Stable control method for two-wheeled vehicle under low-speed and start-stop working conditions

Abstract

The invention discloses a stable control method for a two-wheeled vehicle under low-speed and start-stop working conditions, which belongs to the technical field of two-wheeled vehicle control, and comprises the following steps: collecting state parameters and environmental parameters in the running process of the two-wheeled vehicle, constructing a parameter set, according to the method, multi-dimensional parameters are collected and preprocessed, working condition types are accurately recognized, hierarchical state switching conditions are set, the priority of a stable control target is dynamically adjusted, multi-type control instructions are output, closed-loop correction is carried out, and auxiliary support control is combined. The attitude stability, the steering safety and the power output stability of the two-wheeled vehicle under the low-speed and start-stop full working conditions are remarkably improved, the driving operation difficulty is reduced, the toppling risk is reduced, the driving safety and the riding comfort are improved, the flexibility, the pertinence and the control precision of a control strategy are enhanced, the multi-scene low-speed start-stop operation requirement is met, and the control method is suitable for the multi-scene low-speed start-stop operation requirement. And the practicability and the reliability are higher.

Description

Technical Field

[0001] This invention belongs to the field of two-wheeled vehicle control technology, specifically a method for stable control of two-wheeled vehicles under low speed and start-stop conditions. Background Technology

[0002] Two-wheeled vehicles have become an important means of transportation for short-distance urban travel due to their flexibility, convenience, energy saving and environmental protection advantages. Low-speed driving and starting and stopping are high-frequency scenarios in the daily use of two-wheeled vehicles. In these scenarios, the vehicle speed is low, the power output fluctuates greatly, the vehicle posture is easily disturbed, and the driver's operating intentions are complex. Its stable control performance is directly related to driving safety and comfort.

[0003] However, existing vehicle stability control methods under low-speed and start-stop conditions still have certain shortcomings. Current technologies rely on single-parameter acquisition and lack effective outlier removal and data smoothing preprocessing, resulting in insufficient data reliability and an inability to provide accurate and comprehensive data support for auxiliary support participation in decision-making. The operating condition identification method is crude, lacking multi-feature fusion algorithms and a reliable verification mechanism for matching real-time operating condition features with historical sample databases, making it prone to misjudgment and leading to deviations in auxiliary support participation. The control target priority remains fixed and cannot be dynamically adjusted according to different low-speed start-stop conditions and the vehicle's real-time status, resulting in a lack of targeted auxiliary support control and difficulty in prioritizing attitude stability under conditions prone to attitude instability, such as starting and braking. Furthermore, the auxiliary support participation decision-making lacks a pre-activation mechanism, and the judgment criteria are vague, failing to accurately adapt to conditions such as start-up preparation and low-speed steering. The existing control system lacks support requirements and a closed-loop correction mechanism during independent control. This makes it impossible to optimize control based on actuator feedback, resulting in low control precision and difficulty in achieving accurate attitude control through independent adjustment of the extension and support force of the auxiliary supports on both sides. Furthermore, the lack of hierarchical setting of state switching conditions and the absence of a combined main trigger and auxiliary verification mechanism makes it prone to erroneous intervention and withdrawal of auxiliary supports due to instantaneous parameter fluctuations, leading to control malfunctions. Additionally, the system fails to differentiate the priority of onboard network messages, resulting in delayed transmission of auxiliary support control commands when stability risks occur, hindering rapid response to instability risks. The overall control strategy lacks flexibility, specificity, and reliability, making it difficult to adapt to low-speed and start-stop conditions in various scenarios and effectively mitigating tipping risks. Therefore, a stability control method for two-wheeled vehicles under low-speed and start-stop conditions is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for stabilizing two-wheeled vehicles under low-speed and start-stop conditions, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for stabilizing a two-wheeled vehicle under low-speed and start-stop conditions, comprising the following steps: S1. Collect the state parameters and environmental parameters of the two-wheeled vehicle during operation, and construct a parameter set. The state parameters include at least vehicle speed, acceleration, attitude parameters and driving operation parameters. S2. Based on the parameter set, identify the current operating condition type of the vehicle, wherein the operating condition type includes at least starting condition, low-speed driving condition, braking and stopping condition, and stable stopping condition; S3. Set state switching conditions according to the working condition type. The state switching conditions include at least a main trigger condition and an auxiliary verification condition. S4. Initialize the priority order of stability control targets according to the working condition type. The stability control targets include at least attitude stability control targets, steering safety control targets, and power output smooth control targets. S5. Monitor vehicle parameters in real time and perform state switching determination. When the determination meets the state switching conditions, generate a switching trigger signal. S6. Dynamically adjust the priority order of the stable control targets according to the switching trigger signal; S7. Output control commands according to the adjusted priority order. The control commands include at least one of the following: drive control command, steering control command, braking control command and auxiliary support control command. Closed-loop correction is performed based on the feedback from the actuator to achieve stable attitude control under low speed and start-stop conditions.

