A vehicle control method for extreme cornering working conditions

Abstract

The application discloses a kind of vehicle control methods for limit cornering operating mode, belong to vehicle dynamics control technical field.The method first obtains target longitudinal resultant force, target additional yaw moment and target total anti-roll moment;Establish the mapping relationship between vehicle control target and actuator layer control quantity;Introduce control target slack variable, rewrite each target into soft constraint form containing slack variable;Joint optimization cost function containing control target priority trade-off cost is constructed;Under the condition of satisfying tire adhesion constraint, the optimal distribution of each wheel longitudinal force and front, rear axle active stabilizer bar output torque is obtained.The application realizes the adaptive trade-off of control target under limit operating mode by slack variable mechanism, avoids distribution rigidity and degree of freedom lock, and optimizes stabilizer bar interaxle distribution based on adhesion margin, inhibits single axle tire premature saturation, significantly improves the yaw stability and steering controllability of vehicle in high-speed sharp turn, emergency obstacle avoidance and other limit operating mode.

Description

Technical Field

[0001] This invention belongs to the field of vehicle dynamics and intelligent chassis control technology, and relates to a vehicle control method for extreme cornering conditions. Background Technology

[0002] With the development of electric drive and intelligent chassis technology, in order to improve the yaw stability and body posture stability of vehicles during cornering, independently controllable chassis actuators such as distributed drive motors and active stabilizer bars have been introduced. This enables vehicles to achieve torque vector distribution and anti-roll torque regulation, and to regulate the stability of the motion state of distributed electric drive vehicles from aspects such as tire longitudinal force distribution and tire vertical load distribution.

[0003] Under normal driving conditions, tire adhesion utilization is low and tire force availability is ample. Independent or simple collaborative control of each actuator can achieve different control objectives, thus effectively regulating the vehicle's stable driving state. However, when the vehicle enters extreme conditions such as high-speed sharp turns or emergency obstacle avoidance, the demand for lateral motion control increases significantly, and the output of tire lateral force increases substantially. Under the constraint of limited adhesion conditions, the rapid increase in lateral force significantly compresses the tire's longitudinal force adjustment space, which significantly limits the ability to generate additional yaw moment, thereby restricting the stable control of the vehicle's yaw motion. To improve the feasibility of motion control under extreme conditions, the vehicle needs to simultaneously utilize multiple actuators to coordinate control from two paths: "yaw moment generation" and "tire load transfer adjustment," in order to improve the available tire force space and increase yaw control margin. However, the improvement effect is limited by the collaborative control strategy of the multiple actuators.

[0004] In existing cooperative control strategies, the primary control objective of the active stabilizer bar is to satisfy the vehicle's anti-roll performance, and its output distribution prioritizes roll suppression. Under extreme conditions such as high-speed sharp turns and emergency obstacle avoidance, when the required anti-roll torque approaches the sum of the maximum torques that the front and rear active stabilizers can provide, both stabilizers are forced to operate at saturation output simultaneously. In this situation, tire load adjustment struggles to flexibly distribute the vertical load between the axles based on the difference in tire adhesion between the front and rear axles, making it difficult to provide the necessary load adjustment support for torque vector distribution. This cooperative control strategy, which prioritizes anti-roll control under extreme conditions, limits the coordinated adjustment capability between the active stabilizer bar and torque vector distribution, neglecting the vehicle's primary requirement for yaw stability under extreme conditions. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a vehicle control method for extreme cornering conditions. It employs a hierarchical architecture with upper-level vehicle motion control and lower-level actuator coordinated allocation to achieve coordinated control of vehicle yaw and roll stability. The upper layer, while satisfying basic driving stability constraints, introduces model predictive control to uniformly predict and coordinately solve for yaw and roll stability requirements, outputting control intentions such as target additional yaw moment and target total anti-roll moment. The lower layer establishes a mapping relationship between control targets and actuator control quantities based on these control intentions, and introduces an adjustable control optimization target mechanism in the joint optimization allocation. Through relaxation adjustment, it balances targets such as longitudinal resultant force, additional yaw moment, and total anti-roll moment, allowing non-critical targets to yield appropriately when adhesion is limited, avoiding rigid allocation and degree-of-freedom lock-up. This achieves coordinated solution and execution of torque vector allocation and active stabilizer bars, thereby improving vehicle driving stability and handling controllability under extreme conditions.

