Low speed balance sensitivity method, system for two-wheeled vehicle, two-wheeled vehicle
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-11
- Publication Date
- 2026-08-11
Smart Images
Figure CN122540294A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle attitude perception technology based on vehicle dynamics, and in particular to a sensitive perception method, system, and two-wheeled vehicle for maintaining balance of two-wheeled vehicles (such as motorcycles and electric bicycles) at low speeds or even when stationary. Background Technology
[0002] With increasing urban traffic congestion and growing demand for short-distance travel, two-wheeled vehicles, including electric bicycles and motorcycles, have become an important part of urban transportation systems due to their flexibility and efficiency. In recent years, autonomous driving technology has gradually penetrated from the automotive sector into the two-wheeled vehicle sector, aiming to improve riding safety, reduce accident rates, and promote the comprehensive development of intelligent transportation systems. However, compared to four-wheeled vehicles, two-wheeled vehicles have inherent static instability when stationary or at low speeds, with balance issues being particularly prominent, becoming one of the key bottlenecks in realizing autonomous driving functions.
[0003] The realization of automatic balancing in traditional two-wheeled vehicles, especially for low-speed or stationary balancing maneuvers such as "vehicle stabilization," heavily relies on high-precision, low-latency inertial measurement units (IMUs), high-speed rotating gyroscopes, and fast motor actuators. These systems maintain stability by measuring vehicle roll angle, angular velocity, and other attitude information in real time, using feedback control via high-speed rotating gyroscopes. However, applying traditional attitude perception methods to the attitude perception of two-wheeled vehicles in low-speed, stationary states presents the following problems: 1) The vehicle state data output by the IMU, such as roll angle, heading angle, and roll angular velocity, has inherent data latency, and the motor response has a time constant. In near-static equilibrium, these delays can cause the control system to lag, leading to instability; 2) In extremely low-speed scenarios, the classic vehicle dynamics model based on tire slip angle exhibits divergence, causing the model to fail; 3) Changes in tire width and tire pressure during vehicle roll or environmental changes can affect the model's accuracy; 4) High-speed rotating gyroscopes are expensive and pose certain risks, potentially causing safety issues.
[0004] Inspired by human rider experience, skilled riders do not rely entirely on instantaneous posture feedback, but rather integrate vehicle dynamics prediction with subtle control adjustments to form a "dynamic sense of balance." Current technology lacks a posture perception framework that can simulate this predictive ability, learn and update the model's dynamic parameters in real time, not rely on high-bandwidth sensors, and adapt to low-speed conditions.
[0005] Therefore, there is an urgent need for a new sensing framework that can reduce the requirements for sensor latency and motor response speed, dynamically adapt to the tilting process and environment, and reliably maintain the balance of a two-wheeled vehicle without relying on a high-speed rotating gyroscope in a low-speed stationary state. Summary of the Invention
[0006] The purpose of this invention is to address the technical deficiencies in the prior art by providing a low-speed balance sensitivity method, system, and two-wheeled vehicle for use in two-wheeled vehicles.
[0007] The technical solution adopted to achieve the purpose of this invention is: One aspect of the present invention includes a low-speed balance sensing method for two-wheeled vehicles, comprising the following steps: Model building: Construct and update a low-speed stationary roll situation perception framework based on geometric relationships and vehicle dynamics. The framework includes a dynamic model, an instability index calculation module, and a dynamic prediction module. Calculate roll acceleration Get the current time roll angle yaw rate and front wheel steering angle δ (t), input the dynamic model, and calculate the roll acceleration at the current moment. ; The dynamic prediction module predicts the roll angular velocity: based on Predicting the next discrete time step using the forward Euler integral formula. Lateral roll rate ; The instability index calculation module provides real-time sensing and instability early warning: calculating the tilting moment balance index. , For gravitational torque, This is based on the restoring torque that the motor and steering mechanism can generate under the current state. An alert will be issued when the value exceeds a set threshold; The dynamic model includes: Longitudinal dynamic equation: (1) In equation (1), For vehicle quality, For longitudinal velocity, For longitudinal acceleration, The rolling resistance coefficient, It is the acceleration due to gravity. The air drag coefficient, For windward area, air density; Lateral dynamic equations: (2) In equation (2), For lateral velocity, For lateral acceleration, For dynamic steering gain, For the front wheel steering angle, The coefficient of lateral friction; Roll dynamics equations: (3) In equation (3), This is the roll acceleration. Let be the moment of inertia of the vehicle body about the X-axis. For gravitational torque, For damping torque, This is the centrifugal torque.
