A stability control method and device for a tire underinflated vehicle

Abstract

The application discloses a kind of stability control method and device for tire underpressure vehicle, it is related to vehicle active safety control technical field, wherein method includes: collection vehicle driving state and tire underpressure information, according to underpressure degree correction tire mechanics parameter, establish the vehicle planar dynamics model of fusion vertical posture influence;Combination ideal motion state builds nonlinear model predictive controller, introduces control barrier function and its derivative constraint, guarantee yaw rate is in safe range;Solve the optimization problem with constraint, output wheel target torque and front wheel additional angle, regulate and control vehicle longitudinal and yaw motion;According to vehicle body posture deviation calculation compensation torque, combine load deviation and torque balance condition distribution suspension actuation, realize vehicle body posture correction and tire vertical load reconstruction.The application uses stage-by-stage collaborative control architecture, realizes vehicle planar and vertical dynamics collaborative regulation and control, solves the coupling instability problem induced by tire underpressure.

Description

Technical Field

[0001] This invention relates to the field of active safety control technology for vehicles, and in particular to a stability control method and device for vehicles with underinflated tires. Background Technology

[0002] Vehicle stability under high-speed and complex driving conditions is a key research area in the field of active vehicle safety technology. When vehicles perform maneuvers such as emergency steering and active obstacle avoidance, dynamic characteristics such as increased tire slip angle and deviation of the vehicle's yaw rate from the ideal range can easily occur, leading to safety hazards such as skidding and instability. These problems are further exacerbated under tire pressure conditions. Insufficient tire pressure directly reduces tire lateral stiffness, weakening the tire's lateral force output capability; it also disrupts the original vertical load distribution pattern of the entire vehicle, causing uneven vertical load distribution among the wheels. When a single wheel is underinflated, the dynamic characteristics of the left and right wheels differ significantly, generating additional yaw moment during steering, further increasing the probability of skidding and instability. Simultaneously, the underinflated tire exhibits greater deformation under load, exacerbating vehicle roll and pitch movements, amplifying the vertical load transfer effect between wheels, ultimately forming a complex instability dynamic process where yaw and vertical movements are coupled, significantly reducing vehicle driving safety.

[0003] Currently, vehicle stability control systems are widely used in real-world engineering scenarios. Conventional vehicle stability control schemes often rely on a planar dynamics model of the vehicle to build control logic. By adjusting the driving and braking forces of each wheel and adding yaw torque, they correct the vehicle's yaw motion, bringing the vehicle's operating parameters back to a stable range. However, such control schemes only focus on the vehicle's yaw motion and do not consider the interference caused by changes in the vehicle's vertical characteristics under tire underinflation conditions on the planar control effect. During actual vehicle operation, in addition to yaw and lateral movements in the planar plane, there are also vertical attitude changes such as body roll and pitch. These attitude changes not only interfere with the accuracy of planar control but also cause dynamic fluctuations in the vertical loads of each wheel, directly restricting the normal output of the tire's longitudinal and lateral forces. Tire forces are the core foundation of vehicle stability control; an imbalance in vertical load distribution directly leads to a deterioration in vehicle stability performance. Therefore, control strategies designed solely based on planar dynamics models cannot accurately characterize the vehicle's true dynamic response characteristics under tire underinflation conditions.

[0004] To address the shortcomings of traditional control schemes, current research introduces model predictive control (MMC) algorithms. These algorithms can perform rolling optimization of the control input within the prediction time domain and solve for the optimal control output by incorporating system operational constraints. However, current vehicle stability control schemes based on MMC still primarily focus on vehicle yaw motion as the core control objective, generally neglecting the vehicle's vertical dynamics and the dynamic changes in wheel vertical loads, making it difficult to achieve coordinated control of yaw and vertical motion. Furthermore, traditional MMC often restricts system operation by adding constraints or introducing penalty functions. Hard state constraints can lead to poor control output smoothness. Under extreme driving conditions such as underinflated tires, when the vehicle approaches the stability boundary, relying on penalty function constraints can easily cause vehicle operating parameters to exceed safe limits, significantly increasing the risk of instability.

[0005] Control barrier functions can implement safety constraints and control over the system's operating state. By constructing a safety function and limiting its derivative value, the system state can be continuously maintained within a safe operating range. This method has state prediction capabilities, enabling early control intervention, effectively improving the smoothness of the control process, and increasing the vehicle's safety margin. Combining control barrier functions with model predictive control architecture can reliably achieve safety constraints on the vehicle's operating state while optimizing control performance. Given the various shortcomings of existing technologies, the industry urgently needs a vehicle stability control scheme that is adaptable to tire underinflation conditions, can achieve lateral and vertical coordinated regulation, and possesses reliable safety constraint capabilities. Summary of the Invention

[0006] The main objective of this invention is to provide a stability control method for vehicles with underinflated tires.

[0007] Another object of the present invention is to provide a stability control device for vehicles with underinflated tires.

[0008] The third objective of this invention is to provide an electronic device.

[0009] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, a first aspect of the present invention provides a stability control method for vehicles with underinflated tires, comprising:

[0011] The vehicle's current driving status data and tire underinflation information are obtained. Based on the tire underinflation information, the tire mechanical characteristic parameters are corrected, and a vehicle planar dynamics model including the influence of vertical attitude angles is constructed. Based on the vehicle planar dynamics model and the preset ideal motion state, a nonlinear model predictive controller is constructed, and a control obstacle function is defined with the goal of keeping the yaw rate within the safety boundary. The derivative constraint of the control obstacle function is introduced into the optimization problem of the nonlinear model predictive controller. The optimization problem of the introduced control obstacle function constraint is solved, and the optimal target wheel torque and additional front wheel steering angle control are output. Based on the control, the wheel force and front wheel steering angle are adjusted to regulate the yaw and longitudinal motion of the vehicle. The target anti-pitch moment and target anti-roll moment are calculated based on the deviation between the actual attitude of the vehicle body and the desired attitude. With the goal of reducing the difference between the actual vertical load of the wheel and the standard tire pressure reference load, the suspension is distributed to power in accordance with the torque balance constraint to complete the vehicle body attitude control and tire vertical load reconstruction.

[0012] Optionally, the vehicle's current driving status data and tire underinflation information are obtained, and the tire mechanical characteristic parameters are corrected based on the tire underinflation information, including: The vehicle's current longitudinal speed, lateral speed, yaw rate, pitch rate, roll rate, as well as wheel speed and tire pressure monitoring data are obtained through wheel speed sensors, inertial measurement units, and on-board bus networks. The underinflation coefficient of each tire is determined based on tire pressure monitoring data. The underinflation coefficient is negatively correlated with the deviation of the tire pressure from the standard tire pressure. Based on the underpressure degree coefficient, the rolling resistance coefficient, vertical stiffness, lateral stiffness, longitudinal stiffness and effective rolling radius are modified respectively. Among them, the rolling resistance coefficient increases monotonically with the increase of underpressure degree, while the vertical stiffness, lateral stiffness, longitudinal stiffness and effective rolling radius decrease monotonically with the increase of underpressure degree. The corrected mechanical parameters are substituted into the Unitire tire model to calculate the longitudinal and lateral forces of each wheel, which are then used as inputs for the vehicle's planar dynamics model.