[0006] This invention discloses a vehicle control method for extreme cornering conditions, comprising the following steps: Obtain the longitudinal resultant force of the vehicle target, the target's additional yaw moment, and the target's total anti-roll moment; Establish a mapping relationship between the vehicle-level control target and the actuator-level control quantity. The actuator-level control quantity includes at least the longitudinal force of each wheel and the output torque of the front and rear axle active stabilizer bars. By introducing control target relaxation variables, the target additional yaw moment, target longitudinal resultant force and target total anti-roll moment are rewritten into soft constraint forms containing corresponding relaxation variables, so as to allow deviations in the control target when tire adhesion is limited; Based on the mapping relationship between the vehicle-level control objective and the actuator-level control quantity, the soft constraint form, and the allowance for deviations in the control objective when tire adhesion is limited, a joint optimization cost function is constructed. The joint optimization cost function includes at least the control objective priority trade-off cost based on the slack variables. Under the conditions of satisfying tire adhesion constraints, wheel longitudinal force amplitude constraints, and active stabilizer bar torque amplitude constraints, the joint optimization cost function is solved to obtain the optimal distribution results of the longitudinal forces of each wheel and the output torques of the front and rear axles active stabilizer bars, thereby achieving coordinated control of yaw stability and roll stability under extreme cornering conditions.

[0007] Optionally, the steps of obtaining the target additional yaw moment and the target total anti-roll moment specifically include: A predictive controller based on an upper-level model is constructed. The vehicle body roll angle, roll rate, center of gravity sideslip angle and yaw rate are used as state vectors, and the target total anti-roll moment and target additional yaw moment are used as control inputs. The controller is then used for unified prediction and coordinated solution based on the vehicle dynamics state space model.

[0008] Optionally, the control target relaxation variable includes the target additional yaw moment deviation. Target longitudinal resultant force deviation and the deviation of the target total anti-rolling moment .

[0009] Optionally, the soft constraint form for the target additional yaw moment is:

[0010] in, , These represent the longitudinal driving or braking force of the two front axle wheels, respectively. , These represent the longitudinal driving or braking force of the two rear wheels, respectively. This refers to the front axle track. This refers to the rear axle track width; Add a yaw moment to the target.

[0011] Optionally, the soft constraint form of the target longitudinal resultant force is:

[0012] in, , These represent the longitudinal driving or braking force of the two front axle wheels, respectively. , These represent the longitudinal driving or braking force of the two rear wheels, respectively. This represents the longitudinal resultant force of the vehicle target.

[0013] Optionally, the soft constraint for the target total anti-roll moment is:

[0014] in, , The active stabilizer bars on the front and rear axles respectively output torque; This represents the total anti-tilting moment of the target.

[0015] Optionally, the joint optimization cost function consists of the control objective priority trade-off cost, actuator energy consumption cost, actuator command smoothing cost, and a stabilizer bar inter-axis allocation term based on attachment margin optimization, expressed as:

[0016] in, Represents the decision variable vector of the lower-level executor. and soft constraint relaxation vector To optimize the variables, a minimization solution is performed. This represents the total cost function of the joint optimization; This indicates the trade-off between prioritizing control objectives and their costs. This indicates the energy consumption cost of the actuator; Indicates the cost of executor instruction smoothing; This represents the stabilizer bar inter-axis allocation term optimized based on adhesion margin.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: (1) This invention proposes a vehicle control method for extreme turning conditions. By coordinating the longitudinal motion, yaw motion and tilt motion of the vehicle within a unified control framework, a clear hierarchical relationship and information transmission path are formed between the vehicle-level control target and the actuator-level control capability, effectively avoiding control conflicts caused by multiple independent control targets under extreme conditions.

[0018] (2) This invention introduces a model predictive control method at the vehicle level to uniformly predict and coordinate the control requirements for vehicle yaw stability and roll stability. The target additional yaw moment and the target total anti-roll moment are sent to the actuator as the vehicle control intention, so that torque vector control and active stabilizer bar control can be designed in a coordinated manner at the vehicle level, thereby improving the overall handling stability of the vehicle under extreme conditions such as high-speed sharp turns and emergency obstacle avoidance.

