Transmission device synchronous control coordination method and system

Abstract

The invention discloses a synchronous control coordination method and system for a transmission device, particularly relates to the technical field of synchronous control coordination, and aims to establish a complete dynamic model, analyze load disturbance, parameter perturbation and a dual-motor coupling effect, define nonlinear characteristics of the system and calibrate key parameters. Whether a parameter identification algorithm is introduced to update motor parameters online is determined through factors such as temperature change, mechanical wear and magnetic saturation. In order to solve the dynamic performance limitation caused by fixed ADRC controller parameters, a fuzzy neural network is adopted to optimize the controller parameters online. Through a fuzzification layer, a reasoning layer and an output layer, parameters are dynamically adjusted based on motor rotation angle and rotation speed errors, and a gradient descent algorithm is used to optimize network weights. A cross coupling control thought is adopted, a sliding mode speed coordination controller is designed, an improved reaching law is introduced to suppress buffeting and accelerate convergence, finally motor current is compensated, rotation speed synchronization of double motors is coordinated, and synchronization error fluctuation caused by external interference and parameter difference is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of synchronous control and coordination, and more specifically, to a method and system for synchronous control and coordination of a transmission device. Background Art

[0002] A transmission device is a core component in a mechanical system for transmitting power, adjusting the form of motion, and distributing energy. It converts the output of a power source into the form of motion required by a target device and is a key component in fields such as machinery, vehicles, and industrial equipment. Synchronous control and coordination is a control strategy for achieving a high degree of consistency in the motion states of various units in multiple actuators. Its core goal is to eliminate or suppress synchronous errors caused by external disturbances, parameter differences, or coupling effects, and ensure the optimal overall dynamic performance of the system.

[0003] In the prior art, by calibrating key parameters, the changes in the parameters are ignored, resulting in deviations in the model output due to the input parameters deviating from the actual values.

[0004] To address the above deficiencies, a technical solution is provided herein. Summary of the Invention

[0005] In order to overcome the above deficiencies of the prior art, embodiments of the present invention provide a method and system for synchronous control and coordination of a transmission device to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for synchronous control and coordination of a transmission device, comprising the following steps: Based on the physical structure of a dual-motor steer-by-wire system, establish a complete dynamic model, clarify the nonlinear characteristics of the system by analyzing load disturbances, parameter perturbations, and dual-motor coupling effects, and calibrate key parameters; determine whether to introduce a parameter identification algorithm to update the motor parameters online according to temperature changes, mechanical wear, and magnetic saturation; Design a second-order active disturbance rejection controller to integrate the position loop and speed loop of motor control; in view of the limitations of dynamic performance caused by fixed controller parameters of the active disturbance rejection controller, introduce a fuzzy neural network for online optimization; dynamically adjust the parameters with the motor angle error and speed error as inputs; define the dual-motor speed difference and its derivative as state variables, construct a sliding mode surface and introduce an improved reaching law, and derive a sliding mode control law to output a compensation current to the motor current loop.

[0007] In a preferred embodiment, based on the physical structure of a dual-motor steer-by-wire system, establish a complete dynamic model, clarify the nonlinear characteristics of the system by analyzing load disturbances, parameter perturbations, and dual-motor coupling effects, and calibrate key parameters; Obtain the mathematical model in the d-q axis coordinate system: ; Wherein: and are the d-axis and q-axis stator voltages respectively; and are the d-axis and q-axis inductances respectively, ; and are the d-axis and q-axis stator currents respectively; R is the stator resistance; ω is the rotor angular velocity; is the permanent magnet flux linkage; is the electromagnetic torque; P is the number of pole pairs.

[0008] In a preferred embodiment, the control coefficient is calculated by weighted summation according to temperature change, mechanical wear, and magnetic saturation; if the control coefficient is greater than the system preset threshold, a parameter identification algorithm is introduced to update the motor parameters online.