[0008] In the above technical solution, in equation (2), the dynamic steering gain for: ; in, For the reference gain term, The velocity attenuation coefficient, This is the steering angle saturation coefficient. The roll angle is... This refers to the steering angle of the front wheels.
[0009] In the above technical solution, in equation (2), the transverse friction coefficient To match the tire width Tire pressure and lateral speed Related functions: ; in, The coefficient of friction, ; The reference friction coefficient; For tire width, Standard width; Tire pressure, This is the air pressure influence coefficient. This is the width influence coefficient. These are the composite parameters for tire materials.
[0010] In the above technical solution, in equation (3), c is the damping coefficient; , The distance from the center of gravity projection to the support line is the line connecting the front and rear wheel contact points in the ground coordinate system. , The height of the center of gravity.
[0011] In the above technical solutions, , , Let X and Y represent the coordinates of the displacement vectors of the rear wheel contact point and the front wheel contact point in the ground coordinate system. , Let X and Y be the coordinates of the displacement vector of the vehicle's center of gravity relative to the rear wheel contact point in the ground coordinate system. ; ; , The length is the front and rear wheelbase plus the trail. For the tow distance, The horizontal distance from the center of gravity to the center of the rear wheel. These are the X-axis and Y-axis coordinates of the rear wheel contact point in the vehicle coordinate system. This represents the Z-axis coordinate of the front wheel contact point in the tire coordinate system. In the above technical solutions, Through the following calculations: , for The roll rate at time t, for The roll angle acceleration at time t.
[0012] In the above technical solutions, ; , , ,in For uncontrolled torque, It is the positive limiting torque. It is the negative limiting torque.
[0013] In the above technical solution, the parameters of the low-speed, stationary roll situational awareness framework... Based on the parameter set of the dynamic model Online optimization is performed to select the optimal parameter set for the dynamic model. , For vehicle quality, For the tow distance, For the height of the center of gravity, As the reference friction coefficient, For standard width, Tire pressure, These are the composite parameters for tire materials. The velocity attenuation coefficient, This is the steering angle saturation coefficient. This is the air pressure influence coefficient.
[0014] In the above technical solution, the online optimization method includes the following steps: The particle swarm optimization (PSO) algorithm was used to optimize the parameter set of the dynamic model. Optimization is performed, and the optimal values of the parameters are obtained after optimization. and will Feedback is fed back to the roll situational awareness framework in low-speed, stationary states, enabling dynamic updates and adaptive optimization of model parameters. Another aspect of the present invention includes a system based on the low-speed balance sensitivity method for the two-wheeled vehicle, comprising a dynamic model module, a sensor module, a data preprocessing module, an instability index calculation module, and a dynamic prediction module, wherein: The dynamics model module includes a geometric model support module and a dynamics model driving module. The geometric model support module runs the roll dynamics equation, and the dynamics model module runs the lateral and longitudinal dynamics equations. The roll dynamics equation outputs the distance from the center of gravity projection to the support line. , for and The function, the transverse dynamic equation and the longitudinal dynamic equation are based on and Calculate two-wheeled vehicles and ; The sensor module collects data on the two-wheeled vehicle in real time. , , as well as After preprocessing by the data preprocessing module, the data is transmitted to the instability index calculation module and the dynamics prediction module. The dynamics prediction module then performs a forward Euler integral formula prediction. The instability index calculation module calculates the side tilting moment balance index. ; and The data is transmitted to the control module, which executes a control law to output the front wheel steering angle for the next moment. And driving force.
[0015] In the above technical solution, the system of the low-speed balance sensitivity detection method for two-wheeled vehicles also includes a parameter coupling analysis module and a parameter optimization module. The parameter coupling analysis module and the parameter optimization module optimize the parameters in the roll dynamics equation, the lateral dynamics equation and the longitudinal dynamics equation.
[0016] Another aspect of the present invention includes an electronic device comprising one or more processors and a memory; the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the described low-speed balance sensing method for two-wheeled vehicles.
[0017] In another aspect of the present invention, a computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the described low-speed balance sensitive sensing method for two-wheeled vehicles.