[0019] (3) In view of the problem that the control objectives are difficult to be satisfied simultaneously due to the limited tire adhesion conditions under extreme working conditions, the present invention introduces control objective relaxation variables in the lower-level joint allocation process, and performs unified and weightable modeling of control objectives such as longitudinal resultant force, additional yaw moment and total anti-roll moment, so that multiple control objectives can be adaptively weighed according to the actual capabilities of the system, avoiding rigid control allocation and locked adjustment degrees of freedom.

[0020] (4) This invention constructs a unified mapping relationship between the vehicle control target and the actuator control quantity, and introduces an active stabilizer bar front and rear axle distribution optimization mechanism based on tire force utilization rate in the joint optimization cost function, so that the active stabilizer bar and torque vector distribution can achieve collaborative solution and collaborative execution, fully release the control potential of the active stabilizer bar on the distribution of tire vertical load, suppress premature saturation of single axle tires, and effectively improve the executability and overall control reliability of torque vector control under extreme conditions without changing the basic structure of the vehicle and the existing control system configuration. Attached Figure Description

[0021] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0022] Figure 1 This is a flowchart of a vehicle control method for extreme turning conditions according to the present invention. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0024] A specific embodiment of the present invention, such as Figure 1 As shown, a vehicle control method for extreme cornering conditions includes the following steps: Step 1: Obtain the longitudinal resultant force of the vehicle target.

[0025] Step 11: During vehicle operation, acquire vehicle operating status information in real time, including but not limited to acquiring vehicle operating status, driver input, and tire-related information in each control cycle.

[0026] Specifically, the vehicle's operating status includes the vehicle's longitudinal speed. lateral acceleration Vehicle yaw rate Vehicle center of gravity sideslip angle Body roll angle and vehicle body roll rate ; Driver input includes driver's front wheel steering angle input. ; Tire-related information includes the normal load of each wheel. and road surface adhesion coefficient ,in, These represent the front left, front right, rear left, and rear right wheels, respectively.

[0027] Step 12: Based on the vehicle's longitudinal velocity tracking requirements, construct a longitudinal resultant force control reference for the vehicle. The specific steps are as follows: First, define the vehicle longitudinal velocity error. The expression is:

[0028] in, The desired longitudinal vehicle speed can be provided by the driver's accelerator pedal input, brake pedal input, or speed planning module; This represents the vehicle's longitudinal speed.

[0029] Then, the desired longitudinal acceleration command is generated using a PID control law, expressed as:

[0030] in, The desired longitudinal acceleration; For proportional gain; For integral gain; This is the differential gain; This is the integral term for the longitudinal velocity error; This represents the rate of change of longitudinal velocity error.

[0031] Finally, based on the vehicle's longitudinal dynamics, the desired longitudinal acceleration is converted into the target longitudinal resultant force, expressed as follows:

[0032] in, The target longitudinal resultant force of the vehicle is used as the longitudinal target input for the longitudinal torque distribution of the four wheels; For the overall vehicle weight; The desired longitudinal acceleration is generated by a PID control law.

[0033] Step 2: Calculate the vehicle yaw stability control target and roll stability control target.

[0034] (1) Calculation of the desired target for yaw stability control; Among them, the desired target for yaw stability control includes the desired yaw angular velocity. The sum is the expected centroid side slip angle. .

[0035] Based on a two-degree-of-freedom vehicle lateral dynamics model, the understeering gradient of the vehicle is defined. for:

[0036] in, For the overall vehicle weight; The wheelbase of the vehicle, and satisfying the following conditions. ; This is the distance from the vehicle's center of gravity to the front axle. This is the distance from the vehicle's center of gravity to the rear axle. This is the equivalent lateral stiffness of the front axle; This is the equivalent lateral stiffness of the rear axle.

[0037] Based on this, the desired yaw rate is calculated by combining the vehicle's longitudinal velocity and the driver's front wheel steering angle input. The yaw response is used to characterize the ideal yaw response of a vehicle under given speed and steering input conditions, and its expression is:

[0038] in, The correction coefficients, which vary with vehicle speed and lateral acceleration, are determined through calibration.

[0039] Minimizing the centroid sideslip angle is taken as one of the lateral stability control objectives, and the expected value of the centroid sideslip angle is set as follows:

[0040] in, The desired sideslip angle is used to characterize the control objective of maintaining lateral stability of the vehicle under extreme conditions.

