A control method based on differential speed torque distribution

Abstract

The present invention provides a control method based on differential speed and torque distribution, which relates to the field of intelligent control technology. The method includes: calculating the difference between an initial input and a preset speed command to generate a speed deviation signal and a deviation change rate; establishing a dynamic membership function library using a fuzzy controller, mapping the speed deviation and its change rate into fuzzy linguistic variables, performing inference operations based on a preset fuzzy control rule library, and outputting a frequency adjustment value and a torque compensation coefficient for the frequency converter; reading the generated torque compensation coefficient and the shaft torque values ​​measured by two torque meters, calculating the dynamic difference between the current total load torque and the target torque, establishing a torque distribution weight model based on the differential transmission characteristics, and generating differentiated torque correction commands for the two motors based on the real-time speed value. The present invention improves the control performance of the differential drive.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a control method based on differential speed and torque distribution. Background Art

[0002] Traditional differential control methods typically rely on fixed transmission ratios or simple proportional distribution strategies. Therefore, some methods struggle to adapt to complex and variable load conditions. Particularly in dual-motor drives, due to factors such as sudden load changes and speed differential fluctuations, some traditional methods suffer from the following issues:

[0003] For example, fixed control parameters cannot adapt to changes in speed deviation and torque demand in real time, resulting in adjustment lag and affecting stability; the static weight model is difficult to accurately reflect the differential transmission characteristics, which can easily cause single-side motor overload or efficiency reduction; frequency regulation and torque compensation instructions are difficult to coordinate due to dimensional differences, which may cause control oscillation or actuator saturation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a control method based on differential speed torque distribution, thereby improving the control performance of differential drive.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A control method based on differential speed torque distribution, comprising:

[0007] Step 1: Real-time acquisition of the speed and position feedback signals of the two load motors. The encoder converts the physical quantities into digital pulse signals to generate the real-time speed value and angular displacement data of each motor as the initial input for the speed closed-loop control.

[0008] Step 2: Calculate the difference between the initial input and the preset speed command to generate a speed deviation signal and the deviation change rate. A dynamic membership function library is established through the fuzzy controller to map the speed deviation and its change rate into fuzzy linguistic variables. Inference operations are performed based on the preset fuzzy control rule library to output the frequency adjustment value and torque compensation coefficient of the inverter.

[0009] Step 3: Read the torque compensation coefficient generated in step 2 and the shaft torque values ​​measured by the two torque meters, calculate the dynamic difference between the current total load torque and the target torque, establish a torque distribution weight model based on the differential transmission characteristics, and combine it with the real-time speed value obtained in step 1 to generate differentiated torque correction commands for the two motors;

[0010] Step 4: Couple the frequency adjustment value output from step 2 with the torque correction command generated in step 3, eliminate the control dimension difference through normalization, and use a weighted fusion algorithm to generate the final inverter output parameters, including the frequency setting value, voltage amplitude, and phase compensation value;

[0011] Step 5: Send the final inverter output parameters to the corresponding inverter through the real-time controller's communication bus to drive the load motor to execute the new operating state.

[0012] Furthermore, step 1: real-time acquisition of the speed and position feedback signals of the two load motors, conversion of the physical quantities into digital pulse signals through encoders, and generation of real-time speed values ​​and angular displacement data for each motor as the initial input for speed closed-loop control, including:

[0013] Step 1.1: For each motor's encoder output pulse signal, use a high-speed counter to count the rising or falling edges of the pulses in real time and record the precise timestamp of each pulse triggering. At the same time, count the number of pulses within the unit time window and, combined with the encoder's calibrated number of pulses per revolution, calculate the current speed value as the speed feedback.

[0014] Step 1.2: Measure the time interval between two consecutive pulses, capture the time difference between adjacent pulses through a timer, and calculate the instantaneous pulse period;

[0015] Step 1.3: Use the accumulator to count the total number of encoder pulses, calculate the angular displacement based on the number of pulses per revolution, convert the number of revolutions into the absolute rotation angle of the motor shaft, and perform differential calculation on the angular displacements of adjacent sampling periods to obtain the incremental angle change.

[0016] Step 1.4: Align the real-time speed value calculated in step 1.1 with the rotation angle generated in step 1.3 to eliminate timing deviations caused by signal transmission delays. Convert the speed value to a standard value and the angle value to radians. After unifying the dimensions, use them as the initial input for speed closed-loop control.

[0017] Furthermore, step 2: calculating the difference between the initial input and the preset speed command to generate a speed deviation signal and a deviation change rate, including:

[0018] Step 2.1: Based on the normalized standard value output in step 1.4, perform algebraic subtraction between the real-time speed value of each motor and the preset speed command in each control cycle to obtain an instantaneous speed deviation signal;

[0019] Step 2.2: Perform sliding average filtering on the original speed deviation signal: store the deviation values ​​between the current cycle and the previous N cycles in a cache queue, calculate the weighted average value, and output the filtered speed deviation signal;

[0020] Step 2.3: Based on the filtered speed deviation signal, perform a differential operation on the deviation value between the current cycle and the previous cycle to obtain the deviation change rate.

[0021] Furthermore, a dynamic membership function library is established through the fuzzy controller to map the speed deviation and its rate of change into fuzzy language variables. The inference operation is performed according to the preset fuzzy control rule library to output the frequency adjustment amount and torque compensation coefficient of the inverter, including:

[0022] The speed deviation and change rate output in step 2.3 are divided into 7 fuzzy sets. The membership function is dynamically adjusted according to the real-time working conditions. Based on the preset 49 rules, rule activation and weight calculation are performed. The activated rules are operated with an "AND" operation. The minimum value of the input variable membership is taken as the rule trigger strength to generate the fuzzy output area of ​​the frequency adjustment amount and the torque compensation coefficient. The centroid method is used to calculate the weighted average membership of each point in the fuzzy output area to obtain the frequency adjustment amount and the torque compensation coefficient.

[0023] Furthermore, step 3: reading the torque compensation coefficient generated in step 2 and the shaft torque values ​​measured by the two torque meters, and calculating the dynamic difference between the current total load torque and the target torque, including:

[0024] The original shaft torque signals output by the two torque meters are low-pass filtered to obtain the filtered shaft torque values ​​of the two motors.

[0025] Perform algebraic addition on the filtered shaft torque values ​​of the two motors to obtain the current total load torque value;

[0026] Adjusting the target torque setting value according to the real-time working condition of the differential to obtain an adjusted target torque;

[0027] The total load torque is calculated as a difference from the dynamically adjusted target torque to obtain a dynamic difference.