[0013] Optionally, a vehicle planar dynamics model incorporating the influence of vertical attitude angles is constructed, including: Based on the Newton-Euler theorem, the planar dynamics equations of a four-wheel independent drive vehicle are established, and the vertical attitude motion parameters corresponding to pitch and roll are coupled and integrated into the longitudinal, lateral, and yaw dynamics calculation processes, respectively. Using the front wheel angle as the transformation reference, coordinate conversion is performed on the forces on the front and rear wheels to obtain the longitudinal and lateral forces that the wheels actually exert on the vehicle body under steering conditions. The longitudinal vehicle speed, lateral vehicle speed, and yaw rate are selected as the target state variables of the model, and the pitch rate and roll rate collected in real time are used as dynamic parameters input into the model to fully represent the coupling relationship between vertical attitude and vehicle planar motion.

[0014] Optionally, based on the vehicle's planar dynamics model and a preset ideal motion state, a nonlinear model predictive controller is constructed, and a control obstacle function is defined with the objective of keeping the yaw rate within a safe boundary, including: The vehicle's longitudinal velocity, lateral velocity, and yaw rate are selected as the controller's state variables, and the output variables are set to be equal to the state variables. The torque of each wheel and the additional front wheel steering angle are selected as control inputs to form a control vector. Discretize the nonlinear state-space equations in the vehicle planar dynamics model to obtain a discrete-time system; The ideal motion state reference value of the vehicle is calculated based on the linear two-degree-of-freedom reference model. The range of the reference value is limited by the boundary of road adhesion conditions and tire underpressure characteristics. The safe operating range of yaw rate is determined by combining the road surface adhesion coefficient, vehicle longitudinal speed, and tire underinflation coefficient. Based on the boundary of the safe range, a control obstacle function is constructed to divide the vehicle stable operating state area. By differentiating the control barrier function along the system dynamics, an explicit relationship is established between the derivative of the control barrier function and the current control quantity.

[0015] Optionally, the optimization problem of the introduced control obstacle function constraint is solved to output the optimal target longitudinal force of the wheel and the additional front wheel steering angle control quantity, including: The derivative constraint of the control obstacle function is loaded into the controller optimization model, and non-negative relaxation variables are added to ensure the feasibility of optimization solution under boundary conditions. By combining state tracking deviation, control output amplitude, control quantity fluctuation amplitude, tire load bearing, and relaxation variable value, penalty terms are constructed respectively, and combined to form a multi-objective comprehensive optimization function. Four types of constraints are set: upper and lower limits of control amplitude, rate of change of control, road friction limit, and control obstacle function. The range of legal values ​​for optimization solution is defined. The optimization problem is solved in the finite time domain using sequential quadratic programming or interior point method to obtain the optimal control sequence. The control quantity at the first moment in the optimal control sequence is extracted as the controller output at the current moment. The controller output includes the target torque of each wheel and the additional front wheel steering angle.

[0016] Optionally, the yaw and longitudinal movements of the vehicle can be controlled by adjusting the wheel forces and front wheel steering angle based on the control parameters, including: The target torque of each wheel is distributed to the actuator of the corresponding wheel, and independent control of each wheel is achieved by adjusting the hub motor; The additional front wheel steering angle is superimposed with the front wheel steering angle and then output to the steering actuator, which adjusts the actual front wheel steering angle through the steer-by-wire system or the electric power steering system. The system detects the vehicle's motion status in real time, compares it with the ideal motion reference state, and corrects the control output in a closed loop to constrain the vehicle's yaw and longitudinal movement to stably follow the desired trajectory.

[0017] Optionally, the target anti-pitch moment and target anti-roll moment are calculated based on the deviation between the actual and desired vehicle body posture. With the goal of reducing the difference between the actual vertical load on the wheels and the reference load at standard tire pressure, the suspension is distributed with power according to moment balance constraints, including: The vehicle's actual pitch and roll angles are collected by an inertial measurement unit. The vehicle's horizontal attitude is set as the desired attitude control value, and the deviation between the actual attitude and the desired attitude is calculated. The proportional-integral-derivative control algorithm is used to process the two types of attitude deviations respectively, and the target anti-pitch moment and target anti-roll moment used to counteract the attitude deviation are obtained. An optimization function is constructed with the active suspension of each wheel as the power source and the actual vertical load of the wheel approaching the standard tire pressure reference load as the objective. Based on the obtained attitude control torque, the vertical force balance relationship of the whole vehicle, and the output limit of the suspension actuator, multiple constraints are set. Solving the constraint optimization model yields the optimal suspension power for each wheel, driving the suspension mechanism to complete attitude correction, and synchronously adjusting the vertical load distribution of the wheels to achieve coordinated stability control of the vehicle in both the plane and vertical directions.

[0018] To achieve the above objectives, a second aspect of the present invention provides a stability control device for vehicles with underinflated tires, comprising: The state modeling module is used to acquire the vehicle's current driving state data and tire underinflation information, correct the tire mechanical characteristic parameters based on the tire underinflation information, and construct a vehicle planar dynamics model that includes the influence of vertical attitude angles. The controller construction module is used to construct a nonlinear model predictive controller based on the vehicle planar dynamics model and the preset ideal motion state, and to define a control obstacle function with the objective of keeping the yaw rate within the safety boundary, and to introduce the derivative constraint of the control obstacle function into the optimization problem of the nonlinear model predictive controller; The motion control module is used to solve the optimization problem of the introduced control obstacle function constraint, output the optimal target wheel torque and additional front wheel steering angle control amount, adjust the wheel force and front wheel steering angle based on the control amount, and regulate the yaw motion and longitudinal motion of the vehicle. The attitude load module is used to calculate the target anti-pitch moment and target anti-roll moment based on the deviation between the actual attitude of the vehicle body and the desired attitude. With the goal of reducing the difference between the actual vertical load of the wheels and the standard tire pressure reference load, it distributes the suspension as power according to the torque balance constraint to complete the vehicle body attitude control and tire vertical load reconstruction.

[0019] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0020] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing a stability control method for underinflated vehicles as described in the first aspect embodiment.

[0021] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a stability control method for underinflated vehicles as described in the first aspect embodiment.

