Vehicle steering control method and vehicle

Abstract

The application provides a vehicle steering control method and a vehicle, relates to the technical field of vehicle control, and aims to solve the problem of how to improve the vehicle steering control precision in a high-order intelligent driving scene. The method comprises the following steps: acquiring vehicle driving data, vehicle steering state data and an advanced driving assistance system (ADAS) target angle signal; the ADAS target angle signal is used for reflecting the steering wheel rotation angle expected by the ADAS; a position type PID control algorithm and a tracking differential control algorithm are adopted; based on the vehicle driving data, the vehicle steering state data and the ADAS target angle signal, the torque of a steering system is calculated; and the vehicle steering is controlled based on the torque of the steering system.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle steering control method and a vehicle. Background Technology

[0002] Steer-by-wire (SBW) technology, as an important development direction for automotive steering systems, has significant advantages in improving driving agility and optimizing cabin layout. However, in practical applications, its performance still has many shortcomings that need to be addressed, especially in basic power assist control and advanced intelligent driving scenarios. At the basic power assist control level, the main problems are insufficient adaptability of the power assist logic and poor smoothness of the power assist output. In advanced intelligent driving scenarios, key shortcomings are exposed, including lag in dynamic response, poor human-machine collaboration, and a lack of target angle verification in Advanced Driving Assistance Systems (ADAS). These shortcomings not only affect driving comfort and handling but may also bring potential driving safety risks, hindering the large-scale promotion and application of SBW technology. Summary of the Invention

[0003] The purpose of this application is to provide a vehicle steering control method and a vehicle, aiming to solve the problem of how to improve the accuracy of vehicle steering control in advanced intelligent driving scenarios.

[0004] In a first aspect, this application provides a vehicle steering control method applied to a steer-by-wire (SBW) system. The method includes: acquiring vehicle driving data, vehicle steering state data, and an advanced driver assistance system (ADAS) target angle signal; the ADAS target angle signal is used to reflect the steering wheel rotation angle desired by the ADAS; using a position-based PID control algorithm and a tracking derivative control algorithm, the torque of the steering system is calculated based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal; and the vehicle steering is controlled based on the torque of the steering system.

[0005] The vehicle steering control method provided in this application embodiment acquires and comprehensively analyzes various vehicle data, and uses position-type PID and tracking differential control algorithms to control the actual output torque of the vehicle, thereby accurately regulating the steering torque, improving control accuracy and stability, and ensuring driving safety and comfort.

[0006] In some embodiments, the ADAS target angle signal is verified for safety to obtain the safe limit ADAS target angle; the torque of the steering system is calculated based on vehicle driving data, vehicle steering state data, ADAS target angle signal and safe limit ADAS target angle using position-based PID control algorithm and tracking derivative control algorithm.

[0007] In some embodiments, the maximum target angle of ADAS at the current vehicle speed is determined based on the vehicle's current speed and a preset correspondence; the preset correspondence is used to reflect the maximum target angle of ADAS corresponding to different vehicle speeds; and the safety-limited ADAS target angle is determined based on the ADAS target angle signal, the maximum target angle of ADAS at the current vehicle speed, and the historical safety-limited ADAS target angle.

[0008] In some embodiments, a position-based PID control algorithm and a tracking derivative control algorithm are used to calculate the torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal. This includes: using a position-based PID control algorithm and a tracking derivative control algorithm, and calculating the PID torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal; determining the torque of the steering system based on the PID torque of the steering system when the integral term of the PID controller has not entered the integral saturation region; and determining the torque of the steering system based on the PID saturation limit torque when the integral term of the PID controller has entered the integral saturation region.

[0009] In some embodiments, when the steering system is a steering wheel, the vehicle steering state data includes the steering wheel angle and steering wheel speed; the vehicle driving data includes the vehicle speed; using a position-based PID control algorithm and a tracking differential control algorithm, based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal, the PID torque of the steering system is calculated, including: using the position-based PID control algorithm, based on a first angle difference and vehicle speed, to obtain the target PID steering wheel speed; the first angle difference is the angle difference between the safety-limiting ADAS target angle and the steering wheel angle; using the tracking differential control algorithm, based on the ADAS target angle signal, to obtain the target differential steering wheel speed; using the target PID steering wheel speed and the target differential steering wheel speed, to determine the target steering wheel speed; and using the first speed difference, to obtain the PID torque of the steering wheel; the first speed difference is the speed difference between the target steering wheel speed and the steering wheel speed.

[0010] In some embodiments, when the steering system is an actuator motor, the vehicle steering state data includes rack angle and rack speed; the vehicle driving data includes vehicle speed; using a position-based PID control algorithm and a tracking derivative control algorithm, based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal, the PID torque of the steering system is calculated, including: determining the target rack angle and the derivative target rack angle according to the safety limit ADAS target angle and the ADAS target angle signal; using the position-based PID control algorithm, obtaining the target PID rack speed based on the second angle difference and vehicle speed; the second angle difference is the angle difference between the target rack angle and the rack angle; using the tracking derivative control algorithm, obtaining the target derivative rack speed based on the derivative target rack angle; determining the target rack speed based on the target PID rack speed and the target derivative rack speed; and obtaining the PID torque of the actuator motor based on the second speed difference; the second speed difference is the speed difference between the target rack speed and the rack speed.

[0011] In some embodiments, determining the torque of the steering system based on the PID saturation limiting torque includes: determining whether the rack steering state is established based on the differential target rack angle; if the rack steering state is established, reducing the PID saturation limiting torque based on a preset ratio to obtain a PID reduction torque, and then determining the torque of the actuator motor based on the PID reduction torque; if the rack steering state is not established, determining the torque of the actuator motor based on the PID saturation limiting torque.

[0012] In some embodiments, vehicle steering state data includes steering wheel angle and steering wheel torque; while employing a position-based PID control algorithm and a tracking derivative control algorithm to calculate the PID torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal, the method further includes: obtaining the human-machine co-driving hand force coefficient; determining whether the human-machine co-driving state is established based on the human-machine co-driving hand force coefficient and steering wheel torque; when the human-machine co-driving state is established, saturating the proportional coefficient and integral coefficient in the position-based PID control algorithm until the constraint release condition is met; wherein, the constraint release condition includes: a first angle difference is less than a preset angle difference threshold, and the duration is greater than a preset time threshold; a second angle difference is less than a preset angle difference threshold; wherein, the first angle difference is the angle difference between the safety-limited ADAS target angle and the steering wheel angle; the second angle difference is the angle difference between the target rack angle and the rack angle.

[0013] In some embodiments, before calculating the torque of the steering system, the method further includes: obtaining the fault status of the SBW system itself; if the SBW system is not faulty and the vehicle driving data and vehicle steering status data are normal, sending status feedback information to the ADAS; the status feedback information is used to indicate that the SBW system is not faulty; in response to a handshake request sent by the ADAS, performing multi-level handshake verification with the ADAS; the handshake request is sent by the ADAS when it receives the status feedback information; the multi-level handshake verification is used to confirm the legitimate communication identity of the SBW system and the ADAS, and to verify the stability of the communication link and the reliability of data transmission.

[0014] Secondly, this application provides a vehicle steering control device, which includes: an acquisition unit and a control unit; the acquisition unit is used to acquire vehicle driving data, vehicle steering state data, and an advanced driver assistance system (ADAS) target angle signal; the ADAS target angle signal is used to reflect the steering wheel rotation angle desired by the ADAS; the control unit is used to calculate the torque of the steering system based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal using a position-based PID control algorithm and a tracking derivative control algorithm; and to control the vehicle steering based on the torque of the steering system.

[0015] In some embodiments, the control unit is specifically used to perform safety verification on the ADAS target angle signal to obtain a safety-limited ADAS target angle; and to calculate the torque of the steering system based on vehicle driving data, vehicle steering state data, ADAS target angle signal and safety-limited ADAS target angle using a position-based PID control algorithm and a tracking derivative control algorithm.

[0016] In some embodiments, the control unit is specifically used to determine the maximum target angle of ADAS at the current vehicle speed based on the vehicle's current speed and a preset correspondence; the preset correspondence is used to reflect the maximum target angle of ADAS corresponding to different vehicle speeds; and to determine the safety-limited ADAS target angle based on the ADAS target angle signal, the maximum target angle of ADAS at the current vehicle speed, and the historical safety-limited ADAS target angle.