[0028] Furthermore, a torque distribution weight model is established based on the differential transmission characteristics. Combined with the real-time speed value obtained in step 1, differential torque correction instructions for the two motors are generated, including:

[0029] Based on the real-time speed values ​​of the two motors in step 1, calculate the instantaneous speed difference between the two motors and determine the direction of the speed difference; if the speed of the first motor is higher than that of the second, the speed difference is defined as positive, otherwise it is negative;

[0030] Based on the nonlinear mapping relationship between the differential transmission ratio and the speed difference-torque distribution, the absolute value of the speed difference is converted into a dynamic weight coefficient using a preset dynamic weight function table. The larger the absolute value of the speed difference, the more the weight coefficient shifts toward the higher load side.

[0031] The polarity of the dynamic weight coefficient is determined based on the direction of the speed difference. If the speed difference is positive, the dynamic weight coefficient is allocated to the second motor as its torque correction ratio, and the remaining ratio is allocated to the first motor. If it is negative, the opposite is true.

[0032] The dynamic difference between the total load torque and the target torque calculated in step 3 is proportionally distributed according to the determined dynamic weight coefficient to generate torque corrections for each of the two motors. The correction for the motor on the high-weight side is equal to the product of the dynamic difference and the weight coefficient, and the correction for the motor on the low-weight side is the remaining portion of the dynamic difference.

[0033] The torque correction values ​​of the two motors are superimposed on the torque compensation coefficient output in step 2 to generate the final differentiated torque correction command.

[0034] Furthermore, step 4: couple the frequency adjustment value outputted in step 2 with the torque correction command generated in step 3, eliminate the control dimension difference through normalization processing, and use the weighted fusion algorithm to generate the final inverter output parameters, including the frequency setting value, voltage amplitude and phase compensation value, including:

[0035] Step 4.1: Based on the maximum adjustable frequency range of the inverter, convert the original value of the frequency adjustment amount into a percentage value of the maximum frequency range to obtain a dimensionless frequency adjustment proportional coefficient;

[0036] Step 4.2: Based on the rated torque range of the motor, convert the original value of the torque correction amount into a percentage of the rated torque to obtain the dimensionless torque correction proportional coefficient;

[0037] Step 4.3: Assign weight factors to the frequency adjustment proportional coefficient and the torque correction proportional coefficient according to the preset frequency control priority and torque compensation priority, respectively; wherein the sum of the frequency adjustment weight factor and the torque correction weight factor is 1;

[0038] Step 4.4: Multiply the normalized frequency adjustment proportional coefficient by its weight factor to obtain the weighted frequency component; and multiply the normalized torque correction proportional coefficient by its weight factor to obtain the weighted torque component;

[0039] Step 4.5: Perform algebraic superposition of the weighted frequency component and the weighted torque component to generate comprehensive control parameters;

[0040] Step 4.6: Based on the frequency setting range of the inverter, convert the frequency-related components in the comprehensive parameters into actual frequency setting values; based on the adjustment range of the motor drive voltage, convert the voltage-related components in the comprehensive parameters into voltage amplitude adjustment values; based on the allowable deviation range of phase compensation, convert the phase-related components in the comprehensive parameters into phase compensation values;

[0041] Step 4.7: Perform dynamic smoothing filtering on the inverse normalized frequency setting value, voltage amplitude adjustment value, and phase compensation value to generate the final inverter output parameters.

[0042] Furthermore, step 5: sending the final inverter output parameters to the corresponding inverter via the communication bus of the real-time controller to drive the load motor to execute the new operating state, including:

[0043] Step 5.1: Encapsulate the frequency setting value, voltage amplitude adjustment value, and phase compensation value generated in step 4.7 into a data frame according to a preset communication protocol format to obtain an encapsulated data frame;

[0044] Step 5.2: Perform transmission check on the encapsulated data frame: Calculate the cyclic redundancy check code based on the data frame content and append it to the end of the frame; if framed transmission is required, then split the long data packet into packets and add the frame sequence number and total packet number identifier;

[0045] Step 5.3: Send the data frame to the target inverter via the communication bus interface of the real-time controller at a preset fixed period; if a bus conflict is detected, a backoff algorithm is used to delay and resend;

[0046] Step 5.4: After receiving the data frame, the inverter performs verification and analysis to obtain the parsed parameters;

[0047] Step 5.5: Based on the parsed parameters, the frequency setting value is converted into the switching frequency reference of the inverter, the voltage amplitude adjustment amount is mapped into the PWM duty cycle adjustment amount, and the phase compensation amount is converted into the phase offset angle of the three-phase voltage to generate a new PWM drive signal to drive the load motor to operate through the PWM signal.

[0048] The above solution of the present invention includes at least the following beneficial effects:

[0049] The fuzzy controller dynamically adjusts the membership function and rule base, maps the speed deviation and its rate of change in real time, and accurately outputs the frequency adjustment amount and torque compensation coefficient, shortening the response time. Based on the dynamic weight model of the speed difference direction and amplitude, combined with the nonlinear characteristics of the differential transmission, the differentiated distribution of torque correction commands is achieved, which improves load balancing and reduces the risk of unilateral motor overload. Normalization processing and weighted fusion algorithm are used to eliminate dimensional conflicts of frequency, voltage and phase parameters, ensure the coordination of comprehensive control parameters, and avoid actuator saturation.

[0050] Real-time monitoring of motor feedback data triggers iterative updates to form an adaptive adjustment link, which can maintain high-precision control under complex working conditions. Through data frame verification, backoff retransmission and smoothing filtering mechanisms, it effectively suppresses communication delays and noise interference, ensuring the reliable execution of control instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of a control method based on differential speed torque distribution provided by an embodiment of the present invention.

[0052] Figure 2 1 is a flow chart of step 1 of a control method based on differential speed torque distribution provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] like Figure 1 As shown, an embodiment of the present invention provides a control method based on differential speed torque distribution, including:

[0055] Step 1: Real-time acquisition of the speed and position feedback signals of the two load motors. The encoder converts the physical quantities into digital pulse signals to generate the real-time speed value and angular displacement data of each motor as the initial input for the speed closed-loop control.

[0056] Step 2: Calculate the difference between the initial input and the preset speed command to generate a speed deviation signal and the deviation change rate. A dynamic membership function library is established through the fuzzy controller to map the speed deviation and its change rate into fuzzy linguistic variables. Inference operations are performed based on the preset fuzzy control rule library to output the frequency adjustment value and torque compensation coefficient of the inverter.