[0006] Preferably, the parameter set includes vehicle operating status parameters, vehicle body posture parameters, external environment parameters, and driver operation command parameters; Parameter acquisition is achieved through vehicle-mounted sensors, which include at least an inertial measurement unit, wheel speed sensors, steering angle sensors, and / or slope sensors. Furthermore, preprocessing is performed on the collected raw data, including outlier removal, data smoothing, and / or normalization, wherein the normalization process uses a linear normalization formula: , In the formula, These are the normalized parameter values. The original value of the parameter. The maximum value of the parameter. This is the minimum value of the parameter.

[0007] Preferably, the working condition identification is based on a multi-feature fusion algorithm, including extracting the temporal features and / or correlation features of the parameter set and inputting them into the working condition identification model to output the working condition type; The operating condition identification model includes at least one of the following: rule-based decision model, decision tree model, support vector machine model, neural network model, or probability model.

[0008] Preferably, the operating condition identification includes a reliability verification mechanism, which includes matching real-time operating condition features with a historical operating condition sample library; when the matching result is lower than a preset confidence threshold, a secondary identification process is triggered, and the feature weights or model parameters are adjusted to update the operating condition identification result.

[0009] Preferably, the state switching conditions are set separately for switching between different working conditions, and each working condition switching scenario includes a main triggering condition and an auxiliary verification condition. Among them, the main triggering condition for switching from starting condition to low-speed driving condition includes at least the vehicle speed reaching a preset low-speed threshold and satisfying the steady-state characteristics of power output, and the auxiliary verification condition includes at least the stability of driver operation commands or attitude parameters.

[0010] Preferably, the state switching determination includes a switching validity determination mechanism, which includes performing continuous satisfaction determination and / or logical consistency verification on the main triggering condition and auxiliary verification condition; When the condition is detected to be met only momentarily or not in accordance with consistency requirements, the generation of the switching trigger signal is suppressed, and the stability of the condition is verified using a logical verification formula. , In the common formula, This is the conditional validity judgment value, where T is the duration of continuous monitoring. Let the condition be satisfied at time t; when When the preset threshold is reached, a switching trigger signal is generated; if the threshold is not reached, it is determined that the condition is met momentarily and no switching trigger signal is generated.

[0011] Preferably, the stability control objectives include at least: vehicle attitude stability control objective, power output smooth control objective, braking stability control objective and / or attitude correction control objective; Furthermore, initial priorities are set according to the type of operating condition, with the vehicle attitude stability control target having the highest priority under starting, braking and stopping, or parking stability conditions.

[0012] Preferably, the real-time monitoring adopts a combination of high-frequency monitoring of core parameters and periodic verification of auxiliary parameters. The core parameters include at least attitude parameters, wheel speed parameters and driving operation parameters, and the auxiliary parameters include at least slope parameters, power source status and / or auxiliary support contact status. A switching trigger signal is generated through joint logic, which includes at least main trigger condition verification, auxiliary verification condition verification, and validity determination.

[0013] Preferably, the priority dynamic adjustment is based on vehicle attitude parameters, vehicle speed change rate, steering input, slope parameters and / or auxiliary support contact status; The priority dynamic adjustment adopts at least one of fuzzy logic, rule weight allocation, or learning-based update algorithm; When a state transition occurs, the priority is adjusted according to the new operating condition type; when no transition occurs, the priority is fine-tuned based on the vehicle's real-time operating status; dynamic updates of the priority are achieved through adjustment formulas. , In the formula, This is the adjusted priority quantization value. The initial priority quantization value set for S4, To retain coefficients for priority, The priority adjustment amount is generated based on fuzzy rules. During the adjustment process, if a state switch is triggered, the priority of the core control target is adjusted according to the working condition type after the switch. If no switch is triggered, the priority is fine-tuned based on the real-time operating status of the vehicle.

