Torque distribution method and system based on error driving, terminal equipment and medium

Abstract

The invention provides a torque distribution method and system based on error driving, terminal equipment and a medium. The method comprises the steps that the acceleration error and the error change rate of a target vehicle at the current moment are acquired; determining the acceleration error and the error change rate as feedback signals, inputting the feedback signals into a preset fuzzy control system, and performing fuzzy reasoning to obtain PID control parameters; the PID control parameters are utilized, and the torque increment at the current moment is calculated through a PID algorithm; obtaining a target torque at a previous moment, superposing the target torque at the previous moment and the torque increment at the current moment, and calculating to obtain a target torque at the current moment; and according to the target torque at the current moment, torque distribution control of the target vehicle is achieved. According to the method, decoupling of torque distribution and vehicle inherent parameters and environment parameters is achieved, and dependence on sensor configuration and complex model calibration can be effectively reduced.

Description

Technical Field

[0001] This invention application relates to the field of driver assistance, and more particularly to an error-driven torque distribution method, system, terminal device, and medium. Background Technology

[0002] In the longitudinal control of assisted driving, actuator configurations include various forms, such as acceleration actuator + braking torque actuator, acceleration actuator + deceleration actuator, drive torque actuator + deceleration actuator, and drive torque actuator + braking torque actuator. Among them, the drive torque actuator + braking torque actuator configuration is one of the mainstream configurations in electric vehicles because both drive and braking are controlled by torque signals, offering wide adaptability and high potential control accuracy.

[0003] Acceleration and torque conversion are crucial components of torque signal control. However, current technologies often employ model-based solutions based on dynamic mechanisms for acceleration and torque conversion. These solutions rely on accurate vehicle dynamics models, which require inputting numerous inherent vehicle parameters such as vehicle mass, frontal area, and drag coefficient. Furthermore, they necessitate the acquisition of complex operating condition information, such as slope gradient and road surface adhesion coefficient, through sensors. Consequently, the accuracy of the system's torque distribution is highly dependent on the sensor configuration. Summary of the Invention

[0004] This invention application provides an error-driven torque distribution method, system, terminal device, and medium to solve the technical problem of how to reduce the dependence of torque distribution accuracy on sensor configuration.

[0005] To address the aforementioned technical problems, this invention provides an error-driven torque distribution method, comprising: Obtain the acceleration error of the target vehicle at the current moment; obtain the rate of change of the error of the target vehicle at the current moment; The acceleration error and the rate of change of the error are determined as feedback signals, and the feedback signals are input into a preset fuzzy control system and fuzzy inference is performed to obtain PID control parameters. Using the PID control parameters, the torque increment at the current moment is calculated using the PID algorithm; the target torque at the previous moment is obtained, and the target torque at the previous moment and the torque increment at the current moment are superimposed to calculate the target torque at the current moment; Based on the target torque at the current moment, torque distribution control of the target vehicle is achieved.

[0006] As a preferred embodiment, the step of inputting the feedback signal into a preset fuzzy control system and performing fuzzy inference to obtain PID control parameters includes: The feedback signal is input into a preset fuzzy control system and fuzzified to obtain the error fuzzy variable and the error rate of change fuzzy variable, respectively. According to the preset fuzzy control rule table, fuzzy inference is performed on the error fuzzy variable and the error change rate fuzzy variable to obtain the output fuzzy variables corresponding to the proportional coefficient, integral coefficient and differential coefficient; The output fuzzy variables are defuzzified using the centroid method to calculate estimated values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, which are then determined as the PID control parameters.

[0007] As a preferred embodiment, the step of using the PID control parameters to calculate the torque increment at the current moment through a PID algorithm specifically involves: The torque increment at the current moment is calculated using the following formula: ; Among them, T increment (k) represents the torque increment at the current moment. The acceleration error at the current moment, The acceleration error at the previous moment is given by K, where n refers to the nth moment. p K is an estimated value of the proportionality coefficient. i K represents the number of estimated values ​​for the integral coefficients. d K is an estimate of the differential coefficients. p_ori K is the calibration value used to estimate the scaling factor. i_ori K is the calibration value used to scale the estimated values ​​of the integral coefficients. d_ori This is the calibration value used to estimate the scaling factor.

[0008] As a preferred embodiment, obtaining the acceleration error of the target vehicle at the current moment includes: Determine the current motion state of the target vehicle; Based on the motion state, a velocity estimate is obtained; based on the velocity estimate, a differential is performed to obtain the actual acceleration of the target vehicle at the current moment; Obtain the target vehicle's requested acceleration at the current moment, and calculate the acceleration error at the current moment based on the actual acceleration and the requested acceleration.

