A method, device, equipment and medium for torque control of a heavy-duty joint module

Abstract

This invention discloses a torque control method, device, equipment, and medium for heavy-duty joint modules, relating to the field of robot control technology. The method includes: acquiring real-time torque output values ​​and desired torque values ​​to determine the initial torque deviation; evaluating load disturbances based on the deviation to obtain load disturbance compensation; calculating the basic torque control amount by combining the two; acquiring real-time operating temperature; performing gain scheduling compensation on the basic torque control amount to obtain the target torque control amount and outputting it to the drive module to control the output torque. This invention effectively overcomes control model mismatch caused by changes in thermal parameters by introducing temperature gain scheduling compensation, and simultaneously improves anti-interference capability by utilizing load disturbance observation, achieving high-precision and high-stability torque control across the entire temperature range.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to a method, device and medium for torque control of heavy-duty joint modules. Background Technology

[0002] The joint modules of special-purpose androids need to continuously output large torque in complex dynamic tasks such as carrying heavy loads and resisting posture disturbances. The accuracy of torque control directly determines the robot's working ability and operational safety.

[0003] Currently, most existing methods for torque control of heavy-duty joint modules adopt the classic PID feedback control framework from the industrial robot field. However, due to the structural compactness constraints of special-purpose robots, the heat dissipation conditions of the joint modules are extremely demanding. Under continuous high torque output conditions, the temperature rises sharply, leading to time-varying parameters such as increased winding resistance, weakened permanent magnet flux linkage, and altered grease viscosity. These thermally induced parameter changes cause a severe mismatch between the control law designed based on a fixed physical model and the actual controlled object. Existing control strategies lack effective online compensation methods for thermally induced time-varying parameters, and can only take passive measures such as derating protection when the temperature exceeds the threshold. They cannot maintain adaptive matching of the control model throughout the entire operating range of temperature changes, resulting in decreased torque output accuracy, lag in dynamic response, and even system oscillation. Summary of the Invention

[0004] Based on the technical problems existing in the prior art, this invention proposes a torque control method, device and medium for heavy-duty joint modules. By introducing temperature gain scheduling compensation and load disturbance observation, high-precision torque control is achieved across the entire temperature range.

[0005] In a first aspect, embodiments of the present invention provide a torque control method for a heavy-duty joint module, applied to a heavy-duty joint module of an automaton robot. The heavy-duty joint module includes a control module, a drive module, a deceleration module, and an output module. The control module is connected to the drive module, the deceleration module, and the output module respectively. The drive module is connected to the output module through the deceleration module. The drive module is also equipped with multiple temperature sensors. The method includes:

[0006] The control module acquires the real-time torque output value and the desired torque value of the output module, and determines the initial torque deviation based on the desired torque value and the real-time torque output value.

[0007] The real-time load disturbance of the heavy-duty joint module is evaluated based on the initial torque deviation to obtain the load disturbance compensation amount;

[0008] The base torque adjustment amount is calculated based on the initial torque deviation and the load disturbance compensation amount.

[0009] The real-time operating temperature collected by the temperature sensor is obtained, and the gain scheduling compensation of the basic torque control amount is performed according to the real-time operating temperature to obtain the target torque control amount;

[0010] The target torque adjustment amount is output to the drive module to control the drive module to drive the output module to output torque via the deceleration module.

[0011] In some embodiments of the present invention, the control module is further provided with a gain scheduling unit, wherein the step of performing gain scheduling compensation on the basic torque control amount according to the real-time operating temperature to obtain the target torque control amount includes:

[0012] The control module constructs a temperature gradient feature vector based on the winding temperature and shell temperature collected by the multiple temperature sensors.

[0013] The temperature gradient feature vector is input into the gain scheduling unit to obtain the gain scheduling coefficient;

[0014] The target torque control amount is obtained by scaling the base torque control amount proportionally according to the gain scheduling coefficient.

[0015] In some embodiments of the present invention, before inputting the temperature gradient feature vector to the gain scheduling unit, the method further includes:

[0016] Acquire multiple sets of historical operating data of the heavy-duty joint module under calibrated working conditions, wherein the historical operating data includes winding temperature, shell temperature, torque output value and corresponding optimal values ​​of control parameters;

[0017] Based on the historical operating data, a thermodynamic model and a torque output characteristic model of the heavy-duty joint module are established.

[0018] Based on the thermodynamic model and the torque output characteristic model, the input variable of the gain scheduling unit is determined to be the temperature gradient feature vector, and the output variable is the gain scheduling coefficient.

[0019] Based on the input variables and the output variables, the historical operation data is fuzzified using a fuzzy membership function to generate a fuzzy rule library. Each fuzzy rule in the fuzzy rule library is used to characterize the mapping relationship between the temperature gradient feature vector and the gain scheduling coefficient.

[0020] The fuzzy rule base is written into the gain scheduling unit to complete the construction of the gain scheduling unit.

[0021] In some embodiments of the present invention, calculating the base torque adjustment amount based on the initial torque deviation and the load disturbance compensation amount includes:

[0022] The control module adds the initial torque deviation to the load disturbance compensation to obtain the comprehensive deviation.

[0023] The comprehensive deviation is input to the nonlinear state error feedback control law to perform nonlinear combination of the comprehensive deviation and output the basic torque adjustment amount.