[0022] The embodiments of the present invention have the following beneficial effects: 1. This solution adopts a phased collaborative control architecture, which sequentially completes the recovery of the vehicle's vertical attitude, the reconstruction of the tire's vertical load, and the control of the vehicle's yaw stability. It can effectively address the yaw-vertical coupling instability problem caused by tire underinflation and achieve coordinated regulation of the vehicle's planar dynamics and vertical dynamics.

[0023] 2. Based on the active suspension, the vertical load distribution of the wheels is optimized, which improves the imbalance of wheel load under under-pressure conditions, fully taps the output potential of the longitudinal and lateral forces of the tires, and improves the basic stability of vehicle driving from the dynamics source.

[0024] 3. A nonlinear safety constraint mechanism based on the control barrier function is introduced, which not only ensures that the vehicle's operating state is always within the safe range, but also optimizes the control output characteristics, significantly improves the smoothness of the control process, and avoids safety risks caused by sudden control changes under extreme conditions.

[0025] 4. Under combined conditions of underinflated tires and emergency steering, it can accurately track the ideal operating state of the vehicle, effectively suppress overshoot and oscillation of yaw rate and body slip angle, and significantly reduce body roll and pitch motion, thus comprehensively optimizing the vehicle's dynamic response performance.

[0026] 5. It is adapted to extreme driving scenarios such as underinflated tires, and can continuously maintain the vehicle within a stable operating range, significantly improving the vehicle's active safety capabilities and driving reliability under complex working conditions. Attached Figure Description

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a stability control method for vehicles with underinflated tires, provided as an embodiment of the present invention; Figure 2 This is a schematic diagram of the vehicle dynamics model under tire underinflation conditions provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a two-degree-of-freedom planar reference model of a vehicle provided in an embodiment of the present invention; Figure 4 A logic block diagram of a vehicle layered vertical stability controller provided in an embodiment of the present invention; Figure 5 This is a simulation result diagram of the vehicle's planar motion state under dual lane change conditions provided in an embodiment of the present invention; Figure 6 This is a simulation result diagram of the vertical motion state of a vehicle under dual lane change conditions provided in an embodiment of the present invention; Figure 7 The simulation results of the control output of the dual-track-change system provided in the embodiments of the present invention are shown in the figure. Figure 8 This is a structural diagram of a stability control device for vehicles with underinflated tires, provided in an embodiment of the present invention. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0030] The following description, with reference to the accompanying drawings, describes a stability control method and apparatus for vehicles with underinflated tires according to an embodiment of the present invention.

[0031] Example 1 This invention provides a stability control method for vehicles with underinflated tires. Figure 1 This is a schematic flowchart illustrating a stability control method for vehicles with underinflated tires, provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: Step S1: Obtain the vehicle's current driving status data and tire underinflation information; correct the tire mechanical characteristic parameters based on the tire underinflation information; and construct a vehicle planar dynamics model that includes the influence of vertical attitude angles.

[0032] In this embodiment, the system first utilizes the vehicle's wheel speed sensors and inertial measurement unit, along with the vehicle's onboard bus network, to collect real-time data on the vehicle's full-dimensional operating status. The collected data specifically includes the vehicle's longitudinal speed, lateral speed, yaw rate, pitch rate, and roll rate. Simultaneously, it reads the rotational speed signals of each wheel and the tire pressure data output by the tire pressure monitoring module, thus comprehensively understanding the vehicle's real-time driving conditions and tire operating status. Based on the collected tire pressure monitoring data, operators compare it with the standard tire pressure values ​​to calculate the under-inflation coefficient for each tire. The magnitude of this coefficient is negatively correlated with the deviation of the tire pressure from the standard tire pressure.

[0033] After obtaining the underinflation coefficients for each tire, the core mechanical properties of the tires are corrected one by one based on these coefficients. When a tire is underinflated, its performance parameters change systematically. The rolling resistance coefficient gradually increases as the underinflation deepens, while the vertical stiffness, lateral stiffness, longitudinal stiffness, and effective rolling radius all decrease with increasing underinflation. All corrected mechanical parameters are then input into the Unitire tire model. The model calculations accurately determine the longitudinal and lateral forces of the four wheels (left front, right front, left rear, and right rear), providing the foundational mechanical input data for building the subsequent vehicle dynamics model.

[0034] After completing the calculations of tire mechanical parameters and wheel forces, dynamic modeling is carried out based on the classical Newton-Euler theorem, such as... Figure 2 As shown, the model built in this study is a seven-degree-of-freedom planar dynamics model of a vehicle. During the overall modeling process, the interaction between the vehicle's vertical motion and planar motion was fully considered. The vertical attitude motion parameters corresponding to the vehicle's pitch and roll were directly coupled and integrated into the overall calculation process of the vehicle's longitudinal, lateral, and yaw dynamics. The final vehicle planar dynamics equations are as follows:

[0035] in, These represent the vehicle's longitudinal speed, lateral speed, and vertical speed, respectively. Represents the rate of change of the vehicle's longitudinal speed and the rate of change of its lateral speed; These represent the vehicle's yaw rate, pitch rate, and roll rate, respectively. The equivalent longitudinal force transmitted from the four wheels to the vehicle body. The equivalent lateral force transmitted from the four wheels to the vehicle body; These represent the vehicle's front wheelbase, rear wheelbase, and half-track width, respectively; m represents the vehicle's gross vehicle weight. These are the moments of inertia of the vehicle about the X-axis, Y-axis, and Z-axis, respectively.

[0036] During vehicle operation, the front wheels will steer, at which point the direction of the force on the wheels will change. This step uses the front wheel steering angle as the coordinate conversion reference to perform coordinate transformation calculations on the forces on the front and rear wheels under steering conditions, separating the longitudinal and lateral forces that are ultimately transmitted to the vehicle body from the overall force on the wheels. The corresponding force calculation formulas are as follows:

[0037]

[0038] in, The front wheel steering angle output by the driver. The longitudinal force is exerted by the left front wheel itself. The longitudinal force is exerted by the right front wheel itself. The longitudinal force is exerted by the left rear wheel itself. The longitudinal force acting on the right rear wheel itself; The lateral force is from the left front wheel itself. The lateral force is exerted by the right front wheel itself. The lateral force is from the left rear wheel itself. The force is the lateral force exerted by the right rear wheel itself.

[0039] After the entire model is built, this solution selects the vehicle's longitudinal speed, lateral speed, and yaw rate as the core state variables of the model. Simultaneously, the pitch rate and roll rate, collected in real time by the system, are continuously fed into the model as dynamic parameters. This setup allows for a complete and realistic representation of the coupling relationship between the vehicle's vertical attitude changes and its planar motion state, laying a solid model foundation for the subsequent implementation of vehicle stability control strategies.