[0009] As a preferred embodiment, obtaining the speed estimate based on the motion state includes: When the motion state is a driving state, the first estimated wheel speed of the target vehicle is obtained by the minimum wheel speed method. When the motion state is braking, the second estimated wheel speed of the target vehicle is obtained by the maximum wheel speed method. The average of the first estimated wheel speed and the second estimated wheel speed is determined as the speed estimate of the target vehicle.

[0010] As a preferred embodiment, obtaining the error change rate of the target vehicle at the current moment includes: Obtain the target vehicle's actual acceleration at the previous moment; The error change rate of the target vehicle at the current moment is obtained by performing differential processing based on the actual acceleration at the previous moment and the actual acceleration at the current moment.

[0011] As a preferred embodiment, the step of implementing torque distribution control of the target vehicle based on the target torque at the current moment includes: When the target torque at the current moment is greater than zero, the drive torque actuator is controlled according to the target torque at the current moment to realize the torque distribution control of the target vehicle; When the target torque at the current moment is less than zero, the braking torque actuator is controlled according to the target torque at the current moment to realize the torque distribution control of the target vehicle.

[0012] Accordingly, this application also provides an error-driven torque distribution system, including an acquisition module, a fuzzy inference module, a calculation module, and a control module; wherein, The acquisition module is used to acquire the acceleration error of the target vehicle at the current moment; and to acquire the error change rate of the target vehicle at the current moment. The fuzzy inference module is used to determine the acceleration error and the rate of change of error as feedback signals, and input the feedback signals into a preset fuzzy control system and perform fuzzy inference to obtain PID control parameters. The calculation module is used to calculate the torque increment at the current moment using the PID control parameters and the PID algorithm; obtain the target torque at the previous moment, and superimpose the target torque at the previous moment and the torque increment at the current moment to calculate the target torque at the current moment. The control module is used to implement torque distribution control of the target vehicle based on the target torque at the current moment.

[0013] As a preferred embodiment, the fuzzy inference module inputs the feedback signal into a preset fuzzy control system and performs fuzzy inference to obtain PID control parameters, including: The fuzzy inference module inputs the feedback signal into a preset fuzzy control system and performs fuzzification processing to obtain the error fuzzy variable and the error rate of change fuzzy variable, respectively. According to the preset fuzzy control rule table, fuzzy inference is performed on the error fuzzy variable and the error change rate fuzzy variable to obtain the output fuzzy variables corresponding to the proportional coefficient, integral coefficient and differential coefficient; The output fuzzy variables are defuzzified using the centroid method to calculate estimated values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, which are then determined as the PID control parameters.

[0014] As a preferred embodiment, the calculation module uses the PID control parameters to calculate the torque increment at the current moment through a PID algorithm, specifically: The calculation module calculates the torque increment at the current moment according to the following formula: ; Among them, T increment (k) represents the torque increment at the current moment. The acceleration error at the current moment, The acceleration error at the previous moment is given by K, where n refers to the nth moment. p K is an estimated value of the proportionality coefficient. i K represents the number of estimated values ​​for the integral coefficients. d K is an estimate of the differential coefficients. p_ori K is the calibration value used to estimate the scaling factor. i_ori K is the calibration value used to scale the estimated values ​​of the integral coefficients. d_ori This is the calibration value used to estimate the scaling factor.

[0015] As a preferred embodiment, the acquisition module acquires the acceleration error of the target vehicle at the current moment, including: The acquisition module determines the current motion state of the target vehicle; Based on the motion state, a velocity estimate is obtained; based on the velocity estimate, a differential is performed to obtain the actual acceleration of the target vehicle at the current moment; Obtain the target vehicle's requested acceleration at the current moment, and calculate the acceleration error at the current moment based on the actual acceleration and the requested acceleration.

[0016] As a preferred embodiment, the acquisition module obtains a speed estimate based on the motion state, including: When the motion state is a driving state, the acquisition module uses the minimum wheel speed method to obtain the first estimated wheel speed of the target vehicle; When the motion state is braking, the acquisition module uses the maximum wheel speed method to obtain the second estimated wheel speed of the target vehicle; The acquisition module determines the average of the first estimated wheel speed and the second estimated wheel speed as the speed estimate of the target vehicle.

[0017] As a preferred embodiment, the acquisition module acquires the error change rate of the target vehicle at the current moment, including: The acquisition module acquires the actual acceleration of the target vehicle at the previous moment; The error change rate of the target vehicle at the current moment is obtained by performing differential processing based on the actual acceleration at the previous moment and the actual acceleration at the current moment.