[0009] In a preferred embodiment, online parameter identification and recursive least squares method are introduced; based on the voltage equation (d-q axis) of PMSM: ; The equation is discretized and arranged into a linear regression form: ; where y(k) is the measured output; is the regression matrix; θ is the parameter to be identified; is the noise term; The recursive least squares method updates the parameter estimation value through iteration, and the formula is as follows: ; where represents the parameter estimation value at the current moment; λ is the forgetting factor, used to balance the weights of historical data and new data; P(k) is the covariance matrix, and the initial value is usually set as a diagonal matrix; K(k) represents the Kalman gain matrix.

[0010] In a preferred embodiment, a second-order active disturbance rejection controller is designed to integrate the position loop and speed loop of motor control; the second-order active disturbance rejection controller includes 3 components, namely, a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law.

[0011] In a preferred embodiment, the tracking differentiator realizes the smooth transition and fast response of the input signal by arranging the transition process, minimizing the overshoot and oscillation of the system; the extended state observer observes the system state based on the input and output signals of the controlled object and compensates for the system disturbance; the nonlinear state error feedback control law performs nonlinear processing on the input error signal and outputs a control signal.

[0012] In a preferred embodiment, a fuzzy neural network controller is selected to dynamically adjust and values, based on the motor angular error and motor speed error , As the input, after being calculated by the fuzzy neural network, the output is , The change amount of , , and automatically adjust the parameters , numerical values.

[0013] In a preferred embodiment, based on the cross-coupling control idea, the state variables of the dual-motor system are taken as: ; where: , are the actual rotational speeds of motors 1 and 2 respectively; The state equation of the system is: ; where: ; Combining the following two formulas: ; ; where: k > 0, 0 < α < 1, λ > 0, and sng is the sign function; the output of the sliding mode speed coordination controller is obtained as: ; and the obtained compensation current is fed back to the motor current loop.

[0014] In a preferred embodiment, it includes a system modeling and parameter identification module, an active disturbance rejection control module, a fuzzy neural network optimization module, and a sliding mode synchronization control module; The system modeling and parameter identification module is used to establish a dynamic model including load disturbance, parameter perturbation, and dual-motor coupling effect based on the physical structure of the dual-motor by-wire steering system; and update the motor parameter drift in real time; The system modeling and parameter identification module includes an update control module, and the update control module is used to calculate the control coefficient by weighted summation according to temperature change, mechanical wear, and magnetic saturation, and judge whether to introduce a parameter identification algorithm to update the motor parameters online according to the value of the control coefficient; The active disturbance rejection control module internally includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback; the tracking differentiator is used to smooth the input signal and extract the differential signal to suppress overshoot and oscillation; the extended state observer is used to estimate the internal and external disturbances of the system; the nonlinear state error feedback is used to dynamically adjust the control signal to improve the disturbance rejection ability; The fuzzy neural network optimization module is used to optimize the ADRC parameters online according to the motor angle error and speed error; The sliding mode synchronization control module is used to solve the synchronization error caused by the dynamic inconsistency of the dual-motor and disturbances.

[0015] In a preferred embodiment, the sliding mode synchronization control module internally includes a state variable definition module, an improved reaching law design module, and a current loop compensation module. The state variable definition module is used to quantify the rotational speed difference and synchronization error of the two motors; the improved reaching law design is used to accelerate the reaching speed and suppress chattering; the current loop compensation module is used to inject the compensation current into the motor current loop to quickly synchronize the two motors.

[0016] Technical effects and advantages of the present invention: A synchronization control and coordination method for a transmission device according to the present invention, based on the physical structure of a dual-motor by-wire steering system, establishes a complete dynamic model. By analyzing load disturbances, parameter perturbations, and the coupling effect of the two motors, the nonlinear characteristics of the system are clarified, and key parameters are calibrated; according to temperature changes, mechanical wear, and magnetic saturation, it is determined whether it is necessary to introduce a parameter identification algorithm to update the motor parameters online; avoiding model errors caused by parameter changes and enhancing the accuracy of the model.