[0018] In another aspect of the present invention, a computer program product includes computer-executable instructions that, when executed, implement the aforementioned low-speed balance sensitive sensing method for two-wheeled vehicles.
[0019] In another aspect of the invention, a two-wheeled vehicle includes the aforementioned electronic equipment.
[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. Achieve advanced early warning and reduce hardware dependence: By predicting instability trends based on models, it provides a longer warning lead time than simply relying on IMU roll rate feedback. This allows the control system to intervene before instability occurs, significantly reducing the stringent requirements on IMU data update rate, zero-drift stability, and motor response speed, and making it possible to use lower-cost and safer sensor solutions.
[0021] 2. Overcoming the bottleneck of low-speed models: The innovative introduction of a roll dynamics model based on the precise geometric relationship between the center of gravity and the support line completely avoids the problem of traditional dynamics models based on tire slip angle failing due to mathematical singularities at extremely low speeds or when stationary, ensuring the effectiveness and reliability of the model near 0 speed.
[0022] 3. Possesses self-learning and adaptive capabilities: The dynamic model parameters are calibrated online using the Particle Swarm Optimization (PSO) algorithm based on actual operating data. This enables the system to automatically adapt to different tire characteristics (wear, tire pressure), vehicle load variations, and diverse ground friction conditions, greatly improving the system's generalization ability and robustness in complex real-world environments.
[0023] 4. Enhanced System Safety: By introducing a "predictive" mechanism into the balance control perception layer, the passive response mode is fundamentally changed. It does not rely on high-speed rotating gyroscopes to maintain balance, reducing the safety risks associated with gyroscopes. Combined with adaptive optimization of the model, it can maintain high-precision situational awareness under various boundary conditions, thereby providing more reliable and timely decision-making basis for the upper-level controller. This improves the overall safety of low-speed operation of two-wheeled vehicles from the perception level. Attached Figure Description
[0024] Figure 1 This is a block diagram of the overall system structure of the sensitive sensing frame for low-speed balance of two-wheeled vehicles described in this invention.
[0025] Figure 2This is a schematic diagram of the ground coordinate system.
[0026] Figure 3 This is a schematic diagram of the vehicle coordinate system.
[0027] Figure 4 This is a schematic diagram of the vehicle as a whole.
[0028] Figure 5 This is a schematic diagram showing the distance from the vehicle's center of gravity to the ground and the contact points of the front and rear wheels.
[0029] Figure 6 A simplified diagram of a tire.
[0030] Figure 7 Heatmap of maximum torque, maximum front wheel steering angle, and final roll angle under the torque flat index.
[0031] Figure 8 This is a heatmap showing the maximum torque, maximum front wheel steering angle, and final roll angle under the roll angle index. Detailed Implementation
[0032] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0033] This invention proposes a low-speed balance-sensitive perception framework for two-wheeled vehicles. Its core design concept originates from the mechanism of human riders performing low-speed balance maneuvers such as "stationary riding"—that is, not relying entirely on instantaneous posture feedback, but rather forming a "dynamic sense of balance" through prediction and fine-tuning. This invention aims to construct a self-learning tilt-prone sensitivity perception framework based on IMU data and vehicle dynamics models, without relying on additional mechanical structures such as gyroscopes. By establishing instability tendency indicators, determining the warning lead time of these indicators, and combining this with a prediction module based on dynamic equations, the vehicle can control its posture in advance, thereby reducing the requirements for IMU latency and motor response time.
[0034] Example 1 A low-speed balance sensing method for two-wheeled vehicles includes the following steps: Model building: Construct and update a low-speed stationary roll situation perception framework based on geometric relationships and vehicle dynamics. The framework includes a dynamic model, an instability index calculation module, and a dynamic prediction module. Calculate roll acceleration Get the current time roll angle yaw rate and front wheel steering angle δ (t), input the dynamic model, and calculate the roll acceleration at the current moment. ; The dynamic prediction module predicts the roll angular velocity: based on Predicting the next discrete time step using the forward Euler integral formula. Lateral roll rate ; The instability index calculation module provides real-time sensing and instability early warning: calculating the tilting moment balance index. , For gravitational torque, This is based on the restoring torque that the motor and steering mechanism can generate under the current state. An alert will be issued when the value exceeds a set threshold; The dynamic model includes: Longitudinal dynamic equation: (1) In equation (1), For vehicle quality, For longitudinal velocity, For longitudinal acceleration, The rolling resistance coefficient, It is the acceleration due to gravity. The air drag coefficient, For windward area, air density; Lateral dynamic equations: (2) In equation (2), For lateral velocity, For lateral acceleration, For dynamic steering gain, For the front wheel steering angle, The coefficient of lateral friction; Roll dynamics equations: (3) In equation (3), This is the roll acceleration. Let be the moment of inertia of the vehicle body about the X-axis. For gravitational torque, For damping torque, This is the centrifugal torque.