[0018] As a preferred embodiment, the control module implements torque distribution control of the target vehicle based on the target torque at the current moment, including: When the target torque at the current moment is greater than zero, the control module controls the drive torque actuator according to the target torque at the current moment to realize the torque distribution control of the target vehicle; When the target torque at the current moment is less than zero, the control module controls the braking torque actuator according to the target torque at the current moment to realize the torque distribution control of the target vehicle.

[0019] Accordingly, this application also provides a terminal device, characterized in that it includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the error-driven torque distribution method described in any of the above embodiments.

[0020] Accordingly, this application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the error-driven torque distribution method described in any of the above embodiments.

[0021] Compared with the prior art, this invention application has the following beneficial effects: This invention provides an error-driven torque distribution method, system, terminal device, and medium. The torque distribution method includes: acquiring the acceleration error of a target vehicle at the current moment; acquiring the error change rate of the target vehicle at the current moment; determining the acceleration error and the error change rate as feedback signals, and inputting the feedback signals into a preset fuzzy control system for fuzzy inference to obtain PID control parameters; using the PID control parameters, calculating the torque increment at the current moment through a PID algorithm; acquiring the target torque at the previous moment, and superimposing the target torque at the previous moment and the torque increment at the current moment to calculate the target torque at the current moment; and realizing torque distribution control of the target vehicle based on the target torque at the current moment. This invention uses acceleration error and error rate of change as control feedback signals, obtains PID control parameters through a fuzzy control system, and then determines the target torque at the current moment to achieve longitudinal control of the target vehicle. This decouples torque distribution from inherent vehicle parameters (such as vehicle mass, drag coefficient, and frontal area) and environmental parameters (such as slope gradient), reducing dependence on sensor configuration and complex model calibration, and significantly lowering hardware and development costs. Furthermore, fuzzy control significantly enhances the robustness of the target vehicle to complex operating conditions and model parameter disturbances. In addition, this application uses a pre-set fuzzy control system to obtain PID control parameters and calculates the torque increment at the current moment through a PID algorithm, overcoming the limitations of traditional fixed-parameter PID in nonlinear vehicle dynamics control. Acceleration error provides the control amplitude basis, and error rate of change provides the correction rate and correction direction basis. The combination of these two achieves dual-dimensional control of acceleration error amplitude and rate of change. The introduction of the fuzzy control system enables the target vehicle to adapt to changes in operating conditions, effectively suppressing overshoot and oscillation, and improving the speed and stability of dynamic response. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an embodiment of the error-driven torque distribution method provided in this invention application.

[0023] Figure 2 This is a flowchart illustrating a preferred embodiment of the error-driven torque distribution method provided in this invention application.

[0024] Figure 3 A fuzzy surface diagram illustrating an application example of the scaling factor provided in this invention application.

[0025] Figure 4 A fuzzy surface diagram illustrating an application example of the integral coefficients provided in this invention application.

[0026] Figure 5This is a fuzzy surface diagram illustrating an application example of the differential coefficients provided in this invention application.

[0027] Figure 6 This is a flowchart illustrating an embodiment of the error-driven torque distribution system provided in this invention application. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 Please refer to Figure 1 , Figure 1 This invention application provides a torque distribution method based on error-driven principles, comprising steps S101 to S104; each step is detailed below: Step S101: Obtain the acceleration error of the target vehicle at the current moment; obtain the error change rate of the target vehicle at the current moment.

[0030] In this step, the acceleration error of the target vehicle at the current moment can be calculated based on the actual acceleration at the current moment and the requested acceleration at the current moment.

[0031] In some embodiments, the requested acceleration can be determined based on the target vehicle's status using existing methods. This vehicle status can be obtained through an ADAS system that uses modules such as cameras / radar to perceive and analyze the target vehicle's surrounding environment.

[0032] In some preferred embodiments, step S101, obtaining the acceleration error of the target vehicle at the current moment, includes: determining the motion state of the target vehicle at the current moment; obtaining a speed estimate based on the motion state; performing differentiation processing based on the speed estimate to obtain the actual acceleration of the target vehicle at the current moment; obtaining the requested acceleration of the target vehicle at the current moment; and calculating the acceleration error at the current moment based on the actual acceleration and the requested acceleration.

[0033] Currently, existing technologies for acceleration estimation typically use multiple input sources, including accelerometers and vehicle speed sensors, and employ Kalman filtering algorithms to estimate vehicle speed and acceleration. It's important to note that these algorithms cannot eliminate the influence of road slope. Therefore, this implementation method filters and differentiates the estimated vehicle speed value to obtain the actual acceleration (estimated value) unaffected by road slope.