[0024] In some embodiments of the present invention, the step of inputting the comprehensive deviation to a nonlinear state error feedback control law to perform a nonlinear combination of the comprehensive deviation includes:

[0025] When the real-time operating temperature is lower than the first temperature threshold, the proportional gain is increased;

[0026] When the real-time operating temperature is higher than the second temperature threshold, the differential gain is reduced.

[0027] In some embodiments of the present invention, obtaining the real-time operating temperature collected by the temperature sensor includes:

[0028] The control module acquires winding temperature signals and housing temperature signals through the multiple temperature sensors;

[0029] The winding temperature signal and the housing temperature signal are filtered and synchronously sampled to obtain the real-time operating temperature.

[0030] In some embodiments of the present invention, before determining the initial torque deviation based on the desired torque value and the real-time torque output value, the method further includes:

[0031] The control module acquires the desired torque value and determines the cutoff frequency of the low-pass filter based on the historical temperature change rate of the heavy-duty joint module.

[0032] The desired torque value is low-pass filtered according to the cutoff frequency to obtain the filtered desired torque value.

[0033] The initial torque deviation is determined based on the filtered expected torque value and the real-time torque output value.

[0034] In a second aspect, embodiments of the present invention provide a torque control device for a heavy-duty joint module, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the torque control method for the heavy-duty joint module as described in the first aspect above.

[0035] Thirdly, embodiments of the present invention provide an electronic device including a torque control device for a heavy-duty joint module as described in the second aspect above.

[0036] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for performing the torque control method for a heavy-duty joint module as described in the first aspect above.

[0037] Compared with the prior art, the present invention provides a torque control method, device and medium for heavy-duty joint modules, which has the following beneficial effects:

[0038] This invention uses multiple temperature sensors to collect winding and shell temperatures in real time, constructs a temperature gradient feature vector, and inputs it into a gain scheduling unit. It then outputs gain scheduling coefficients online that match the current thermal state, proportionally scaling and compensating for the basic torque control. This effectively suppresses the impact of thermally induced parameter drifts caused by temperature increases, such as increased winding resistance, permanent magnet flux decay, and changes in grease viscosity, on torque control accuracy, enabling the control system to maintain adaptive matching across the entire temperature operating range. Furthermore, by establishing a thermodynamic model and a torque output characteristic model through offline calibration and generating a fuzzy rule base based on fuzzy membership functions, the determination of the gain scheduling coefficients does not rely on a precise mathematical model. This achieves data-driven adaptive compensation, reducing engineering implementation difficulty and improving system robustness and reliability. Attached Figure Description

[0039] Figure 1 This is a flowchart of the torque control method for the heavy-duty joint module of the present invention;

[0040] Figure 2 This is a flowchart illustrating how the target torque control amount is obtained by gain compensation of the basic torque control amount according to the present invention.

[0041] Figure 3 This is a flowchart of the process before the temperature gradient feature vector is input to the gain scheduling unit in this invention;

[0042] Figure 4 This is a flowchart illustrating the calculation of the foundation torque adjustment based on the initial torque deviation and load disturbance compensation amount according to the present invention.

[0043] Figure 5 This is a flowchart illustrating the nonlinear combination of the comprehensive deviation in this invention;

[0044] Figure 6 This is a flowchart illustrating the process of acquiring the real-time operating temperature from a temperature sensor, as described in this invention.

[0045] Figure 7 This is a flowchart illustrating the process before determining the initial torque deviation in this invention.

[0046] Figure 8 This is a schematic diagram of the heavy-duty joint module of the present invention;

[0047] Figure 9 A structural diagram of a torque regulation control device for a heavy-duty joint module provided in another embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0050] Reference Figure 8 This invention proposes a torque control method for a heavy-duty joint module, which is applied to the heavy-duty joint module of an embodied robot. The heavy-duty joint module includes a control module 100, a drive module 200, a deceleration module 300, and an output module 400. The control module 100 is connected to the drive module 200, the deceleration module 300, and the output module 400 respectively. The drive module 200 is connected to the output module 400 through the deceleration module 300. The drive module 200 is also equipped with multiple temperature sensors. The control module 100 is equipped with a gain scheduling unit 110.

[0051] It should be noted that the control module 100 first obtains the real-time torque output value and the desired torque value from the output module 400. Based on the difference between the real-time torque output value and the desired torque value, it determines the initial torque deviation to characterize the instantaneous deviation of the current torque output. Subsequently, based on the initial torque deviation, it uses a preset load disturbance evaluation strategy to estimate the real-time load disturbance of the heavy-duty joint module of the embodied robot and obtain the load disturbance compensation amount to offset the influence of nonlinear disturbances such as external impacts and internal friction on the torque output.

[0052] Furthermore, the control module 100 calculates the basic torque adjustment amount based on the initial torque deviation and load disturbance compensation amount to ensure the dynamic response quality of the heavy-duty joint module under strong disturbances. Then, it acquires real-time operating temperatures from multiple temperature sensors, constructs a temperature gradient feature vector, and inputs this vector to the gain scheduling unit 110 located within the control module. The gain scheduling unit 100 has a pre-set mapping relationship between the temperature gradient feature vector and the gain scheduling coefficient. This mapping relationship is based on the thermodynamic model of the joint module and offline calibration of the torque output characteristics, and can output a gain scheduling coefficient matching the temperature condition online based on the input real-time temperature gradient feature vector. Finally, the control module 100 scales the basic torque adjustment amount proportionally according to the gain scheduling coefficient to obtain the temperature-compensated target torque adjustment amount, and outputs the target torque adjustment amount to the drive module 200 to control the drive module 200 to drive the output module 400 to output the desired torque via the deceleration module 300.