[0017] In some embodiments, the control unit is specifically used to employ a position-based PID control algorithm and a tracking derivative control algorithm to calculate the PID torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal; when the integral term of the PID controller has not entered the integral saturation region, the torque of the steering system is determined based on the PID torque of the steering system; when the integral term of the PID controller has entered the integral saturation region, the torque of the steering system is determined based on the PID saturation limit torque.

[0018] In some embodiments, when the steering system is a steering wheel, the vehicle steering state data includes the steering wheel angle and steering wheel speed; the vehicle driving data includes the vehicle speed; the control unit is specifically used to use a position-based PID control algorithm to obtain a target PID steering wheel speed based on a first angle difference and the vehicle speed; the first angle difference is the angle difference between the safety-limiting ADAS target angle and the steering wheel angle; using a tracking differential control algorithm to obtain a target differential steering wheel speed based on the ADAS target angle signal; determining the target steering wheel speed based on the target PID steering wheel speed and the target differential steering wheel speed; and obtaining the PID torque of the steering wheel based on the first speed difference; the first speed difference is the speed difference between the target steering wheel speed and the steering wheel speed.

[0019] In some embodiments, when the steering system is an actuator motor, the vehicle steering state data includes rack angle and rack speed; the vehicle driving data includes vehicle speed; the control unit is specifically used to determine the target rack angle and the derivative target rack angle based on the safety limit ADAS target angle and the ADAS target angle signal; using a position-based PID control algorithm, based on a second angle difference and vehicle speed, the target PID rack speed is obtained; the second angle difference is the angle difference between the target rack angle and the rack angle; using a tracking derivative control algorithm, based on the derivative target rack angle, the target derivative rack speed is obtained; the target rack speed is determined based on the target PID rack speed and the target derivative rack speed; the PID torque of the actuator motor is obtained based on the second speed difference; the second speed difference is the speed difference between the target rack speed and the rack speed.

[0020] In some embodiments, the control unit is specifically configured to determine whether the rack rotation state is established based on the differential target rack angle; if the rack rotation state is established, the PID saturation limiting torque is reduced based on a preset ratio to obtain the PID reduction torque, and then the torque of the actuator motor is determined based on the PID reduction torque; if the rack rotation state is not established, the torque of the actuator motor is determined based on the PID saturation limiting torque.

[0021] In some embodiments, vehicle steering state data includes steering wheel angle and steering wheel torque; the control unit is further configured to acquire human-machine co-driving hand force coefficient; based on the human-machine co-driving hand force coefficient and steering wheel torque, determine whether the human-machine co-driving state is established; if the human-machine co-driving state is established, saturate limit the proportional coefficient and integral coefficient in the position-type PID control algorithm until the limit release condition is met; wherein, the limit release condition includes: a first angle difference is less than a preset angle difference threshold, and the duration is greater than a preset time threshold; a second angle difference is less than a preset angle difference threshold; wherein, the first angle difference is the angle difference between the safety limit ADAS target angle and the steering wheel angle; the second angle difference is the angle difference between the target rack angle and the rack angle.

[0022] In some embodiments, before calculating the torque of the steering system, the control unit is further configured to obtain the fault status of the SBW system itself; if the SBW system is not faulty and the vehicle driving data and vehicle steering status data are normal, send status feedback information to the ADAS; the status feedback information is used to indicate that the SBW system is not faulty; in response to a handshake request sent by the ADAS, perform multi-level handshake verification with the ADAS; the handshake request is sent by the ADAS when it receives the status feedback information; the multi-level handshake verification is used to confirm the legitimate communication identity of the SBW system and the ADAS, and to verify the stability of the communication link and the reliability of data transmission.

[0023] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0024] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0025] Fifthly, the present invention provides a vehicle that includes the electronic equipment described in the third aspect, or the vehicle that includes the computer-readable storage medium described in the fourth aspect.

[0026] Sixthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, it causes the electronic device to implement the method described in the first aspect.

[0027] The beneficial effects of the second to sixth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of a vehicle steering control system provided in an embodiment of this application; Figure 2 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 3A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 4 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 5 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 6 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 7 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 8 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 9 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 10 A flowchart of a vehicle steering control method provided in an embodiment of this application; Figure 11 This is a schematic diagram of the structure of a vehicle steering control device provided in an embodiment of this application; Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0030] Reference numerals: Vehicle steering control system 100, data acquisition module 10, control module 20, execution module 30. Detailed Implementation

[0031] In the embodiments of this application, the terms "first," "second," "third," "fourth," "fifth," and "sixth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," "fourth," "fifth," and "sixth" may explicitly or implicitly include one or more of that feature.

[0032] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0033] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0034] In the embodiments of this application, "parallel," "perpendicular," and "equal" include the described situation and situations similar to the described situation, where the range of similarity is within an acceptable deviation range, which is determined by those skilled in the art taking into account the measurement under discussion and the error associated with the measurement of a particular quantity (i.e., the limitations of the measurement system). For example, "parallel" includes absolute parallelism and approximate parallelism, where the acceptable deviation range for approximate parallelism can be, for example, a deviation within 5°; "perpendicular" includes absolute perpendicularity and approximate perpendicularity, where the acceptable deviation range for approximate perpendicularity can also be, for example, a deviation within 5°. "Equal" includes absolute equality and approximate equality, where the acceptable deviation range for approximate equality can be, for example, a difference between the two equals being less than or equal to 5% of either one.

[0035] According to the background technology, basic power steering is a core component of SBW technology that ensures a basic driving experience. However, its shortcomings in practical applications directly affect driving feel and comfort, specifically in the following two aspects: First, the power steering logic is simplistic, resulting in insufficient scenario adaptability. Existing SBW systems rely heavily on a fixed speed-steering angle mapping relationship for basic power steering adjustment. The adjustment logic is relatively simple, typically adjusting the amount of power steering based solely on vehicle speed. This means providing greater power steering at low speeds to reduce steering effort and reducing it at high speeds to ensure steering stability. However, real-world driving scenarios are complex and varied. Relying solely on speed cannot achieve precise matching of power steering characteristics. For example, in different road conditions such as switching between curves and straightaways, driving uphill or downhill, or sudden changes in road surface adhesion coefficient, the system cannot adjust the power steering parameters accordingly, easily leading to abrupt steering feel and affecting the driver's confidence in controlling the vehicle. Second, the power steering lacks smoothness, compromising driving comfort. In scenarios requiring frequent steering, such as parking or low-speed maneuvering, or during rapid acceleration or deceleration, the power steering output of the SBW system is prone to jerking during transitions. This fluctuation in steering assist is directly transmitted to the steering wheel, causing the driver to experience noticeable steering jerking or abrupt changes. This not only reduces driving comfort but may also increase driver fatigue, especially during prolonged low-speed driving or frequent parking, where the problem is more pronounced.

[0036] In advanced intelligent driving scenarios, the Side-by-Side (SBW) system, as the core actuator for lateral vehicle control, directly impacts the control accuracy, stability, and safety of the intelligent driving system due to performance defects. This is primarily reflected in three aspects: dynamic response, human-machine collaborative driving, and safety verification. Firstly, lag in dynamic response affects the accuracy of intelligent driving control. Limited by hardware conditions such as motor performance and transmission characteristics, the SBW system often exhibits slow response and excessively long turning times when following steering system commands. This lag in dynamic response leads to deviations in lateral vehicle control, requiring the intelligent driving system to frequently adjust the steering wheel to correct the driving trajectory. This results in phenomena such as the vehicle "dragging" along, deviating from the lane center, or veering too far to the left or right from the lane line, severely impacting the driving stability and safety of advanced intelligent driving. Secondly, the smoothness of human-machine collaborative driving is lacking, resulting in a poor switching experience. In human-machine co-driving mode, coordination issues can easily arise when switching steering control between the driver and the intelligent driving system: when the driver manually intervenes in steering, it may conflict with the steering commands of the intelligent driving system, resulting in the steering wheel being "grabbed"; and when the driver exits manual control and resumes intelligent driving control, the steering wheel rebound speed of the SBW system is too fast, giving the driver a noticeable abrupt feeling. This uneven control switching not only reduces the driving experience but may also lead to the driver misjudging the vehicle's status, increasing operational risks. Thirdly, there is a lack of effective judgment on the rationality and safety of ADAS target angles. If the ADAS system mistakenly sends a high-angle steering command due to sensor misjudgment, algorithm defects, or other reasons, especially in high-speed driving scenarios, the SBW system will directly perform steering operations according to the incorrect command, which can easily cause the vehicle to sway violently laterally, or even cause the vehicle to lose control, posing a serious threat to driving safety.