[0041] (2) Calculation of desired target for roll stability control; The desired target for roll stability control includes the desired roll angle of the vehicle under a given lateral acceleration. and desired roll rate .

[0042] Based on the vehicle roll dynamics equation, the expression is:

[0043] in, The moment of inertia of the vehicle body about the roll axis; This is the roll damping coefficient; The equivalent roll stiffness includes the suspension roll recovery stiffness and the equivalent recovery stiffness caused by gravity. This refers to the vehicle body roll angle acceleration; This refers to the vehicle body roll rate; This refers to the vehicle body roll angle; The height from the center of mass to the center of roll; It is lateral acceleration; This is the total anti-roll control torque used for vehicle body attitude control; Under steady-state tilt equilibrium conditions, let The roll balance relationship is obtained. To avoid the desired target and control input becoming interdependent, the desired roll angle is calculated using a reference model without active attitude control. The expression is: .

[0044] Based on this, the desired roll angle of the vehicle under a given lateral acceleration is constructed, expressed as follows: .

[0045] Minimizing the desired roll rate is one of the objectives of lateral stability control. The desired roll rate is set as follows:

[0046] in, This represents the desired roll angle.

[0047] Step 3: Construct an upper-level model predictive controller, and predict the target additional yaw moment based on the upper-level model predictive controller. Total anti-rolling moment of the target .

[0048] Construct an upper-level model predictive controller, which includes state vectors. Control input vector and external disturbance vector The expression is:

[0049] in, This refers to the vehicle body roll angle; This refers to the vehicle body roll rate; The sideslip angle is the angle between the vehicle's center of gravity and its body. The vehicle's yaw rate; The target is the total anti-rolling moment; Add a yaw moment to the target; It is lateral acceleration; Input the front wheel steering angle for the driver.

[0050] Establish a continuous-time state-space model, expressed as:

[0051] in, The first derivative of the state vector with respect to time. ; Indicates the vehicle body roll angle acceleration; This indicates the sideslip angular velocity of the vehicle's center of gravity. This refers to the vehicle's yaw acceleration. This is the system state matrix; To control the input matrix; Represents the perturbation input matrix; To control the input vector; This represents the external disturbance vector.

[0052] Furthermore, the system state matrix Control input matrix and the perturbation input matrix They are represented as follows:

[0053] in, The moment of inertia of the vehicle body about the roll axis; This is the roll damping coefficient; This is the equivalent roll stiffness; The yaw moment of inertia of the vehicle; The height from the center of mass to the center of roll; This is the equivalent lateral stiffness of the front axle; This is the equivalent lateral stiffness of the rear axle.

[0054] Furthermore, select the vehicle body roll angle. Body roll rate Vehicle center of gravity sideslip angle With vehicle yaw rate As state tracking variables, and given their expected values ​​respectively. , , , Therefore, the four expected values ​​are unified into an expected state vector. The expression is:

[0055] Obtaining the control state equation With the desired state vector Then, an upper-level model predictive controller is constructed, and the target additional yaw moment is obtained by online solution. and the target total anti-rolling moment .

[0056] Step 4: Model the mapping relationship between the control target and the control variables at the actuator layer.

[0057] The control target is the longitudinal resultant force of the vehicle obtained in steps 1 to 3. Additional yaw moment of the target and the target total anti-rolling moment .

[0058] Obtain the longitudinal resultant force of the vehicle target Additional yaw moment of the target and the target total anti-rolling moment Then, the longitudinal drive / braking force of each wheel (four wheels) is applied. , , , and the output torque of the front and rear axle active stabilizer bars , As a decision variable for lower-level joint allocation, a mapping relationship is constructed between the vehicle-level control objective and the actuator-level control quantity to achieve coordinated allocation of longitudinal resultant force, additional yaw moment and total anti-roll moment among multiple actuators.

[0059] Furthermore, the target longitudinal resultant force of the vehicle is obtained by superimposing the longitudinal control quantities of each wheel:

[0060] Furthermore, the target additional yaw moment formed at the vehicle's center of gravity by the longitudinal force difference between the left and right wheels is expressed as:

[0061] in, This refers to the front axle track. This refers to the rear axle track.