[0053] The control method of the present invention will be further described below with reference to the accompanying drawings.

[0054] Reference Figure 1 , Figure 1 This is a flowchart of a torque control method for a heavy-duty joint module provided in an embodiment of the present invention. The torque control method for the heavy-duty joint module includes, but is not limited to, the following steps:

[0055] Step S11: The control module obtains the real-time torque output value and the desired torque value from the output module, and determines the initial torque deviation based on the desired torque value and the real-time torque output value.

[0056] It should be noted that the control module acquires the real-time torque output value of the output module through a torque sensor installed on the output module, and simultaneously receives the desired torque value from the external host computer. The initial torque deviation is obtained by subtracting the desired torque value from the real-time torque output value. The initial torque deviation represents the instantaneous deviation between the current torque output and the desired output, and serves as the basic input for all subsequent control calculations.

[0057] It should be noted that the initial torque deviation is determined based on the expected torque value and the real-time torque output value, and is expressed by the following first formula:

[0058] ;

[0059] in, The initial torque deviation at time t The expected torque value at time t This represents the real-time torque output value at time t.

[0060] Step S12: Evaluate the real-time load disturbance of the heavy-duty joint module based on the initial torque deviation to obtain the load disturbance compensation amount.

[0061] It should be noted that the control module has a built-in preset load disturbance assessment strategy, which is preferably a nonlinear disturbance observer. The control module inputs the initial torque deviation to the nonlinear disturbance observer. Based on the initial torque deviation and the real-time speed and position signals of the heavy-duty joint module, the nonlinear disturbance observer estimates in real time the time-varying disturbances such as external impact loads and internal nonlinear friction currently borne by the heavy-duty joint module, and outputs the load disturbance compensation amount. The load disturbance compensation amount is used to offset the adverse effects of external impacts and internal nonlinear friction on the torque output, so that the subsequent control quantity calculation can be based on a clean deviation signal.

[0062] The control module estimates the real-time load disturbance using a nonlinear disturbance observer to obtain the load disturbance compensation amount. The simplified first-order observer form used in this embodiment is expressed by the following second formula:

[0063] ;

[0064] in, The load disturbance compensation amount at time t For the observer time constant, For the Laplace operator, This represents the initial torque deviation.

[0065] Step S13: Calculate the foundation torque adjustment amount based on the initial torque deviation and load disturbance compensation amount;

[0066] It should be noted that the control module adds the initial torque deviation to the load disturbance compensation to obtain the comprehensive deviation, which is expressed by the following third formula:

[0067] ;

[0068] in, The comprehensive deviation at time t For the initial torque deviation, This is the load disturbance compensation amount.

[0069] The overall deviation is then input into a preset control law model, preferably an active disturbance rejection (AD) control law. The AD control law, through its internal nonlinear state error feedback control law, uses the fal function to nonlinearly combine the overall deviation, outputting the basic torque adjustment, as expressed in the following fourth formula:

[0070] ;

[0071] in, The basic torque adjustment amount at time t, For proportional gain, For differential gain, Nonlinear factor (0 < < 1) For linear interval threshold, This is the derivative of the overall deviation.

[0072] The fal function is defined as follows:

[0073] ,when ;

[0074] ,when ;

[0075] in, These are the input variables for the fal function. It is a symbolic function.

[0076] It should be noted that the basic torque adjustment is a preliminary control command generated based on deviation and disturbance compensation without considering the effect of temperature. It can ensure the dynamic response quality and anti-interference capability of the heavy-duty joint module under strong disturbance.

[0077] Step S14: Obtain the real-time operating temperature collected by the temperature sensor, and perform gain scheduling compensation on the basic torque control amount based on the real-time operating temperature to obtain the target torque control amount.

[0078] It should be noted that multiple temperature sensors are respectively installed at the windings of the drive module and the housing of the reduction module to collect the winding temperature signal and housing temperature signal in real time during the operation of the heavy-duty joint module. The control module filters and synchronously samples the collected winding temperature signal and housing temperature signal to eliminate high-frequency noise and sampling asynchrony error, so as to obtain the real-time operating temperature that can truly reflect the current thermal state of the drive module.

[0079] Furthermore, the control module dynamically adjusts the gain scheduling compensation intensity of the basic torque regulation amount based on the acquired real-time operating temperature, so that the target torque regulation amount obtained after compensation can adapt to the control requirements under different temperature conditions. This effectively overcomes the control model mismatch problem caused by changes in thermal parameters, ensuring that the heavy-duty joint module can maintain high-precision torque output quality and stable dynamic control performance throughout the entire temperature operating range.

[0080] It should be noted that the target torque control amount is obtained by performing gain scheduling compensation on the basic torque control amount based on the real-time operating temperature, and is expressed by the following fifth formula:

[0081] ;

[0082] in, The target torque control amount at time t, The gain scheduling compensation coefficient is determined based on the real-time operating temperature T. It is the basic torque control quantity.