[0037] To address the aforementioned technical problems, this application provides a vehicle steering control method and a vehicle that can comprehensively analyze various vehicle data to track the target steering angle of the vehicle, thereby controlling the vehicle steering deviation and improving the vehicle's response accuracy and control stability. The vehicle steering control method includes: acquiring vehicle driving data, vehicle steering state data, and an Advanced Driver Assistance System (ADAS) target angle signal; the ADAS target angle signal is used to reflect the steering wheel rotation angle desired by the ADAS; employing a position-based PID control algorithm and a tracking derivative control algorithm, based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal, to calculate the steering system torque; and controlling the vehicle steering based on the steering system torque.

[0038] The contents provided in this application will be described below with reference to the accompanying drawings.

[0039] Figure 1 This is a schematic diagram of a vehicle steering control system provided in an embodiment of this application, as shown below. Figure 1As shown, the vehicle steering control system 100 includes a data acquisition module 10, a control module 20, and an execution module 30, wherein the control module 20 is connected to the data acquisition module 10 and the execution module 30 respectively.

[0040] The acquisition module 10 is used to acquire vehicle driving data, vehicle steering status data and advanced driver assistance system (ADAS) target angle signal. The ADAS target angle signal is used to reflect the steering wheel rotation angle expected by ADAS.

[0041] In some embodiments, vehicle driving data includes steering wheel angle, steering wheel speed, steering wheel torque, rack angle and rack speed, vehicle speed, vehicle speed signal status, etc., which are subsequently provided to the control module 20 for processing.

[0042] Control module 20 is used to calculate the torque of the steering system based on vehicle driving data, vehicle steering state data and ADAS target angle signal by using position PID control algorithm and tracking derivative control algorithm, and to control vehicle steering based on the torque of the steering system.

[0043] In some embodiments, the control module 20 may consist of an ADAS system and an SBW system, or the control module 20 may include an SBW system alone.

[0044] In some embodiments, the control module 20 generates a control command based on the calculated torque of the steering system. The control command carries information such as torque parameters and execution timing, and is transmitted to the execution module 30 through a preset communication bus (such as a CAN bus).

[0045] After receiving the control command, the execution module 30 first verifies the safety verification information in the command. After the verification is successful, it parses the torque parameters and execution timing, drives the internal motor, hydraulic or electro-hydraulic actuator to output the corresponding torque, and drives the steering wheel or steering rack to complete the steering action according to the angle and response speed expected by ADAS.

[0046] In some embodiments, the execution module 30 includes a steering wheel motor and an SBW execution motor.

[0047] It should be noted that the application scenarios of the embodiments in this application are not limited. The system architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of vehicle technology and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0048] Figure 2A flowchart of a vehicle steering control method provided in this application embodiment, the method being applied to a steer-by-wire (SBW) system, such as... Figure 2 As shown, the method includes the following steps S101-S103: S101. Acquire vehicle driving data, vehicle steering status data, and target angle signal from Advanced Driver Assistance System (ADAS).

[0049] Among them, the ADAS target angle signal is used to reflect the steering wheel rotation angle expected by ADAS.

[0050] In some embodiments, vehicle driving data includes vehicle speed, vehicle speed signal status, steering wheel angle, steering wheel speed, steering wheel torque, etc.

[0051] In some embodiments, the ADAS target angle signal is generated by the lateral control module of the ADAS system to reflect the steering wheel rotation angle desired by the ADAS. For example, when the ADAS system is in lane keeping mode, the ADAS target angle signal is the steering wheel rotation angle required to keep the vehicle in the current lane; when the ADAS system is in lane changing mode, the ADAS target angle signal is the steering wheel rotation angle required to complete the lane changing action.

[0052] In some embodiments, the aforementioned data and signals are transmitted via the vehicle's CAN bus to ensure the real-time performance and reliability of data interaction, thereby meeting the real-time requirements of steering control.

[0053] S102. Using position-based PID control algorithm and tracking derivative control algorithm, the torque of the steering system is calculated based on vehicle driving data, vehicle steering state data and ADAS target angle signal.

[0054] In some embodiments, specifically, the tracking differential control algorithm first preprocesses the ADAS target angle signal. This algorithm can extract the differential signal of the target angle signal and perform smoothing filtering on the target angle signal, thereby suppressing the disturbance caused by the sudden change of the ADAS target angle signal, avoiding step changes in steering torque, and improving the smoothness of steering control.

[0055] The position-based PID control algorithm calculates the difference between the preprocessed ADAS target angle signal and the actual steering wheel angle to obtain the angle deviation value. Then, it combines the vehicle speed and driving trajectory deviation in the vehicle driving data to perform proportional (P), integral (I), and derivative (D) operations on the angle deviation value to obtain the basic steering torque. Finally, it integrates the filtered optimization parameters output by the tracking derivative control algorithm to dynamically correct the basic steering torque and obtain the final steering system torque.

[0056] It should be noted that the parameters of the position-type PID control algorithm (proportional coefficient, integral time constant, derivative time constant) can be calibrated according to the steering system characteristics of different vehicle models, and the filtering parameters of the tracking derivative control algorithm can be adaptively adjusted according to the working mode of the ADAS system to ensure control accuracy under different operating conditions.

[0057] S103, Torque control of vehicle steering based on steering system.

[0058] In some embodiments, the steering system torque signal calculated in S102 is sent to the vehicle's steer-by-wire actuator. The steer-by-wire actuator outputs the corresponding steering assist or active steering torque according to the torque signal, drives the steering tie rod to cause the wheel to deflect, so that the actual steering wheel angle gradually approaches the angle corresponding to the ADAS target angle signal, thereby achieving accurate tracking of the vehicle's steering state to the ADAS target angle.

[0059] For example, when a vehicle deviates from its lane while driving, the ADAS system generates a corresponding target angle signal. This method calculates the steering torque through the above steps and controls the steering actuator to automatically return the vehicle to the target lane without the need for manual intervention by the driver, thus improving driving safety and comfort.

[0060] This method, through the synergistic effect of positional PID control algorithm and tracking derivative control algorithm, takes into account both the response speed and stability of steering control, solves the problems of overshoot and oscillation that are prone to occur in traditional single PID control, and meets the high precision requirements of lateral control of ADAS system.

[0061] The vehicle steering control method provided in this application embodiment acquires and comprehensively analyzes various vehicle data, and uses position-type PID and tracking differential control algorithms to control the actual output torque of the vehicle, thereby accurately regulating the steering torque, improving control accuracy and stability, and ensuring driving safety and comfort.

[0062] Figure 3 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 3 As shown, S102 can be implemented as follows: S201-S202: S201. Perform safety verification on the ADAS target angle signal to obtain the safety limit ADAS target angle.

[0063] Due to environmental factors and malfunctions, ADAS target signals may become abnormal, potentially leading to loss of steering control if used directly. Therefore, it is necessary to eliminate anomalies through safety verification, define a reasonable range, and output a safe limit ADAS target angle to provide reliable input for subsequent torque calculations, ensuring safe and stable steering.

[0064] In some embodiments, reasonable threshold values ​​for ADAS target angles under different operating conditions are preset by combining vehicle parameters, driving conditions, etc., and then real-time ADAS target angle signals and vehicle driving status data are collected to verify the rationality of the ADAS target angle signals. Finally, a signal that has passed verification is output.

[0065] S202. The position-based PID control algorithm and the tracking derivative control algorithm are adopted to calculate the torque of the steering system based on vehicle driving data, vehicle steering state data, ADAS target angle signal and safety limit ADAS target angle.

[0066] Precise calculation of steering torque is key to achieving smooth tracking of the target angle in ADAS. A single control algorithm is insufficient to simultaneously consider the response speed, steady-state control accuracy, and anti-interference capability of the steering system: position-based PID control algorithms have small steady-state errors, but they are less adaptable to sudden disturbances during driving (such as uneven road surfaces and crosswinds); tracking derivative control algorithms have fast tracking characteristics and interference suppression capabilities, and can effectively smooth signal abrupt changes. By using position-based PID control and tracking derivative control in synergy, both steering angle tracking accuracy and system dynamic performance can be considered, ensuring the smoothness and safety of vehicle steering control.

[0067] In some embodiments, vehicle driving data, steering state data, ADAS target angle signal, and safety limit ADAS target angle obtained via S201 are collected. Then, a position-based PID and tracking differential control fusion algorithm is constructed: the deviation between the target angle and the actual steering angle is calculated, the deviation is input into the position-based PID control algorithm and the parameters are dynamically adapted to output the basic steering torque. At the same time, the basic steering torque is corrected based on the disturbance compensation amount output by the tracking differential control algorithm. Combined with the steering system state, dynamic constraints are applied, and finally, a precise steering torque that meets the control requirements is output.