[0040] Step S2: Based on the vehicle planar dynamics model and the preset ideal motion state, a nonlinear model predictive controller is constructed, and a control obstacle function is defined with the goal of keeping the yaw rate within the safety boundary. The derivative constraint of the control obstacle function is introduced into the optimization problem of the nonlinear model predictive controller.

[0041] To accurately calibrate the ideal operating state of the vehicle during driving, this application introduces a dedicated reference model to complete the relevant calculations, such as... Figure 3 As shown, this model is a linear two-degree-of-freedom reference model for a vehicle. This model is also constructed following the Newton-Euler theorem, and the corresponding dynamic differential equations are derived, the specific expressions of which are:

[0042] in, , These correspond to the lateral stiffness of the front and rear wheels of the vehicle, respectively.

[0043] In this embodiment, the steady-state response of the vehicle under the front wheel angle step input condition is selected as the standard reference. Based on this, the expected values ​​of the vehicle's lateral speed and yaw rate are calculated. The corresponding calculation formulas are as follows:

[0044] in, This represents the desired lateral speed. This represents the expected value of the yaw rate. The stability factor is calculated using the following formula: .

[0045] The actual driving process of a vehicle is limited by the road surface's adhesion, and the additional factor of underinflation of tires directly leads to a decrease in vehicle stability. Therefore, it is necessary to set reasonable operating boundaries for yaw rate and lateral speed. The corresponding constraints are as follows:

[0046] in, This is the maximum permissible value for yaw rate. This refers to the maximum permissible lateral speed. denoted as the road surface adhesion coefficient, representing the maximum adhesion capacity that the road surface can provide; g is the acceleration due to gravity.

[0047] The vehicle's longitudinal reference speed is primarily controlled by the accelerator pedal opening. To simplify the control logic and ensure operational stability, this solution will... Set to a fixed value. This represents the desired longitudinal vehicle speed. Combining the reference parameters in the longitudinal, lateral, and yaw directions, a comprehensive system reference vector is formed, expressed as:

[0048] in, This is the system state reference vector, used to characterize the ideal operating state of the vehicle; This represents the expected longitudinal speed of the vehicle. This represents the desired lateral speed of the vehicle. This represents the expected value of the vehicle's yaw rate.

[0049] Based on the nonlinear vehicle planar dynamics system constructed in the previous step, this application further constructs an NMPC planar stability controller. Taking into account the vehicle's motion characteristics, the vehicle's longitudinal velocity is selected. Lateral velocity yaw rate As the core state variable of the controller, and stipulating that the system output variable should be consistent with the state variable, the corresponding expression is:

[0050] in, Let be the system state vector. This is the system output vector.

[0051] Based on the overall vehicle drive and steering control requirements, the torque of the four wheels and the additional steering angle of the front wheels are determined as the system control inputs, thus forming a complete control vector:

[0052] in, The torque of the left front wheel. This refers to the torque of the right front wheel. This refers to the torque of the left rear wheel. This refers to the torque of the right rear wheel; The additional steering angle applied to the front wheels is the steering compensation amount output by the controller.

[0053] Because the control process introduces additional front wheel steering angle parameters, the force state of the wheels will change accordingly. Therefore, it is necessary to revise the original calculation formulas for longitudinal and lateral forces of the wheels. Based on the revised wheel force relationships, the vehicle's planar dynamics differential equations can be rearranged into a standard nonlinear state-space form, as shown in the following expression:

[0054] in, The first derivative of the system state vector represents the real-time rate of change of each state variable; This is a nonlinear dynamics mapping function for vehicles, used to establish the operational relationship between the state vector, control vector, and rate of change of state.

[0055] The equivalent wheel force mentioned above is calculated by combining the force of a single wheel with the steering angle. The specific calculation relationship is shown below:

[0056]

[0057]

[0058] in, The base front wheel steering angle is input for the driver. Add a steering angle to the front wheel output of the controller. The moment of inertia of the wheel during rotation; The angular velocity of each wheel; The effective rolling radius of each wheel; These represent the torque of each wheel.

[0059] Considering that the controller is a discrete control system, the continuous state-space system also needs to be discretized. The expression obtained after discretization is:

[0060] Where k represents the current sampling time of the system, and k+1 represents the next sampling time; The preset system discrete time interval; This is a nonlinear dynamics mapping function for vehicles, used to characterize the operational relationship between state variables and control variables.

[0061] Based on the hardware structure and actuator operating characteristics of a four-wheel independent drive vehicle, amplitude constraints are set for both the control quantity itself and its variation. Due to limitations in mechanical structure and output capacity, the torque that each wheel can output has a definite physical limit, thus yielding the constraint conditions for the control quantity:

[0062] in, This is the lower limit of the control vector. This is the upper limit value of the control vector; , , , These are the minimum torques that can be output by the front left, front right, rear left, and rear right wheels, respectively. , , , These represent the maximum torque that can be output by the four wheels; Minimum additional steering angle for the front wheels. The maximum additional steering angle is set for the front wheels. To avoid abrupt changes in control commands affecting vehicle ride comfort, constraints are also set on the changes in control variables:

[0063] in, To control the change in quantity, , These are the lower and upper limits of the change in the control quantity, respectively.

[0064] Meanwhile, based on the road friction ellipse theory in vehicle dynamics, the range of the resultant force of the longitudinal and lateral forces of a single wheel is limited, and the corresponding constraint formula is:

[0065] in, For the vertical load corresponding to the wheel, the subscript is... These refer to the front left, front right, rear left, and rear right wheels, respectively; the meanings of the other parameters are the same as described above.

[0066] When tires are underinflated, the vehicle's operating state is highly likely to approach the stability boundary, significantly increasing the risk of vehicle instability. To address this issue, this application introduces a control barrier function into the NMPC controller to construct safety barrier constraints. This solution uses yaw rate as the core control object to define the vehicle's safe operating domain, which must satisfy... .

[0067] Considering the significant decrease in lateral load-bearing capacity of the wheel after tire underinflation, the upper limit of yaw rate is adaptively optimized. The optimized calculation formula is as follows:

[0068] in, This is the underinflation coefficient, used to quantify the underinflation state of the tire. The higher the degree of tire underinflation, the smaller the value of this coefficient.

[0069] Based on the above boundary conditions, define the corresponding control barrier function:

[0070] in, This is a control obstacle function used to determine whether the vehicle's current operating state is within a safe range.

[0071] This control barrier function allows for a clear division of different vehicle operating state regions, where the specific definitions of the safety set, safety boundary, and interior of the safety set are as follows:

[0072]

[0073]

[0074] in, For vehicle operation safety set, it represents the legal range of values ​​for yaw rate; The safety boundary is the dividing point between a stable state and a dangerous state. The internal area of ​​the safety cluster represents the range in which the vehicle operates completely stably. It is the field of real numbers.

