Vehicle anti-roll cooperative control method and device, electronic equipment and storage medium

Abstract

The application discloses a vehicle anti-rollover collaborative control method and device based on multi-source signal fusion, electronic equipment and a storage medium, and the method comprises the following steps: acquiring multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, front road curvature information, vehicle speed and front wheel steering angle; after the multi-source signals are fused, a vehicle roll state is estimated through an extended Kalman filter; based on the vehicle roll state and the multi-source signals, a vehicle control amount is output through a sliding mode controller of an enhanced sliding mode surface; the vehicle control amount is decomposed into a suspension roll torque and an active front wheel steering correction amount by adopting a QP optimization algorithm; suspension stiffness and damping are adjusted according to the suspension roll torque, and the front wheel steering angle is controlled according to the active front wheel steering correction amount, so that the collaborative control is completed. The application breaks the information island problem of the traditional independent closed-loop control architecture based on the multi-source data fusion driven collaborative control.

Description

Technical Field

[0001] This application relates to the field of vehicle anti-roll control technology, and in particular to a vehicle anti-roll cooperative control method, device, electronic device, and storage medium. Background Technology

[0002] The current mainstream vehicle anti-roll active control systems mainly include: active steering system (AFS), active suspension system and active stabilizer bar system.

[0003] Traditional suspension systems (such as CDC continuous damping control) and AFS (Active Front Steering) employ independent closed-loop control architectures. The goal of suspension control is to suppress roll angle by increasing damping force; the goal of AFS control is to correct yaw rate through active steering. Current technology treats suspension and steering as two independent subsystems. The suspension system does not sense steering intention, and the AFS system does not acquire roll moment information. When a vehicle enters a curve: AFS increases the steering angle to improve yaw tracking, which generates additional roll moment; while the suspension simultaneously increases damping force to resist roll, it weakens the tire's lateral force. The two systems cancel each other out, reducing handling efficiency and increasing energy consumption.

[0004] Existing feedback control relies on real-time vehicle status feedback signals such as IMU / wheel speed sensors. For sudden road disturbances (such as sudden curvature changes or impacts from uneven road surfaces), the system needs to wait for a significant change in vehicle attitude before it begins to respond, resulting in passive catch-up control. This may lead to uncontrollable peak roll angle, overshoot of control input (causing excessive compensation by actuators), and simultaneous deterioration of ride comfort and safety. Summary of the Invention

[0005] Therefore, it is necessary to provide a vehicle anti-roll cooperative control method, device, electronic device, and storage medium that can solve the problems of multi-system control conflict, response lag, poor parameter adaptability, and actuator chattering in the prior art.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] The first aspect of the present invention provides a vehicle anti-roll cooperative control method based on multi-source signal fusion, comprising:

[0008] Acquire multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, road curvature information ahead, vehicle speed, and front wheel steering angle;

[0009] After fusing the multi-source signals, the vehicle roll state is estimated using an extended Kalman filter.

[0010] Based on the vehicle roll state and multi-source signals, the vehicle control quantity is output by the sliding mode controller that enhances the sliding surface;

[0011] The vehicle control variables are decomposed into suspension roll moment and active front wheel steering correction using the QP optimization algorithm.

[0012] The suspension stiffness and damping are adjusted according to the suspension roll moment, and the front wheel steering angle is controlled according to the active front wheel steering correction amount to complete the coordinated control.

[0013] Furthermore, the vehicle roll state is estimated using an extended Kalman filter, including:

[0014] In the prediction phase of the extended Kalman filter, a discretized vehicle dynamics model and the previous time-state are utilized. Combined with control input Calculate the state prediction value Covariance Matrix ;

[0015] During the update phase of the extended Kalman filter, the multi-source signals are fused to construct a measurement vector, and the vehicle roll state is obtained by correcting the state estimation through Kalman gain.