[0068] Figure 4 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 4 As shown, S201 can be implemented as follows: S301-S302: S301. Based on the vehicle's current speed and the preset correspondence, determine the maximum target angle of ADAS at the current vehicle speed.

[0069] The preset correspondence is used to reflect the maximum target angle of ADAS corresponding to different vehicle speeds.

[0070] In some embodiments, in the preset correspondence, the vehicle speed and the corresponding maximum target angle of ADAS show a negative correlation trend, that is, the higher the vehicle speed, the smaller the maximum target angle of ADAS allowed by the system, thereby ensuring the stability and safety of vehicle steering at different vehicle speeds.

[0071] In some embodiments, the basic data in the preset correspondence can be obtained through vehicle personalized calibration. For different vehicle models and configurations, combined with core parameters such as chassis parameters, steering system characteristics, and tire specifications, multi-condition real vehicle calibration is carried out in a standard test site or simulated environment. Steering safety limit data at different vehicle speeds are collected, and the initial maximum vehicle turning angle value corresponding to each speed gear is determined to form a basic preset correspondence adapted to the vehicle.

[0072] In some embodiments, the maximum target angle of ADAS does not exceed the threshold value of the high-order intelligent driving end angle. This threshold value is obtained by SBW learning the current left and right end angles of the vehicle steering wheel during vehicle use and taking the smaller value, thereby completing dynamic updates. This avoids the deviation between the theoretical end angle and the actual end angle caused by factors such as mechanical wear and changes in component gaps during long-term vehicle use, and ensures the accuracy of vehicle steering angle control.

[0073] In some embodiments, the SBW system collects the current vehicle speed in real time, queries a preset correspondence based on the current vehicle speed, and if the vehicle speed is a node value, it directly matches the corresponding ADAS maximum target angle. If it is between two nodes, it calculates the angle through interpolation to ensure a smooth transition.

[0074] For example, assuming the advanced intelligent driving end-point angle threshold is MAX, the preset correspondence is shown in Table 1 below. If the current vehicle speed is 35km / h, according to the data in Table 1, 35km / h is between 30km / h (190°) and 40km / h (120°). According to linear interpolation, A = 190° + (120° - 190°) × (35 - 30) / (40 - 30) = 155°, that is, the current maximum target angle of ADAS is 155°.

[0075] Table 1

[0076] S302. Based on the ADAS target angle signal, the maximum ADAS target angle at the current vehicle speed, and the historical safe limit ADAS target angle, determine the safe limit ADAS target angle.

[0077] In some embodiments, S302 specifically includes: SBW acquiring the ADAS target angle in real time, the maximum target angle determined in S301, and the historical safety-limited ADAS target angle (initial value is the angle at the moment of ADAS activation); First limit: the smaller of the ADAS target angle and the maximum ADAS target angle at the current vehicle speed is taken to obtain angle a; Second limit: a is converted into target speed (calculated according to signal and scheduling cycle), and the smaller of the maximum angular velocity corresponding to the current vehicle speed is taken to obtain speed b; the speed change rate is calculated (the difference between the current b and the previous b); the final safety-limited angle = historical safety-limited angle + speed change rate, realizing dual limits of angle and angular velocity.

[0078] For example, suppose the signal period is 10ms, the scheduling period is 5ms, the initial activation angle is 80°, the maximum angle of the advanced intelligent driving terminal is 375°, and the current vehicle speed is 50km / h (corresponding to a maximum target angle of 90° and a maximum angular velocity of 210° / s). At time t-1, the historical safe ADAS limiting angle is 85°, a1=88°, b1=210° / s; at time t, the ADAS target angle is 95°, the first level of limitation gives a2=90°, the converted speed is 200° / s, the second level of limitation gives b2=200° / s; the speed change rate is -10° / s, and the current safe limiting ADAS target angle is 85°-10° / s=75°.

[0079] In some embodiments, the maximum angular velocity corresponding to the current vehicle speed can be obtained through calibration. For different vehicle models and configurations, and considering core parameters such as chassis parameters, steering system characteristics, and tire specifications, multi-condition real-vehicle calibration is conducted in a standard test track or simulated environment. Safety limit data of angular velocity at different vehicle speeds are collected to determine the initial maximum vehicle angular velocity corresponding to each speed gear, forming a correspondence that reflects the maximum angular velocity of the vehicle at different speeds. For example, the correspondence between vehicle speed and maximum angular velocity is shown in Table 2 below.

[0080] Table 2

[0081] Figure 5 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 5 As shown, S102 can be implemented as follows: S401-S403: S401 employs a position-based PID control algorithm and a tracking derivative control algorithm to calculate the PID torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal.

[0082] In some embodiments, vehicle driving data includes at least one of vehicle speed, wheel speed, and lateral acceleration, and steering state data includes at least one of the following: actual steering wheel angle, angular velocity, and motor output torque. The tracking differential control algorithm smooths the ADAS target angle signal, and the output differential signal is input into the position PID controller along with the above data. The PID torque is calculated by weighting the proportional term (related to angle deviation), the integral term (accumulated integral of deviation), and the differential term (rate of change of deviation, corrected by the differential signal), thereby improving torque stability.

[0083] S402. When the integral term of the PID controller has not entered the integral saturation region, the torque of the steering system is determined based on the PID torque of the steering system.

[0084] In some embodiments, the integral saturation judgment criteria are: the integral value is in the same direction as the increment and the steering wheel angle reaches the deviation below the steady-state error; when not saturated, the torque is dynamically weighted and corrected according to the vehicle speed based on the PID torque (high speed reduces gain for stable steering, low speed increases gain for fast response).

[0085] S403. When the integral term of the PID controller enters the integral saturation region, the torque of the steering system is determined based on the PID saturation limit torque.

[0086] In some embodiments, when the saturation judgment condition is met, the integral stops accumulating and maintains the current value. The PID saturation limiting torque is calculated by a fixed integral value plus a real-time proportional term and a derivative term.

[0087] Figure 6 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 6 As shown, S102 can be implemented as follows: S501-S504: When the steering system is a steering wheel, the vehicle steering state data includes the steering wheel angle and steering wheel speed; the vehicle driving data includes the vehicle speed; using a position-based PID control algorithm and a tracking derivative control algorithm, based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal, the PID torque of the steering system is calculated, including: S501: The position-based PID control algorithm is adopted to obtain the target PID steering wheel speed based on the first angle difference and vehicle speed.

[0088] The first angle difference is the angle difference between the target angle of the safety-limiting ADAS and the steering wheel angle.

[0089] For example, calculating the steering wheel speed using a PID controller primarily employs a position-based PID controller. Generally, the PID formula is as follows:

[0090] Since the SBW system uses a discrete PID controller, the position-based PID controller needs to be converted into a discrete form. Therefore, the discrete formula for calculating the PID steering wheel speed is as follows:

[0091] in, It is the control output at time k; The deviation at time k; It is a proportionality coefficient used to quickly respond to deviations; It is the integral coefficient, used to eliminate steady-state error. The integral term is multiplied by the sampling time T to maintain unit consistency. These are the differential coefficients, used to predict changes in deviation. The differential term is divided by T to approximate the derivative. T is the sampling time.

[0092] In some embodiments, since the control of the SBW system relies on an electronic controller (such as an ECU) to calculate and execute steering commands, and the electronic controller can only perform discrete data acquisition and calculation with a fixed sampling period, it cannot directly run the position PID control algorithm in the continuous time domain. Therefore, it is necessary to use numerical approximation methods such as difference to replace derivative and summation to replace integral to convert the position PID formula describing the real-time changes of the control quantity in the continuous time domain into a recursive calculation form with discrete sampling time as the node, so as to obtain the discrete PID formula, thereby adapting to the digital control logic of the SBW system and realizing precise and real-time control of the steering motor output.

[0093] For example, the discrete PID formula is as follows:

[0094] in, It is the control output at time k; The deviation at time k; It is a proportionality coefficient used to quickly respond to deviations; It is the integral coefficient, used to eliminate steady-state error. The integral term is multiplied by the sampling time T to maintain unit consistency. These are the differential coefficients, used to predict changes in deviation. The differential term is divided by T to approximate the derivative. T is the sampling time.