[0057] Step 3: Read the torque compensation coefficient generated in step 2 and the shaft torque values ​​measured by the two torque meters, calculate the dynamic difference between the current total load torque and the target torque, establish a torque distribution weight model based on the differential transmission characteristics, and combine it with the real-time speed value obtained in step 1 to generate differentiated torque correction commands for the two motors;

[0058] Step 4: Couple the frequency adjustment value output from step 2 with the torque correction command generated in step 3, eliminate the control dimension difference through normalization, and use a weighted fusion algorithm to generate the final inverter output parameters, including the frequency setting value, voltage amplitude, and phase compensation value;

[0059] Step 5: Send the final inverter output parameters to the corresponding inverter through the real-time controller's communication bus to drive the load motor to execute the new operating state.

[0060] In this embodiment of the present invention, real-time speed and position signals are collected by an encoder and dynamically adjusted using a fuzzy controller. This allows for rapid response to load changes, reduces speed fluctuations, and improves robustness in nonlinear and tightly coupled scenarios. Inference operations based on fuzzy linguistic variables and a rule base can handle complex dynamic characteristics without requiring precise mathematical models, adapt to motor parameter drift or external interference, and enhance control flexibility. A torque distribution weight model, combined with real-time speed values, dynamically adjusts the torque output of the two motors based on the differential transmission characteristics, ensuring balanced shaft load and avoiding overload or power waste on a single motor. Real-time correction commands based on the deviation between measured and target torques enable coordinated control of multiple motors, improving transmission stability and reliability (e.g., reducing tire wear and improving ride smoothness in vehicle differential scenarios). By coupling frequency adjustment variables with torque correction commands, dimensional differences are eliminated and unified inverter output parameters (frequency, voltage amplitude, and phase compensation) are generated. This simplifies multivariable control complexity, improves control accuracy and integration, and simultaneously adjusts frequency and voltage phase to optimize motor energy efficiency (e.g., reduce reactive power loss). Dynamic torque compensation reduces mechanical shock and extends equipment life.

[0061] In a preferred embodiment of the present invention, step 1: real-time acquisition of the speed and position feedback signals of two load motors, conversion of the physical quantities into digital pulse signals via encoders, and generation of real-time speed values ​​and angular displacement data for each motor as initial inputs for speed closed-loop control, includes:

[0062] Step 1.1: For each motor's encoder output pulse signal, use a high-speed counter to count the rising or falling edges of the pulses in real time and record the precise timestamp of each pulse triggering. At the same time, count the number of pulses within the unit time window and, combined with the encoder's calibrated number of pulses per revolution, calculate the current speed value as the speed feedback.

[0063] Step 1.2: Measure the time interval between two consecutive pulses, capture the time difference between adjacent pulses through a timer, and calculate the instantaneous pulse period;

[0064] Step 1.3: Use the accumulator to count the total number of encoder pulses, calculate the angular displacement based on the number of pulses per revolution, convert the number of revolutions into the absolute rotation angle of the motor shaft, and perform differential calculation on the angular displacements of adjacent sampling periods to obtain the incremental angle change.

[0065] Step 1.4: Align the real-time speed value calculated in step 1.1 with the rotation angle generated in step 1.3 to eliminate timing deviations caused by signal transmission delays. Convert the speed value to a standard value and the angle value to radians. After unifying the dimensions, use them as the initial input for speed closed-loop control.

[0066] In this embodiment of the present invention, the encoder continuously outputs pulse signals during motor operation. A high-speed counter monitors and counts the rising and falling edges of each pulse in real time, simultaneously recording the precise moment of the pulse triggering. By counting the total number of pulses within a pre-set unit time window and combining this with the encoder's calibrated number of pulses per revolution, the current motor speed can be calculated. This speed value serves as speed feedback information for subsequent control.

[0067] The time interval between two adjacent encoder pulses is measured using a timer. After capturing the time difference between the arrival of adjacent pulses, the instantaneous pulse period is calculated. This period reflects the temporal characteristics of adjacent position changes during motor rotation, providing a basis for analyzing instantaneous changes in motor speed.

[0068] The accumulator is started to continuously accumulate and count the total number of pulses generated by the encoder. Based on the number of pulses per revolution of the encoder, the total number of accumulated pulses is converted into the absolute rotation angle of the motor shaft. At the same time, the angular displacement within two adjacent sampling periods is differentially calculated to obtain the incremental angle change of the motor in a short period of time, thereby obtaining the dynamic change of the motor rotation angle.

[0069] The real-time speed values ​​calculated in step 1.1 and the rotation angles generated in step 1.3 are aligned one-to-one according to their timestamps to eliminate temporal order deviations caused by delays in signal transmission. The speed values ​​are then converted to a standardized form, and the angle values ​​are converted from conventional units to radians to ensure that both quantities have the same dimensionality. Finally, these processed speed and angle data serve as the initial input data for the speed closed-loop control.

[0070] Through high-speed counter counting and precise timestamp recording, the motor speed and position information can be accurately obtained, providing a reliable data basis for subsequent control; the measurement of instantaneous pulse period can capture subtle changes in motor speed and improve the response capability to dynamic working conditions. The cumulative counting calculates angular displacement and incremental angle change, which can not only grasp the absolute position of the motor, but also understand the angle change trend during its rotation process, and realize comprehensive monitoring of the motor motion state. Timestamp alignment effectively solves the timing problems caused by signal transmission delay and ensures the consistency of speed and angle data; unified dimension makes the data meet the input requirements of the control algorithm, improves the accuracy and stability of closed-loop control, reduces the control error caused by data mismatch, and enhances reliability and robustness.

[0071] In a preferred embodiment of the present invention, step 2: calculating the difference between the initial input and the preset speed command to generate a speed deviation signal and a deviation change rate includes:

[0072] Step 2.1: Based on the normalized standard value output in step 1.4, perform algebraic subtraction between the real-time speed value of each motor and the preset speed command in each control cycle to obtain an instantaneous speed deviation signal;

[0073] Step 2.2: Perform sliding average filtering on the original speed deviation signal: store the deviation values ​​between the current cycle and the previous N cycles in a cache queue, calculate the weighted average value, and output the filtered speed deviation signal;

[0074] Step 2.3: Based on the filtered speed deviation signal, perform a differential operation on the deviation value between the current cycle and the previous cycle to obtain the deviation change rate.