[0014] Preferably, the closed-loop correction includes collecting feedback signals from the actuator and comparing them with the expected effect of the control command; when the deviation exceeds a threshold, a correction amount is generated and the control command is updated; The closed-loop correction employs at least one of proportional-integral-derivative PID control, model predictive control (MPC), or sliding mode control.

[0015] Preferably, the operating condition type further includes start-up preparation operating condition and / or low-speed steering operating condition; When it is determined that the vehicle is in a starting preparation state, braking and stopping state, parking stability state and / or low speed steering state, the auxiliary support actuator is triggered to enter the pre-activation state and perform support to participate in the determination. When the attitude parameters meet the preset instability triggering conditions or when it is determined that there is a slope condition or a road surface height difference condition, an auxiliary support intervention control command is output, so that the auxiliary support actuators on the left and right sides of the vehicle adjust the extension amount and / or support force in an independent closed-loop manner to maintain the preset attitude target. When the vehicle speed exceeds the preset speed threshold and the attitude parameters meet the stability conditions, an auxiliary support exit control command is output to retract and lock the auxiliary support actuator.

[0016] Preferably, the priority dynamic adjustment includes a stability risk triggering mechanism, which triggers a stability risk event when the attitude parameters exceed the stability boundary threshold, the attitude change rate exceeds the threshold, the vehicle speed change rate exceeds the threshold, and / or an abnormal contact of the auxiliary support is detected. Upon triggering a stability risk event, the attitude stability control objective is forcibly set to the highest priority, and at least one or a combination of the following control strategies are executed: a) Perform dynamic amplitude limiting or speed limiting control on the drive control output; b) Limit the steering angle and / or steering angular velocity of the steering control output; c) Output auxiliary support intervention control commands to provide physical stability support; Furthermore, the control commands are sent through the vehicle network, and the network messages corresponding to the stability control commands have a higher communication priority than the steering control messages and drive control messages, which is used to preempt vehicle network communication resources when a stability risk event occurs.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention effectively solves the above-mentioned core problems by collecting and preprocessing multi-dimensional parameters, accurately identifying working condition types, setting hierarchical state switching conditions, dynamically adjusting the priority of stable control targets, outputting multiple types of control commands and performing closed-loop correction, and combining auxiliary support control. It significantly improves the attitude stability, steering safety and power output smoothness of two-wheeled vehicles under low speed and start-stop conditions, reduces the difficulty of driving operation, reduces the risk of tipping over, and improves driving safety and ride comfort. At the same time, it enhances the flexibility, pertinence and control accuracy of the control strategy, adapts to the low-speed start-stop operation needs of multiple scenarios, and has stronger practicality and reliability. 2. This invention extracts the temporal and correlation features of parameters by employing a multi-feature fusion algorithm, and combines various working condition identification models such as rule-based judgment models and neural network models to output working condition types, which greatly improves the accuracy and adaptability of working condition identification. It can accurately distinguish various low-speed start-stop working conditions such as starting, low-speed driving, braking and stopping, and parking stability. A reliability verification mechanism is introduced, which matches real-time working condition features with a historical working condition sample library to verify the credibility of the identification results. When the matching result is lower than a preset threshold, a secondary identification is triggered and the feature weights or model parameters are adjusted, which effectively avoids the problem of misidentification of working conditions caused by instantaneous parameter fluctuations, further improves the reliability of working condition identification, and ensures that the stable control strategy can accurately adapt to different working condition requirements. 3. This invention initializes the priority ranking of stability control targets according to different operating conditions, clarifying the initial priority of core control targets such as attitude stability, steering safety, and smooth power output, thus achieving targeted adaptation of control targets. For operating conditions where attitude is easily unbalanced, such as starting, braking and stopping, and parking stability, the vehicle attitude stability control target is set as the highest priority to ensure vehicle attitude balance and reduce the risk of tipping over from the source. For low-speed driving conditions, both attitude stability and smooth power output are taken into account to improve ride comfort and driving safety. In the prior art, if smooth power output is fixed as the highest priority, attitude stability may be ignored due to power adjustment during starting conditions, leading to vehicle tipping over. Through targeted priority initialization, it is ensured that core stability requirements are prioritized under different operating conditions, improving the targeting and effectiveness of stability control. 4. This invention dynamically adjusts the priority ranking of stability control targets based on switching trigger signals and real-time operating parameters such as vehicle attitude parameters, speed change rate, and slope parameters, using algorithms such as fuzzy logic and rule-based weight allocation. Compared to the fixed control priorities in existing technologies, this invention achieves dynamic adaptation and flexible adjustment of control priorities. It can quickly adjust the priority ranking to adapt to the core control requirements of the new operating condition when the operating condition changes, such as appropriately increasing the priority of the power output smoothness control target when switching from a starting condition to a low-speed driving condition, balancing attitude stability and power smoothness. Furthermore, it can fine-tune the priorities when the vehicle state fluctuates even without a change in operating condition. When abnormal attitude parameters are detected, the priority of the attitude stability control target is temporarily increased to prioritize preventing the vehicle from tipping over. This significantly improves the pertinence and flexibility of the stability control strategy, ensuring that core stability requirements are prioritized in different scenarios, further enhancing the stability and safety of vehicle operation. Attached Figure Description