[0095] S502: The tracking differential control algorithm is adopted to obtain the target differential steering wheel speed based on the ADAS target angle signal.

[0096] The target differential steering wheel rotation speed focuses on the slope of the change in the ADAS target angle, and the angle following timeliness is improved by tracking differential processing.

[0097] In some embodiments, the ADAS target angle signal sent by the ADAS system is used as the input signal v, which is then processed by the tracking differential formula to output the differential component x2 of the input signal. This differential component is the ADAS target steering wheel speed, which directly reflects the slope of the change in the ADAS target angle.

[0098] For example, the formula for tracking differential processing is as follows:

[0099]

[0100] in, is the input signal; r is the filter factor; Input signal The filtered signal; Input signal The differential.

[0101] In some embodiments, in order to adapt the differential control effect at different vehicle speeds, it is necessary to obtain the differential correction coefficient through a two-dimensional lookup table. The lookup table input parameters are the current vehicle speed and the ADAS target steering wheel speed obtained above. Finally, the target differential steering wheel speed is calculated by the formula: target differential steering wheel speed = differential correction coefficient × ADAS target steering wheel speed.

[0102] By introducing differential control, which directly correlates with the slope of the change in the ADAS target angle, the steering wheel angle following time in advanced intelligent driving scenarios can be significantly shortened, improving the timeliness of control response.

[0103] S503. Determine the target steering wheel speed based on the target PID steering wheel speed and the target differential steering wheel speed.

[0104] In some embodiments, the target PID steering wheel speed and the target differential steering wheel speed obtained above are added together to obtain the target steering wheel speed.

[0105] In some embodiments, the Kp coefficient of the target PID steering wheel speed and the coefficient of the target differential steering wheel speed can be adjusted respectively to control the magnitude of the target steering wheel speed value.

[0106] S504. Based on the first speed difference, the PID torque of the steering wheel is obtained.

[0107] The first speed difference is the speed difference between the target steering wheel speed and the steering wheel speed.

[0108] In some embodiments, the coefficients of Kp and Ki are obtained by looking up tables based on the speed difference and vehicle speed. The PID torque is obtained by multiplying the Kp coefficient by the speed difference and adding the Ki coefficient by the speed difference.

[0109] Figure 7 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 7 As shown, S102 can be implemented as follows: S601-S604: S601. Determine the target rack angle and the differential target rack angle based on the safety limit ADAS target angle and the ADAS target angle signal.

[0110] In some embodiments, the rack angle, the safety limit ADAS target angle, and the ADAS target angle are first acquired in real time. The target rack angle = safety limit ADAS target angle ÷ preset transmission ratio; the differential target rack angle = ADAS target angle ÷ preset transmission ratio, where the transmission ratio is a pre-calibrated fixed value that provides input for subsequent dual-path control.

[0111] For example, if the transmission ratio is 10, the safety limit ADAS target angle is 50°, and the ADAS target angle is 55°, then the target rack angle = 50° ÷ 10 = 5°, and the differential target rack angle = 55° ÷ 10 = 5.5°.

[0112] S602: The position-based PID control algorithm is adopted to obtain the target PID rack speed based on the second angle difference and the vehicle speed.

[0113] The second angle difference is the angle difference between the target rack angle and the rack angle.

[0114] In some embodiments, the second angle difference is first calculated as target rack angle - real-time rack angle. Based on the second angle difference and vehicle speed, a preset two-dimensional lookup table (pre-calibrated on a bench / actual vehicle) is used to obtain the Kp coefficient. The target PID rack speed is calculated as Kp coefficient × second angle difference, dynamically adapting to different operating conditions.

[0115] For example, when the vehicle speed is 30km / h and the second angle difference is 2°, Kp=8, and the target PID rack speed is 8×2° / s=16° / s; when the vehicle speed is 60km / h and the angle difference remains unchanged, Kp=6, and the speed is 6×2° / s=12° / s.

[0116] S603. The tracking differential control algorithm is adopted to obtain the target differential rack speed based on the differential target rack angle.

[0117] In some embodiments, the differential target rack angle is input into the tracking differential to obtain the initial rotational speed, and then the differential correction coefficient is obtained by combining the vehicle speed query with a two-dimensional lookup table. The final target differential rack rotational speed = initial rotational speed × correction coefficient, thereby improving the accuracy of the rotational speed.

[0118] For example, the initial rotational speed of the differential target rack angle of 5.5° is 18° / s after tracking differentiation. When the vehicle speed is 40km / h, the correction factor is 0.95, and the final rotational speed is 18×0.95=17.1° / s; when the vehicle speed is 50km / h, the correction factor is 0.92, and the final rotational speed is 18×0.92=16.56° / s.

[0119] S604. Determine the target rack speed based on the target PID rack speed and the target derivative rack speed.

[0120] In some embodiments, the target rack speed = target PID rack speed × PID adjustment coefficient + target derivative rack speed × derivative adjustment coefficient. The two adjustment coefficients can be calibrated independently, and the weights of the two speed components can be flexibly allocated to optimize the response characteristics.

[0121] For example, with a target PID speed of 16° / s and a derivative speed of 17.1° / s, a PID adjustment coefficient of 1.0, and a derivative adjustment coefficient of 0.9, the target speed = 16 × 1.0 + 17.1 × 0.9 = 31.39° / s; the adjustment coefficient can change the final speed.

[0122] S605. Based on the second speed difference, the PID torque of the actuator motor is obtained.

[0123] The second speed difference is the speed difference between the target rack speed and the rack speed.

[0124] In some embodiments, the second speed difference = target rack speed - real-time rack speed, and the torque is calculated using positional PID: PID torque = Kp × speed difference + Ki × integral term + Kd × rate of change. Kp and Ki are obtained from a table based on the speed difference and vehicle speed. Kp is preset or fine-tuned to achieve closed-loop control.

[0125] For example, with Kd=0.5, the second speed difference is 3.39° / s, and at a vehicle speed of 40km / h, Kp=12, Ki=0.8, the integral term is 2.5, the rate of change is 1° / s², and the PID torque is 12×3.39+0.8×2.5+0.5×1=43.18N·m; at a vehicle speed of 60km / h, Kp=10, Ki=0.6, and the torque is 35.9N·m.

[0126] Figure 8 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 8 As shown, S102 can be implemented as follows: S701-S703: S701. Based on the differential target rack angle, determine whether the rack rotation state is valid.

[0127] In some embodiments, the rack steering state is specifically a rack reversing state, which is determined by combining the change characteristics of the differential target rack angle. The differential target rack angle is obtained from the ADAS target angle sent by the ADAS system through preset calculation steps. Specifically, by acquiring the differential target rack angle at the current moment and the differential target rack angle at the previous moment in real time, the change in the differential target rack angle at the two moments is calculated, and then it is determined whether the change in angle at the current moment is opposite to the change in angle at the previous moment; at the same time, the duration of the opposite angle change is timed. If the duration of the opposite angle change exceeds a preset threshold, the rack steering state (i.e., rack reversing state) is determined to be established.

[0128] For example, the preset threshold for the duration of opposite angle changes is set to 50ms. If the change in the differential target rack angle at time t is +2° / s (indicating the rack deflects in the first direction), and the change in the differential target rack angle at time t-1 is -1.8° / s (indicating the rack deflects in the second direction opposite to the first direction), and this state of opposite angle changes lasts for 52ms from time t-1, exceeding the preset threshold of 50ms, then the rack steering state can be determined to be established. It should be noted that the preset threshold can be adaptively adjusted according to the steering system parameters and driving conditions of different vehicle models, and is not limited to 50ms. Those skilled in the art can determine the optimal threshold through actual vehicle testing.

[0129] S702. When the rack and pinion rotation state is established, the PID saturation limiting torque is reduced based on a preset ratio to obtain the PID reduction torque, and then the torque of the actuator motor is determined based on the PID reduction torque.

[0130] In some embodiments, the determination of the rack steering state needs to trigger the reduction operation of the PID saturation limiting torque. The triggering time is the rising edge of the rack commutation, that is, the torque reduction process is immediately started when the duration of the detected opposite angle change reaches a preset threshold. The preset ratio is achieved through a preset proportional coefficient, which is a positive number less than 1. Its value needs to be determined in conjunction with actual vehicle calibration. The calibration process needs to cover different driving speeds (such as low speed, medium speed, and high speed), different steering angles, and different load conditions to ensure that under various conditions, the PID reduction torque can make the torque output of the actuator motor adapt to the rack commutation requirements, avoiding steering shock or response delay.