[0075] In this embodiment of the present invention, during each control cycle, the normalized real-time speed value of each motor, obtained after processing in step 1.4, is extracted, and a pre-set speed command is retrieved. An algebraic subtraction operation is performed on each motor's real-time speed value and the corresponding pre-set speed command, thereby obtaining an instantaneous speed deviation signal for each motor during that control cycle, which intuitively reflects the difference between the motor's current speed and the target speed.

[0076] A cache queue is set up to store the original speed deviation signal. At the arrival of each new control cycle, the speed deviation value of the current cycle is stored in the queue along with the deviation values ​​of the previous N cycles. Then, a weighted average is calculated for all the deviation values ​​in the queue according to a pre-set weight distribution scheme. This method filters out any noise and interference that may be present in the original signal, and outputs a smoother and more stable filtered speed deviation signal.

[0077] Based on the filtered speed deviation signal obtained in step 2.2, the deviation values ​​between the current control cycle and the previous control cycle are compared and a differential operation is performed. By calculating the difference between the two cycle deviation values, the change in speed deviation between adjacent cycles is obtained, and then the deviation change rate is calculated. This parameter reflects the changing trend of the speed deviation and provides a key basis for subsequent control strategy adjustments.

[0078] Step 2.1 uses algebraic subtraction to convert the difference between the actual motor speed and the preset command into a quantifiable instantaneous speed deviation signal, providing a clear adjustment direction and target for the subsequent control algorithm. The sliding average filtering operation in step 2.2 effectively removes random noise and interference fluctuations in the original speed deviation signal, making the deviation signal smoother and more stable, avoiding control misjudgments caused by signal mutations, and enhancing control reliability. Step 2.3 calculates the deviation change rate to keenly capture the changing trend of the speed deviation, allowing the control to not only adjust the current speed deviation, but also predict the development trend of the deviation, adjust the control strategy in advance, improve dynamic response capabilities and control accuracy, and achieve more efficient and stable speed closed-loop control.

[0079] In a preferred embodiment of the present invention, a dynamic membership function library is established through a fuzzy controller, the speed deviation and its rate of change are mapped into fuzzy linguistic variables, and an inference operation is performed according to a preset fuzzy control rule library to output the frequency adjustment amount and torque compensation coefficient of the inverter, including:

[0080] The speed deviation and change rate output in step 2.3 are divided into 7 fuzzy sets. The membership function is dynamically adjusted according to the real-time working conditions. Based on the preset 49 rules, rule activation and weight calculation are performed. The activated rules are operated with an "AND" operation. The minimum value of the input variable membership is taken as the rule trigger strength to generate the fuzzy output area of ​​the frequency adjustment amount and the torque compensation coefficient. The centroid method is used to calculate the weighted average membership of each point in the fuzzy output area to obtain the frequency adjustment amount and the torque compensation coefficient.

[0081] In this embodiment of the present invention, the speed deviation and rate of change output from step 2.3 are evenly divided into seven fuzzy sets based on their numerical ranges, such as "negative large," "negative medium," "negative small," "zero," "positive small," "positive medium," and "positive large." During motor operation, the system monitors operating condition changes, such as load fluctuations and environmental interference, in real time and dynamically modifies the shape and parameters of the membership function based on these actual operating conditions. For example, if the load suddenly increases, the boundary values ​​and distribution of the membership function are adjusted accordingly, ensuring that the fuzzy sets can more accurately describe the current speed deviation and rate of change.

[0082] Fuzzy rule activation and weight calculation:

[0083] The system has 49 pre-set fuzzy control rules, each corresponding to a different combination of speed deviation and rate of change fuzzy sets. When new speed deviation and rate of change data are input, the system checks these rules one by one to determine which rules' prerequisites are met, that is, to activate the corresponding rules. For activated rules, the system calculates the rule weight based on the membership of the input variables in the rule to the corresponding fuzzy set. For example, if the premise of a rule is "speed deviation is negative and large and the rate of change is positive and small", the system will find the membership of the current speed deviation in the "negative and large" set, as well as the membership of the rate of change in the "positive and small" set, and use these two membership values ​​as the basis for calculating the rule weight.

[0084] Rule reasoning and fuzzy output generation:

[0085] The system performs an AND operation on the activated rules. Specifically, for each activated rule, the minimum value of the input variable membership is taken and used as the trigger strength for that rule. This process generates a series of rules with different trigger strengths, which together form the fuzzy output range for the frequency adjustment and torque compensation coefficients. Each point in this range corresponds to a different degree of frequency adjustment and torque compensation potential, reflecting the comprehensive response of fuzzy control to various operating conditions.

[0086] The generated fuzzy output region is processed using the centroid method. Specifically, the weighted average of the membership degree for each point within the fuzzy output region is calculated. By multiplying each point's coordinate value with its corresponding membership degree, summing all these products, and finally dividing by the sum of all membership degrees, an accurate numerical result is obtained. These results correspond to the frequency adjustment value and torque compensation coefficient of the inverter, respectively. This converts the fuzzy control result into precise parameters that can actually be used for inverter regulation.

[0087] Dynamically adjusting the membership function enables the fuzzy controller to flexibly adapt to changes in the motor's operating state based on real-time operating conditions. It also exhibits excellent anti-interference capabilities for complex situations such as sudden load changes and parameter drift, improving system robustness. 49 preset rules cover a variety of speed deviation and rate-of-change combinations. Combined with "AND" operations and trigger strength calculations, this allows for refined adjustment of the inverter's frequency and torque, avoiding the simple linear adjustment of traditional control methods and making the control process more tailored to actual needs. The center-of-gravity defuzzification method converts fuzzy outputs into precise parameters, effectively avoiding sudden changes in the controlled quantity. It outputs smooth frequency adjustment and torque compensation coefficients, ensuring smooth motor operation, reducing mechanical shock, and extending equipment life. Furthermore, this fuzzy control method can optimize motor energy efficiency and reduce energy consumption.

[0088] In a preferred embodiment of the present invention, step 3: reading the torque compensation coefficient generated in step 2 and the shaft torque values ​​measured by the two torque meters, and calculating the dynamic difference between the current total load torque and the target torque, includes:

[0089] The original shaft torque signals output by the two torque meters are low-pass filtered to obtain the filtered shaft torque values ​​of the two motors.

[0090] Perform algebraic addition on the filtered shaft torque values ​​of the two motors to obtain the current total load torque value;

[0091] Adjusting the target torque setting value according to the real-time working condition of the differential to obtain an adjusted target torque;

[0092] The total load torque is calculated as a difference from the dynamically adjusted target torque to obtain a dynamic difference.