[0017] Design a second-order active disturbance rejection controller, which includes three core modules: a tracking differentiator, an extended state observer, and a nonlinear state feedback; integrate the position loop and speed loop of the motor control. Aiming at the dynamic performance limitation caused by the fixed parameters of the ADRC controller, a fuzzy neural network is introduced for online optimization. Taking the motor angle error and speed error as inputs, the parameters are dynamically adjusted through the fuzzyfication layer, fuzzy rule inference layer, and output layer. The gradient descent algorithm is used to optimize the network weights and membership function parameters. Based on the cross-coupling control idea, a sliding mode speed coordination controller is designed to solve the problem of dual-motor synchronization. Define the rotational speed difference and its derivative of the two motors as state variables, construct a sliding mode surface and introduce an improved reaching law to suppress system chattering while accelerating convergence. Deduce the sliding mode control law to output the compensation current to the motor current loop, quickly coordinate the rotational speed synchronization of the two motors, and reduce the synchronization error fluctuation caused by parameter differences or external disturbances. Description of the drawings

[0018] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings; Figure 1 It is a schematic flow chart of a synchronization control and coordination method for a transmission device according to the present invention; Figure 2 It is a schematic structural diagram of a synchronization control and coordination system for a transmission device according to the present invention. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] A synchronous control and coordination method for a transmission device of the present invention is based on the physical structure of a dual-motor by-wire steering system to establish a complete dynamic model. By analyzing load disturbances, parameter perturbations, and the coupling effect of the dual motors, the nonlinear characteristics of the system are clarified, and key parameters are calibrated. Whether to introduce a parameter identification algorithm to update the motor parameters online is determined according to temperature changes, mechanical wear, and magnetic saturation, avoiding model errors caused by parameter changes and enhancing the accuracy of the model.

[0021] Design a second-order active disturbance rejection controller (ADRC), which includes three core modules: a tracking differentiator, an extended state observer, and a nonlinear state feedback. Integrate the position loop and speed loop of the motor control. Aiming at the dynamic performance limitations caused by the fixed parameters of the ADRC controller, a fuzzy neural network is introduced for online optimization. Taking the motor angle error and speed error as inputs, the parameters are dynamically adjusted through a fuzzy layer, a fuzzy rule inference layer, and an output layer. The gradient descent algorithm is used to optimize the network weights and membership function parameters. Based on the cross-coupling control idea, a sliding mode speed coordination controller is designed to solve the dual-motor synchronization problem. Define the speed difference between the dual motors and its derivative as state variables, construct a sliding mode surface, and introduce an improved reaching law to suppress system chattering while accelerating convergence. Deduce the sliding mode control law to output the compensation current to the motor current loop, quickly coordinate the synchronization of the dual-motor speeds, and reduce the synchronization error fluctuations caused by parameter differences or external disturbances.

[0022] Embodiment 1 A synchronous control and coordination method for a transmission device of the present invention, as Figure 1 shown, includes the following steps: Based on the physical structure of the dual-motor by-wire steering system, establish a complete dynamic model. By analyzing load disturbances, parameter perturbations, and the coupling effect of the dual motors, clarify the nonlinear characteristics of the system, and calibrate key parameters. Determine whether to introduce a parameter identification algorithm to update the motor parameters online according to temperature changes, mechanical wear, and magnetic saturation. Design a second-order active disturbance rejection controller, integrate the position loop and speed loop of the motor control. Aiming at the dynamic performance limitations caused by the fixed parameters β1 and β2 of the active disturbance rejection controller, introduce a fuzzy neural network for online optimization. Dynamically adjust the parameters with the motor angle error and speed error as inputs. Define the speed difference between the dual motors and its derivative as state variables, construct a sliding mode surface, and introduce an improved reaching law. Deduce the sliding mode control law to output the compensation current to the motor current loop.