[0035] Example 2 This embodiment is a further detailed description based on Embodiment 1.
[0036] A low-speed balance sensing method for two-wheeled vehicles includes the following steps: model building and calculation of roll angle acceleration. Predicting roll rate and calculating roll moment balance index .
[0037] Step 1, the model building steps are as follows: Build and update a dynamics model for two-wheeled vehicles in a low-speed stationary state - a low-speed stationary state roll situational awareness framework.
[0038] First, the divergence of the sideslip angle in the current classic two-wheeled vehicle dynamics model under low-speed stationary conditions is analyzed, and a geometrically supported two-wheeled vehicle dynamics model for low speed is proposed. This model integrates the geometric relationship between the front and rear wheel contact points and the center of gravity position during the roll process, and introduces detailed parameters into the dynamics to simulate the vehicle roll process. Figures 2-6 A schematic diagram of the geometric relationships used in low-speed balance analysis of two-wheeled vehicles.
[0039] S11, construct detailed longitudinal and lateral dynamic models.
[0040] Idealizing the motorcycle as a point mass, with mass... Taking the center of the rear wheel of the vehicle as the point of contact with the ground. Points, front, left, and top are... Establish the vehicle coordinate system along the positive direction of the axis, such as... Figure 3 As shown, a multi-degree-of-freedom mechanical analysis is conducted using the motorcycle as a whole as the research object.
[0041] (1) In the longitudinal direction, rolling resistance and air resistance The formula for calculating air resistance is: ,in For vehicle quality, For longitudinal velocity, The rolling resistance coefficient, It is the acceleration due to gravity. It is the air drag coefficient. It is the windward area. It is the air density, and its direction is opposite to the direction of the vehicle's movement.
[0042] In summary, the longitudinal dynamic equation is:
[0043] This is the longitudinal acceleration.
[0044] (2) Laterally, the tilting of the vehicle body directly causes sideslip, and the control system changes the sideslip phenomenon through steering. This embodiment defines in detail the force resisting the tilting generated by sideslip, which is determined by dynamic steering gain. and front wheel cornering definition.
[0045] The dynamic steering gain The calculation formula is:
[0046] in: The reference gain term is the static nominal steering force gain, which represents the lateral force that can be generated per unit steering angle under ideal conditions. This is the speed decay term, which simulates the characteristic that steering sensitivity decreases as speed increases; at low speeds, this term approaches 1. The velocity attenuation coefficient, The larger the value, the faster the gain decreases with speed; This is a steering angle saturation term used to simulate the nonlinear saturation characteristics at large steering angles. The steering angle saturation coefficient is used to determine the tire force growth. When the front wheel steering angle is too large, the tire force growth exhibits non-linear saturation, resulting in a decrease in gain. This is the roll angle effect term, reflecting the geometric attenuation of the force perpendicular to the wheel plane in the lateral component of the inertial coordinate system when the vehicle rolls. This is the roll angle.
[0047] Meanwhile, the model takes into account the effect of friction. The vehicle is subjected to lateral frictional damping in the same direction as the tipping, causing the vehicle to tip over. Coefficient of friction Establish a model regarding tire width, tire pressure, and speed:
[0048] in: The reference friction coefficient; For tire width, Standard width; Tire pressure, This is the air pressure influence coefficient. These are the composite parameters for tire materials. It describes the effect that the maximum friction coefficient decreases linearly as the air pressure increases and the grounding area decreases.
[0049] In summary, the longitudinal dynamic equation is:
[0050] This is lateral acceleration.
[0051] S12, construct the roll dynamics equations based on the geometric relationship between the vehicle's center of gravity and the fulcrum.