[0018] Figure 1 The present invention describes the operation flow of a stability control method for two-wheeled vehicles under low-speed and start-stop conditions. Figure 1 ; Figure 2 The present invention describes the operation flow of a stability control method for two-wheeled vehicles under low-speed and start-stop conditions. Figure 2 ; Figure 3 The present invention describes the operation flow of a stability control method for two-wheeled vehicles under low-speed and start-stop conditions. Figure 3 ; Figure 4 The present invention describes the operation flow of a stability control method for two-wheeled vehicles under low-speed and start-stop conditions. Figure 4 . Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example Please see Figure 1-4 As shown, the present invention provides a technical solution comprising the following steps: S1. Collect the state parameters and environmental parameters of the two-wheeled vehicle during operation, and construct a parameter set. The state parameters include at least vehicle speed, acceleration, attitude parameters and driving operation parameters. S2. Based on the parameter set, identify the current operating condition type of the vehicle, wherein the operating condition type includes at least starting condition, low-speed driving condition, braking and stopping condition, and stable stopping condition; S3. Set state switching conditions according to the working condition type. The state switching conditions include at least a main trigger condition and an auxiliary verification condition. S4. Initialize the priority order of stability control targets according to the working condition type. The stability control targets include at least attitude stability control targets, steering safety control targets, and power output smooth control targets. S5. Monitor vehicle parameters in real time and perform state switching determination. When the determination meets the state switching conditions, generate a switching trigger signal. S6. Dynamically adjust the priority order of the stable control targets according to the switching trigger signal; S7. Output control commands according to the adjusted priority order. The control commands include at least one of the following: drive control command, steering control command, braking control command and auxiliary support control command. Closed-loop correction is performed based on the feedback from the actuator to achieve stable attitude control under low speed and start-stop conditions.

[0021] In this embodiment, the parameter set includes vehicle operating status parameters, vehicle body posture parameters, external environment parameters, and driver operation command parameters; Parameter acquisition is achieved through vehicle-mounted sensors, which include at least an inertial measurement unit, wheel speed sensors, steering angle sensors, and / or slope sensors. Furthermore, preprocessing is performed on the collected raw data, including outlier removal, data smoothing, and / or normalization, wherein the normalization process uses a linear normalization formula: , In the formula, These are the normalized parameter values. The original value of the parameter. The maximum value of the parameter. This is the minimum value of the parameter.

[0022] In this embodiment, the working condition identification is based on a multi-feature fusion algorithm, which includes extracting the temporal features and / or correlation features of the parameter set and inputting them into the working condition identification model to output the working condition type. The operating condition identification model includes at least one of the following: rule-based decision model, decision tree model, support vector machine model, neural network model, or probability model.

[0023] In this embodiment, the operating condition identification includes a reliability verification mechanism, which includes matching real-time operating condition features with a historical operating condition sample library; when the matching result is lower than a preset confidence threshold, a secondary identification process is triggered, and the feature weights or model parameters are adjusted to update the operating condition identification result.

[0024] In this embodiment, the state switching conditions are set separately for switching between different working conditions, and each working condition switching scenario includes a main trigger condition and an auxiliary verification condition. Among them, the main triggering condition for switching from starting condition to low-speed driving condition includes at least the vehicle speed reaching a preset low-speed threshold and satisfying the steady-state characteristics of power output, and the auxiliary verification condition includes at least the stability of driver operation commands or attitude parameters.