[0083] As a simplified implementation of this embodiment, the gain scheduling compensation coefficient Based on real-time operating temperature Determine the gain scheduling compensation coefficient. The value of is expressed by the following sixth and seventh formulas:

[0084] ,when ;

[0085] ,when ;

[0086] in, For real-time operating temperature, For the preset normal operating temperature threshold, The preset maximum allowable operating temperature threshold, This is the preset temperature compensation slope coefficient.

[0087] Step S15: Output the target torque adjustment amount to the drive module to control the drive module to drive the output module to output torque via the deceleration module.

[0088] It should be noted that the drive module receives the target torque adjustment amount sent by the control module, generates a corresponding PWM drive signal according to the target torque adjustment amount, and outputs the corresponding rotational motion and torque under the control of the PWM drive signal; the rotational motion is decelerated and amplified by the deceleration module and then transmitted to the output module, which drives the external load to output the desired torque.

[0089] The relationship between the duty cycle of the PWM drive signal and the target torque control amount is expressed by the following eighth formula:

[0090] ;

[0091] in, The PWM duty cycle at time t For the preset PWM conversion coefficient, Let t be the target torque control amount at time t.

[0092] Additionally, in one embodiment, reference is made to Figure 2 ,exist Figure 1 Step S14 of the illustrated embodiment involves gain scheduling compensation of the basic torque control amount based on the real-time operating temperature to obtain the target torque control amount, and also includes, but is not limited to, the following steps:

[0093] Step S21: The control module constructs a temperature gradient feature vector based on the winding temperature and shell temperature collected by multiple temperature sensors.

[0094] Step S22: Input the temperature gradient feature vector into the gain scheduling unit to obtain the gain scheduling coefficient;

[0095] Step S23: Scale the base torque control amount proportionally according to the gain scheduling coefficient to obtain the target torque control amount.

[0096] It should be noted that the temperature gradient feature vector incorporates information on the gradient difference between the winding temperature and the shell temperature, as well as the rate of change of the shell temperature, enabling a comprehensive characterization of the internal thermal distribution and thermal change trends of the heavy-duty joint module. In actual operation, the winding, as an active heat source, responds to temperature faster than the shell temperature. The gradient difference between the two directly reflects the severity of the current thermal load and the state of heat conduction, while the rate of change of the shell temperature reflects the trend direction of heat accumulation. The combination of these two features provides more accurate and timely characteristic information for compensating for thermally induced parameter drift.

[0097] Specifically, the temperature gradient feature vector is constructed, as expressed by the following ninth formula:

[0098] ;

[0099] in, For temperature gradient feature vectors, For winding temperature value, For shell temperature value, The rate of change of shell temperature within a preset time window, superscript This indicates the matrix transpose.

[0100] The control module inputs the temperature gradient feature vector to a preset gain scheduling unit. This unit internally stores a mapping relationship between the temperature gradient feature vector and the gain scheduling coefficient, based on the thermodynamic model and offline calibration of the torque output characteristics of the heavy-duty joint module. The gain scheduling unit, based on the input temperature gradient feature vector, outputs a gain scheduling coefficient online that matches the current temperature conditions through its internal inference mechanism. The value of the gain scheduling coefficient dynamically adjusts as the temperature gradient feature vector changes: when the temperature gradient increases or the rate of temperature change accelerates, it indicates that the thermally induced parameter drift is becoming more severe, and the gain scheduling coefficient increases accordingly; when the temperature gradient decreases or the temperature tends to stabilize, the gain scheduling coefficient returns to its nominal value.

[0101] The control module scales the base torque adjustment proportionally to obtain the temperature-compensated target torque adjustment, which is expressed by the following formula (number ten):

[0102] ;

[0103] in, For the target torque control amount, For gain scheduling coefficient, It is the basic torque control quantity.

[0104] Furthermore, the scaling process directly adjusts the amplitude of the control command to compensate for the attenuation or distortion of torque output caused by changes in thermal parameters. Throughout the process, the determination of the gain scheduling coefficient does not depend on the precise mathematical model of the heavy-duty joint module, but rather achieves data-driven adaptive compensation through a preset mapping relationship.

[0105] Additionally, in one embodiment, reference is made to Figure 3 ,exist Figure 2 After constructing the temperature gradient feature vector in step S21 of the illustrated embodiment, and before inputting the temperature gradient feature vector to the gain scheduling unit in step S22, the following steps are included, but are not limited to:

[0106] Step S31: Obtain multiple sets of historical operating data of the heavy-duty joint module under the calibration conditions. The historical operating data includes winding temperature, shell temperature, torque output value and corresponding optimal values ​​of control parameters.

[0107] It should be noted that the calibration conditions cover various typical operating states that heavy-duty joint modules may experience, including but not limited to light-load start-up, continuous operation under rated load, overload impact, frequent acceleration and deceleration, and continuous operation under full load. Each set of historical operating data includes winding temperature, housing temperature, torque output value, and the corresponding optimal values ​​of control parameters. The optimal values ​​of control parameters refer to the combination of control parameters that achieves the best torque control effect under that set of operating conditions, obtained through experimental calibration or optimization algorithms.