[0023] Specifically; Based on the physical structure of the dual-motor by-wire steering system, establish a complete dynamic model. By analyzing load disturbances, parameter perturbations, and the coupling effect of the dual motors, clarify the nonlinear characteristics of the system, and calibrate key parameters.

[0024] Furthermore, a mathematical model in the d-q axis coordinate system is obtained: ; In the formula: , are the d-axis and q-axis stator voltages respectively; , are the d-axis and q-axis inductances respectively, ; , are the d-axis and q-axis stator currents respectively; R is the stator resistance; ω is the rotor angular velocity; is the permanent magnet flux linkage; is the electromagnetic torque; P is the number of pole pairs.

[0025] Motor parameters will drift due to factors such as temperature change, mechanical wear, and magnetic saturation, resulting in model mismatch and affecting control accuracy; although traditional ADRC has a low dependence on the model, if the parameter error is too large, the disturbance estimation ability of the extended state observer will decline.

[0026] Calculate the control coefficient by weighted summation according to temperature change, mechanical wear, and magnetic saturation; the specific formula is as follows: ; Where K represents the control coefficient; wd represents temperature change, the greater the temperature change, the greater the control coefficient, and vice versa; ms represents mechanical wear, the greater the mechanical wear, the greater the control coefficient, and vice versa; bh represents magnetic saturation, the greater the magnetic saturation, the greater the control coefficient, and vice versa; a, b, and c are the weight coefficients of temperature change, mechanical wear, and magnetic saturation respectively.

[0027] Specifically, an infrared thermal imager can be used for non-contact measurement, and the local overheating area can be located through the thermal image to obtain the temperature change; a piezoelectric accelerometer can be used to detect vibrations in the range of 1 Hz to 10 kHz; mechanical wear can be captured; a Hall sensor can be installed in the air gap or stator teeth to measure the local magnetic flux density.

[0028] If the control coefficient is greater than the system preset threshold, a parameter identification algorithm is introduced to update the motor parameters online; specifically, online parameter identification is introduced, such as the recursive least squares method (RLS); taking the voltage equation of PMSM (d-q axis) as an example: ; Discretize the equation and organize it into a linear regression form: ; Where y(k) is the measured output; is the regression matrix; θ is the parameter to be identified; is the noise term.

[0029] RLS updates the parameter estimation value through iteration, and the formula is as follows: ; Where The parameter estimation value representing the current moment; λ is the forgetting factor used to balance the weights of historical data and new data; P(k) is the covariance matrix, and its initial value is usually set as a diagonal matrix; K(k) represents the Kalman gain matrix.

[0030] Design a second-order active disturbance rejection controller to integrate the position loop and speed loop of motor control; the second-order active disturbance rejection controller consists of three components, namely the tracking differentiator (TD), the extended state observer (ESO), and the nonlinear state error feedback control law (NLSEF).

[0031] The tracking differentiator realizes the smooth transition and fast response of the input signal by arranging the transition process, minimizing the overshoot and oscillation of the system, and its expression is: ; In the formula: is the tracking signal of the input signal v, is 's differential signal; r is the speed factor; h is the precision factor; fhan() is the fastest comprehensive function.

[0032] The extended state observer observes the system state according to the input and output signals of the controlled object and compensates for the system disturbance, and its expression is: ; In the formula: 、 are the tracking observation value and differential observation value of the output signal y respectively; is the observation value of the total disturbance inside and outside the system; 、 、 are error correction factors; is the compensation factor.

[0033] The nonlinear state error feedback control law performs nonlinear processing on the input error signal and outputs the control signal, and its expression is: ; In the formula: 、 are error signals; α is the nonlinear factor; δ is the filtering factor; fal() is the nonlinear function.