[0062] Furthermore, the total anti-roll moment of the target vehicle is provided jointly by the front and rear axle active stabilizer bars, and their relationship is expressed as follows:

[0063] in, , The active stabilizer bars on the front and rear axles respectively output torque.

[0064] Furthermore, the mapping relationship between the above control objectives and the actuator-level control variables can be expressed in matrix form as follows:

[0065] =

[0066]

[0067] in, This represents the decision variable vector of the lower-level actuator; This represents the target vector output by the executor layer; A mapping matrix representing the mapping relationship between control objectives and actuator-level control variables. , Represents a real number.

[0068] Step 5: Model the mapping relationship between the control objective and the actuator control quantity under extreme conditions.

[0069] To address the problem that limited tire adhesion under extreme conditions makes it difficult to strictly meet the overall vehicle layer control target, this invention introduces a control target relaxation variable. , , The overall vehicle objective is rewritten as a soft constraint with permissible deviations, and then expanded to the control quantities of each actuator to ensure that the requirements for yaw stability and vehicle trajectory controllability can be prioritized when tire force resources are limited.

[0070] Furthermore, the soft constraint form for the target additional yaw moment is as follows:

[0071] in, Add a yaw moment deviation to the target.

[0072] Furthermore, the soft constraint form of the target longitudinal resultant force is as follows:

[0073] in, The longitudinal resultant force deviation is the target.

[0074] Furthermore, the soft constraint for the target total anti-roll moment is: .

[0075] This invention, through the aforementioned three soft constraint equations, allows for adjustment when tire adhesion is insufficient. , , It assumes part of the target deviation, thereby avoiding the lower-level optimization from being infeasible due to strict equality targets or the adjustment degree of freedom being locked.

[0076] Furthermore, under extreme operating conditions, the expression that balances the mapping relationship between the control objective and the actuator control quantity is as follows: + s

[0077] in, This represents the target vector output by the executor layer; This represents the soft constraint relaxation vector.

[0078] Step 6: Solve the optimal distribution of longitudinal torque of each wheel and output torque of the front and rear axle active stabilizer bars in a coordinated and optimized manner for extreme working conditions.

[0079] Specifically, based on establishing the target mapping relationship and introducing slack variables in steps 4 and 5 to achieve a trade-off control target modeling, a joint optimization cost function oriented towards extreme working conditions is constructed. Under the premise of satisfying tire adhesion constraints, the cost function is minimized online using a quadratic programming method to obtain the optimal allocation result of the longitudinal torque of each wheel and the output torque of the front and rear axle active stabilizer bars.

[0080] Specifically, the joint optimization cost function consists of the control objective priority trade-off cost, actuator energy consumption cost, actuator command smoothing cost, and a stabilizer bar inter-axis allocation term based on attachment margin optimization, and its expression is:

[0081] in, Represents the decision variable vector of the lower-level executor. and soft constraint relaxation vector To optimize the variables, a minimization solution is performed. This represents the total cost function of the joint optimization; This indicates the trade-off between prioritizing control objectives and their costs. This indicates the energy consumption cost of the actuator; Indicates the cost of executor instruction smoothing; This represents the stabilizer bar inter-axis allocation term optimized based on adhesion margin.

[0082] Furthermore, control the priorities and trade-offs. This is used to clarify the priority of each objective and the allowable deviation.

[0083] To achieve adaptive trade-offs in control objectives under capacity constraints, and to reflect the priority of different objectives through weights, a cost-priority trade-off for control objectives is constructed. The expression is:

[0084] in, Add yaw moment deviation to the target; The longitudinal resultant force deviation of the target; The target total anti-rolling moment deviation; , , These are the weighting coefficients for the corresponding target deviations. By adjusting the weights, the order and degree of trade-off between yaw stability, longitudinal motion, and roll stability under extreme conditions can be achieved.

[0085] Furthermore, the energy consumption cost of the actuator Used to suppress excessive control amounts and unnecessary energy consumption.