[0093] In this embodiment of the present invention, the system acquires the raw shaft torque signals from the two torque meters and then initiates a low-pass filtering process. This low-pass filter scans the raw signals, allowing low-frequency signals to pass smoothly while suppressing high-frequency noise and interference. By processing the raw signal data point by point, it removes any high-frequency fluctuations, ultimately outputting smooth, stable values ​​for the shaft torques of the two motors, providing reliable data for subsequent calculations.

[0094] The system extracts the torque values ​​of the two motor shaft systems after low-pass filtering, performs algebraic addition operations, and accumulates the torque values ​​borne by the two motors to obtain the current total load torque value of the entire shaft system. This value intuitively reflects the actual load size borne by the current system.

[0095] Real-time monitoring of the operating conditions of the differential, including information such as motor speed, load change trends, and operating mode switching. Based on the pre-set working condition-torque mapping rules and the current actual working conditions, the target torque setting value is dynamically adjusted. For example, when the differential is in the heavy-load startup phase, the system will increase the target torque setting value; while when running at a light load and constant speed, the target torque will be appropriately reduced to make the target torque more in line with actual needs. The calculated current total load torque value and the dynamically adjusted target torque setting value are subtracted. Through the subtraction operation, the dynamic difference between the two is obtained. This difference clearly shows the gap between the current actual load torque and the expected torque, providing a key basis for subsequent torque distribution and control strategy adjustments.

[0096] Low-pass filtering effectively removes noise interference from the original torque signal, making the shaft torque data more real and reliable, avoiding calculation errors caused by signal fluctuations, and dynamically adjusting the target torque according to the real-time working conditions of the differential, so that the system can flexibly respond to different operating scenarios and load changes, ensuring the rationality and effectiveness of the target torque setting, improving the system's adaptability, and accurately calculating the dynamic difference between the total load torque and the target torque, providing clear adjustment direction and quantitative indicators for subsequent torque distribution and motor control, helping to achieve precise coordination of the torque output of the two motors, avoiding overload or uneven load of a single motor, improving the stability and operating efficiency of the transmission system, and extending the service life of the equipment.

[0097] In a preferred embodiment of the present invention, a torque distribution weight model is established based on the differential transmission characteristics, and combined with the real-time speed value obtained in step 1, differential torque correction instructions for the two motors are generated, including:

[0098] Based on the real-time speed values ​​of the two motors in step 1, calculate the instantaneous speed difference between the two motors and determine the direction of the speed difference; if the speed of the first motor is higher than that of the second, the speed difference is defined as positive, otherwise it is negative;

[0099] Based on the nonlinear mapping relationship between the differential transmission ratio and the speed difference-torque distribution, the absolute value of the speed difference is converted into a dynamic weight coefficient using a preset dynamic weight function table. The larger the absolute value of the speed difference, the more the weight coefficient shifts toward the higher load side.

[0100] The polarity of the dynamic weight coefficient is determined based on the direction of the speed difference. If the speed difference is positive, the dynamic weight coefficient is allocated to the second motor as its torque correction ratio, and the remaining ratio is allocated to the first motor. If it is negative, the opposite is true.

[0101] The dynamic difference between the total load torque and the target torque calculated in step 3 is proportionally distributed according to the determined dynamic weight coefficient to generate torque corrections for each of the two motors. The correction for the motor on the high-weight side is equal to the product of the dynamic difference and the weight coefficient, and the correction for the motor on the low-weight side is the remaining portion of the dynamic difference.

[0102] The torque correction values ​​of the two motors are superimposed on the torque compensation coefficient output in step 2 to generate the final differentiated torque correction command.

[0103] In this embodiment of the present invention, the real-time speed values ​​of the two motors collected in step 1 are extracted and numerically subtracted to obtain the instantaneous speed difference between the two motors. The system then determines the sign of this difference: if the speed of the first motor is higher than that of the second, the speed difference is defined as positive; conversely, if the speed of the first motor is lower than that of the second, the speed difference is defined as negative. This operation clearly defines the relative speed relationship between the two motors.

[0104] Based on the differential's transmission ratio characteristics and the pre-established nonlinear mapping between speed difference and torque distribution, a preset dynamic weighting function table is called. The calculated absolute value of the speed difference is used as an input parameter, and the corresponding mapping value is searched in the function table to convert it into a dynamic weighting coefficient. This process follows a nonlinear rule: the larger the absolute value of the speed difference, the more the weighting coefficient shifts toward the higher load side, increasing nonlinearly to accommodate torque distribution requirements under varying speed differences. Based on the speed difference direction determined in step 1, the system determines how the dynamic weighting coefficients are distributed. When the speed difference is positive, the dynamic weighting coefficient is allocated to the second motor with the lower speed as a torque correction factor; the remaining weighting coefficient is allocated to the first motor. If the speed difference is negative, the dynamic weighting coefficient is allocated to the first motor with the lower speed, ensuring that the weighting coefficient is always allocated to the motor requiring increased torque.

[0105] The dynamic difference between the total load torque and the target torque calculated in step 3 is retrieved, and the difference is proportionally distributed based on the determined dynamic weight coefficient. For the motor on the high-weight side, its torque correction is obtained by multiplying the dynamic difference by the weight coefficient; while the torque correction for the motor on the low-weight side is the remainder after subtracting the high-weight side correction from the dynamic difference. This achieves differentiated distribution of the torque corrections for the two motors, and the torque corrections for the two motors calculated in step 4 are superimposed on the torque compensation coefficient output in step 2. The compensation coefficient is incorporated into the correction amount, and the dual requirements of speed deviation adjustment and actual load deviation adjustment are comprehensively considered. Finally, differentiated torque correction instructions for each of the two motors are generated to drive the motors to adjust the torque output.

[0106] The weight coefficient is adjusted in real time based on the speed difference, and nonlinear mapping is performed in combination with the differential transmission characteristics, so that the torque distribution can dynamically adapt to the changes in motor speed, ensure the load balance of the two motors, avoid overload of a single motor, and improve the stability of the transmission system. The weight polarity is flexibly allocated according to the direction of the speed difference to achieve coordinated adjustment of the motor torque output. Under conditions such as vehicle turning and equipment speed change, the power on both sides of the differential is ensured to be reasonably distributed, and the system response speed and operating efficiency are improved. The load torque difference and the torque compensation coefficient are superimposed, taking into account the current load deviation and speed adjustment requirements, and optimizing the motor torque output strategy. It can not only quickly respond to load changes, but also maintain speed stability, reduce mechanical wear, and extend the service life of the equipment. Through the preset weight function table and regularized process, this method can be adapted to different types of differentials and motor systems without the need for complex model derivation, facilitating engineering implementation and parameter adjustment, and improving the versatility and scalability of the control scheme.