[0131] For example, the preset proportional coefficient is determined to be 0.6 after calibration on a real vehicle. If the current PID saturation limit torque is 100 N·m, after the weakening operation is triggered on the rising edge of the rack commutation, the PID weakening torque is calculated according to the preset ratio. The specific calculation method is: PID weakening torque = PID saturation limit torque × preset proportional coefficient, that is, 100 N·m × 0.6 = 60 N·m. Subsequently, the 60 N·m PID weakening torque is used as the control input of the actuator motor torque, which is converted into the corresponding drive signal by the motor controller to control the actuator motor to output the corresponding torque, so as to achieve smooth steering during the rack commutation process.

[0132] S703. When the rack rotation state is not established, the torque of the actuator motor is determined based on the PID saturation limit torque.

[0133] In some embodiments, the rack steering state not being established includes two scenarios: first, the change in the differential target rack angle at the current moment is the same as the change in angle at the previous moment, and the rack maintains deflection motion in the same direction; second, the change in angle at the current moment is opposite to that at the previous moment, but the duration of the opposite state does not reach the preset threshold, which is considered an instantaneous angle fluctuation. In this case, there is no need to weaken the PID saturation limiting torque; the preset PID saturation limiting torque is directly used as the control reference for the actuator motor torque. The motor controller outputs the corresponding control signal according to the PID saturation limiting torque, causing the actuator motor to output torque according to the conventional steering control logic, ensuring the steering stability and control accuracy of the rack in the non-reversing state.

[0134] For example, if the current differential target rack angle change is +3° / s and the previous angle change was +2.5° / s, and the two are in the same direction, the rack rotation state is not established. In this case, the preset PID saturation limit torque of 80 N·m is taken as the control basis, and the actuator motor outputs the corresponding torque under the action of this torque to drive the rack to deflect smoothly in the target direction. If the current angle change is -2° / s and the previous angle change was +1.5° / s, and the two are in opposite directions, but the duration is only 30ms, which does not reach the preset threshold of 50ms, the rack rotation state is still determined to be not established, and the actuator motor torque is also determined based on the PID saturation limit torque of 80 N·m.

[0135] During the execution of advanced intelligent driving functions in a vehicle, there may be situations where the driver needs to intervene. Due to the uncertainty of human operation, in order to ensure the driving safety of the vehicle, it is necessary to limit the steering control of the vehicle when the driver intervenes.

[0136] Figure 9 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 9As shown, while employing position-based PID control algorithm and tracking derivative control algorithm, and calculating the PID torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal, the method also includes: S801, Obtain the human-machine co-driving hand force coefficient.

[0137] In some embodiments, the human-machine co-driving force coefficient is calculated in real time by the vehicle's advanced driver assistance system (ADAS) and sent to the steering control system (such as the SBW controller). Its value ranges from [0,1] and is used to quantify the driving authority allocation ratio between the driver and the advanced intelligent driving system. Specifically, the ADAS system can output the human-machine co-driving force coefficient based on the vehicle's current driving scenario (such as highway cruising, urban following, emergency obstacle avoidance, etc.), driver operating behavior (such as steering wheel torque, pedal action, line of sight, etc.), and the activation status of intelligent driving functions, through a preset authority allocation model.

[0138] For example, when the ADAS system activates full-speed adaptive cruise control (ACC) and lane centering assist (LCC) functions, and the sensors do not detect any effective operation of the steering wheel by the driver (i.e., the steering wheel torque is less than a preset operation threshold), the output human-machine co-driving force coefficient is 0, indicating that the driving authority is completely controlled by the advanced intelligent driving system; when the driver actively turns the steering wheel, and the steering wheel torque detected by the sensors is greater than the preset participation threshold, the ADAS system adjusts the human-machine co-driving force coefficient to 1, indicating that the driving authority is completely given to the driver; and in the scenario where the driver and the intelligent driving system cooperate in control, the human-machine co-driving force coefficient can be dynamically selected according to the control weight of the two, such as 0.3, 0.6, etc.

[0139] In some embodiments, the acquisition frequency of the human-machine co-driving hand force coefficient is consistent with the control cycle of the steering system, preferably 10ms-20ms, to ensure the real-time nature of permission allocation and thus guarantee the stability of steering control. Simultaneously, the acquired human-machine co-driving hand force coefficient needs to be filtered, for example, using a moving average filtering algorithm to remove high-frequency noise from the signal, avoiding misjudgments in subsequent state assessments due to sudden coefficient changes.

[0140] S802. Based on the human-machine co-driving hand force coefficient and steering wheel torque, determine whether the human-machine co-driving state is valid.

[0141] In some embodiments, it can be determined whether the human-machine co-driving state is established by setting multi-dimensional judgment conditions. The judgment conditions include at least the human-machine co-driving hand force coefficient threshold condition, the steering wheel torque threshold condition, and the duration threshold condition. When all conditions are met at the same time, the human-machine co-driving state is determined to be established.

[0142] For example, the preset threshold for the human-machine co-driving hand force coefficient is 0 (i.e., the condition is satisfied as long as the hand force coefficient is not 0), the preset threshold for the absolute value of the steering wheel torque is 1.2 Nm, and the preset threshold for the duration is 100 ms. In this case, if the acquired human-machine co-driving hand force coefficient is >0, and the absolute value of the steering wheel torque collected by the sensor is >1.2 Nm, and the duration of this state satisfying the first two conditions is >100 ms, then the human-machine co-driving state is determined to be established. If any of the above conditions are not met, for example, if the human-machine co-driving hand force coefficient is 0, or the absolute value of the steering wheel torque is 1.0 Nm (less than 1.2 Nm), or the absolute value of the steering wheel torque is >1.2 Nm but the duration is only 80 ms (less than 100 ms), then the human-machine co-driving state is determined to be not established.

[0143] In some embodiments, the steering wheel torque threshold and duration threshold can be calibrated and adjusted according to parameters such as vehicle type, curb weight, and steering ratio. For example, for small passenger cars, the absolute value threshold of steering wheel torque can be calibrated to 1.0 Nm-1.5 Nm, and the duration threshold can be calibrated to 80 ms-120 ms; for medium-sized commercial vehicles, the absolute value threshold of steering wheel torque can be calibrated to 1.5 Nm-2.0 Nm, and the duration threshold can be calibrated to 100 ms-150 ms, to adapt to the steering characteristic requirements of different vehicle types.

[0144] S803. When the human-machine co-driving state is established, the proportional coefficient and integral coefficient in the position-type PID control algorithm are saturated and limited until the limit release condition is met.

[0145] The conditions for lifting the restriction include: the first angle difference is less than a preset angle difference threshold and the duration is greater than a preset time threshold; the second angle difference is less than a preset angle difference threshold; wherein, the first angle difference is the angle difference between the safety restriction ADAS target angle and the steering wheel angle; and the second angle difference is the angle difference between the target rack angle and the rack angle.

[0146] In some embodiments, the position-based PID control algorithm includes control of steering wheel torque and control of actuator motor torque.

[0147] In some embodiments, when the human-machine co-driving state is determined to be established, the saturation limit of the proportional coefficient (Kp) and integral coefficient (Ki) in the position-type PID control algorithm is activated to constrain the values ​​of Kp and Ki within a preset effective range, so as to avoid the sudden increase of PID torque output due to excessive Kp and Ki, which could lead to loss of control of the steering system.

[0148] For example, in the preset position-based PID control algorithm for the steering wheel motor, the saturation limit range of Kp is [0.5, 2.0], and the saturation limit range of Ki is [0.01, 0.1]. In the position-based PID control algorithm for the execution motor, the saturation limit range of Kp is [0.8, 2.5], and the saturation limit range of Ki is [0.02, 0.15]. When the human-machine co-driving state is determined to be established, if the current Kp value is 2.8 (exceeding the upper limit of the limit range of 2.5), it is forcibly adjusted to 2.5; if the current Ki value is 0.005 (exceeding the lower limit of the limit range of 0.01), it is forcibly adjusted to 0.01, ensuring that the PID control parameters are always within the safe value range.

[0149] For example, the threshold values ​​for the first angle difference and the second angle difference decrease as the vehicle speed increases, as specifically calibrated in Table 3 below. These threshold values ​​are applicable to determining the release of the restrictions on the steering wheel angle difference and rack angle difference. Table 3

[0150] In some embodiments, the preset time threshold for lifting the restriction is 30ms. That is, the saturation restriction on Kp and Ki in the position-based PID control algorithm can be lifted only when the angle difference is less than the preset angle difference threshold at the corresponding vehicle speed and the duration of this state is greater than 30ms. For example, when the vehicle speed is 60km / h, the corresponding preset angle difference threshold is 12°. If the first angle difference remains at 10° (less than 12°) for 35ms, the saturation restriction on Kp and Ki is lifted, and their normal value range is restored. If the first angle difference remains at 10° for only 25ms, the saturation restriction state is maintained.