[0108] Step S32: Based on historical operating data, establish a thermodynamic model and a torque output characteristic model for the heavy-duty joint module;

[0109] It should be noted that the thermodynamic model is used to characterize the heat transfer law and temperature distribution characteristics of the heavy-duty joint module under different operating conditions, and the torque output characteristic model is used to characterize the mapping relationship between the output torque and control parameters of the heavy-duty joint module under different temperature conditions. By establishing the thermodynamic model and the torque output characteristic model, the design of the subsequent fuzzy rule base can be based on the real physical characteristics of the heavy-duty joint module rather than empirical assumptions.

[0110] The thermodynamic model describes the relationship between winding temperature, shell temperature, operating time, and thermal power, expressed by the following eleventh formula:

[0111] ;

[0112] in, For equivalent heat capacity, For equivalent thermal resistance, For ambient temperature, This is for heat power loss.

[0113] The torque output characteristic model describes the relationship between the output torque and control parameters and temperature, expressed by the following twelfth formula:

[0114] ;

[0115] in, This is the torque output characteristic mapping function. For control parameters, For winding temperature, This refers to the shell temperature.

[0116] Step S33: Based on the thermodynamic model and the torque output characteristic model, determine that the input variable of the gain scheduling unit is the temperature gradient feature vector, and the output variable is the gain scheduling coefficient.

[0117] It's important to note that the thermodynamic model provides not only absolute temperature values, but also extracts spatial and temporal temperature gradient features. This is because the torque output decay of actuators (such as motors and hydraulic valves) depends primarily on uneven temperature distribution (e.g., expansion differences caused by temperature variations, magnetic performance degradation), rather than the absolute temperature at a single point. The temperature gradient feature vector more directly reflects these thermally induced disturbances and is therefore chosen as the input.

[0118] The torque output characteristic model quantitatively describes the attenuation and hysteresis effect of temperature gradient on actual torque. After receiving the current temperature gradient vector, the gain scheduling unit uses the torque output characteristic model to predict the torque deviation that will occur if no compensation is made in real time, and then calculates the gain scheduling coefficient in reverse. The gain scheduling coefficient is positively correlated with the performance loss risk predicted by the temperature gradient. The larger the gradient, the higher the compensation intensity. When the gradient is stable, the coefficient returns to the nominal value, thus completing the quantitative mapping from the physical thermal state to the control gain, thereby maintaining the stability and response quality of the heavy-duty joint module output under variable temperature conditions.

[0119] Step S34: Based on the input and output variables, the historical operation data is fuzzified using the fuzzy membership function to generate a fuzzy rule base. Each fuzzy rule in the fuzzy rule base is used to characterize the mapping relationship between the temperature gradient feature vector and the gain scheduling coefficient.

[0120] It should be noted that for each set of historical operating data, the winding temperature, shell temperature, and shell temperature change rate are mapped to the corresponding fuzzy subset (such as low, medium, high, very high, etc.) through a fuzzy membership function. At the same time, the optimal value of the corresponding control parameter is mapped to the fuzzy subset of the gain scheduling coefficient, thus forming a fuzzy rule. All fuzzy rules together constitute the fuzzy rule library. Each fuzzy rule in the fuzzy rule library is used to characterize the mapping relationship between a temperature gradient feature vector and the gain scheduling coefficient.

[0121] Specifically, this embodiment takes the triangular membership function as an example. The membership function of the j-th fuzzy subset is expressed by the following thirteenth formula:

[0122] ;

[0123] in, Input variables Compared to the first a fuzzy subset The membership value, , , These are the left endpoint, vertex, and right endpoint of the membership function of the triangle, respectively.

[0124] For each set of historical operating data, the winding temperature, shell temperature, and shell temperature change rate are input into the corresponding membership function to obtain the membership values ​​of each input variable under different fuzzy subsets. The corresponding optimal gain scheduling coefficient is then used as the output. The i-th fuzzy rule formed in this way is expressed by the following fourteenth formula:

[0125] ;

[0126] in, For the first A fuzzy rule, , , These are the fuzzy subsets corresponding to winding temperature, shell temperature, and shell temperature change rate, respectively. Let j, k, l, and m be the fuzzy subsets corresponding to the gain scheduling coefficients, and let j, k, l, and m be the index numbers of the corresponding subsets.

[0127] Step S35: Write the fuzzy rule base into the gain scheduling unit to complete the construction of the gain scheduling unit.

[0128] It should be noted that the fuzzy rule base is written into the gain scheduling unit to complete the construction of the gain scheduling unit. The gain scheduling unit constructed through the above steps has the ability to perform fuzzy inference based on the real-time input temperature gradient feature vector and output the corresponding gain scheduling coefficients online, without the need for complex online optimization calculations during operation.

[0129] Specifically, the generated fuzzy rule base is written into the gain scheduling unit to complete the construction. After construction, the gain scheduling unit outputs the gain scheduling coefficients through fuzzy inference based on the real-time input temperature gradient feature vector, and calculates them using the following formula (number fifteen):

[0130] ;

[0131] in, The total number of activated fuzzy rules. For the first The activation strength of each rule (obtained by fuzzy logic operation from the input membership value). For the first The gain scheduling coefficient output by each rule.