[0107] In a preferred embodiment of the present invention, step 4: performing a coupling operation on the frequency adjustment value outputted from step 2 and the torque correction command generated from step 3, eliminating the control dimension difference through normalization processing, and using a weighted fusion algorithm to generate the final inverter output parameters, including the frequency setting value, voltage amplitude, and phase compensation amount, including:

[0108] Step 4.1: Based on the maximum adjustable frequency range of the inverter, convert the original value of the frequency adjustment amount into a percentage value of the maximum frequency range to obtain a dimensionless frequency adjustment proportional coefficient;

[0109] Step 4.2: Based on the rated torque range of the motor, convert the original value of the torque correction amount into a percentage of the rated torque to obtain the dimensionless torque correction proportional coefficient;

[0110] Step 4.3: Assign weight factors to the frequency adjustment proportional coefficient and the torque correction proportional coefficient according to the preset frequency control priority and torque compensation priority, respectively; wherein the sum of the frequency adjustment weight factor and the torque correction weight factor is 1;

[0111] Step 4.4: Multiply the normalized frequency adjustment proportional coefficient by its weight factor to obtain the weighted frequency component; and multiply the normalized torque correction proportional coefficient by its weight factor to obtain the weighted torque component;

[0112] Step 4.5: Perform algebraic superposition of the weighted frequency component and the weighted torque component to generate comprehensive control parameters;

[0113] Step 4.6: Based on the frequency setting range of the inverter, convert the frequency-related components in the comprehensive parameters into actual frequency setting values; based on the adjustment range of the motor drive voltage, convert the voltage-related components in the comprehensive parameters into voltage amplitude adjustment values; based on the allowable deviation range of phase compensation, convert the phase-related components in the comprehensive parameters into phase compensation values;

[0114] Step 4.7: Perform dynamic smoothing filtering on the inverse normalized frequency setting value, voltage amplitude adjustment value, and phase compensation value to generate the final inverter output parameters.

[0115] In this embodiment of the present invention, after obtaining the original value of the frequency adjustment value output in step 2, the original adjustment value is converted to a percentage of the maximum frequency range, with reference to the maximum adjustable frequency range of the inverter (e.g., 0-50 Hz). For example, if the maximum frequency is 50 Hz and the frequency adjustment value is 10 Hz, the frequency adjustment value is converted to a 20% proportionality factor, resulting in a dimensionless, standardized value.

[0116] The system converts the original torque correction value generated in step 3 into a percentage of the rated torque based on the motor's rated torque range (e.g., 0-100 Nm). For example, if the rated torque is 100 Nm and the torque correction is 15 Nm, it is converted to a 15% proportional coefficient to eliminate the dimensional difference between torque and frequency.

[0117] Based on the preset control strategy (e.g., prioritizing speed stability during high-speed operation and torque compensation under heavy loads), weighting factors are assigned to the frequency adjustment proportional coefficient and the torque correction proportional coefficient. For example, the frequency adjustment weight is set to 0.6 and the torque correction weight is set to 0.4 (the sum of the two is 1) to reflect the different control priorities under different operating conditions. The normalized frequency adjustment proportional coefficient is multiplied by the corresponding weighting factor to obtain a weighted frequency component (e.g., 20% × 0.6 = 12%). Simultaneously, the torque correction proportional coefficient is multiplied by the corresponding weighting factor to obtain a weighted torque component (e.g., 15% × 0.4 = 6%), respectively reflecting the contributions of frequency and torque to the integrated control. The weighted frequency component and the weighted torque component are algebraically added to generate a comprehensive control parameter (e.g., 12% + 6% = 18%). This parameter integrates the dual requirements of frequency adjustment and torque correction, forming a unified control command basis.

[0118] Convert the frequency-related components of the integrated parameters to actual values ​​based on the actual frequency range of the inverter. For example, if the frequency component of the integrated parameters is 12% and the maximum frequency is 50 Hz, the frequency setting value is 50 Hz x 12% = 6 Hz. Based on the motor drive voltage adjustment range (e.g., 0-380 V), convert the voltage-related components of the integrated parameters to specific voltage values ​​(e.g., using proportional mapping).

[0119] Phase compensation amount: According to the phase tolerance range (such as ±15°), the phase-related components in the comprehensive parameters are converted into actual compensation angles.

[0120] Dynamic smoothing filtering is performed on the inverse-normalized frequency, voltage, and phase parameters. Using a low-pass filter or sliding average algorithm, this eliminates sudden changes or high-frequency noise that may occur during the conversion process, ensuring continuous and stable output parameters. Through normalization, control parameters with different dimensions, such as frequency (Hz) and torque (Nm), are converted into dimensionless proportional coefficients, resolving unit conflicts when coupling multiple variables and facilitating subsequent weighted fusion operations. A weight factor allocation mechanism dynamically adjusts the control priority of frequency and torque based on actual operating conditions (for example, prioritizing frequency in speed regulation scenarios and torque in heavy-load scenarios), improving the system's adaptability to complex operating conditions. The weighted fusion algorithm achieves synergistic effects between frequency regulation and torque correction, avoiding system oscillations caused by over-adjustment of a single parameter. For example, while frequency regulation suppresses speed deviations, torque correction simultaneously compensates for load fluctuations, ensuring stable and efficient motor operation. Dynamic smoothing filtering effectively suppresses sudden changes in parameters, reduces the impact of inverter output changes, reduces motor electromagnetic losses and mechanical wear, and extends equipment life. It also improves system operational smoothness (for example, reducing vehicle jerking).

[0121] In a preferred embodiment of the present invention, step 5: sending the final inverter output parameter to the corresponding inverter via the communication bus of the real-time controller to drive the load motor to execute the new operating state includes:

[0122] Step 5.1: Encapsulate the frequency setting value, voltage amplitude adjustment value, and phase compensation value generated in step 4.7 into a data frame according to a preset communication protocol format to obtain an encapsulated data frame;

[0123] Step 5.2: Perform transmission check on the encapsulated data frame: Calculate the cyclic redundancy check code based on the data frame content and append it to the end of the frame; if framed transmission is required, then split the long data packet into packets and add the frame sequence number and total packet number identifier;

[0124] Step 5.3: Send the data frame to the target inverter via the communication bus interface of the real-time controller at a preset fixed period; if a bus conflict is detected, a backoff algorithm is used to delay and resend;

[0125] Step 5.4: After receiving the data frame, the inverter performs verification and analysis to obtain the parsed parameters;

[0126] Step 5.5: Based on the parsed parameters, the frequency setting value is converted into the switching frequency reference of the inverter, the voltage amplitude adjustment amount is mapped into the PWM duty cycle adjustment amount, and the phase compensation amount is converted into the phase offset angle of the three-phase voltage to generate a new PWM drive signal to drive the load motor to operate through the PWM signal.

