature and current thresholds. When the detected temperature is below the set threshold, i.e., when T < T... s When the temperature deviation is -ΔT, the system enters standby mode and shuts down the cooling system to save energy; when the temperature deviation is within ±5℃, the system switches to PID control mode, superimposing the feedforward control quantity and the feedback control quantity at the output, U t =αU f +(1-α)U b Dynamically match heat dissipation power with actual heat generation requirements; when the detected temperature exceeds T... max Or when the current exceeds the safety threshold, i.e., when T > T max or I>I max The system immediately enters protection mode, activates maximum heat dissipation power, and triggers an alarm to prevent equipment damage due to overheating.

[0071] The specific state transition logic is shown in the table below:

[0072] Table 1 State Transition Logic

[0073] state Triggering conditions action standby <![CDATA[T<T sct -5℃]]> Turn off the radiator PID control <![CDATA[T-T text ≤5℃]]> Feedforward + Feedback Joint Control Protect <![CDATA[T>T max ]]> Full power cooling triggers alarm.

[0074] Step S5 involves the implementation process. After the system is powered on, it first initializes and calibrates the sensors, then enters the real-time data acquisition stage, simultaneously acquiring current and temperature signals. The feedforward module calculates the heat dissipation power Q in real time, the feedback module performs PID calculations, and the control quantity synthesis module outputs the PWM duty cycle. The control quantity synthesis module superimposes the feedforward and feedback outputs, and finally, the actuator achieves precise control of the heat dissipation power.

[0075] Furthermore, the PID parameter self-tuning in step three employs a relay feedback method, including the following steps: injecting a step disturbance signal during system startup; recording the system oscillation period Tu and amplitude Au; and calculating the initial parameters K according to the Ziegler-Nichols formula. p =0.6K u T i =0.5T u T d =0.125T u Set the online adjustment factor β = 1 - e (-t / τ) This enables dynamic adjustment of parameters.

[0076] The core principle of this invention lies in constructing a feedforward-feedback dual closed-loop collaborative control architecture. This architecture, relying on a high-precision sensing module and a fuzzy adaptive algorithm, enables early prediction and dynamic, precise adjustment of heat dissipation intensity. Specifically, a LEMLAH 50-P Hall current sensor is used to collect key parameters such as the operating current of the static inverter in real time. Combined with an experimentally calibrated current-heat generation mathematical model (where the heat conversion coefficient k is obtained by fitting the temperature rise rate under constant current, and the temperature hysteresis characteristic is calibrated through current change experiments), the expected heat generation power of the motor is calculated in real time, forming a feedforward control quantity. This avoids the inherent hysteresis of traditional cooling systems that "heat up first and then dissipate heat" from the source. Simultaneously, a high-linearity PT1000 platinum resistance temperature detection network is used to collect real-time temperature signals from the core area of ​​the driver, and the temperature deviation is input into an incremental digital PID algorithm for processing. To improve algorithm adaptability, the system uses a fuzzy adaptive tuning module to optimize PID parameters. The error e and error change rate ec are fuzzified into 7 linguistic variable levels through a two-dimensional fuzzy controller. The triangular membership function is used to simplify the calculation. The parameter adjustment rules of the proportional, integral, and derivative links are established based on practical experience. Then, the output is clarified by the area centroid method. Finally, a precise PID adjustment parameter lookup table is generated to ensure high precision of feedback control.

[0077] This invention avoids heat dissipation lag in advance through feedforward prediction and ensures control accuracy through feedback correction. Combined with intelligent state switching and parameter self-tuning technology, it effectively solves the problems of lag response, insufficient control accuracy and poor adaptability of traditional heat dissipation systems. It significantly improves the heat dissipation efficiency and operational stability of servo motor drivers, effectively extends the service life of equipment, greatly reduces the risk of overheating failure, and provides key support for the long-term reliable operation of servo motor drivers, thus achieving the purpose of the invention.

Claims

1. A closed-loop heat dissipation system for SFC based on a servo motor driver, characterized in that, The system includes a static inverter power module, an intelligent servo drive, an adaptive cooling system, an independent power supply unit, a Hall current sensor, and a high-linearity PT1000 platinum resistance temperature detection network. The PT1000 platinum resistance temperature detection network is used to collect real-time temperature signals from the core area of ​​the intelligent servo drive. The Hall current sensor is used to collect real-time operating current parameters of the static inverter power module and feed back the data current to the intelligent servo drive. The intelligent servo drive completes closed-loop current monitoring. The intelligent servo drive integrates a control module, which constructs a dual closed-loop control system with temperature and current feedback based on the real-time operating current parameters and real-time temperature signals to adjust the heat dissipation intensity of the adaptive cooling system.

2. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 1, characterized in that, The control module is equipped with a feedforward-feedback composite control architecture. The feedforward channel uses the real-time operating current I of the motor as the input, and the feedback channel uses the temperature signal T collected by the PT1000 platinum resistance temperature detection network as the feedback. The control module realizes the orderly switching of the system's operating state through SFC.

3. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 2, characterized in that, The feedforward channel is equipped with a current-heat generation mathematical model, which calculates the motor's heat generation power in real time based on the motor's thermal conversion coefficient k. The motor's thermal conversion coefficient k is calibrated through a temperature rise rate fitting experiment under constant current. The feedback channel uses an incremental digital PID algorithm to process the temperature deviation signal, and the control module is equipped with a parameter self-tuning module to realize online optimization of PID control parameters.

4. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 3, characterized in that, The parameter self-tuning module consists of a fuzzy adaptive tuning PID controller composed of a two-dimensional fuzzy controller and a PID controller. The two-dimensional fuzzy controller divides the fuzzy language E and EC of the error e and the error change rate ec into 7 linguistic variable levels, and outputs the variable ΔK. P ΔK I and ΔK D Fuzzy linguistic variable K P ′、K I ′ and K D It is also divided into 7 levels of language variables; the fuzzy adaptive tuning PID controller adjusts the PID parameters according to the preset fuzzy control rules, which are based on the operator's practical experience and E, EC and K. P ′、K I ′ and K D The logical relationship between ' is established; the fuzzy adaptive tuning PID controller uses the area centroid method to complete the declarative processing of the output quantity. By calculating the centroid of the area enclosed by the fuzzy set membership function curve and the horizontal coordinate, the horizontal coordinate value corresponding to the centroid is selected as the representative value of the fuzzy set, and thus ΔK is obtained. P ΔK I ΔK D The clear values ​​are then used to obtain a lookup table of clear values ​​for the PID adjustment parameters.

5. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 1, characterized in that, The calculation of the control quantity for the feedforward channel also includes establishing a heat generation prediction model: Where k represents the motor thermal conversion system, and ζ represents the inertia compensation coefficient.

6. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 1, characterized in that, The control module is equipped with a state machine, which is divided into three main operating states: standby state, adjustment state, and protection state. The state machine switches states based on the comparison results of the detected temperature and current signals with preset thresholds. The state transition logic of the state machine is as follows: When the detection temperature T < T sct At -5℃, the system enters standby mode and shuts down the adaptive cooling system; When the temperature deviation TT text When the temperature is ≤5℃, the system enters the regulation state, and outputs the superimposed feedforward control quantity and the feedback control quantity, U. t =αU f +(1-α)U b The heat dissipation power is dynamically adjusted, and α is the weighting coefficient of the feedforward control. When the detection temperature T > T max When the current I exceeds the safety threshold, the system enters a protection state, activates the maximum heat dissipation power of the adaptive cooling system, and triggers an alarm.

7. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 4, characterized in that, The parameter self-tuning module employs a relay feedback method, specifically including: injecting a step disturbance signal during the system startup phase; recording the system oscillation period Tu and amplitude Au; and calculating the initial PID parameters K according to the Ziegler-Nichols formula. p =0.6K u T i =0.5T u T d =0.125T u Set the online adjustment factor β = 1 - e (-t / τ) This enables dynamic adjustment of PID parameters.

8. The SFC closed-loop heat dissipation system based on a servo motor driver according to claim 1, characterized in that, include: The signal acquisition module includes a Hall current sensor and a PT1000 platinum resistance temperature sensor. The Hall current sensor has a measurement range of 0-50A and an accuracy of ±0.5%. The PT1000 platinum resistance temperature sensor has a temperature measurement range of -50 to 150℃ and an accuracy of ±0.3℃. The control decision module includes a microprocessor unit with a built-in control algorithm and a state machine management unit for implementing SFC running state switching. The actuator includes a brushless DC fan with a PWM speed regulation range of 10% to 100% and a speed regulation range of 0 to 2500 r / min; the microprocessor unit also includes: a fault diagnosis submodule in the intelligent driver, which monitors the health status in real time; a data storage submodule, which records historical operating data; and a communication interface that supports the Modbus-RTU protocol.

9. The SFC closed-loop heat dissipation control system based on a servo motor driver according to claim 1, characterized in that, The adaptive cooling system includes: a permanent magnet synchronous motor for driving a cooling fan; a cooling duct with an axial intake and radial exhaust structure; and temperature monitoring points located at the stator windings and bearings of the permanent magnet synchronous motor. During axial intake, the airflow enters the cooling duct along the axis of the motor shaft and can directly pass through the core heat-generating areas of the motor stator and rotor, resulting in a short and uniform intake path. During radial exhaust, the airflow is discharged perpendicular to the shaft direction, forming a 90-degree angle with the intake direction, quickly removing heat from the core area and preventing hot air backflow.

10. A closed-loop heat dissipation method for SFC based on a servo motor driver, characterized in that, Includes the following steps: Step S1: Construct a feedforward-feedback composite control architecture; Step S2, establish the current-heat generation mathematical model Q=k×I 2 , where k is the thermal conversion coefficient of the motor calibrated experimentally; Step S3: The temperature deviation signal is processed using an incremental digital PID algorithm, and a parameter self-tuning module is set to achieve online optimization of control parameters; Step S4: Design a three-state operation mechanism based on the state machine's state transition logic: Step S5: The orderly switching of the operating state is achieved through SFC. The control process includes: system initialization, calibration of Hall current sensor and PT1000 platinum resistance temperature sensor, synchronous acquisition of current signal I and temperature signal T, real-time calculation of heat generation power Q by feedforward module, execution of PID calculation by feedback module and output of PWM duty cycle by control quantity integration module.

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