[0132] Additionally, in one embodiment, reference is made to Figure 4 ,exist Figure 1 In step S13 of the illustrated embodiment, calculating the foundation torque adjustment amount based on the initial torque deviation and load disturbance compensation amount, the steps include, but are not limited to, the following:

[0133] Step S41: The control module adds the initial torque deviation to the load disturbance compensation to obtain the comprehensive deviation.

[0134] Step S42: Input the comprehensive deviation into the nonlinear state error feedback control law to perform nonlinear combination of the comprehensive deviation and output the basic torque adjustment.

[0135] It should be noted that in step S11, the control module obtains the initial torque deviation by subtracting the desired torque value from the real-time torque output value. However, this initial torque deviation not only includes the true deviation information between the desired torque and the actual output, but also incorporates various load disturbance factors experienced by the heavy-duty joint module during actual operation, including external impact loads, internal nonlinear friction, and changes in inertial force. If the initial torque deviation containing disturbances is directly input into the control law for calculation, the disturbance component will be amplified as part of the deviation signal, leading to unnecessary fluctuations or even oscillations in the control quantity. This embodiment estimates the real-time load disturbance and outputs a load disturbance compensation amount. This load disturbance compensation amount is approximately equal in amplitude to the disturbance component in the initial torque deviation amount but opposite in direction. Therefore, by superimposing the initial torque deviation amount and the load disturbance compensation amount, the control module can effectively cancel out the disturbance component in the initial torque deviation amount, ensuring that only the true deviation information is retained in the comprehensive deviation amount, thus providing a clean input signal for subsequent calculation of the basic torque control amount.

[0136] Heavy-duty joint modules exhibit strong nonlinear dynamic characteristics during actual operation, including friction nonlinearity, flux saturation nonlinearity, and gap nonlinearity. Linear control laws have inherent limitations in handling these nonlinear characteristics, making it difficult to maintain ideal control performance under all operating conditions. Therefore, this embodiment introduces the fal function. The fal function has nonlinear characteristics of small gain for large errors and large gain for small errors. That is, when the error is large, a relatively small gain is used to avoid excessive control quantity leading to system overshoot or even instability; when the error is small, a relatively large gain is used to ensure that the heavy-duty joint module can quickly eliminate residual errors and improve steady-state accuracy. By using the fal function to nonlinearly combine the comprehensive deviation and its derivative, the nonlinear state error feedback control law can dynamically adjust the control gain according to the magnitude of the deviation, so that the control law has good dynamic response characteristics and steady-state control accuracy throughout the entire operating range.

[0137] Additionally, in one embodiment, reference is made to Figure 5 ,exist Figure 4 In step S42 of the illustrated embodiment, the comprehensive deviation is input to the nonlinear state error feedback control law to perform a nonlinear combination of the comprehensive deviation. This step also includes, but is not limited to, the following steps:

[0138] Step S51: When the real-time operating temperature is lower than the first temperature threshold, increase the proportional gain;

[0139] Step S52: When the real-time operating temperature is higher than the second temperature threshold, reduce the differential gain.

[0140] It should be noted that when the real-time operating temperature is below the first temperature threshold, the control module increases the proportional gain of the nonlinear state error feedback control law. The proportional gain determines the instantaneous response strength of the control law to the deviation signal. Under low-temperature conditions, the viscosity of the lubricating grease in the drive module increases, the damping of the mechanical transmission components increases, and the resistance of the motor windings is at a low level. Consequently, the electrical and mechanical time constants of the heavy-duty joint module are relatively small. Under these conditions, appropriately increasing the proportional gain can compensate for the adverse effects of increased system damping on the response speed under low-temperature conditions, ensuring that the dynamic response performance of the heavy-duty joint module does not deteriorate due to increased damping at low temperatures.

[0141] When the real-time operating temperature exceeds the second temperature threshold, the control module reduces the differential gain of the nonlinear state error feedback control law. The differential gain determines the control law's response strength to the rate of change of the deviation signal, primarily affecting the system's damping characteristics and stability. Under high-temperature conditions, the viscosity of the lubricating grease in the drive module decreases, reducing the damping of the mechanical transmission components. Simultaneously, the increased resistance of the motor windings slows the electromagnetic torque response, resulting in a decreasing damping ratio. If the original differential gain remains unchanged, the system may oscillate or even become unstable due to insufficient damping. Therefore, appropriately reducing the differential gain decreases the control law's sensitivity to the rate of change of the deviation, avoiding system oscillations caused by excessive damping compensation, and ensuring stable operation of the torque control system under high-temperature conditions.

[0142] Additionally, in one embodiment, reference is made to Figure 6 ,exist Figure 1 In step S14 of the illustrated embodiment, obtaining the real-time operating temperature collected by the temperature sensor also includes, but is not limited to, the following steps:

[0143] Step S61: The control module acquires winding temperature signals and housing temperature signals through multiple temperature sensors;

[0144] Step S62: Filter and synchronously sample the winding temperature signal and the housing temperature signal to obtain the real-time operating temperature.