[0025] In this embodiment, the state switching determination includes a switching validity determination mechanism, which includes performing continuous satisfaction determination and / or logical consistency verification on the main triggering condition and auxiliary verification condition. When the condition is detected to be met only momentarily or not in accordance with consistency requirements, the generation of the switching trigger signal is suppressed, and the stability of the condition is verified using a logical verification formula. , In the common formula, This is the conditional validity judgment value, where T is the duration of continuous monitoring. Let the condition be satisfied at time t; when When the preset threshold is reached, a switching trigger signal is generated; if the threshold is not reached, it is determined that the condition is met momentarily and no switching trigger signal is generated.

[0026] In this embodiment, the stability control target includes at least: vehicle attitude stability control target, power output smooth control target, braking stability control target and / or attitude correction control target; Furthermore, initial priorities are set according to the type of operating condition, with the vehicle attitude stability control target having the highest priority under starting, braking and stopping, or parking stability conditions.

[0027] In this embodiment, the real-time monitoring adopts a combination of high-frequency monitoring of core parameters and periodic verification of auxiliary parameters. The core parameters include at least attitude parameters, wheel speed parameters and driving operation parameters, and the auxiliary parameters include at least slope parameters, power source status and / or auxiliary support contact status. A switching trigger signal is generated through joint logic, which includes at least main trigger condition verification, auxiliary verification condition verification, and validity determination.

[0028] In this embodiment, the priority dynamic adjustment is based on vehicle attitude parameters, vehicle speed change rate, steering input, slope parameters and / or auxiliary support contact status; The priority dynamic adjustment adopts at least one of fuzzy logic, rule weight allocation, or learning-based update algorithm; When a state transition occurs, the priority is adjusted according to the new operating condition type; when no transition occurs, the priority is fine-tuned based on the vehicle's real-time operating status; dynamic updates of the priority are achieved through adjustment formulas. , In the formula, This is the adjusted priority quantization value. The initial priority quantization value set for S4, To retain coefficients for priority, The priority adjustment amount is generated based on fuzzy rules. During the adjustment process, if a state switch is triggered, the priority of the core control target is adjusted according to the working condition type after the switch. If no switch is triggered, the priority is fine-tuned based on the real-time operating status of the vehicle.

[0029] In this embodiment, the closed-loop correction includes collecting feedback signals from the actuator and comparing them with the expected effect of the control command; when the deviation exceeds a threshold, a correction amount is generated and the control command is updated; The closed-loop correction employs at least one of proportional-integral-derivative PID control, model predictive control (MPC), or sliding mode control.

[0030] In this embodiment, the operating condition type further includes starting preparation operating condition and / or low-speed steering operating condition; When it is determined that the vehicle is in a starting preparation state, braking and stopping state, parking stability state and / or low speed steering state, the auxiliary support actuator is triggered to enter the pre-activation state and perform support to participate in the determination. When the attitude parameters meet the preset instability triggering conditions or when it is determined that there is a slope condition or a road surface height difference condition, an auxiliary support intervention control command is output, so that the auxiliary support actuators on the left and right sides of the vehicle adjust the extension amount and / or support force in an independent closed-loop manner to maintain the preset attitude target. When the vehicle speed exceeds the preset speed threshold and the attitude parameters meet the stability conditions, an auxiliary support exit control command is output to retract and lock the auxiliary support actuator.

[0031] In this embodiment, the priority dynamic adjustment includes a stability risk triggering mechanism. When the attitude parameters exceed the stability boundary threshold, the attitude change rate exceeds the threshold, the vehicle speed change rate exceeds the threshold, and / or an abnormal contact of the auxiliary support is detected, a stability risk event is triggered. Upon triggering a stability risk event, the attitude stability control objective is forcibly set to the highest priority, and at least one or a combination of the following control strategies are executed: a) Perform dynamic amplitude limiting or speed limiting control on the drive control output; b) Limit the steering angle and / or steering angular velocity of the steering control output; c) Output auxiliary support intervention control commands to provide physical stability support; Furthermore, the control commands are sent through the vehicle network, and the network messages corresponding to the stability control commands have a higher communication priority than the steering control messages and drive control messages, which is used to preempt vehicle network communication resources when a stability risk event occurs.