[0075] To ensure the safety set possesses forward invariance and prevent the vehicle's operating state from deviating from the safe range, derivative constraints need to be added to the control obstacle function:

[0076] in, To control the rate of change of the barrier function; These are non-negative slack variables used to ensure the feasibility of solving under extreme conditions; These are K-type functions, commonly used nonlinear constraint functions in control systems. This embodiment uses a linear function as the specific form:

[0077] Where λ is the constant coefficient of the linear function, and h is the control barrier function.

[0078] By combining multiple control objectives such as vehicle tracking, energy consumption, stability, and load limiting, objective functions with different functions are constructed sequentially. It is mainly used to penalize the deviation between the actual state quantity of the vehicle and the reference state quantity, thereby ensuring that the vehicle state can accurately track the ideal reference trajectory.

[0079] in, In the prediction time domain, it represents the number of steps the controller predicts forward; The system output at time k+i is predicted based on the information at time k. This is the system reference output vector; This is the positive semi-definite diagonal weight matrix corresponding to the state error, used to adjust the weight of the state tracking error.

[0080] objective function The purpose of penalizing the amplitude of the control variable is to effectively reduce the energy consumption of the entire control system.

[0081] in, In the time domain, this represents the number of steps in which the controller is effectively activated; The control quantity at time k+i is obtained based on the information at time k. This is the positive definite diagonal weight matrix corresponding to the control quantity, used to adjust the penalty weights of the control output amplitude.

[0082] objective function Used to constrain the range of change in the control quantity, it can effectively suppress frequent oscillations in the controller output and improve control smoothness.

[0083] in, This is the control quantity from the previous prediction step; This is a semi-positive definite diagonal weight matrix corresponding to the change in the control quantity, used to adjust the penalty weights for fluctuations in the control quantity.

[0084] Tire underinflation directly alters the vertical load distribution across all wheels. To further improve overall vehicle stability and operational robustness, this solution sets an objective function. The load rate of each tire is penalized to allow sufficient stability margin for tire operation. The specific expression is as follows:

[0085] In the formula, the wheel forces and vertical loads are all real-time values ​​at the corresponding moment, and are used as a whole to characterize the proportion of the single wheel load to the ultimate bearing capacity.

[0086] objective function Specifically penalizing slack variables aims to minimize their values ​​and prevent them from interfering with control accuracy.

[0087] in, This is the vector of slack variables corresponding to each prediction step; This is the weight matrix corresponding to the slack variables. All elements in this matrix are selected with large values ​​to strengthen the constraint effect on the slack variables.

[0088] By integrating the objective functions of each of the above sub-items, we obtain the overall optimization objective function. At the same time, all constraints required for system operation are summarized, thereby completing the overall construction of the constrained finite-time domain optimization model.

[0089] Step S3: Solve the optimization problem of the introduced control obstacle function constraint, output the optimal target longitudinal force of the wheel and the additional front wheel steering angle control quantity, and adjust the wheel force and front wheel steering angle based on the control quantity to regulate the yaw motion and longitudinal motion of the vehicle.

[0090] This section addresses the optimization problem constrained by a control obstacle function, ultimately outputting the optimal target longitudinal force on the wheel and the additional front wheel steering angle control quantity. The obtained control quantity is used to adjust the wheel force state and the front wheel deflection angle, thereby achieving effective control over the vehicle's yaw and longitudinal motion.

[0091] This application defines the complete control sequence in the control time domain as:

[0092] in, This is the predicted complete control sequence at the current time k. This represents the control vector obtained by solving for the future k+i time based on the system information collected at time k. To control the time domain, it represents the total number of steps in the optimization solution.

[0093] Based on the multiple objective functions and all constraints established above, this scheme employs either sequential quadratic programming or the interior-point method to solve the optimization problem. The solution process requires simultaneously satisfying a series of constraints, including system dynamics, control variables, tire adhesion, and safety boundaries. The specific constraint expressions and parameter explanations are as follows:

[0094] The first equation is the recursive equation for the discrete state of the vehicle: Let be the system state vector predicted at time k+i; Δt is the system sampling time interval. It is a nonlinear dynamics mapping function for vehicles, representing the operational relationship between state variables, control variables, and the rate of change of state; , These are the state vector and control vector of the previous prediction step, respectively.

[0095] The second formula is for controlling the amplitude constraint: , These are the lower and upper limits of the control vector, used to limit the maximum output range of the actuator.

[0096] The third equation is a constraint on the rate of change of the control quantity: , These are the lower and upper limits of the change in the control quantity, used to limit the magnitude of sudden changes in the control command.

[0097] The fourth formula is the tire friction adhesion constraint: The road surface adhesion coefficient; This represents the vertical load on the wheel at the current moment. , These represent the longitudinal and lateral forces acting on the wheel, respectively. This formula is used to ensure that the resultant force of the wheel does not exceed the road surface adhesion limit; the subscript j represents different wheels.

[0098] The fifth equation is the control obstacle function constraint: This is a control obstacle function used to determine whether the vehicle's yaw motion is within a safe range; The vehicle's yaw rate; It is a linear K-type function; These are slack variables used to ensure that the optimization problem can be solved normally under extreme conditions.

[0099] The entire set of constraint rules ensures that the calculation results conform to the actual operating patterns of the vehicle and meet the requirements of real vehicle use.

[0100] After completing the optimization solution, the optimal control sequence within the finite control time domain can be obtained. According to the execution rules of model predictive control rolling optimization, the control quantity corresponding to the first time step in the sequence is selected as the final output result of the controller at the current sampling time. The output includes the target driving torque corresponding to the four wheels and the additional steering angle of the front wheels.

[0101] The calculated target drive torque for each wheel is sent to the corresponding wheel's drive actuator. By adjusting the output torque of the hub motor, the longitudinal force on each wheel is independently and precisely controlled. The calculated additional front wheel steering angle is superimposed on the driver-input base front wheel steering angle, and the synthesized total steering angle command is sent to the steering actuator. The actual front wheel deflection angle is changed using the steer-by-wire system or electric power steering system. During system operation, wheel forces, front wheel steering angle, and various vehicle motion parameters are continuously collected. The measured parameters are compared with ideal motion reference values, and closed-loop corrections are performed to continuously constrain the vehicle's longitudinal and yaw motion, ensuring that the vehicle stably follows the preset desired trajectory under actual driving conditions.

[0102] Step S4: Calculate the target anti-pitch moment and target anti-roll moment based on the deviation between the actual attitude of the vehicle body and the desired attitude. With the goal of reducing the difference between the actual vertical load of the wheel and the standard tire pressure reference load, distribute the suspension as power according to the torque balance constraint to complete the vehicle body attitude control and tire vertical load reconstruction.