[0145] It should be noted that the control module collects winding temperature signals and housing temperature signals separately through multiple temperature sensors. This invention places independent temperature sensors at the windings of the drive module and the housing of the reduction module. The winding temperature reflects the direct thermal state of the active heat source (motor windings), is sensitive to changes in current and load, and has a fast response speed. The housing temperature reflects the overall heat accumulation and heat dissipation state of the joint module, with relatively gradual changes, reflecting the combined effect of heat conduction and thermal balance. Including both in the acquisition range allows for comprehensive acquisition of the thermal state information of the heavy-duty joint module from both the heat source and overall heat accumulation dimensions, providing a rich data foundation for subsequent temperature compensation. The multiple temperature sensors operate independently without interfering with each other, ensuring physical redundancy and reliability of temperature data acquisition.

[0146] Furthermore, the control module filters and synchronously samples the acquired winding temperature signal and housing temperature signal to obtain the real-time operating temperature. Since the raw signal acquired by the temperature sensor inevitably contains high-frequency noise components such as PWM high-frequency switching noise from the drive module, motor electromagnetic interference, and mechanical vibration noise, directly using the raw signal for subsequent temperature compensation calculations would introduce unnecessary control disturbances and reduce compensation accuracy.

[0147] Therefore, the control module first performs low-pass filtering on the winding temperature signal and the housing temperature signal to remove high-frequency noise components and retain the true thermal change trend information. Simultaneously, since the winding temperature sensor and the housing temperature sensor are physically independent, their signal transmission paths and response times may differ. If their sampling times are not synchronized, the subsequently constructed temperature gradient feature vector will be distorted due to the inconsistent time base of the data. Therefore, the control module synchronously samples the filtered winding temperature signal and the housing temperature signal, that is, samples and holds both signals at the same time to ensure that the acquired winding temperature value and housing temperature value are strictly aligned in time. After the above filtering and synchronous sampling processing, the real-time operating temperature that accurately reflects the current joint thermal state is obtained, serving as a reliable input for subsequent temperature compensation.

[0148] Additionally, in one embodiment, reference is made to Figure 7 ,exist Figure 1 In the embodiment shown, after the control module obtains the real-time torque output value and the desired torque value from the output module, and before determining the initial torque deviation based on the desired torque value and the real-time torque output value, the following steps are included, but not limited to:

[0149] Step S71: The control module obtains the desired torque value and determines the cutoff frequency of the low-pass filter based on the historical temperature change rate of the heavy-duty joint module.

[0150] It should be noted that the control module obtains the desired torque value and determines the cutoff frequency of the low-pass filter based on the historical temperature change rate of the heavy-duty joint module, as expressed by the following sixteenth formula:

[0151] ;

[0152] in, This is the cutoff frequency of the low-pass filter. The preset cutoff frequency reference value, This is the preset cutoff frequency adjustment coefficient. These are characteristic values ​​representing the temperature change trend.

[0153] It should be noted that the characteristic value of temperature change trend Calculated based on historical temperature change rate series An increase indicates a faster temperature rise, at which point the cutoff frequency... The lower the value, the better the filtering effect.

[0154] Furthermore, the desired torque value originates from an external host computer or motion planner, which may contain high-frequency components generated by communication noise, quantization errors, or the planning algorithm itself. If these high-frequency components are directly input to the torque control system, they may excite the mechanical resonance mode of the heavy-duty joint module or cause drastic fluctuations in the control input. Simultaneously, during the rapid temperature rise phase, the system's damping and response characteristics drift with temperature changes, and its sensitivity to high-frequency components also alters. Therefore, the cutoff frequency of the low-pass filter needs to have adaptive adjustment capabilities to match the filtering requirements under different temperature conditions.

[0155] Specifically, the historical temperature change rate reflects the temperature change trend of the heavy-duty joint module during recent operation. The control module acquires the temperature change rate sequence within a preset historical time period and determines the temperature change trend characteristic value based on this sequence. When the temperature change trend characteristic value increases, it indicates that the joint module is in a rapid temperature rise phase, and the system parameter drift intensifies. At this time, the control module lowers the cutoff frequency of the low-pass filter to enhance the filtering effect and suppress the adverse excitation of the system by high-frequency command components. When the temperature change trend characteristic value decreases, it indicates that the temperature is stabilizing. At this time, the control module raises the cutoff frequency of the low-pass filter to maintain the system's command tracking response speed. The determination of the above cutoff frequency is based on a preset mapping relationship.

[0156] Step S72: Perform low-pass filtering on the desired torque value according to the cutoff frequency to obtain the filtered desired torque value;

[0157] It should be noted that the control module performs low-pass filtering on the desired torque value based on the determined cutoff frequency, obtaining the filtered desired torque value, which is expressed by the following formula (number seventeen):

[0158] ;

[0159] in, Let t be the desired torque value after filtering. Let be the filtering time constant, and s be the Laplace operator. Let t be the original expected torque value at time t.

[0160] A low-pass filter allows signal components below the cutoff frequency to pass through while attenuating signal components above the cutoff frequency. After low-pass filtering, high-frequency noise and abrupt changes in the desired torque value are effectively suppressed, making the command signal smoother and reducing the impact of command abrupt changes on the control system.

[0161] Step S73: Determine the initial torque deviation based on the filtered expected torque value and the real-time torque output value.