[0034] In some preferred embodiments, the motion state of the target vehicle can be divided into a driving state and a braking state. Obtaining a speed estimate based on the motion state includes: when the motion state is driving, obtaining a first estimated wheel speed of the target vehicle using the minimum wheel speed method; when the motion state is braking, obtaining a second estimated wheel speed of the target vehicle using the maximum wheel speed method; and determining the average of the first estimated wheel speed and the second estimated wheel speed as the speed estimate of the target vehicle.

[0035] It should be noted that speed estimation is more accurate when the vehicle is unstable, by differentiating between braking and driving conditions and using the maximum / minimum wheel speed method. Therefore, driving and braking states are theoretically methods to determine the first and second estimated wheel speeds separately, taking into account the effects of wheel slippage (during driving) and skid (during braking) on ​​wheel speed, respectively.

[0036] However, considering that if the target vehicle is under ADAS system control, the longitudinal control function of the assisted driving or autonomous driving will automatically disengage when the target vehicle becomes unstable, and other high-priority vehicle stability algorithms (such as ABS / ESP / TCS) will intervene, so the overall operating condition will be relatively stable. Taking this into account, the step of determining the average of the first estimated wheel speed and the second estimated wheel speed as the speed estimate of the target vehicle is an engineering simplification method, which is suitable for the relatively stable vehicle situation in ADAS control scenarios, and can effectively reduce the complexity of the overall algorithm.

[0037] In some preferred embodiments, the target vehicle may be equipped with wheel speed sensors to monitor the wheel speeds of the four wheels in order to determine whether the target vehicle is in a driving or braking state at the current moment.

[0038] When the motion state is a driving state, the minimum wheel speed method can be used to obtain the first estimated wheel speed of the target vehicle: ; in, For the first estimated wheel speed, The speed of the left front wheel. Right front wheel speed, Left rear wheel speed, Right rear wheel speed, The radius is the wheel radius.

[0039] When the motion state is braking, the second estimated wheel speed of the target vehicle can be obtained using the maximum wheel speed method: in This is the second estimated wheel speed.

[0040] From the first and second estimated wheel speeds described above, the estimated speed of the target vehicle is obtained: ; in, This is the estimated speed of the target vehicle.

[0041] After obtaining the speed estimate, the actual acceleration can be obtained through differentiation. Since this acceleration value directly corresponds to the wheel speed, this estimate is the actual vehicle acceleration value and does not include information such as the vehicle's gradient-equivalent acceleration. Therefore, the aforementioned actual acceleration... for: ; After discretization, it is represented as: ; in, Let K be the actual acceleration of the target vehicle at time k (or at time step k). This is the velocity estimate at time k. dt is the speed estimate at time k-1, and dt is the module scheduling period.

[0042] Furthermore, combining the requested acceleration mentioned above, the acceleration error can be obtained: ; In the formula, For acceleration error, To request acceleration.

[0043] If only acceleration error is considered, the system will be in an oscillating state when it is about to reach the control target, and it will be impossible to predict the evolution trend of the deviation in advance, which will easily lead to overshoot. It is not sensitive to transient changes and cannot capture the inflection point (overshoot point) of the system's dynamic response. Therefore, this embodiment differentiates the acceleration error to obtain another control feedback, namely the error change rate.

[0044] Furthermore, obtaining the error change rate of the target vehicle at the current moment includes: obtaining the actual acceleration of the target vehicle at the previous moment; and performing differential processing based on the actual acceleration at the previous moment and the actual acceleration at the current moment to obtain the error change rate of the target vehicle at the current moment.

[0045] For example, the rate of change of error can be expressed as: ; In the formula, This represents the rate of change of the aforementioned error.

[0046] After discretization, it is represented as: ; in Let be the rate of change of error at time k. Let the acceleration error be at time k. This represents the acceleration error at time k-1.

[0047] The acceleration error value provides the control amplitude basis for subsequent control, while the differential value of the acceleration error provides the correction rate and correction direction basis for control. By combining the two, "amplitude-rate" dual-dimensional control can be achieved, ensuring steady-state accuracy and optimizing dynamic response.

[0048] Step S102: The acceleration error and the rate of change of the error are determined as feedback signals, and the feedback signals are input into a preset fuzzy control system and fuzzy inference is performed to obtain PID control parameters.

[0049] In this step, the feedback signal is used as the input to the fuzzy control system, and the PID control parameters output by the fuzzy control system are input to the PID algorithm in the subsequent step S102 to obtain the torque increment at the current moment, which can achieve the purpose of adaptive adjustment.