[0103] This step calculates the required target anti-pitch moment and target anti-roll moment based on the deviation between the actual and desired vehicle posture. At the same time, the optimization target is to reduce the difference between the actual vertical load of the wheel and the standard tire pressure reference load. Combined with the torque balance constraint, the suspension is rationally distributed to achieve vehicle posture adjustment and tire vertical load reconstruction.

[0104] To achieve coordinated control of both the planar and vertical motion of the vehicle, this application designs a hierarchical vertical stability controller, the overall control logic of which is as follows: Figure 4 As shown, the controller adopts a two-layer architecture, consisting of an upper attitude control layer and a lower power distribution optimization layer from top to bottom. The two layers work together to complete the vertical state control of the entire vehicle.

[0105] In the upper-level attitude control stage, the vehicle pitch angle and roll angle are selected as the core control objects, and the vehicle's horizontal attitude is set as the desired attitude of the system. The vehicle pitch angle and roll angle are collected in real time by the onboard inertial measurement unit to calculate the deviation between the actual vehicle attitude and the preset desired attitude. This embodiment uses a PID control algorithm to process the attitude deviation, calculating the target anti-roll moment and target anti-pitch moment used to counteract the vehicle attitude deviation. The formula for calculating the anti-roll moment is as follows:

[0106] in, The target is the anti-lateral tilting moment; For proportionality coefficient, For integral coefficients, These are the differential coefficients, and the three are the core tuning parameters of the PID controller; Let k be the vehicle body roll angle deviation value at time k; This represents the roll angle deviation value corresponding to the j-th sampling time. k is the system sampling time interval; 1 represents the previous sampling time. The calculation logic for the anti-pitch moment is completely consistent with that for the anti-roll moment.

[0107] By outputting the corresponding anti-pitch and anti-roll moments, the pitch and roll phenomena of the vehicle body can be effectively suppressed, and the vehicle body attitude can be gradually corrected to a stable state.

[0108] After entering the lower-level power distribution stage, the working forces of the four-wheel active suspension are used as optimization decision variables, and the optimization objective is to make the actual vertical load of each wheel infinitely close to the reference vertical load under standard tire pressure. At the same time, minimizing energy consumption is also considered, and the corresponding objective function is constructed as follows:

[0109] Where J is the objective function; subscript These represent the front left, front right, rear left, and rear right wheels, respectively. The weighting coefficients for each wheel load error term are used to distinguish the control priorities of different wheels; Power is supplied to the suspension at the corresponding wheel position; The suspension spring force at the wheel position; This is the vertical reference load of the wheel under standard tire pressure conditions.

[0110] Based on the overall vehicle mechanical balance and the working characteristics of the suspension actuators, several constraints are set for the optimization process. First, roll moment balance constraints are set:

[0111] in, This is half the vehicle's track width, which is half the distance between the centers of the left and right wheels; the meanings of the other parameters remain the same as before.

[0112] Secondly, set pitch moment balance constraints:

[0113] in, For the target anti-pitch moment; The distance from the vehicle's center of gravity to the front axle is the front wheelbase. This is the rear axle distance from the vehicle's center of gravity to the rear axle.

[0114] Considering the physical output limit of the active suspension mechanism, upper and lower limits of dynamic settings are further applied to the single-wheel suspension:

[0115] in, For single-wheel suspension, the minimum power is set. The maximum power for a single-wheel suspension is determined by the hardware parameters of the suspension actuator.

[0116] Solving the constrained optimization problem yields the optimal force distribution for each wheel's active suspension. This optimal force drives the suspension mechanism to perform corresponding actions, rapidly correcting vehicle pitch and roll, and rationally redistributing vertical loads to each wheel, improving the overall tire stress state and maximizing the output of longitudinal and lateral forces. Ultimately, this achieves coordinated control of vehicle planar and vertical stability under underinflated tire conditions.

[0117] In the application of one embodiment of the present invention, the implementation process is as follows: To verify the practical application effect of the vehicle stability control method proposed in this invention under tire underinflation conditions, this section sets up a simulation test environment and selects typical driving conditions to carry out simulation verification.

[0118] This simulation uses a double lane change condition to simulate an emergency steering scenario for a vehicle. The target vehicle speed is set to 80 km / h, and the road adhesion coefficient is set to 0.85. At the same time, the tire pressure of the left rear tire is set to be 40% lower than the standard tire pressure to simulate the underinflated tire condition, thereby examining the impact of the underinflated state on the vehicle's dynamic characteristics and driving stability.

[0119] The simulation results of the vehicle's planar motion state are as follows Figure 5 As shown. Figure 5 The main focus is on presenting the variation curves of key planar state parameters such as vehicle yaw rate and lateral velocity, and comparing the vehicle response characteristics under no control strategy and with the control strategy of this invention.

[0120] The vehicle yaw rate is constrained for safety based on the vehicle dynamics model and control obstacle function described above, and the corresponding expression is:

[0121] in, For the control barrier function; The vehicle's yaw rate; This is the upper limit for the safe yaw rate of the vehicle.

[0122] Depend on Figure 5 It is known that during the process of tire underinflation and emergency steering, without the application of control, the vehicle's yaw rate and lateral speed fluctuate greatly, the response curve shows obvious abrupt changes, and the vehicle's driving state tends to be unstable. After adopting the control method of the present invention, the changes in the vehicle's yaw rate and lateral speed are gradual, the dynamic response characteristics are significantly optimized, and the vehicle can stably track the desired driving state.

[0123] To quantitatively evaluate the control effect, the maximum value (MAX), mean absolute error (MAE), and root mean square error (RMSE) were selected as evaluation indicators. The relevant statistical results are shown in Table 1.

[0124] Table 1

[0125] As shown in Table 1, under uncontrolled conditions, the maximum yaw rate of the vehicle is 19.60°, the mean absolute error is 2.60°, and the root mean square error is 4.92°. After applying the control strategy of this invention, the maximum yaw rate is reduced to 6.28°, the mean absolute error is reduced to 0.85°, and the root mean square error is reduced to 1.52°. For the lateral velocity, under uncontrolled conditions, the maximum value is 5.84°, the mean absolute error is 0.88°, and the root mean square error is 1.62°. After applying control, the maximum lateral velocity is 2.65°, the mean absolute error is 0.31°, and the root mean square error is 0.61°. All evaluation indicators show significant improvement, indicating that this invention can effectively suppress fluctuations in vehicle planar motion parameters and avoid the risk of vehicle instability.

[0126] The simulation results of the vehicle's vertical motion state are as follows: Figure 6 As shown. Figure 6 It displays the dynamic changes in vehicle pitch angle, roll angle, and vertical load on each wheel, which are used to characterize the vehicle's vertical attitude and tire stress state.