[0127] In this embodiment of the present invention, the parameters generated in step 4.7, such as the frequency setting value, voltage amplitude adjustment value, and phase compensation value, are structured and encapsulated according to a preset communication protocol format (e.g., Modbus or CANopen). For example, each parameter is assigned a fixed data bit length and register address, and frame header and footer identifiers and device address information are added to form a complete data frame. This ensures that the parameters are uniformly formatted and semantically clear during transmission.

[0128] A CRC checksum is calculated on the contents of the encapsulated data frame, generating a checksum and appending it to the end of the frame. This checksum verifies data integrity and avoids parameter transmission errors caused by electromagnetic interference, bus noise, and other factors. If the data volume is large and requires framed transmission, the system splits the long data packet into multiple short data frames, adding a frame sequence number (e.g., "Frame 1 of 3") and a total packet count identifier to each subframe to ensure that the receiving end can reassemble the complete data in sequence. Data frames are sent to the target inverter via the real-time controller's communication bus interface (e.g., CAN, EtherCAT) at a fixed cycle (e.g., 1ms). If a bus conflict is detected during transmission (e.g., multiple devices simultaneously occupying the bus), a backoff algorithm (e.g., random delayed retransmission) is triggered to avoid data collisions and ensure that high-priority instructions are transmitted first.

[0129] After receiving a data frame, the inverter first verifies the CRC checksum at the end of the frame. If the check succeeds, the parameters in the data frame are parsed according to the communication protocol (for example, extracting the register value corresponding to the frequency setting value). If the check fails, the frame is discarded and an error message is sent to the controller, requesting data retransmission. The parsed frequency setting value is converted into the inverter's switching frequency reference. For example, if the frequency setting is 30Hz, the inverter is controlled to generate a sinusoidal modulation signal with a base frequency of 30Hz. The voltage amplitude adjustment is mapped to the PWM duty cycle adjustment. For example, if the voltage needs to be increased by 10%, the high-level duration of the PWM signal is increased, and the amplitude adjustment is achieved by changing the average voltage value.

[0130] The phase compensation amount is converted into a three-phase voltage phase offset angle (e.g., phase A leads by 5°, phase B lags by 5°). Phase compensation is achieved by adjusting the trigger timing of the three-phase PWM signals, optimizing the motor's magnetic field distribution. Finally, the inverter generates a new PWM drive signal based on these parameters, driving the load motor to the adjusted operating state.

[0131] Communication protocol encapsulation and CRC check ensure that parameters are not distorted or missed during transmission, effectively resist electromagnetic interference in industrial environments, and reduce the risk of misjudgment of control instructions. Frame processing and conflict avoidance mechanisms improve bus compatibility in high-load scenarios and ensure data integrity when multiple devices work together. Data frames are sent at a fixed period (e.g., 1ms) to meet real-time control requirements and ensure the motor can quickly respond to dynamic speed and torque adjustment commands. Priority-driven conflict resolution mechanisms (e.g., backoff algorithms) ensure the timely transmission of critical control commands and avoid system delays caused by bus congestion. The inverter's parameter verification and parsing mechanism ensures the accuracy of received frequency, voltage, and phase parameters, preventing abnormal motor operation (e.g., overvoltage and overcurrent) caused by data errors. Dynamic PWM signal generation directly affects motor magnetic field control. Through coordinated adjustment of frequency, amplitude, and phase, smooth motor speed transitions and precise torque output matching load requirements are achieved, reducing mechanical shock and energy loss. Based on standard communication protocols (e.g., Modbus and CANopen), the control process is adaptable to different inverter and controller brands, lowering the system integration threshold. A verification failure retransmission mechanism and error feedback link enable the system to automatically identify and correct transmission layer failures, reducing manual intervention and improving the stability and fault tolerance of the automation system.

[0132] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A control method based on differential speed torque distribution, characterized in that: The method comprises: Step 1: Real-time acquisition of the speed and position feedback signals of the two load motors. The encoder converts the physical quantities into digital pulse signals to generate the real-time speed value and angular displacement data of each motor as the initial input for the speed closed-loop control. Step 2: Calculate the difference between the initial input and the preset speed command to generate a speed deviation signal and deviation change rate. A dynamic membership function library is established through a fuzzy controller. The speed deviation and its change rate are mapped into fuzzy linguistic variables. Inference operations are performed based on the preset fuzzy control rule library to output the frequency adjustment value and torque compensation coefficient of the inverter. This includes: dividing the speed deviation and change rate into seven fuzzy sets, dynamically adjusting the membership function according to real-time operating conditions, executing rule activation and weight calculation based on 49 preset rules, performing an "AND" operation on the activated rules, taking the minimum value of the input variable membership as the rule trigger strength, and generating a fuzzy output region for the frequency adjustment value and torque compensation coefficient. The centroid method is used to calculate the weighted average membership of each point in the fuzzy output region to obtain the frequency adjustment value and torque compensation coefficient. Step 3: Read the torque compensation coefficient generated in step 2 and the shaft torque values ​​measured by the two torque meters, calculate the dynamic difference between the current total load torque and the target torque, establish a torque distribution weight model based on the differential transmission characteristics, and combine the real-time speed value obtained in step 1 to generate differentiated torque correction instructions for the two motors, including: based on the real-time speed values ​​of the two motors, calculate the instantaneous speed difference between the two motors, and determine the direction of the speed difference; if the speed of the first motor is higher than that of the second, define the speed difference as positive, otherwise it is negative; according to the nonlinear mapping relationship between the differential transmission ratio and the speed difference-torque distribution, the absolute value of the speed difference is converted into a dynamic weight coefficient through a preset dynamic weight function table; wherein, the speed difference The larger the absolute value, the nonlinear increase in the amplitude of the weight coefficient shifting toward the high-load side; the polarity of the distribution of the dynamic weight coefficient is determined according to the direction of the speed difference; if the speed difference is positive, the dynamic weight coefficient is distributed to the second motor as its torque correction ratio, and the remaining ratio is distributed to the first motor; if it is negative, the opposite is true; the dynamic difference between the total load torque and the target torque is proportionally distributed according to the determined dynamic weight coefficient to generate the torque correction amount of each of the two motors; among which, the correction amount of the motor on the high-weight side is equal to the product of the dynamic difference and the weight coefficient, and the correction amount on the low-weight side is the remaining part of the dynamic difference; the torque correction amounts of the two motors are superimposed on the torque compensation coefficient to generate the final differentiated torque correction instruction; Step 4: Couple the frequency adjustment value output from step 2 with the torque correction command generated in step 3, eliminate the control dimension difference through normalization, and use a weighted fusion algorithm to generate the final inverter output parameters, including the frequency setting value, voltage amplitude, and phase compensation value; Step 5: Send the final inverter output parameters to the corresponding inverter through the real-time controller's communication bus to drive the load motor to execute the new operating state.