[0050] In some preferred embodiments, such as Figure 2 As shown, step S102, which involves inputting the feedback signal into a preset fuzzy control system and performing fuzzy inference to obtain PID control parameters, includes steps S201 to S203; each step is detailed below: Step S201: Input the feedback signal into a preset fuzzy control system and perform fuzzification processing to obtain the error fuzzy variable and the error rate of change fuzzy variable, respectively. Step S202: According to the preset fuzzy control rule table, perform fuzzy inference on the error fuzzy variable and the error change rate fuzzy variable to obtain the output fuzzy variables corresponding to the proportional coefficient, integral coefficient and differential coefficient; Step S203: The output fuzzy variable is defuzzified using the centroid method to calculate the estimated values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, which are then determined as the PID control parameters.

[0051] In some examples, the universes of discourse for the acceleration error and the rate of change of the aforementioned input can be respectively... and Mpss and Mpsss are the units of acceleration and rate of change of acceleration, respectively, and can also be expressed as meters per second squared and meters per second cubic.

[0052] After fuzzifying the acceleration error, we get: smaller Si, zero Zi, larger Li.

[0053] After fuzzifying the error rate of change, we get: decrease Di, keep Mi, and increase Ii.

[0054] Among them, Si, Zi, Li, Di, Mi and Ii are the names of the universe of discourse after the input variables are fuzzified, and can also be called fuzzy variables.

[0055] In terms of physical meaning, Si, Zi, and Li divide the acceleration error into three intervals, corresponding to the small error, zero error, and large error intervals, respectively; correspondingly, Di, Mi, and Ii divide the rate of change of error into three intervals, corresponding to the decreasing, maintaining, and increasing intervals, respectively.

[0056] In fuzzy control systems, the fuzzified domain and functional relationships are the core bridge connecting precise input quantities and fuzzy control rules, realizing the mapping from precise quantities to fuzzy quantities and adapting to fuzzy inference logic.

[0057] The core of fuzzy control is based on fuzzy rules of natural language (such as increasing the control quantity when the error is large), while the inputs collected by the system (such as acceleration error and error derivative) are precise values.

[0058] Defining the universe of discourse and membership functions (such as trigonometric functions or Gaussian functions) can transform precise input values ​​into membership degrees of corresponding fuzzy sets (such as Si, Zi, Li, Di, Mi, and Ii mentioned above), allowing precise data to be recognized and processed by fuzzy rules.

[0059] The domain of discourse divides the range of input variables by discretization or continuousization, mapping the infinite precise numerical space to a finite fuzzy set space. This process significantly reduces the number of fuzzy rules, eliminates the need to formulate rules for each precise value, simplifies the design and maintenance of the rule base, and improves the real-time performance of the control system.

[0060] The shape of the membership function (e.g., a gentle Gaussian function, a steep trigonometric function) determines the rate of change of the membership degree as the input changes. For example: A smooth membership function is less sensitive to input noise and can enhance the system's anti-interference ability; A steep membership function can improve the system's sensitivity to changes in input.

[0061] By combining a reasonable range of the domain of discourse, the robustness and response speed of the system can be balanced, avoiding frequent jitter of the control output due to small fluctuations in the input.

[0062] The domain normalizes input variables with different dimensions to a unified numerical range, while the membership function transforms them into dimensionless membership values, ensuring that multiple input variables can participate in fuzzy inference within the same logical framework and output consistent fuzzy control quantities.

[0063] As described above, the effective range of variables is defined by the domain of discourse, and the membership function defines the degree to which a variable belongs to a fuzzy set. The combination of these two provides the necessary input premises for the reasoning process of fuzzy control.

[0064] The fuzzy control rule tables mentioned above can be shown in Tables 1 to 5 below: Table 1 shows the relationship between input variables, subdomains, and membership functions after fuzzification. Since this fuzzy control system incorporates a PID algorithm for adaptive control, it has three outputs, with a defined output range of (-1, 1), which serve as subsequent control inputs.

[0065] Table 2 shows the relationship between output variables, subdomains, and membership functions after fuzzification. Table 3. Input and output fuzzy rule design for proportional coefficients. Table 4. Input and output fuzzy rule design for integral coefficients. Table 5. Input and output fuzzy rule design for differential coefficients. Furthermore, the fuzzy surface designs for the proportional coefficient, integral coefficient, and differential coefficient are respectively as follows: Figures 3 to 5 As shown in the figure, the coordinate axes err and err-dot refer to the acceleration error and the rate of change of error, respectively.

[0066] This implementation method uses the centroid method to defuzzify the output fuzzy variables, calculates the estimated values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, and determines these estimated values ​​as PID control parameters.

[0067] Step S103: Using the PID control parameters, calculate the torque increment at the current moment using the PID algorithm; obtain the target torque at the previous moment, and superimpose the target torque at the previous moment and the torque increment at the current moment to calculate the target torque at the current moment.