[0127] This invention calculates the compensation torque through the upper-level attitude control system, wherein the formula for calculating the anti-rolling torque is as follows:

[0128] in, for The target's anti-tilting moment at all times; This is the proportionality coefficient; The integral coefficient; These are the differential coefficients; Let k be the vehicle body roll angle deviation value at time k; This represents the roll angle deviation value corresponding to the j-th sampling time. The system sampling time interval; This is the previous sampling time. The vehicle body pitch moment is solved using the same calculation logic.

[0129] The lower-level power distribution stage constructs an optimization function with the objective of minimizing load deviation, expressed as:

[0130] in, To optimize the objective function; The weighting coefficient for the load error of each wheel; Power is supplied to the corresponding suspension of the wheels; This refers to the suspension spring force. The vertical reference load of the wheel under standard tire pressure; subscript These correspond to the front left, front right, rear left, and rear right wheels, respectively.

[0131] Combination Figure 6 Analysis shows that without control, the vehicle's pitch and roll angles deviate significantly, resulting in poor vehicle stability. Simultaneously, the vertical load distribution on underinflated wheels and other wheels is uneven, leading to poor tire contact stress. By employing the control method of this invention, the layered vertical stability controller outputs anti-pitch and anti-roll moments, effectively suppressing vehicle pitch and roll. Through optimized power distribution via the suspension, the actual vertical load on each wheel gradually approaches the reference load corresponding to standard tire pressure, resulting in a more reasonable distribution of tire vertical load and significantly improving the stress conditions and adhesion performance of underinflated tires.

[0132] Simulation results of controller output control quantity are as follows Figure 7 As shown. Figure 7 Record the core control quantities output by the controller throughout the simulation process, such as the longitudinal force of the target wheel, the additional front wheel steering angle, and the suspension action force.

[0133] This invention defines the control sequence for a finite-time optimization problem as:

[0134] in, For the present The complete control sequence at time t; Based on Time information prediction Timing control vector; To control the time domain.

[0135] The optimization solution process must simultaneously satisfy the discrete state equations of the system, the control amplitude constraints, the control change constraints, the tire friction constraints, and the control obstacle function constraints. The entire set of constraints ensures that the control output meets the physical characteristics of the vehicle mechanism and the driving safety requirements.

[0136] Depend on Figure 7 As can be seen, the control quantities output by the controller of this invention are continuous, stable, and free from abrupt changes or oscillations. Based on the real-time operating status of the vehicle, the controller dynamically outputs the target torque for all four wheels and the additional front wheel steering angle, adjusting the longitudinal force on the wheels and the front wheel deflection angle to complete the vehicle's planar motion control; simultaneously, it outputs power to the suspension of each wheel to coordinate vehicle posture correction and tire load reconstruction. The control quantity output pattern matches the changes in vehicle state, the control logic is reasonable, and it can stably drive each actuator to complete the corresponding actions.

[0137] In summary Figure 5 , Figure 6 , Figure 7 As shown in Table 1, the simulation results indicate that under the emergency steering condition of double lane change with the left rear tire underinflated by 40%, the cooperative control method proposed in this invention can ensure that the vehicle's planar motion parameters stably track the target value, the yaw rate is always within the safe limit, and there is no obvious overshoot or oscillation; the vehicle's pitch and roll motions are effectively suppressed, and the vehicle's vertical attitude remains stable; the vertical load distribution of each wheel is optimized, and the tire adhesion capacity is fully utilized.

[0138] This invention achieves coordinated control of vehicle planar and vertical motion under tire underinflation conditions through a hierarchical architecture that combines planar stability control and vertical stability control. This effectively improves the overall driving stability and safety of the vehicle and verifies the effectiveness and engineering feasibility of the control method in tire underinflation scenarios.

[0139] Example 2 This invention provides a stability control device for vehicles with underinflated tires. Figure 8 This is a schematic diagram of a stability control device for vehicles with underinflated tires, provided in an embodiment of the present invention. Figure 8 As shown, the device includes: The state modeling module 100 is used to acquire the current driving state data of the vehicle and the tire underinflation information, correct the tire mechanical characteristic parameters based on the tire underinflation information, and construct a vehicle planar dynamics model that includes the influence of vertical attitude angle. The controller construction module 200 is used to construct a nonlinear model predictive controller based on the vehicle planar dynamics model and the preset ideal motion state, and to define a control obstacle function with the objective of keeping the yaw rate within the safety boundary, and to introduce the derivative constraint of the control obstacle function into the optimization problem of the nonlinear model predictive controller. The motion control module 300 is used to solve the optimization problem of the introduced control obstacle function constraint, output the optimal target torque of the wheel and the additional front wheel steering angle control amount, adjust the wheel force and front wheel steering angle based on the control amount, and regulate the yaw motion and longitudinal motion of the vehicle. The attitude load module 400 is used to calculate the target anti-pitch moment and the target anti-roll moment based on the deviation between the actual attitude of the vehicle body and the desired attitude. With the goal of reducing the difference between the actual vertical load of the wheels and the standard tire pressure reference load, the module distributes the suspension as power according to the torque balance constraint to complete the vehicle body attitude control and tire vertical load reconstruction.

[0140] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0141] Example 3 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0142] Example 4 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0143] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0144] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0145] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A stability control method for vehicles with underinflated tires, characterized in that, include: The vehicle's current driving status data and tire underinflation information are obtained. Based on the tire underinflation information, the tire mechanical characteristic parameters are corrected, and a vehicle planar dynamics model including the influence of vertical attitude angles is constructed. Based on the vehicle planar dynamics model and the preset ideal motion state, a nonlinear model predictive controller is constructed, and a control obstacle function is defined with the goal of keeping the yaw rate within the safety boundary. The derivative constraint of the control obstacle function is introduced into the optimization problem of the nonlinear model predictive controller. The optimization problem of the introduced control obstacle function constraint is solved, and the optimal target wheel torque and additional front wheel steering angle control are output. Based on the control, the wheel force and front wheel steering angle are adjusted to regulate the yaw and longitudinal motion of the vehicle. The target anti-pitch moment and target anti-roll moment are calculated based on the deviation between the actual attitude of the vehicle body and the desired attitude. With the goal of reducing the difference between the actual vertical load of the wheel and the standard tire pressure reference load, the suspension is distributed to power in accordance with the torque balance constraint to complete the vehicle body attitude control and tire vertical load reconstruction.