2. The control method based on differential speed torque distribution according to claim 1, characterized in that: Step 1: Collect the speed and position feedback signals of the two load motors in real time. Convert the physical quantities into digital pulse signals through the encoder to generate the real-time speed value and angular displacement data of each motor as the initial input for the speed closed-loop control, including: Step 1.1: For each motor's encoder output pulse signal, use a high-speed counter to count the rising or falling edges of the pulses in real time and record the precise timestamp of each pulse triggering. At the same time, count the number of pulses within the unit time window and, combined with the encoder's calibrated number of pulses per revolution, calculate the current speed value as the speed feedback. Step 1.2: Measure the time interval between two consecutive pulses, capture the time difference between adjacent pulses through a timer, and calculate the instantaneous pulse period; Step 1.3: Use the accumulator to count the total number of encoder pulses, calculate the angular displacement based on the number of pulses per revolution, convert the number of revolutions into the absolute rotation angle of the motor shaft, and perform differential calculation on the angular displacements of adjacent sampling periods to obtain the incremental angle change. Step 1.4: Align the real-time speed value calculated in step 1.1 with the rotation angle generated in step 1.3 to eliminate timing deviations caused by signal transmission delays. Convert the speed value to a standard value and the angle value to radians. After unifying the dimensions, use them as the initial input for speed closed-loop control.

3. The control method based on differential speed torque distribution according to claim 2, characterized in that: Step 2: Calculate the difference between the initial input and the preset speed command to generate a speed deviation signal and deviation change rate, including: Step 2.1: Based on the normalized standard value output in step 1.4, perform algebraic subtraction between the real-time speed value of each motor and the preset speed command in each control cycle to obtain an instantaneous speed deviation signal; Step 2.2: Perform sliding average filtering on the original speed deviation signal: store the deviation values ​​between the current cycle and the previous N cycles in a cache queue, calculate the weighted average value, and output the filtered speed deviation signal; Step 2.3: Based on the filtered speed deviation signal, perform a differential operation on the deviation value between the current cycle and the previous cycle to obtain the deviation change rate.

4. The control method based on differential speed torque distribution according to claim 3, characterized in that: Step 3: Read the torque compensation coefficient generated in step 2 and the shaft torque values ​​measured by the two torque meters, and calculate the dynamic difference between the current total load torque and the target torque, including: The original shaft torque signals output by the two torque meters are low-pass filtered to obtain the filtered shaft torque values ​​of the two motors. Perform algebraic addition on the filtered shaft torque values ​​of the two motors to obtain the current total load torque value; Adjusting the target torque setting value according to the real-time working condition of the differential to obtain an adjusted target torque; The total load torque is calculated as a difference from the dynamically adjusted target torque to obtain a dynamic difference.

5. The control method based on differential speed torque distribution according to claim 4, characterized in that: Step 4: Couple the frequency adjustment output from step 2 with the torque correction command generated in step 3, eliminate the control dimension difference through normalization, and use a weighted fusion algorithm to generate the final inverter output parameters, including the frequency setting value, voltage amplitude, and phase compensation, including: Step 4.1: Based on the maximum adjustable frequency range of the inverter, convert the original value of the frequency adjustment amount into a percentage value of the maximum frequency range to obtain a dimensionless frequency adjustment proportional coefficient; Step 4.2: Based on the rated torque range of the motor, convert the original value of the torque correction amount into a percentage of the rated torque to obtain the dimensionless torque correction proportional coefficient; Step 4.3: Assign weight factors to the frequency adjustment proportional coefficient and the torque correction proportional coefficient according to the preset frequency control priority and torque compensation priority, respectively; wherein the sum of the frequency adjustment weight factor and the torque correction weight factor is 1; Step 4.4: Multiply the normalized frequency adjustment proportional coefficient by its weight factor to obtain the weighted frequency component; and multiply the normalized torque correction proportional coefficient by its weight factor to obtain the weighted torque component; Step 4.5: Perform algebraic superposition of the weighted frequency component and the weighted torque component to generate comprehensive control parameters; Step 4.6: Based on the frequency setting range of the inverter, convert the frequency-related components in the comprehensive parameters into actual frequency setting values; based on the adjustment range of the motor drive voltage, convert the voltage-related components in the comprehensive parameters into voltage amplitude adjustment values; based on the allowable deviation range of phase compensation, convert the phase-related components in the comprehensive parameters into phase compensation values; Step 4.7: Perform dynamic smoothing filtering on the inverse normalized frequency setting value, voltage amplitude adjustment value, and phase compensation value to generate the final inverter output parameters.

6. The control method based on differential speed torque distribution according to claim 5, characterized in that: Step 5: Send the final inverter output parameters to the corresponding inverter through the real-time controller's communication bus to drive the load motor to execute the new operating state, including: Step 5.1: Encapsulate the frequency setting value, voltage amplitude adjustment value, and phase compensation value generated in step 4.7 into a data frame according to a preset communication protocol format to obtain an encapsulated data frame; Step 5.2: Perform transmission check on the encapsulated data frame: Calculate the cyclic redundancy check code based on the data frame content and append it to the end of the frame; if framed transmission is required, then split the long data packet into packets and add the frame sequence number and total packet number identifier; Step 5.3: Send the data frame to the target inverter via the communication bus interface of the real-time controller at a preset fixed period; if a bus conflict is detected, a backoff algorithm is used to delay and resend; Step 5.4: After receiving the data frame, the inverter performs verification and analysis to obtain the parsed parameters; Step 5.5: Based on the parsed parameters, the frequency setting value is converted into the switching frequency reference of the inverter, the voltage amplitude adjustment amount is mapped into the PWM duty cycle adjustment amount, and the phase compensation amount is converted into the phase offset angle of the three-phase voltage to generate a new PWM drive signal to drive the load motor to operate through the PWM signal.

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