[0032] Working principle: Through the collaborative work of multiple sensors such as the on-board inertial measurement unit, wheel speed sensor, and steering angle sensor, various key parameters during vehicle operation are comprehensively collected, covering vehicle operating status, vehicle posture, external environment, and driver operation commands, to construct a complete multi-dimensional parameter set. After collection, the raw data undergoes preprocessing operations such as outlier removal, data smoothing, and normalization. The purpose is to eliminate abnormal data caused by external interference and sensor errors, reduce data fluctuations, and make the processed parameters more stable and reliable, ensuring that subsequent steps such as operating condition identification and control strategy formulation based on these parameters can be carried out accurately and efficiently. Based on the preprocessed complete parameter set, a multi-feature fusion algorithm is used to extract temporal and related features from the parameter set. These features are then input into a pre-defined operating condition recognition model. By combining the synergistic effects of one or more models such as rule-based judgment, decision trees, and neural networks, the current operating condition of the vehicle is accurately identified, clearly distinguishing core operating conditions such as starting, low-speed driving, braking and stopping, and stable parking. It can also further identify extended operating conditions such as starting preparation and low-speed turning. To improve the reliability of the recognition results, a reliability verification mechanism is introduced. The real-time extracted operating condition features are matched and compared with a historical operating condition sample library. If the credibility of the matching result is lower than a preset threshold, a secondary recognition process is immediately triggered. The recognition results are updated and corrected by adjusting feature weights or model parameters to avoid misjudgment of operating conditions caused by instantaneous parameter fluctuations, thus ensuring the accuracy and stability of operating condition recognition. Based on the identified different operating conditions, state switching conditions for switching between each operating condition are set accordingly. Each operating condition switching scenario is clearly divided into primary trigger conditions and auxiliary verification conditions. The two work together to complete the judgment preparation for operating condition switching. The primary trigger condition is the core basis for operating condition switching, used to accurately capture key nodes where the operating condition undergoes substantial changes, ensuring the timeliness of the switching. The auxiliary verification conditions are used to supplement and verify the effectiveness of the primary trigger condition, avoiding false switching caused by instantaneous parameter fluctuations. For example, when switching from starting operating condition to low-speed driving operating condition, the primary trigger condition is that the vehicle speed reaches a preset low-speed threshold and the power output tends to be steady, while the auxiliary verification conditions are that the driver's operation commands are stable and the vehicle body posture parameters are stable. By setting the switching conditions in layers, the judgment criteria for operating condition switching are made more scientific and rigorous, providing a clear and explicit basis for subsequent state switching judgments. Based on the identified current operating condition type, the priorities of stability control objectives are initially set, clarifying the initial order of control objectives such as attitude stability, steering safety, smooth power output, braking stability, and attitude correction. The core principle is to set priorities based on the core stability requirements of different operating conditions, ensuring that the most critical stability objectives are prioritized. Specifically, in operating conditions where the vehicle's attitude is prone to imbalance and the risk of tipping is high, such as starting, braking to a stop, and parking stability, the vehicle attitude stability control objective is set as the highest priority, prioritizing the protection of vehicle attitude balance. In relatively stable operating conditions such as low-speed driving, the priorities of various control objectives are considered, and attitude stability, smooth power output, and steering safety are reasonably balanced. By setting the initial priorities, it is ensured that the stability control strategy can accurately adapt to the core requirements of different operating conditions. A combination of high-frequency monitoring of core parameters and periodic verification of auxiliary parameters is used to monitor vehicle operating parameters in real time. Core parameters closely related to stability control, such as attitude parameters, wheel speed parameters, and driving operation parameters, are monitored at high frequency to ensure that subtle changes in vehicle status can be captured quickly. Auxiliary parameters, such as slope parameters, power source status, and auxiliary support contact status, are verified periodically to ensure comprehensive monitoring. During the monitoring process, the state switching conditions