2. The method according to claim 1, characterized in that, Acquire current vehicle driving status data and tire underinflation information, and correct tire mechanical characteristic parameters based on the tire underinflation information, including: The vehicle's current longitudinal speed, lateral speed, yaw rate, pitch rate, roll rate, as well as wheel speed and tire pressure monitoring data are obtained through wheel speed sensors, inertial measurement units, and on-board bus networks. The underinflation coefficient of each tire is determined based on tire pressure monitoring data. The underinflation coefficient is negatively correlated with the deviation of the tire pressure from the standard tire pressure. Based on the underpressure degree coefficient, the rolling resistance coefficient, vertical stiffness, lateral stiffness, longitudinal stiffness, and effective rolling radius are modified. Among them, the rolling resistance coefficient increases monotonically with the increase of underpressure degree, while the vertical stiffness, lateral stiffness, longitudinal stiffness, and effective rolling radius decrease monotonically with the increase of underpressure degree. The corrected mechanical parameters are substituted into the Unitire tire model to calculate the longitudinal and lateral forces of each wheel, which are then used as inputs for the vehicle's planar dynamics model.

3. The method according to claim 2, characterized in that, Construct a vehicle planar dynamics model that includes the influence of vertical attitude angles, including: Based on the Newton-Euler theorem, the planar dynamics equations of a four-wheel independent drive vehicle are established, and the vertical attitude motion parameters corresponding to pitch and roll are coupled and integrated into the longitudinal, lateral, and yaw dynamics calculation processes, respectively. Using the front wheel angle as the transformation reference, coordinate conversion is performed on the forces on the front and rear wheels to obtain the longitudinal and lateral forces that the wheels actually exert on the vehicle body under steering conditions. The longitudinal vehicle speed, lateral vehicle speed, and yaw rate are selected as the target state variables of the model, and the pitch rate and roll rate collected in real time are used as dynamic parameters input into the model to fully represent the coupling relationship between vertical attitude and vehicle planar motion.

4. The method according to claim 3, characterized in that, Based on the vehicle's planar dynamics model and the preset ideal motion state, a nonlinear model predictive controller is constructed, and a control obstacle function is defined with the objective of keeping the yaw rate within the safety boundary, including: The vehicle's longitudinal velocity, lateral velocity, and yaw rate are selected as the controller's state variables, and the output variables are set to be equal to the state variables. The torque of each wheel and the additional front wheel steering angle are selected as control inputs to form a control vector. Discretize the nonlinear state-space equations in the vehicle planar dynamics model to obtain a discrete-time system; The ideal motion state reference value of the vehicle is calculated based on the linear two-degree-of-freedom reference model. The range of the reference value is limited by the boundary of road adhesion conditions and tire underpressure characteristics. The safe operating range of yaw rate is determined by combining the road surface adhesion coefficient, vehicle longitudinal speed, and tire underinflation coefficient. Based on the boundary of the safe range, a control obstacle function is constructed to divide the vehicle stable operating state area. By differentiating the control barrier function along the system dynamics, an explicit relationship is established between the derivative of the control barrier function and the current control quantity.

5. The method according to claim 4, characterized in that, Solve the optimization problem of the introduced control obstacle function constraint, and output the optimal target longitudinal force of the wheel and the additional front wheel steering angle control quantity, including: The derivative constraint of the control obstacle function is loaded into the controller optimization model, and non-negative relaxation variables are added to ensure the feasibility of optimization solution under boundary conditions. By combining state tracking deviation, control output amplitude, control quantity fluctuation amplitude, tire load bearing, and relaxation variable value, penalty terms are constructed respectively, and combined to form a multi-objective comprehensive optimization function. Four types of constraints are set: upper and lower limits of control amplitude, rate of change of control, road friction limit, and control obstacle function. The range of legal values ​​for optimization solution is defined. The optimization problem is solved in the finite time domain using sequential quadratic programming or interior point method to obtain the optimal control sequence. The control quantity at the first moment in the optimal control sequence is extracted as the controller output at the current moment. The controller output includes the target torque of each wheel and the additional front wheel steering angle.

6. The method according to claim 5, characterized in that, Based on the control quantity, the wheel force and front wheel steering angle are adjusted to regulate the vehicle's yaw and longitudinal movements, including: The target torque of each wheel is distributed to the actuator of the corresponding wheel, and independent control of each wheel is achieved by adjusting the hub motor; The additional front wheel steering angle is superimposed with the front wheel steering angle and then output to the steering actuator, which adjusts the actual front wheel steering angle through the steer-by-wire system or the electric power steering system. The system monitors the vehicle's motion status in real time, compares the output with the ideal motion reference state, and corrects the closed-loop control output to constrain the vehicle's yaw and longitudinal movement to stably follow the desired trajectory.

7. The method according to claim 6, characterized in that, The target anti-pitch moment and target anti-roll moment are calculated based on the deviation between the actual and desired vehicle body posture. The goal is to reduce the difference between the actual vertical load on the wheels and the reference load at standard tire pressure. The suspension is then distributed with power according to moment balance constraints, including: The vehicle's actual pitch and roll angles are collected by an inertial measurement unit. The vehicle's horizontal attitude is set as the desired attitude control value, and the deviation between the actual attitude and the desired attitude is calculated. The proportional-integral-derivative control algorithm is used to process the two types of attitude deviations respectively, and the target anti-pitch moment and target anti-roll moment used to counteract the attitude deviation are obtained. An optimization function is constructed with the active suspension of each wheel as the power source and the actual vertical load of the wheel approaching the standard tire pressure reference load as the objective. Based on the obtained attitude control torque, the vertical force balance relationship of the whole vehicle, and the output limit of the suspension actuator, multiple constraints are set. Solving the constraint optimization model yields the optimal suspension power for each wheel, driving the suspension mechanism to complete attitude correction, and synchronously adjusting the vertical load distribution of the wheels to achieve coordinated stability control of the vehicle in both the plane and vertical directions.

8. A stability control device for vehicles with underinflated tires, characterized in that, include: The state modeling module is used to acquire the vehicle's current driving state data and tire underinflation information, correct the tire mechanical characteristic parameters based on the tire underinflation information, and construct a vehicle planar dynamics model that includes the influence of vertical attitude angles. The controller construction module is used to construct a nonlinear model predictive controller based on the vehicle planar dynamics model and the preset ideal motion state, and to define a control obstacle function with the objective of keeping the yaw rate within the safety boundary, and to introduce the derivative constraint of the control obstacle function into the optimization problem of the nonlinear model predictive controller; The motion control module is used to solve the optimization problem of the introduced control obstacle function constraint, output the optimal target wheel torque and additional front wheel steering angle control quantity, adjust the wheel force and front wheel steering angle based on the control quantity, and regulate the yaw motion and longitudinal motion of the vehicle. The attitude load module is used to calculate the target anti-pitch moment and target anti-roll moment based on the deviation between the actual attitude of the vehicle body and the desired attitude. With the goal of reducing the difference between the actual vertical load of the wheels and the standard tire pressure reference load, it distributes the suspension as power according to the torque balance constraint to complete the vehicle body attitude control and tire vertical load reconstruction.

9. An electronic device, characterized in that, Including processor and memory; The processor runs a program corresponding to the executable program code stored in the memory to implement the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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