[0034] Aiming at the dynamic performance limitation caused by the fixed controller parameters β1 and β2 of the active disturbance rejection controller, a fuzzy neural network is introduced for online optimization; the motor angle error and speed error are used as inputs to dynamically adjust the parameters; the double-motor speed difference and its derivative are defined as state variables, a sliding mode surface is constructed and an improved reaching law is introduced, and the sliding mode control law is derived to output the compensation current to the motor current loop.

[0035] Furthermore, the nonlinear state feedback controller directly outputs the control signal to the controlled object by performing nonlinear processing on the error signal. The control signal directly affects the compensation ability of the controller for the system state error, and its expression is: ; Wherein: parameter , The numerical values of have an important impact on the stability and control accuracy of the system. Setting them too small will weaken the dynamic response ability of the system, and setting them too large will make the system overly sensitive to interference, causing oscillations and thus reducing the stability of the system.

[0036] In addition, when the working state of the system changes, it is difficult to obtain the best control performance with fixed β1 and β2 values; considering the above characteristics, a fuzzy neural network controller is selected to dynamically adjust the numerical values of β1 and β2 to improve the dynamic performance of the system. The controller uses the motor rotation angle error and the motor speed error , As inputs, the changes in β1 and β2 are obtained through fuzzy neural network calculations , , and the numerical values of the parameters β1 and β2 are automatically adjusted.

[0037] The fuzzy neural network consists of four layers of networks, specifically as follows: The input layer contains 2 nodes, which respectively receive the input variables , , and directly output to the next layer without any data processing. The input and output of each node can be expressed as: ; The fuzzification layer contains 7 nodes to realize the fuzzification of each input variable. The fuzzy subsets are set as {PB, PM, PS, ZO, NS, NM, NB}, where , The universes of discourse of are [-13, 13] and [-15, 15] respectively, , The universe of discourse of is [-5, 5]. The Gaussian function is selected as the membership function, and the output of each node is: ; Wherein: , respectively represent the center value and the width value of the membership function.

[0038] The fuzzy inference layer contains 49 nodes to realize the fuzzy inference of each input variable according to the fuzzy rules. Its expression is: ; Wherein: , are the number of nodes in the fuzzification layer; j is the number of nodes in the fuzzy inference layer.

[0039] The output layer contains 2 nodes to realize the defuzzification of each fuzzy quantity, and the output quantities are the changes in β1 and β2 , . ; Wherein: is the connection weight from the fuzzy inference layer to the output layer.

[0040] Next, define the controller performance index function: ; where: r(k) and y(k) respectively represent the input and output of the controller, and in the motor system, they respectively represent the reference rotation angle and the actual rotation angle θ of the motor; r(k) - y(k) represents the error e at the k-th iteration.

[0041] To minimize E, the gradient descent algorithm is selected to correct the network parameters, including the connection weights , the center value of the Gaussian function and the width value . First, calculate the partial derivatives of the objective function with respect to each parameter; then correct according to the gradient optimization algorithm , , . The specific formulas are as follows: ; . Where: η is the learning rate (η ∈ [0, 1]); α is the momentum factor (α ∈ [0, 1]).

[0042] Since the actual parameters of the two motors cannot be exactly the same, and there are factors such as load disturbances during operation, it will cause the problem of asynchronous rotation speeds of the two motors, which will further have an adverse impact on the motor rotation angle tracking accuracy, system service life, energy consumption, etc. Therefore, it is necessary to improve the synchronization performance of the dual-motor system and reduce the synchronization error between the motors. Based on the cross-coupling control idea, this invention designs a sliding-mode speed coordination controller using the sliding-mode control algorithm to solve the asynchronous problem of the dual-motor system; the state variables of the dual-motor system are taken as: ; where: , are the actual rotation speeds of motor 1 and motor 2 respectively.

[0043] The state equation of the system is: ; where: .

[0044] The traditional power reaching law can suppress the chattering of the system to a certain extent, but the speed is slow and the reaching time is long when far from the sliding surface. Adding an exponential reaching term to the traditional power reaching law speeds up the speed of the system approaching the sliding surface and realizes fast convergence.