[0052] To address the problem of model failure caused by divergence in the calculation of the sideslip angle at low speeds, this embodiment uses a geometrically based model instead of the traditional sideslip angle model.
[0053] Taking the rear wheel contact point when the vehicle is upright as Points, front, left, and top are... The positive direction of the axis defines the ground coordinate system, such as... Figure 2 As shown. To establish the geometric model, the multi-rigid-body model of the two-wheeled vehicle is simplified. It is assumed that the tire is a rigid body and the contact surface between the tire and the ground is an arc, as shown below. Figure 3 As shown, the contact point between the tire and the ground is located on the tire's arc surface and the horizontal ground; the ground is a rigid horizontal surface; under low-speed stationary conditions, the longitudinal slippage of the tire is ignored.
[0054] First, calculate the contact point of the rear wheel.
[0055] Based on the established ground coordinate system and vehicle coordinate system, the spatial equations for the connection between the front and rear wheel pivot points and the movement of the center of gravity during the roll process are calculated. In the vehicle coordinate system, the initial position of the rear wheel center is:
[0056] Where r is the radius of the tire contact surface arc. .
[0057] Let the rear wheel contact point be in the vehicle coordinate system as The coordinates in the ground coordinate system are .in, These are the X-axis and Y-axis coordinates of the rear wheel contact point in the vehicle coordinate system. Let X, Y, and Z be the coordinates of the rear wheel contact point in the ground coordinate system. Transforming the vehicle coordinate system to the ground coordinate system, and considering the roll angle φ, the rotation matrix is defined as follows:
[0058] Define the translation matrix as , Let each axis represent the translation of the tire coordinate system relative to the ground coordinate system. Then, the coordinates of the rear wheel contact point are transformed to the ground coordinate system:
[0059] Will , , Substitution have to:
[0060] Following the approach of transforming the coordinates of the rear wheel contact point to the ground coordinate system, the center of the rear wheel during the roll process is obtained. The coordinates are:
[0061] Will , , Substitute Calculated The rear wheel contact point satisfies both the tire contact arc surface constraint and the ground constraint in the vehicle coordinate system. Regarding the tire contact arc surface constraint:
[0062] For ground constraints:
[0063] Since the tire is a rigid body and the tire's contact surface with the ground is a circular arc, the line connecting the center of the rear wheel to the ground is perpendicular to the ground. Therefore, by geometric relations, we can obtain:
[0064] in This represents the Y-axis coordinate of the rear wheel center in the ground coordinate system. The Z-axis coordinate of the rear wheel center in the ground coordinate system Summarized as follows: , In the vehicle coordinate system, when the vehicle is upright and not steering, the steering axis passes through point [point missing]. , The length is the front and rear wheelbase plus the trail, such as Figure 4 As shown, its steering axis direction vector is .
[0065] When a front wheel steering angle exists, the initial coordinates of the front wheel center are:
[0066] This is the drag distance.
[0067] Let the contact point of the front wheel in the vehicle coordinate system be... In the ground coordinate system, ,in These represent the coordinates of the front wheel contact point on the X, Y, and Z axes of the tire coordinate system. These represent the X, Y, and Z coordinates of the front wheel contact point in the ground coordinate system. Since the steering axis is not perpendicular to the ground, the resulting lateral and longitudinal offsets need to be considered. When the steering angle is... At that time, the tire plane normal vector in the vehicle coordinate system is .
[0068] To simplify the rotational model, we assume that the lateral position of the front wheel contact point in the vehicle coordinate system is... Lateral position from the center of the front wheel and longitudinal position Add a offset caused by tire roll Decision, and This describes the coupling relationship between the front wheel steering angle and body roll. Therefore:
[0069] The front wheel contact point satisfies both the tire contact arc surface constraint and the ground constraint in the vehicle coordinate system. Regarding the ground constraint:
[0070] Transform the coordinates of the front wheel contact point to the ground coordinate system:
[0071] Substitution have to:
[0072] calculate Z-coordinate:
[0073] With grounding constraints, let have to:
[0074] Solving for:
[0075] Simplifying, we get:
[0076] The front wheel contact point in the vehicle coordinate system is:
[0077] The coordinates of the front and rear wheel contact points in the ground coordinate system are as follows:
[0078] The support line is through and The spatial straight line has the following parametric equation:
[0079] The center of gravity is fixed in the vehicle coordinate system as follows:
[0080] in The horizontal distance from the center of gravity to the center of the rear wheel. For horizontal offset, For the height of the center of gravity, The coordinates of the center of gravity in the vehicle coordinate system.