[0151] In some embodiments, even if the human-machine co-driving state is exited (e.g., the human-machine co-driving hand force coefficient is 0, or the driver's hand torque is less than a preset threshold and the duration is greater than the preset threshold), Kp and Ki still need to be saturated and limited until the above-mentioned limitation release conditions are met. This is because if the limitations of Kp and Ki are released too early, the calculated PID torque will increase sharply due to the potentially large angle difference at this time, causing the steering wheel to return to center at an extremely fast speed (e.g., 800° / s rotation speed), which may lead to loss of vehicle control. Therefore, the limitations need to be maintained until the angle difference stabilizes within a safe range.

[0152] Before controlling the torque of the vehicle's steering system, it is necessary to confirm whether there are any faults in the ADAS and SBW systems and to ensure that the communication between the ADAS and SBW systems is normal in order to ensure that the system is operating normally.

[0153] Figure 10 A flowchart of another vehicle steering control method provided in the embodiments of this application is shown below. Figure 10 As shown, the method also includes: S901, Obtain the fault status of the SBW system itself.

[0154] In some embodiments, the electronic control unit of the SBW system collects the operating parameters of each core component in the system in real time through a built-in fault diagnosis module. The core components include, but are not limited to, steering actuator motor, torque sensor, angle sensor, communication bus, power module, etc.

[0155] S902. If the SBW system is not faulty and the vehicle driving data and vehicle steering status data are normal, send status feedback information to ADAS.

[0156] The status feedback information is used to indicate that the SBW system has not experienced a fault.

[0157] In some embodiments, the collected real-time parameters can be compared with the normal operating threshold range of each component according to the preset diagnostic logic: if the real-time parameter of a certain component exceeds the threshold range (for example, the working current of the steering motor is greater than the rated maximum current, or the feedback signal of the angle sensor jumps or has no signal), it is determined that the SBW system has a corresponding type of fault, and fault status information containing fault code and fault component identification is generated; if the real-time parameters of all components are within the normal threshold range, it is determined that the SBW system has no fault, and a fault-free status identifier is generated.

[0158] In some embodiments, the SBW system and ADAS communicate and interact via CAN signals.

[0159] S903: In response to the handshake request sent by ADAS, perform multi-level handshake verification with ADAS.

[0160] The handshake request is sent by ADAS when it receives status feedback information; the multi-level handshake verification is used to confirm the legitimate communication identity between the SBW system and ADAS, and to verify the stability of the communication link and the reliability of data transmission.

[0161] In some embodiments, during the interaction between the SBW system and the ADAS system, the SBW system first collects the vehicle speed signal status, Ready signal, steering wheel torque sensor signal status, steering wheel angle sensor signal status, and rack angle sensor signal status in real time. After verifying that all are normal and the SBW system is fault-free, the SBW system reports a normal fault status to the ADAS system. Upon receiving this fault status, the ADAS system detects that the driver has performed an advanced intelligent driving activation operation on the steering wheel (such as pressing the activation button), and then sends an ADAS handshake request with a Ready status. After receiving a Ready handshake request, the SBW system performs a handshake check in the Ready phase. If the check is successful, it sends a handshake status of Ready to the ADAS system. If the check fails, it sends a fault reason to the ADAS system. If the ADAS system continues to request, the SBW system repeats the handshake check until it succeeds or the request terminates. After receiving the Ready status, the ADAS system sends a handshake request with a status of Active to the SBW system. The SBW system performs a handshake check in the Active phase. If the check is successful, it sends a handshake status of Active to the ADAS system. If the check fails, it sends a fault reason to the ADAS system. If the ADAS system continues to request, the SBW system repeats the handshake check until it succeeds or the request terminates.

[0162] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0163] This application embodiment can divide the vehicle steering control device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0164] Figure 11 This is a schematic diagram of a vehicle steering control device provided in an embodiment of this application, used to implement the vehicle steering control method provided in the above embodiments, such as... Figure 11 As shown, the vehicle steering control device 600 includes an acquisition unit 601 and a control unit 602. The acquisition unit 601 is used to acquire vehicle driving data, vehicle steering state data, and an advanced driver assistance system (ADAS) target angle signal. The ADAS target angle signal is used to reflect the steering wheel rotation angle desired by the ADAS. The control unit 602 is used to calculate the steering system torque based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal using a position-based PID control algorithm and a tracking derivative control algorithm. The control unit 602 controls the vehicle steering based on the steering system torque.

[0165] In some embodiments, the control unit 602 is specifically used to perform safety verification on the ADAS target angle signal to obtain a safety-limited ADAS target angle; and to calculate the torque of the steering system based on vehicle driving data, vehicle steering state data, ADAS target angle signal and safety-limited ADAS target angle using a position-type PID control algorithm and a tracking derivative control algorithm.

[0166] In some embodiments, the control unit 602 is specifically used to determine the maximum target angle of ADAS at the current vehicle speed based on the vehicle's current speed and a preset correspondence; the preset correspondence is used to reflect the maximum target angle of ADAS corresponding to different vehicle speeds; and to determine the safety-limited ADAS target angle based on the ADAS target angle signal, the maximum target angle of ADAS at the current vehicle speed, and the historical safety-limited ADAS target angle.

[0167] In some embodiments, the control unit 602 is specifically used to employ a position-based PID control algorithm and a tracking derivative control algorithm to calculate the PID torque of the steering system based on vehicle driving data, vehicle steering state data, and ADAS target angle signal; when the integral term of the PID controller has not entered the integral saturation region, the torque of the steering system is determined based on the PID torque of the steering system; when the integral term of the PID controller has entered the integral saturation region, the torque of the steering system is determined based on the PID saturation limit torque.

[0168] In some embodiments, when the steering system is a steering wheel, the vehicle steering state data includes the steering wheel angle and steering wheel speed; the vehicle driving data includes the vehicle speed; the control unit 602 is specifically used to use a position-based PID control algorithm to obtain a target PID steering wheel speed based on a first angle difference and the vehicle speed; the first angle difference is the angle difference between the safety limit ADAS target angle and the steering wheel angle; to use a tracking differential control algorithm to obtain a target differential steering wheel speed based on the ADAS target angle signal; to determine the target steering wheel speed based on the target PID steering wheel speed and the target differential steering wheel speed; and to obtain the PID torque of the steering wheel based on the first speed difference; the first speed difference is the speed difference between the target steering wheel speed and the steering wheel speed.

[0169] In some embodiments, when the steering system is an actuator motor, the vehicle steering state data includes rack angle and rack speed; the vehicle driving data includes vehicle speed; the control unit 602 is specifically used to determine the target rack angle and the derivative target rack angle based on the safety limit ADAS target angle and the ADAS target angle signal; using a position-based PID control algorithm, based on the second angle difference and the vehicle speed, to obtain the target PID rack speed; the second angle difference is the angle difference between the target rack angle and the rack angle; using a tracking derivative control algorithm, based on the derivative target rack angle, to obtain the target derivative rack speed; based on the target PID rack speed and the target derivative rack speed, to determine the target rack speed; and based on the second speed difference, to obtain the PID torque of the actuator motor; the second speed difference is the speed difference between the target rack speed and the rack speed.

[0170] In some embodiments, the control unit 602 is specifically used to determine whether the rack rotation state is established based on the differential target rack angle; if the rack rotation state is established, the PID saturation limiting torque is reduced based on a preset ratio to obtain the PID reduction torque, and then the torque of the actuator motor is determined based on the PID reduction torque; if the rack rotation state is not established, the torque of the actuator motor is determined based on the PID saturation limiting torque.

[0171] In some embodiments, the vehicle steering state data includes steering wheel angle and steering wheel torque; the control unit 602 is further configured to acquire the human-machine co-driving hand force coefficient; determine whether the human-machine co-driving state is established based on the human-machine co-driving hand force coefficient and steering wheel torque; when the human-machine co-driving state is established, saturate the proportional coefficient and integral coefficient in the position-type PID control algorithm until the restriction release condition is met; wherein, the restriction release condition includes: a first angle difference is less than a preset angle difference threshold, and the duration is greater than a preset time threshold; a second angle difference is less than a preset angle difference threshold; wherein, the first angle difference is the angle difference between the safety restriction ADAS target angle and the steering wheel angle; the second angle difference is the angle difference between the target rack angle and the rack angle.