[0086] To mitigate the increased energy consumption caused by excessive longitudinal torque of the wheels and active stabilizer bar torque, or to prevent the actuator from operating under high load for extended periods, an actuator energy consumption cost-saving mechanism is constructed. The expression is:

[0087] in, and These are the weights of the front and rear axle stabilizer bar torque costs, respectively. Represented as the first The longitudinal force cost weight of each wheel, and These represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. Indicates the first Longitudinal drive / braking force of each wheel; , These are the output torques of the front axle active stabilizer bar and the rear axle active stabilizer bar, respectively.

[0088] Furthermore, the cost of executor instruction smoothing Used to suppress chattering and ensure the continuity of actuator output.

[0089] To reduce drastic changes in control commands between adjacent control cycles, improve control smoothness, and suppress chattering, an actuator command smoothing cost is constructed. The expression is:

[0090] in, For the first The increment of longitudinal drive / braking force of each wheel in adjacent control cycles; and These represent the increments of the front and rear axle stabilizer bar torques within adjacent control cycles; , , These are the corresponding penalty weights for the rate of change; Indicates the first The first control cycle Longitudinal drive / braking force of each wheel; Indicates the first The first control cycle Longitudinal drive / braking force of each wheel; , They represent the first , The front axle active stabilizer bar outputs torque per control cycle; , They represent the first , After one control cycle, the active stabilizer bar outputs torque.

[0091] Furthermore, This is a stabilizer bar inter-axle distribution term optimized based on adhesion margin. Its function is to adaptively adjust the front and rear axle torque distribution ratio of the active stabilizer bar according to the difference in tire adhesion margin between the front and rear axles.

[0092] To adaptively distribute the total anti-roll moment between the front and rear axles, and to prioritize the distribution of the anti-roll moment to the axle with higher adhesion margin under extreme conditions, thereby suppressing premature tire saturation on a single axle, an inter-axle distribution term for the stabilizer bar based on adhesion margin optimization is introduced into the cost function. .

[0093] Specifically, firstly, the front axle roll moment distribution coefficient is defined. The expression is:

[0094] in, , The active stabilizer bars on the front and rear axles respectively output torque; This represents a small constant excluding zero.

[0095] A reference distribution coefficient is constructed based on the combined force utilization rate of the front and rear axle tires. The expression is:

[0096] Based on this, a stabilizer bar inter-axis allocation term based on adhesion margin optimization is constructed, with the following expression:

[0097] in, This is the front axle anti-roll moment distribution coefficient; This is a reference distribution coefficient calculated from the combined force utilization rate of the front and rear axle tires; and These are the combined force utilization rates of the front and rear axle tires, respectively. To represent the small constant that prevents the elimination of zero; The guiding weights are used to adjust the influence of the allocation items on the final allocation result.

[0098] Step 7: Execution of control commands and update of control cycle.

[0099] The optimal control commands obtained from the joint optimization solution in step 6 are sent to the corresponding actuators. These commands include longitudinal drive or braking torque commands for each wheel and output torque commands for the front and rear axle active stabilizer bars, causing the vehicle to generate corresponding longitudinal drive force, additional yaw moment, and anti-roll moment within the current control cycle. After receiving the control commands, the actuators complete the actual output of the control quantities based on their own dynamic characteristics and physical constraints, thereby acting on the vehicle's driving state control.

[0100] Physical constraints include tire adhesion constraints, wheel longitudinal force amplitude constraints, and active stabilizer bar moment amplitude constraints.

[0101] 1) Tire adhesion constraint

[0102] in, For the first The lateral force of each wheel These represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. For the first Tire-road adhesion coefficient at each wheel; For the first Normal load of each wheel.

[0103] 2) Wheel longitudinal force amplitude constraint

[0104] in, and The first The minimum and maximum permissible values ​​of the longitudinal force on each wheel are used to characterize the wheel's driving / braking capability boundaries. The parameters are determined by factors such as the maximum output torque of the drive motor, the transmission ratio, and the effective radius of the tires. The determination is limited by factors such as the maximum braking force of the braking system, regenerative braking capability, and adhesion conditions.

[0105] 3) Active stabilizer bar moment amplitude constraint

[0106] in, , These are the output torques of the front axle active stabilizer bar and the rear axle active stabilizer bar, respectively. , These are the maximum output torques of the front axle active stabilizer bar and the rear axle active stabilizer bar, respectively. Their values ​​are determined by actuator structural parameters, hydraulic / motor capabilities, and safety protection strategies.