[0081] In the ground coordinate system:
[0082] The coordinates of the center of gravity in the ground coordinate system.
[0083] The projection point of the center of gravity on the ground is:
[0084] This is the projection point of the vehicle's center of gravity.
[0085] Calculate the distance from the center of gravity to the support line, and let , ,but:
[0086] in:
[0087] D is the signed distance; the perpendicular distance from the centroid projection to the support line is positive on the left. Figure 5 As shown.
[0088] The balance condition for a two-wheeled vehicle is That is, the center of gravity projection falls on the support line. When the center of gravity projection is to the left of the support line, i.e., D... At 0, gravity causes Increased torque:
[0089] At low speeds, ignoring gyroscopic torque, the control torque is achieved by changing the front wheel steering angle. To influence the position of the support line, thereby changing This indirectly affects the gravitational torque.
[0090] Based on torque balance, the vehicle's roll dynamics equation is:
[0091] in: Let X be the moment of inertia of the vehicle body about the X-axis. For damping torque, This is the centrifugal torque.
[0092] , c is the damping coefficient.
[0093] Define state variables , The state-space equation is then: .
[0094] Based on the derived two-wheeled vehicle dynamics model—a low-speed, stationary state tilt situational awareness framework—a control law is designed.
[0095] Step 2, calculate the roll acceleration. The steps are as follows: Using the vehicle state at the current time t, including the real-time roll angle yaw rate and front wheel steering angle δ (t), using the established roll dynamics equations, calculate the roll angular acceleration at the current moment. :
[0096] At low speed, assuming .
[0097] Calculate the predicted roll rate after a future discrete time step Δt. This provides forward-looking information for the control system.
[0098] Step 3, predicting the roll rate, is as follows: To simulate the predictive ability of a human rider, a dynamic prediction module based on the aforementioned geometric relationship support model is introduced. The forward Euler integral method is employed, utilizing the current angular acceleration. One-step prediction of roll rate:
[0099] In the formula, Δt is the discrete time step of the control system, which is usually consistent with the sensor sampling period or the controller operation period.
[0100] The prediction module outputs the predicted roll rate. This information, along with the roll moment balance index A output by the instability evaluation index module, is transmitted to the vehicle control module. This allows the control system to not only react based on the current stability index but also to incorporate model-predicted future attitude change trends, enabling earlier and smoother intervention, thereby reducing the stringent requirements on sensor latency and actuator response speed.
[0101] Step 4, Real-time Sensing and Instability Early Warning: The steps are as follows: This step involves real-time sensing of the vehicle's status and instability assessment during model operation.
[0102] S41 sets the tilt moment balance index.
[0103] The Roll Moment Balance Index is defined as a stability evaluation index for two-wheeled vehicles, and it is defined as the ratio of the gravitational torque to the maximum restoring torque that the motor can provide:
[0104] in For gravitational torque, , It is based on the restoring torque that the motor and steering mechanism can generate under the current state.
[0105] in For uncontrolled torque, It is the positive limiting torque. It is the negative limiting torque. This is the maximum front wheel steering angle limit allowed by the controller.
[0106] S42, Early Warning Judgment.
[0107] Input the real-time data collected by the IMU into the instability index calculation module established in step 1 to calculate the above index. Based on the results of previous model physical rationality verification, the moment when the gravitational torque first exceeds the restoring torque is defined as (i.e., (or a set threshold) is the warning time for the instability evaluation index.
[0108] Compared to comparing only the roll rate, this restoring torque balance index provides a higher warning lead, allowing the control system to intervene earlier and thus reducing the requirements for motor response time constant and sensor delay. For the motor response, this embodiment assumes a first-order inertial model to reduce computational load.
[0109] Example 3 Based on the previous embodiment, this embodiment adds parameter adaptive optimization, performs online optimization of the parameters of the dynamic model in step 1, and feeds back the optimized parameters to the low-speed stationary roll situational awareness framework, so that the model can adapt to changes in vehicle parameters and environment.