[0068] In this embodiment, the PID algorithm can be expressed as follows: ; Among them, K p K i and K dLet e(k) be the proportional coefficient, integral coefficient, and derivative coefficient of the error, respectively; let n be the control error at time k; let n be the control error at time n; and let u(k) be the control output at time k.

[0069] In some embodiments, to prevent the risk of integral explosion, the number of times the integral term is accumulated can be limited to the accumulated value of the last 20 periods. Therefore, the optimized formula is: ; Furthermore, considering engineering parameter tuning, a scaling factor can be added. , and To achieve parameter tuning of the proportional, integral, and derivative coefficients, the scaling factors mentioned above can be taken as calibration values. The calculation formula for the further optimized PID algorithm is then: ; In some preferred embodiments, the acceleration error can be substituted into the control errors e(k) and e(k-1) to calculate the torque increment at the current moment according to the following formula: ; Among them, T increment (k) represents the torque increment at the current moment. The acceleration error at the current time (time k) is... The acceleration error at the previous moment is given by K, where n refers to the nth moment. p K is an estimated value of the proportionality coefficient. i K represents the number of estimated values ​​for the integral coefficients. d K is an estimate of the differential coefficients. p_ori K is the calibration value used to estimate the scaling factor. i_ori K is the calibration value used to scale the estimated values ​​of the integral coefficients. d_ori This is the calibration value used to estimate the scaling factor.

[0070] Furthermore, the target torque from the previous moment can be superimposed with the torque increment at the current moment to calculate the target torque at the current moment: ; In the formula, The target torque at the current moment, This represents the target torque at the previous moment.

[0071] This method can determine that there is only one increase or decrease in the amount replenished at each time point, T. increment(k) is the torque increment. Since the main part is the target torque of the previous moment, it can ensure that the target torque of the current moment is not significantly abrupt and is always closely related to the previous state, thus ensuring good consistency of the control results.

[0072] Step S104: Based on the target torque at the current moment, implement torque distribution control for the target vehicle.

[0073] In this embodiment, different controllers can be activated based on the current value of the target torque (e.g., positive or negative) to achieve torque distribution control of the target vehicle.

[0074] For example, step S104, which involves implementing torque distribution control of the target vehicle based on the target torque at the current moment, includes: when the target torque at the current moment is greater than zero, controlling the drive torque actuator based on the target torque at the current moment to implement torque distribution control of the target vehicle.

[0075] Alternatively, when the target torque at the current moment is less than zero, the braking torque actuator is controlled according to the target torque at the current moment to achieve torque distribution control of the target vehicle.

[0076] When the target torque at the current moment is close to zero, it indicates that the vehicle is in a state of force balance. If there is a slope at this time, the target torque at the previous moment will not be zero, but the torque increment will be close to zero. At this time, the vehicle can maintain a stable state.

[0077] Accordingly, such as Figure 6 As shown, this application also provides an error-driven torque distribution system 600, including an acquisition module 601, a fuzzy inference module 602, a calculation module 603, and a control module 604; wherein, The acquisition module 601 is used to acquire the acceleration error of the target vehicle at the current moment; and to acquire the error change rate of the target vehicle at the current moment. The fuzzy inference module 602 is used to determine the acceleration error and the error change rate as feedback signals, and input the feedback signals into a preset fuzzy control system and perform fuzzy inference to obtain PID control parameters. The calculation module 603 is used to calculate the torque increment at the current moment using the PID control parameters and the PID algorithm; obtain the target torque at the previous moment; and superimpose the target torque at the previous moment and the torque increment at the current moment to calculate the target torque at the current moment. The control module 604 is used to implement torque distribution control of the target vehicle based on the target torque at the current moment.

[0078] As a preferred embodiment, the fuzzy inference module 602 inputs the feedback signal into a preset fuzzy control system and performs fuzzy inference to obtain PID control parameters, including: The fuzzy inference module 602 inputs the feedback signal into the preset fuzzy control system and performs fuzzification processing to obtain the error fuzzy variable and the error change rate fuzzy variable, respectively. According to the preset fuzzy control rule table, fuzzy inference is performed on the error fuzzy variable and the error change rate fuzzy variable to obtain the output fuzzy variables corresponding to the proportional coefficient, integral coefficient and differential coefficient; The output fuzzy variables are defuzzified using the centroid method to calculate estimated values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, which are then determined as the PID control parameters.