set in S3 are checked in real time to determine the state switching. At the same time, a switching validity determination mechanism is introduced to continuously determine whether the main trigger conditions and auxiliary verification conditions are met and to verify logical consistency. If the condition is found to be met only momentarily or does not meet the logical consistency requirements, the generation of the switching trigger signal is suppressed to avoid control disorder caused by false triggering. The switching trigger signal is only generated when the conditions are continuously met and the logic is consistent. The system receives the generated switching trigger signal and, combined with multi-dimensional parameters such as the vehicle's real-time attitude parameters, speed change rate, steering input, slope parameters, and auxiliary support contact status, dynamically adjusts the priority of the set stability control targets using one or more algorithms, including fuzzy logic, rule-based weight allocation, and learning-based update algorithms. The adjustment process involves two scenarios: first, when a working condition switch is triggered, the priority of each control target is readjusted according to the new working condition type to ensure the control strategy quickly adapts to the core requirements of the new working condition; second, when a working condition switch is not triggered, but the vehicle's real-time status fluctuates, the priority is fine-tuned to ensure vehicle stability. Simultaneously, a stability risk trigger mechanism is introduced. If risks such as attitude parameters exceeding thresholds, attitude or speed change rates exceeding thresholds, or abnormal auxiliary support contact are detected, the attitude stability control target is immediately forced to be set to the highest priority to quickly respond to stability risks and ensure vehicle safety. Based on the adjusted stability control target priority ranking, one or more control commands such as drive, steering, braking, and auxiliary support are output to ensure that the control commands are accurately adapted to the current operating conditions and stability requirements. When the vehicle is detected to have an instability risk or is in a stable parking condition, an auxiliary support intervention control command is output to control the auxiliary support actuators on both sides of the vehicle to adjust the extension amount and support force in an independent closed-loop manner to maintain vehicle posture stability. When a stability risk event is triggered, drive limit and steering limit control commands are output simultaneously to ensure vehicle stability. At the same time, a closed-loop correction mechanism is introduced to collect feedback signals from the actuators in real time, compare the actual execution effect with the expected effect of the control command, and if the deviation exceeds a preset threshold, a correction amount is immediately generated and the control command is updated to continuously optimize the execution effect. In addition, the control commands are sent through the vehicle network, and the communication priority of stability control-related network messages is higher than that of other messages such as steering and drive, ensuring that communication resources can be prioritized and control commands can be transmitted quickly when a stability risk occurs, ultimately achieving stable control of vehicle posture under low speed and start-stop conditions.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0034] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for stabilizing a two-wheeled vehicle under low-speed and start-stop conditions, characterized in that, Includes the following steps: S1. Collect the state parameters and environmental parameters of the two-wheeled vehicle during operation, and construct a parameter set. The state parameters include at least vehicle speed, acceleration, attitude parameters and driving operation parameters. S2. Based on the parameter set, identify the current operating condition type of the vehicle, wherein the operating condition type includes at least starting condition, low-speed driving condition, braking and stopping condition, and stable stopping condition; S3. Set state switching conditions according to the working condition type. The state switching conditions include at least a main trigger condition and an auxiliary verification condition. S4. Initialize the priority order of stability control targets according to the working condition type. The stability control targets include at least attitude stability control targets, steering safety control targets, and power output smooth control targets. S5. Monitor vehicle parameters in real time and perform state switching determination. When the determination meets the state switching conditions, generate a switching trigger signal. S6. Dynamically adjust the priority order of the stable control targets according to the switching trigger signal; S7. Output control commands according to the adjusted priority order. The control commands include at least one of the following: drive control command, steering control command, braking control command and auxiliary support control command. Closed-loop correction is performed based on the feedback from the actuator to achieve stable attitude control under low speed and start-stop conditions.

2. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 1, characterized in that: The parameter set includes vehicle operating status parameters, vehicle body posture parameters, external environment parameters, and driver operation command parameters. Parameter acquisition is achieved through vehicle-mounted sensors, which include at least an inertial measurement unit, wheel speed sensors, steering angle sensors, and / or slope sensors. Furthermore, preprocessing is performed on the collected raw data, including outlier removal, data smoothing, and / or normalization.

3. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 2, characterized in that: The working condition identification is based on a multi-feature fusion algorithm, which includes extracting the time-domain features and / or correlation features of the parameter set and inputting them into the working condition identification model to output the working condition type. The operating condition identification model includes at least one of the following: rule-based decision model, decision tree model, support vector machine model, neural network model, or probability model.

4. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 3, characterized in that: The operating condition identification includes a reliability verification mechanism, which includes matching real-time operating condition features with a historical operating condition sample library; when the matching result is lower than a preset confidence threshold, a secondary identification process is triggered, and feature weights or model parameters are adjusted to update the operating condition identification result.

5. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 4, characterized in that: The state switching conditions are set separately for switching between different working conditions, and each working condition switching scenario includes a main trigger condition and an auxiliary verification condition. Among them, the main triggering condition for switching from starting condition to low-speed driving condition includes at least the vehicle speed reaching a preset low-speed threshold and satisfying the steady-state characteristics of power output, and the auxiliary verification condition includes at least the stability of driver operation commands or attitude parameters.

6. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 5, characterized in that: The state transition determination includes a transition validity determination mechanism, which includes performing continuous satisfaction determination and / or logical consistency verification on the main triggering condition and auxiliary verification condition. When the detected condition is a transient fluctuation or does not meet the consistency requirement, the generation of the switching trigger signal is suppressed.

7. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 6, characterized in that: The stability control objectives include at least: vehicle attitude stability control objectives, power output smooth control objectives, braking stability control objectives and / or attitude correction control objectives; Furthermore, initial priorities are set according to the type of operating condition, with the vehicle attitude stability control target having the highest priority under starting, braking and stopping, or parking stability conditions.

8. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 7, characterized in that: The real-time monitoring adopts a combination of high-frequency monitoring of core parameters and periodic verification of auxiliary parameters. The core parameters include at least attitude parameters, wheel speed parameters and driving operation parameters, and the auxiliary parameters include at least slope parameters, power source status and / or auxiliary support contact status. A switching trigger signal is generated through joint logic, which includes at least main trigger condition verification, auxiliary verification condition verification, and validity determination.

9. A method for stabilizing a two-wheeled vehicle under low-speed and start-stop conditions according to claim 8, characterized in that: The priority dynamic adjustment is based on vehicle attitude parameters, vehicle speed change rate, steering input, slope parameters and / or auxiliary support contact status; The priority dynamic adjustment adopts at least one of fuzzy logic, rule weight allocation, or learning-based update algorithm; When a state transition occurs, the priority is adjusted according to the new operating condition type; when no transition occurs, the priority is fine-tuned based on the vehicle's real-time operating status.

10. A method for stabilizing a two-wheeled vehicle under low-speed and start-stop conditions according to claim 9, characterized in that: The closed-loop correction includes collecting feedback signals from the actuator and comparing them with the expected effect of the control command; when the deviation exceeds a threshold, a correction amount is generated and the control command is updated. The closed-loop correction employs at least one of proportional-integral-derivative PID control, model predictive control (MPC), or sliding mode control.

11. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 10, characterized in that: The operating conditions further include start-up preparation operating conditions and / or low-speed steering operating conditions. When it is determined that the vehicle is in a starting preparation state, braking and stopping state, parking stability state and / or low speed steering state, the auxiliary support actuator is triggered to enter the pre-activation state and perform support to participate in the determination. When the attitude parameters meet the preset instability triggering conditions or when it is determined that there is a slope condition or a road surface height difference condition, an auxiliary support intervention control command is output, so that the auxiliary support actuators on the left and right sides of the vehicle adjust the extension amount and / or support force in an independent closed-loop manner to maintain the preset attitude target. When the vehicle speed exceeds the preset speed threshold and the attitude parameters meet the stability conditions, an auxiliary support exit control command is output to retract and lock the auxiliary support actuator.

12. The stability control method for a two-wheeled vehicle under low speed and start-stop conditions according to claim 11, characterized in that: The priority dynamic adjustment includes a stability risk triggering mechanism, which triggers a stability risk event when the attitude parameters exceed the stability boundary threshold, the attitude change rate exceeds the threshold, the vehicle speed change rate exceeds the threshold, and / or an abnormal contact of the auxiliary support is detected. Upon triggering a stability risk event, the attitude stability control objective is forcibly set to the highest priority, and at least one or a combination of the following control strategies are executed: a) Perform dynamic amplitude limiting or speed limiting control on the drive control output; b) Limit the steering angle and / or steering angular velocity of the steering control output; c) Output auxiliary support intervention control commands to provide physical stability support; Furthermore, the control commands are sent through the vehicle network, and the network messages corresponding to the stability control commands have a higher communication priority than the steering control messages and drive control messages, which is used to preempt vehicle network communication resources when a stability risk event occurs.

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