[0045] Combining the following two formulas: ; ; where: k > 0, 0 < α < 1, λ > 0, sng is the sign function. The output of the sliding-mode speed coordination controller is obtained as: ; Since the response speed of the current loop is much faster than that of the speed loop, feeding the obtained compensation current back to the motor current loop can quickly synchronize the two motors.

[0046] Embodiment 2 A synchronous control and coordination system for a transmission device according to the present invention, as Figure 2 shown, includes the following modules: a system modeling and parameter identification module, an active disturbance rejection control module, a fuzzy neural network optimization module, and a sliding mode synchronization control module; The system modeling and parameter identification module is used to establish a dynamic model including load disturbance, parameter perturbation, and double-motor coupling effect based on the physical structure of the dual-motor by-wire steering system; and update the motor parameter drift in real time; The system modeling and parameter identification module includes an update control module, which is used to calculate the control coefficient by weighted summation according to temperature change, mechanical wear, and magnetic saturation, and judge whether to introduce a parameter identification algorithm to update the motor parameters online according to the value of the control coefficient; The active disturbance rejection control module internally includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback; the tracking differentiator is used to smooth the input signal and extract the differential signal to suppress overshoot and oscillation; the extended state observer is used to estimate the internal and external disturbances of the system; the nonlinear state error feedback is used to dynamically adjust the control signal to improve the disturbance rejection ability; The fuzzy neural network optimization module is used to online optimize the ADRC parameters according to the motor angle error and speed error; The sliding mode synchronization control module is used to solve the synchronization error caused by the dynamic inconsistency of the two motors and disturbances; The sliding mode synchronization control module internally includes a state variable definition module, an improved reaching law design module, and a current loop compensation module. The state variable definition module is used to quantify the speed difference and synchronization error of the two motors; the improved reaching law design is used to accelerate the reaching speed and suppress chattering; the current loop compensation module is used to inject the compensation current into the motor current loop to quickly synchronize the two motors.

[0047] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0048] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0049] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0050] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0051] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A synchronous control and coordination method for a transmission device, characterized in that, It includes the following steps: Based on the physical structure of the dual-motor steer-by-wire system, establish a complete dynamic model. By analyzing load disturbance, parameter perturbation, and dual-motor coupling effect, clarify the system's nonlinear characteristics and calibrate key parameters; Determine whether to introduce a parameter identification algorithm to update the motor parameters online according to temperature change, mechanical wear, and magnetic saturation; Design a second-order active disturbance rejection controller (ADRC) to integrate the position loop and speed loop of motor control; Aiming at the dynamic performance limitation caused by the fixed parameters of the ADRC, introduce a fuzzy neural network for online optimization; Dynamically adjust the parameters with the motor angle error and speed error as inputs; Define the speed difference between the two motors and its derivative as state variables, construct a sliding mode surface and introduce an improved reaching law, and derive the sliding mode control law to output the compensation current to the motor current loop.

2. A synchronous control and coordination method for a transmission device according to claim 1, characterized in that: Based on the physical structure of the dual-motor steer-by-wire system, establish a complete dynamic model. By analyzing load disturbance, parameter perturbation, and dual-motor coupling effect, clarify the system's nonlinear characteristics and calibrate key parameters; Obtain the mathematical model in the d-q axis coordinate system: where: and are the stator voltages on the d-axis and q-axis respectively; and are the inductances on the d-axis and q-axis respectively, ; and are the stator currents on the d-axis and q-axis respectively; R is the stator resistance; ω is the rotor angular velocity; is the permanent magnet flux linkage; is the electromagnetic torque; P is the number of pole pairs.

3. A synchronous control and coordination method for a transmission device according to claim 1, characterized in that: Calculate the control coefficient through weighted summation according to temperature change, mechanical wear, and magnetic saturation; If the control coefficient is greater than the system preset threshold, introduce a parameter identification algorithm to update the motor parameters online.