[0162] It should be noted that the control module determines the initial torque deviation based on the filtered desired torque value and the real-time torque output value, as expressed by the following formula (number eighteen):

[0163] ;

[0164] in, for The initial torque deviation at time [time]. for The expected torque value after filtering at time step. for Real-time torque output value at any moment

[0165] The control module calculates the difference between the filtered desired torque value and the real-time torque output value to obtain the initial torque deviation.

[0166] like Figure 9 As shown, Figure 9 This is a structural diagram of a torque control device for a heavy-duty joint module provided in an embodiment of the present invention.

[0167] The present invention also provides a torque control device for a heavy-duty joint module, comprising:

[0168] The processor 901 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0169] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called by the processor 901 to execute the torque control method of the heavy-duty joint module in the embodiments of this application.

[0170] The input / output interface 903 is used to implement information input and output;

[0171] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0172] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0173] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0174] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the torque control method of the above-mentioned heavy-duty joint module.

[0175] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0177] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A torque control method for a heavy-duty joint module, characterized in that, In a heavy-duty joint module applied to an embodied robot, the heavy-duty joint module includes a control module, a drive module, a deceleration module, and an output module. The control module is connected to the drive module, the deceleration module, and the output module respectively. The drive module is connected to the output module through the deceleration module. The drive module is also equipped with multiple temperature sensors. The method includes: The control module acquires the real-time torque output value and the desired torque value of the output module, and determines the initial torque deviation based on the desired torque value and the real-time torque output value. The real-time load disturbance of the heavy-duty joint module is evaluated based on the initial torque deviation to obtain the load disturbance compensation amount. The base torque adjustment amount is calculated based on the initial torque deviation and the load disturbance compensation amount. The real-time operating temperature collected by the temperature sensor is obtained, and the gain scheduling compensation of the basic torque control amount is performed according to the real-time operating temperature to obtain the target torque control amount; The target torque adjustment amount is output to the drive module to control the drive module to drive the output module to output torque via the deceleration module.

2. The torque control method for a heavy-duty joint module according to claim 1, characterized in that, The control module also includes a gain scheduling unit, which performs gain scheduling compensation on the basic torque control amount based on the real-time operating temperature to obtain the target torque control amount, including: The control module constructs a temperature gradient feature vector based on the winding temperature and shell temperature collected by the multiple temperature sensors. The temperature gradient feature vector is input into the gain scheduling unit to obtain the gain scheduling coefficient; The target torque control amount is obtained by scaling the base torque control amount proportionally according to the gain scheduling coefficient.

3. The torque control method for a heavy-duty joint module according to claim 2, characterized in that, Before inputting the temperature gradient feature vector into the gain scheduling unit, the method further includes: Acquire multiple sets of historical operating data of the heavy-duty joint module under calibrated working conditions, wherein the historical operating data includes winding temperature, shell temperature, torque output value and corresponding optimal values ​​of control parameters; Based on the historical operating data, a thermodynamic model and a torque output characteristic model of the heavy-duty joint module are established. Based on the thermodynamic model and the torque output characteristic model, the input variable of the gain scheduling unit is determined to be the temperature gradient feature vector, and the output variable is the gain scheduling coefficient. Based on the input variables and the output variables, the historical operation data is fuzzified using a fuzzy membership function to generate a fuzzy rule base. Each fuzzy rule in the fuzzy rule base is used to characterize the mapping relationship between the temperature gradient feature vector and the gain scheduling coefficient. The fuzzy rule base is written into the gain scheduling unit to complete the construction of the gain scheduling unit.

4. The torque control method for a heavy-duty joint module according to claim 1, characterized in that, The calculation of the base torque adjustment amount based on the initial torque deviation and the load disturbance compensation amount includes: The control module adds the initial torque deviation to the load disturbance compensation to obtain the comprehensive deviation. The comprehensive deviation is input to the nonlinear state error feedback control law to perform nonlinear combination of the comprehensive deviation and output the basic torque adjustment amount.

5. The torque control method for a heavy-duty joint module according to claim 4, characterized in that, The step of inputting the comprehensive deviation into a nonlinear state error feedback control law to perform a nonlinear combination of the comprehensive deviation includes: When the real-time operating temperature is lower than the first temperature threshold, the proportional gain is increased; When the real-time operating temperature is higher than the second temperature threshold, the differential gain is reduced.

6. The torque control method for a heavy-duty joint module according to claim 1, characterized in that, The process of acquiring the real-time operating temperature collected by the temperature sensor includes: The control module acquires winding temperature signals and housing temperature signals through the multiple temperature sensors; The winding temperature signal and the housing temperature signal are filtered and synchronously sampled to obtain the real-time operating temperature.

7. The torque control method for a heavy-duty joint module according to claim 1, characterized in that, Before determining the initial torque deviation based on the expected torque value and the real-time torque output value, the method further includes: The control module acquires the desired torque value and determines the cutoff frequency of the low-pass filter based on the historical temperature change rate of the heavy-duty joint module. The desired torque value is low-pass filtered according to the cutoff frequency to obtain the filtered desired torque value. The initial torque deviation is determined based on the filtered expected torque value and the real-time torque output value.

8. A torque control device for a heavy-duty joint module, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the torque control method for the heavy-duty joint module as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, Includes the torque control device for the heavy-duty joint module as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the torque control method for a heavy-duty joint module as described in any one of claims 1 to 7.

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