[0110] To enable the vehicle dynamics model to adapt to changes in different scenarios (such as tire pressure changes, load changes, and environmental disturbances) in real time, this step executes the particle swarm optimization (PSO) algorithm to optimize parameters.
[0111] The particle swarm optimization (PSO) algorithm was used to optimize the parameter set of the dynamic model. Optimization is performed, and the optimal values of the parameters are obtained after optimization. and will The optimized parameters are fed back to the low-speed stationary state roll situational awareness framework in step 1 to achieve dynamic updating and adaptation of the model parameters.
[0112] Figures 7-8 This diagram illustrates the comparison of the motor response time constant requirements for different instability evaluation indices. The motor response time constant is 0.0375s. Figures 7-8 The invention visually demonstrates the additional lead time obtained by using the roll moment balance index early warning system compared to the traditional roll rate early warning system.
[0113] Example 4 A system based on the low-speed balance sensitivity detection method of the two-wheeled vehicle includes a dynamic model module, a sensor module, a data preprocessing module, an instability index calculation module, and a dynamic prediction module, wherein: The dynamics model module includes a geometric model support module and a dynamics model driving module. The geometric model support module runs the roll dynamics equation, and the dynamics model module runs the lateral and longitudinal dynamics equations. The roll dynamics equation outputs the distance from the center of gravity projection to the support line. , for and The function, the transverse dynamic equation and the longitudinal dynamic equation are based on and Calculate two-wheeled vehicles and ; The sensor module collects data on the two-wheeled vehicle in real time. , , as well as After preprocessing by the data preprocessing module, the data is transmitted to the instability index calculation module and the dynamics prediction module. The dynamics prediction module then performs a forward Euler integral formula prediction. The instability index calculation module calculates the side tilting moment balance index. ; and The data is transmitted to the control module, which executes a control law to output the front wheel steering angle for the next moment. And driving force.
[0114] Preferably, the system of the low-speed balance sensitivity detection method for two-wheeled vehicles further includes a parameter coupling analysis module and a parameter optimization module, which optimize the parameters in the roll dynamics equation, lateral dynamics equation and longitudinal dynamics equation.
[0115] Example 5 An electronic device includes: one or more processors and a memory; the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the low-speed balance sensing method for two-wheeled vehicles described in Embodiment 1.
[0116] Example 6 A computer-readable storage medium storing computer-executable instructions, which, when executed, implement the low-speed balance sensing method for a two-wheeled vehicle as described in Embodiment 1.
[0117] Example 7 A computer program product comprising computer-executable instructions, which, when executed, implement the low-speed balance sensing method for a two-wheeled vehicle as described in Embodiment 1.
[0118] Example 8 A two-wheeled vehicle including the electronic equipment described in Example 5.
[0119] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A low speed balance sensitivity sensing method for a two-wheeled vehicle, characterized by, Includes the following steps: Model building: Construct and update a low-speed stationary roll situation perception framework based on geometric relationships and vehicle dynamics. The framework includes a dynamic model, an instability index calculation module, and a dynamic prediction module. Calculate roll acceleration Get the current time roll angle yaw rate and front wheel steering angle δ (t), input the dynamic model, and calculate the roll acceleration at the current moment. ; The dynamics prediction module predicts a roll angle velocity based on by a forward Euler integration formula the roll angle velocity after ; The instability index calculation module perceives and gives early warning of instability in real time: calculates the roll moment balance index , is the gravity moment, is the restoring moment that can be generated by the motor and steering mechanism under the current state, and when is greater than a set threshold, early warning is given. The dynamic model includes: Longitudinal dynamics equation: (1) In equation (1), For vehicle quality, For longitudinal velocity, For longitudinal acceleration, The rolling resistance coefficient, It is the acceleration due to gravity. The air drag coefficient, For windward area, air density; Lateral dynamic equations: (2) In equation (2), For lateral velocity, For lateral acceleration, For dynamic steering gain, For the front wheel steering angle, The coefficient of lateral friction; Rolling dynamics equation: (3) In formula (3), is a roll angular acceleration, is a moment of inertia of the vehicle body about the X axis, is a gravitational moment, is a damping moment, is a centrifugal moment.