[0107] After completing the execution of control commands within the current control cycle, information such as vehicle operating status, driver input, and tire load is collected again, and key states such as vehicle longitudinal speed, yaw rate, and roll angle are updated. Then, the next control cycle is entered, and steps 1 to 6 are repeated sequentially. Under the action of MPC rolling optimization and feedback updates, a hierarchical collaborative closed-loop control of vehicle longitudinal motion, yaw stability, and roll stability under extreme conditions is achieved.

[0108] Under the aforementioned cost function and physical constraints, online solutions are obtained to determine the optimal longitudinal torque of each wheel and the output torque of the front and rear axle active stabilizer bars. These torques are then used as control commands to output to the corresponding actuators, achieving coordinated control of the active stabilizer bars and torque vector distribution under extreme operating conditions.

[0109] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle control method for extreme cornering conditions, characterized in that, Includes the following steps: Obtain the longitudinal resultant force of the vehicle target, the target's additional yaw moment, and the target's total anti-roll moment; Establish a mapping relationship between the vehicle-level control target and the actuator-level control quantity. The actuator-level control quantity includes at least the longitudinal force of each wheel and the output torque of the front and rear axle active stabilizer bars. By introducing control target relaxation variables, the target additional yaw moment, target longitudinal resultant force and target total anti-roll moment are rewritten into soft constraint forms containing corresponding relaxation variables, so as to allow deviations in the control target when tire adhesion is limited; Based on the mapping relationship between the vehicle-level control objective and the actuator-level control quantity, the soft constraint form, and the allowance for deviations in the control objective when tire adhesion is limited, a joint optimization cost function is constructed. The joint optimization cost function includes at least the control objective priority trade-off cost based on the slack variables. Under the conditions of satisfying tire adhesion constraints, wheel longitudinal force amplitude constraints, and active stabilizer bar torque amplitude constraints, the joint optimization cost function is solved to obtain the optimal distribution results of the longitudinal forces of each wheel and the output torques of the front and rear axles active stabilizer bars, thereby achieving coordinated control of yaw stability and roll stability under extreme cornering conditions.

2. The vehicle control method according to claim 1, characterized in that, The specific steps for obtaining the target's additional yaw moment and total anti-roll moment include: A predictive controller based on an upper-level model is constructed. The vehicle body roll angle, roll rate, center of gravity sideslip angle and yaw rate are used as state vectors, and the target total anti-roll moment and target additional yaw moment are used as control inputs. The controller is then used for unified prediction and coordinated solution based on the vehicle dynamics state space model.

3. The vehicle control method according to claim 1, characterized in that, The control target relaxation variable includes the target additional yaw moment deviation. Target longitudinal resultant force deviation and the deviation of the target total anti-rolling moment .

4. The vehicle control method according to claim 3, characterized in that, The soft constraint form for the target additional yaw moment is: in, , These represent the longitudinal driving or braking force of the two front axle wheels, respectively. , These represent the longitudinal driving or braking force of the two rear wheels, respectively. This refers to the front axle track width; This refers to the rear axle track width; Add a yaw moment to the target.

5. The vehicle control method according to claim 3, characterized in that, The soft constraint form of the longitudinal resultant force of the target is: in, , These represent the longitudinal driving or braking force of the two front axle wheels, respectively. , These represent the longitudinal driving or braking force of the two rear wheels, respectively. This represents the longitudinal resultant force of the vehicle target.

6. The vehicle control method according to claim 3, characterized in that, The soft constraint for the target total anti-roll moment is: in, , The active stabilizer bars on the front and rear axles respectively output torque; This represents the total anti-tilting moment of the target.

7. The vehicle control method according to claim 1, characterized in that, The joint optimization cost function consists of the control objective priority trade-off cost, actuator energy consumption cost, actuator command smoothing cost, and a stabilizer bar inter-axis allocation term based on adhesion margin optimization, and its expression is: in, Represents the decision variable vector of the lower-level executor. and soft constraint relaxation vector To optimize the variables, a minimization solution is performed. This represents the total cost function of the joint optimization; This indicates the trade-off between prioritizing control objectives and their costs. This indicates the energy consumption cost of the actuator; Indicates the cost of executor instruction smoothing; This represents the stabilizer bar inter-axis allocation term optimized based on adhesion margin.

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