[0079] As a preferred embodiment, the calculation module 603 uses the PID control parameters to calculate the torque increment at the current moment through a PID algorithm, specifically: The calculation module 603 calculates the torque increment at the current moment according to the following formula: ; Among them, T increment (k) represents the torque increment at the current moment. The acceleration error at the current moment, The acceleration error at the previous moment is given by K, where n refers to the nth moment. p K is an estimated value of the proportionality coefficient. i K represents the number of estimated values ​​for the integral coefficients. d K is an estimate of the differential coefficients. p_ori K is the calibration value used to estimate the scaling factor. i_ori K is the calibration value used to scale the estimated values ​​of the integral coefficients. d_ori This is the calibration value used to estimate the scaling factor.

[0080] As a preferred embodiment, the acquisition module 601 acquires the acceleration error of the target vehicle at the current moment, including: The acquisition module 601 determines the current motion state of the target vehicle; Based on the motion state, a velocity estimate is obtained; based on the velocity estimate, a differential is performed to obtain the actual acceleration of the target vehicle at the current moment; Obtain the target vehicle's requested acceleration at the current moment, and calculate the acceleration error at the current moment based on the actual acceleration and the requested acceleration.

[0081] As a preferred embodiment, the acquisition module 601 obtains a velocity estimate based on the motion state, including: When the motion state is a driving state, the acquisition module 601 uses the minimum wheel speed method to obtain the first estimated wheel speed of the target vehicle; When the motion state is braking, the acquisition module 601 uses the maximum wheel speed method to obtain the second estimated wheel speed of the target vehicle; The acquisition module 601 determines the average of the first estimated wheel speed and the second estimated wheel speed as the speed estimate of the target vehicle.

[0082] As a preferred embodiment, the acquisition module 601 acquires the error change rate of the target vehicle at the current moment, including: The acquisition module 601 acquires the actual acceleration of the target vehicle at the previous moment; The error change rate of the target vehicle at the current moment is obtained by performing differential processing based on the actual acceleration at the previous moment and the actual acceleration at the current moment.

[0083] As a preferred embodiment, the control module 604 implements torque distribution control of the target vehicle based on the target torque at the current moment, including: When the target torque at the current moment is greater than zero, the control module 604 controls the drive torque actuator according to the target torque at the current moment to realize the torque distribution control of the target vehicle. When the target torque at the current moment is less than zero, the control module 604 controls the braking torque actuator according to the target torque at the current moment to realize the torque distribution control of the target vehicle.

[0084] Accordingly, this application also provides a terminal device, characterized in that it includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the error-driven torque distribution method described in any of the above embodiments.

[0085] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal, connecting various parts of the terminal via various interfaces and lines.

[0086] The memory can be used to store the computer program. The processor implements various functions of the terminal by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0087] Accordingly, this application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the error-driven torque distribution method described in any of the above embodiments.

[0088] Wherein, if the modules of the device / terminal equipment / system integration are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0089] Compared with the prior art, this invention application has the following beneficial effects: This invention provides an error-driven torque distribution method, system, terminal device, and medium. The torque distribution method includes: acquiring the acceleration error of a target vehicle at the current moment; acquiring the error change rate of the target vehicle at the current moment; determining the acceleration error and the error change rate as feedback signals, and inputting the feedback signals into a preset fuzzy control system for fuzzy inference to obtain PID control parameters; using the PID control parameters, calculating the torque increment at the current moment through a PID algorithm; acquiring the target torque at the previous moment, and superimposing the target torque at the previous moment and the torque increment at the current moment to calculate the target torque at the current moment; and realizing torque distribution control of the target vehicle based on the target torque at the current moment. This invention uses acceleration error and error rate of change as control feedback signals, obtains PID control parameters through a fuzzy control system, and then determines the target torque at the current moment to achieve longitudinal control of the target vehicle. This decouples torque distribution from inherent vehicle parameters (such as vehicle mass, drag coefficient, and frontal area) and environmental parameters (such as slope gradient), reducing dependence on sensor configuration and complex model calibration, and significantly lowering hardware and development costs. Furthermore, fuzzy control significantly enhances the robustness of the target vehicle to complex operating conditions and model parameter disturbances. In addition, this application uses a pre-set fuzzy control system to obtain PID control parameters and calculates the torque increment at the current moment through a PID algorithm, overcoming the limitations of traditional fixed-parameter PID in nonlinear vehicle dynamics control. Acceleration error provides the control amplitude basis, and error rate of change provides the correction rate and correction direction basis. The combination of these two achieves dual-dimensional control of acceleration error amplitude and rate of change. The introduction of the fuzzy control system enables the target vehicle to adapt to changes in operating conditions, effectively suppressing overshoot and oscillation, and improving the speed and stability of dynamic response.