2. The low speed balance sensitivity method for two-wheeled vehicles as claimed in claim 1, wherein, Includes the following steps: In the formula (2), the dynamic steering gain Kd is: Kd = - (Kp + Kd) ； wherein, is a reference gain term, is a speed decay coefficient, is a steering angle saturation coefficient, is a roll angle, is a front wheel steering angle, is a tire material compound parameter; Preferably, in equation (2), the transverse friction coefficient To match the tire width Tire pressure and lateral speed Related functions: ； in, The coefficient of friction, ; The reference friction coefficient; For tire width, Standard width; Tire pressure, This is the air pressure influence coefficient; Preferably, in equation (3), c is the damping coefficient; , The distance from the center of gravity projection to the support line is the line connecting the front and rear wheel contact points in the ground coordinate system. , The height of the center of gravity; Preferred, , , Let X and Y represent the coordinates of the displacement vectors of the rear wheel contact point and the front wheel contact point in the ground coordinate system. , Let X and Y be the coordinates of the displacement vector of the vehicle's center of gravity relative to the rear wheel contact point in the ground coordinate system. Furthermore, ； ; , The length is the front and rear wheelbase plus the trail. For the tow distance, The horizontal distance from the center of gravity to the center of the rear wheel. These are the X-axis and Y-axis coordinates of the rear wheel contact point in the vehicle coordinate system. This represents the Z-axis coordinate of the front wheel contact point in the tire coordinate system.
3. The low speed balance sensitivity method for two wheeled vehicles as claimed in claim 1 wherein, By the following calculation: , for The roll rate at time t, for The roll angle acceleration at time t.
4. The low speed balance sensitivity method for two wheeled vehicles as claimed in claim 1 wherein, ； ， ， wherein is the uncontrolled moment, is the positive limit moment, is the negative limit moment.
5. The low speed balance sensitivity method for two wheeled vehicles as claimed in claim 1 wherein, Parameters of a low-speed stationary state roll attitude awareness framework Parameter sets in accordance with a kinetic model Performing online optimization, preferably, , For vehicle quality, For the tow distance, For the height of the center of gravity, As the reference friction coefficient, For standard width, Tire pressure, These are the composite parameters for tire materials. The velocity attenuation coefficient, This is the steering angle saturation coefficient. This is the air pressure influence coefficient; Preferably, the online optimization method includes the following steps: The particle swarm optimization (PSO) algorithm was used to optimize the parameter set of the dynamic model. Optimization is performed, and the optimal values of the parameters are obtained after optimization. and will The optimized parameters are fed back to the low-speed, stationary roll situational awareness framework to achieve dynamic updating and adaptation of the model parameters.
6. A system based on the low-speed balance sensitivity detection method for two-wheeled vehicles as described in any one of claims 1-5, characterized in that, It includes a dynamics model module, a sensor module, a data preprocessing module, an instability index calculation module, and a dynamics prediction module, among which: The dynamics model module includes a geometric model support module and a dynamics model driving module. The geometric model support module runs the roll dynamics equation, and the dynamics model module runs the lateral and longitudinal dynamics equations. The roll dynamics equation outputs the distance from the center of gravity projection to the support line. , for and The function, the transverse dynamic equation and the longitudinal dynamic equation are based on and Calculate two-wheeled vehicles and ; The sensor module collects the real-time data of two-wheeled vehicle 、 、 and After the preprocessing of the data preprocessing module, the data are transmitted to the instability index calculation module and the dynamics prediction module, and the dynamics prediction module performs the forward Euler integral formula prediction ; the instability index calculation module calculates a side roll moment balance index ; and The data is transmitted to the control module, which executes a control law to output the front wheel steering angle for the next moment. And driving force. Preferably, the system of the low-speed balance sensitivity detection method for two-wheeled vehicles further includes a parameter coupling analysis module and a parameter optimization module, which optimize the parameters in the roll dynamics equation, lateral dynamics equation and longitudinal dynamics equation.
7. An electronic device, characterized in that, It includes one or more processors and a memory; the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the low-speed balance sensing method for two-wheeled vehicles as described in claim 1.
8. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions, which, when executed, are used to implement the low-speed balance sensing method for a two-wheeled vehicle as described in claim 1.
9. A computer program product, characterised in that, The aforementioned computer program product includes computer-executable instructions that, when executed, implement the low-speed balance sensing method for a two-wheeled vehicle as described in claim 1.
10. A two-wheeled vehicle characterized by Including the electronic device as described in claim 7.