4. A synchronous control and coordination method for a transmission device according to claim 3, characterized in that: Introduce online parameter identification and recursive least squares method; based on the voltage equation (d-q axis) of PMSM: ; Discretize the equation and organize it into a linear regression form: ; where y(k) is the measured output; is the regression matrix; θ is the parameter to be identified; is the noise term; The recursive least squares method updates the parameter estimation value through iteration, and the formula is as follows: ; where represents the parameter estimation value at the current moment; λ is the forgetting factor, which is used to balance the weights of historical data and new data; P(k) is the covariance matrix, and its initial value is usually set as a diagonal matrix; K(k) represents the Kalman gain matrix.

5. A synchronous control and coordination method for a transmission device according to claim 1, characterized in that: Design a second-order active disturbance rejection controller (ADRC) to integrate the position loop and speed loop of motor control; The second-order ADRC includes three components, namely, a tracking differentiator, an extended state observer, and a nonlinear state error feedback control law.

6. A synchronous control and coordination method for a transmission device according to claim 5, characterized in that: The tracking differentiator realizes the smooth transition and fast response of the input signal by arranging the transition process, minimizing the overshoot and oscillation of the system; The extended state observer observes the system state according to the input and output signals of the controlled object and compensates for the system disturbance; The nonlinear state error feedback control law performs nonlinear processing on the input error signal and outputs a control signal.

7. A synchronous control and coordination method for a transmission device according to claim 1, characterized in that: Select a fuzzy neural network controller to dynamically adjust , the values, using the motor rotation angle error and the motor speed error , as inputs. After calculation by the fuzzy neural network, the output is , the change amount of , , and automatically adjust the parameters , values.

8. A synchronous control and coordination method for a transmission device according to claim 1, characterized in that: Based on the cross-coupling control idea, the state variables of the dual-motor system are taken as: ; where: and are the actual rotational speeds of motor 1 and motor 2 respectively; The state equation of the system is as follows: where: ; Combine the following two formulas: ; ; where: k > 0, 0 < α < 1, λ > 0, sng is the sign function; the output of the sliding mode speed coordination controller is obtained as: ; Feed the obtained compensation current back to the motor current loop.

9. A synchronous control and coordination system for a transmission device, characterized in that: It includes a system modeling and parameter identification module, an active disturbance rejection control module, a fuzzy neural network optimization module, and a sliding mode synchronization control module; The system modeling and parameter identification module is used to establish a dynamic model including load disturbance, parameter perturbation, and dual-motor coupling effect based on the physical structure of the dual-motor steer-by-wire system; Real-time update the motor parameter drift; The system modeling and parameter identification module includes an update control module, which is used to calculate the control coefficient through weighted summation according to temperature change, mechanical wear, and magnetic saturation, and judge whether to introduce a parameter identification algorithm to update the motor parameters online according to the value of the control coefficient; The active disturbance rejection control module internally includes a tracking differentiator, an extended state observer, and a nonlinear state error feedback; The tracking differentiator is used to smooth the input signal and extract the differential signal to suppress overshoot and oscillation; The extended state observer is used to estimate the internal and external disturbances of the system; The nonlinear state error feedback is used to dynamically adjust the control signal to enhance the disturbance rejection ability; The fuzzy neural network optimization module is used to online optimize the ADRC parameters according to the motor angle error and speed error; The sliding mode synchronization control module is used to solve the synchronization error caused by the dynamic inconsistency of the two motors and disturbances.

10. A synchronous control and coordination system for a transmission device according to claim 9, characterized in that: The sliding mode synchronization control module internally includes a state variable definition module, an improved reaching law design module, and a current loop compensation module. The state variable definition module is used to quantify the speed difference and synchronization error of the dual motors; the improved reaching law design is used to accelerate the reaching speed and suppress chattering. The current loop compensation module is used to inject the compensation current into the motor current loop to quickly synchronize the dual motors.

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