[0172] In some embodiments, before calculating the torque of the steering system, the control unit 602 is further configured to acquire the fault status of the SBW system itself; if the SBW system is not faulty and the vehicle driving data and vehicle steering status data are normal, send status feedback information to the ADAS; the status feedback information is used to indicate that the SBW system is not faulty; in response to a handshake request sent by the ADAS, perform multi-level handshake verification with the ADAS; the handshake request is sent by the ADAS when it receives the status feedback information; the multi-level handshake verification is used to confirm the legitimate communication identity of the SBW system and the ADAS, and to verify the stability of the communication link and the reliability of data transmission.

[0173] In the case of implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 12 As shown, the electronic device 700 includes: a processor 702, a communication interface 703, and a bus 704. Optionally, the electronic device 700 may also include a memory 701.

[0174] Processor 702 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 702 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 702 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0175] The communication interface 703 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0176] The memory 701 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0177] In one possible implementation, the memory 701 can exist independently of the processor 702. The memory 701 can be connected to the processor 702 via a bus 704 and is used to store instructions or program code. When the processor 702 calls and executes the instructions or program code stored in the memory 701, it can implement the vehicle steering control method provided in this embodiment of the invention.

[0178] In another possible implementation, the memory 701 can also be integrated with the processor 702.

[0179] The 704 bus can be an extended industry standard architecture (EISA) bus, etc. The 704 bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0180] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0181] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be instructed by computer program instructions to be implemented by related hardware. This program can be stored in the aforementioned computer-readable storage medium. When the computer program instructions are executed on a computer, they cause the computer to perform the vehicle steering control method as described in any of the above embodiments.

[0182] Exemplary examples of computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this disclosure may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0183] This application also provides a computer program product comprising a computer program that, when run on a computer, causes the computer to execute any of the vehicle steering control methods provided in the above embodiments.

[0184] In the description of the embodiments of this application, specific features, structures, materials or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0185] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle steering control method, characterized in that, The method, applied to a steer-by-wire (SBW) system, includes: Acquire vehicle driving data, vehicle steering status data, and Advanced Driver Assistance System (ADAS) target angle signal; the ADAS target angle signal is used to reflect the steering wheel rotation angle desired by ADAS. The position-based PID control algorithm and the tracking derivative control algorithm are used to calculate the torque of the steering system based on the vehicle driving data, the vehicle steering state data and the ADAS target angle signal; The vehicle steering is controlled based on the torque of the steering system.

2. The method according to claim 1, characterized in that, The method employs a position-based PID control algorithm and a tracking derivative control algorithm to calculate the torque of the steering system based on the vehicle driving data, the vehicle steering state data, and the ADAS target angle signal, including: The ADAS target angle signal is subjected to safety verification to obtain the safety-limited ADAS target angle; The torque of the steering system is calculated by employing a position-based PID control algorithm and a tracking derivative control algorithm, based on the vehicle driving data, the vehicle steering state data, the ADAS target angle signal, and the safety-limiting ADAS target angle.

3. The method according to claim 2, characterized in that, The step of performing safety verification on the ADAS target angle signal to obtain a safety-limited ADAS target angle includes: Based on the vehicle's current speed and a preset correspondence, the maximum target angle of ADAS at the current speed is determined; the preset correspondence is used to reflect the maximum target angle of ADAS corresponding to different vehicle speeds. The safety-limited ADAS target angle is determined based on the ADAS target angle signal, the maximum ADAS target angle at the current vehicle speed, and the historical safety-limited ADAS target angle.

4. The method according to claim 1, characterized in that, The method employs a position-based PID control algorithm and a tracking derivative control algorithm to calculate the torque of the steering system based on the vehicle driving data, the vehicle steering state data, and the ADAS target angle signal, including: The position-based PID control algorithm and the tracking derivative control algorithm are used to calculate the PID torque of the steering system based on the vehicle driving data, the vehicle steering state data and the ADAS target angle signal; If the integral term of the PID controller has not entered the integral saturation region, the torque of the steering system is determined based on the PID torque of the steering system. When the integral term of the PID controller enters the integral saturation region, the torque of the steering system is determined based on the PID saturation limit torque.

5. The method according to claim 4, characterized in that, When the steering system is a steering wheel, the vehicle steering state data includes the steering wheel angle and steering wheel rotation speed; the vehicle driving data includes vehicle speed; the calculation of the PID torque of the steering system using a position-based PID control algorithm and a tracking derivative control algorithm, based on the vehicle driving data, the vehicle steering state data, and the ADAS target angle signal, includes: Using the position-based PID control algorithm, the target PID steering wheel speed is obtained based on the first angle difference and the vehicle speed; the first angle difference is the angle difference between the safety limit ADAS target angle and the steering wheel angle. Using the aforementioned tracking differential control algorithm, the target differential steering wheel speed is obtained based on the ADAS target angle signal; The target steering wheel speed is determined based on the target PID steering wheel speed and the target differential steering wheel speed. The PID torque of the steering wheel is obtained based on the first speed difference; the first speed difference is the speed difference between the target steering wheel speed and the steering wheel speed.

6. The method according to claim 4, characterized in that, When the steering system is an actuator motor, the vehicle steering state data includes rack angle and rack speed; the vehicle driving data includes vehicle speed; the calculation of the PID torque of the steering system using a position-based PID control algorithm and a tracking derivative control algorithm, based on the vehicle driving data, vehicle steering state data, and ADAS target angle signal, includes: Based on the safety-limited ADAS target angle and the ADAS target angle signal, determine the target rack angle and the differential target rack angle; Using the position-based PID control algorithm, the target PID rack rotation speed is obtained based on the second angle difference and the vehicle speed; the second angle difference is the angle difference between the target rack angle and the rack angle. Using the aforementioned tracking differential control algorithm, the target differential rack rotation speed is obtained based on the differential target rack angle; The target rack speed is determined based on the target PID rack speed and the target differential rack speed. The PID torque of the actuator is obtained based on the second speed difference; the second speed difference is the speed difference between the target rack speed and the rack speed.

7. The method according to claim 6, characterized in that, The determination of the steering system torque based on PID saturation limiting torque includes: Based on the differential target rack angle, determine whether the rack rotation state is valid; When the rack and pinion rotation state is established, the PID saturation limiting torque is reduced based on a preset ratio to obtain the PID reduction torque, and then the torque of the actuator motor is determined based on the PID reduction torque. If the rack rotation state is not established, the torque of the actuator motor is determined based on the PID saturation limiting torque.

8. The method according to claim 4, characterized in that, The vehicle steering status data includes steering wheel angle and steering wheel torque; While employing a position-based PID control algorithm and a tracking derivative control algorithm, and calculating the PID torque of the steering system based on the vehicle driving data, the vehicle steering state data, and the ADAS target angle signal, the method further includes: Obtain the human-machine co-driving hand force coefficient; Based on the human-machine co-driving hand force coefficient and the steering wheel torque, determine whether the human-machine co-driving state is established; When the human-machine co-driving state is established, the proportional coefficient and integral coefficient in the position-type PID control algorithm are saturated and limited until the limitation release condition is met; wherein, the limitation release condition includes: a first angle difference is less than a preset angle difference threshold and the duration is greater than a preset time threshold; a second angle difference is less than the preset angle difference threshold; wherein, the first angle difference is the angle difference between the safety limitation ADAS target angle and the steering wheel angle; the second angle difference is the angle difference between the target rack angle and the rack angle.

9. The method according to claim 1, characterized in that, Before calculating the torque of the steering system, the method further includes: Obtain the fault status of the SBW system itself; If the SBW system is not malfunctioning and the vehicle driving data and vehicle steering status data are both normal, a status feedback message is sent to the ADAS; the status feedback message is used to indicate that the SBW system is not malfunctioning. In response to a handshake request sent by the ADAS, a multi-level handshake verification is performed with the ADAS; the handshake request is sent by the ADAS when it receives the status feedback information; the multi-level handshake verification is used to confirm the legitimate communication identity of the SBW system and the ADAS, and to verify the stability of the communication link and the reliability of data transmission.

10. A vehicle, characterized in that the vehicle... include: processor; Memory configured to store processor-executable instructions; The processor is configured to execute instructions to implement the method as described in any one of claims 1 to 9.

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