[0090] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A torque distribution method based on error-driven principles, characterized in that, include: Obtain the acceleration error of the target vehicle at the current moment; obtain the rate of change of the error of the target vehicle at the current moment; The acceleration error and the rate of change of the error are determined as feedback signals, and the feedback signals are input into a preset fuzzy control system and fuzzy inference is performed to obtain PID control parameters. Using the PID control parameters, the torque increment at the current moment is calculated by the PID algorithm; the target torque at the previous moment is obtained, and the target torque at the previous moment and the torque increment at the current moment are superimposed to calculate the target torque at the current moment. Based on the target torque at the current moment, torque distribution control of the target vehicle is achieved.

2. The torque distribution method based on error-driven principle as described in claim 1, characterized in that, The step of inputting the feedback signal into a preset fuzzy control system and performing fuzzy inference to obtain PID control parameters includes: The feedback signal is input into a preset fuzzy control system and fuzzified to obtain the error fuzzy variable and the error rate of change fuzzy variable, respectively. According to the preset fuzzy control rule table, fuzzy inference is performed on the error fuzzy variable and the error change rate fuzzy variable to obtain the output fuzzy variables corresponding to the proportional coefficient, integral coefficient and differential coefficient; The output fuzzy variables are defuzzified using the centroid method to calculate estimated values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, which are then determined as the PID control parameters.

3. The torque distribution method based on error-driven principle as described in claim 2, characterized in that, The torque increment at the current moment is calculated using the PID control parameters and the PID algorithm, specifically as follows: The torque increment at the current moment is calculated using the following formula: ； Among them, T increment (k) represents the torque increment at the current moment. The acceleration error at the current moment, The acceleration error at the previous moment is given by K, where n refers to the nth moment. p K is an estimated value of the proportionality coefficient. i K represents the number of estimated values ​​for the integral coefficients. d K is an estimate of the differential coefficients. p_ori K is the calibration value used to estimate the scaling factor. i_ori K is the calibration value used to scale the estimated values ​​of the integral coefficients. d_ori This is the calibration value used to estimate the scaling factor.

4. The torque distribution method based on error-driven method as described in claim 1, characterized in that, The acquisition of the target vehicle's current acceleration error includes: Determine the current motion state of the target vehicle; Based on the motion state, a velocity estimate is obtained; based on the velocity estimate, a differential is performed to obtain the actual acceleration of the target vehicle at the current moment; Obtain the target vehicle's requested acceleration at the current moment, and calculate the acceleration error at the current moment based on the actual acceleration and the requested acceleration.

5. The torque distribution method based on error-driven principle as described in claim 4, characterized in that, The step of obtaining the velocity estimate based on the motion state includes: When the motion state is a driving state, the first estimated wheel speed of the target vehicle is obtained by the minimum wheel speed method. When the motion state is braking, the second estimated wheel speed of the target vehicle is obtained by the maximum wheel speed method. The average of the first estimated wheel speed and the second estimated wheel speed is determined as the speed estimate of the target vehicle.

6. The torque distribution method based on error-driven method as described in claim 4, characterized in that, The step of obtaining the error change rate of the target vehicle at the current moment includes: Obtain the target vehicle's actual acceleration at the previous moment; The error change rate of the target vehicle at the current moment is obtained by performing differential processing based on the actual acceleration at the previous moment and the actual acceleration at the current moment.

7. The torque distribution method based on error-driven method as described in claim 1, characterized in that, The step of implementing torque distribution control of the target vehicle based on the target torque at the current moment includes: When the target torque at the current moment is greater than zero, the drive torque actuator is controlled according to the target torque at the current moment to realize the torque distribution control of the target vehicle; When the target torque at the current moment is less than zero, the braking torque actuator is controlled according to the target torque at the current moment to realize the torque distribution control of the target vehicle.

8. A torque distribution system based on error-driven operation, characterized in that, It includes an acquisition module, a fuzzy inference module, a calculation module, and a control module; among which, The acquisition module is used to acquire the acceleration error of the target vehicle at the current moment; and to acquire the error change rate of the target vehicle at the current moment. The fuzzy inference module is used to determine the acceleration error and the rate of change of error as feedback signals, and input the feedback signals into a preset fuzzy control system and perform fuzzy inference to obtain PID control parameters. The calculation module is used to calculate the torque increment at the current moment using the PID control parameters and the PID algorithm; obtain the target torque at the previous moment, and superimpose the target torque at the previous moment and the torque increment at the current moment to calculate the target torque at the current moment. The control module is used to implement torque distribution control of the target vehicle based on the target torque at the current moment.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the error-driven torque distribution method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device in which the computer-readable storage medium is located to perform the error-driven torque distribution method as described in any one of claims 1 to 7.

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