[0016] Furthermore, the discretized vehicle dynamics model is obtained by discretizing the vehicle dynamics model, and the vehicle dynamics model is as follows:

[0017]

[0018] In the formula, Represents the vehicle dynamics model; Represents state variables, Indicates the centroid sideslip angle. Indicates yaw rate. Indicates the roll angle. Indicates the roll rate. Indicates longitudinal vehicle speed; Indicates control input, Indicates the front wheel steering angle. This represents the anti-roll moment of the active suspension; assuming the longitudinal vehicle speed remains constant, i.e. Meanwhile, assume the roll angle Small, that is , ; , These represent the lateral forces of the front wheels and the rear wheels, respectively. , , , These represent the tire lateral stiffness of the front and rear wheels, respectively. 、 These represent the front and rear tire slip angles, respectively. , ; Indicates the overall vehicle weight; , These represent the roll moment of inertia of the vehicle about its longitudinal axis and its roll moment of inertia about its vertical axis, respectively. , These represent the longitudinal distances from the front wheels to the vehicle's center of gravity and the rear wheels to the vehicle's center of gravity, respectively. This represents the equivalent roll stiffness of the suspension system. This represents the equivalent roll damping of the suspension system; Represents gravitational acceleration; Indicates the height of the vehicle's center of gravity; The unmodeled disturbance term representing the roll direction is assumed to be bounded: .

[0019] Furthermore, the enhanced sliding surface includes a basic sliding surface. and pre-aiming energy item :

[0020]

[0021]

[0022]

[0023] In the formula, Indicates reinforcement of the sliding surface. Indicates the target weight, and ; , They represent the weight coefficients, and , , The time constant is the desired response speed index of the control system. The smaller the value, the faster the system response is required (e.g., in emergency obstacle avoidance scenarios). The larger the value, the slower the system response (such as cruise mode). =0 indicates the ideal roll angle; Indicates information about the curvature of the road ahead. The theoretical yaw rate of the vehicle; Indicates a future time window, , Pre-aiming distance; , It is based on dynamic models and road curvature. The extrapolated future tilt state is calculated using a quasi-static model method.

[0024] Furthermore, based on the current rate of change of road curvature... right Perform adaptive adjustments:

[0025]

[0026] In the formula, Based on the weighting coefficient, It is a curvature-sensitive adjustment factor, and , All are positive numbers.

[0027] Furthermore, the QP optimization algorithm is used to decompose the vehicle control variables into suspension roll moment and active front wheel steering correction, including:

[0028] Design the objective function for:

[0029]

[0030] In the formula, Indicates the time step number in the discretization. To predict the length of the time domain. , , The weight of the centroid sideslip angle error. Yaw rate error weighting Indicates the weight of the roll angle error. Indicates the weight of the roll rate error. Indicates the weight of the front wheel steering angle. Indicates the suspension torque weight; This is the terminal state weight matrix;

[0031] The constraints are:

[0032]

[0033] In the formula, express The maximum value, express The maximum value, express The maximum value, express The maximum value;

[0034] Through the objective function Given the constraints, redesign the allocator state vector. The control vector is The suspension roll moment is obtained. and active front wheel steering correction .

[0035] Furthermore, adjusting the suspension stiffness and damping based on the suspension roll moment, and controlling the front wheel steering angle based on the active front wheel steering correction amount, includes:

[0036] The suspension roll moment is sent to the active suspension via the CAN bus, and the active suspension adjusts the suspension stiffness and damping according to the suspension roll moment;

[0037] The active front wheel steering correction is sent to the active front wheel steering actuator via the CAN bus, and the active front wheel steering actuator controls the front wheel steering angle according to the active front wheel steering correction.

[0038] A second aspect of the present invention provides a vehicle anti-roll cooperative control device based on multi-source signal fusion, comprising:

[0039] The acquisition module acquires multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, road curvature information ahead, vehicle speed, and front wheel steering angle;

[0040] The first calculation module fuses the multi-source signals and estimates the vehicle roll state using an extended Kalman filter.

[0041] The second calculation module outputs vehicle control quantities based on the vehicle roll state and multi-source signals through a sliding mode controller that enhances the sliding surface.

[0042] The third calculation module uses the QP optimization algorithm to decompose the vehicle control quantity into suspension roll moment and active front wheel steering correction quantity;

[0043] The control module adjusts the suspension stiffness and damping according to the suspension roll moment and controls the front wheel steering angle according to the active front wheel steering correction amount to complete the coordinated control.

[0044] A third aspect of the present invention provides an electronic device comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the vehicle anti-roll cooperative control method based on multi-source signal fusion.

[0045] A fourth aspect of the present invention is a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the vehicle anti-roll cooperative control method based on multi-source signal fusion.

[0046] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0047] 1) This invention is based on multi-source data fusion to drive collaborative control, breaking the information silo problem of traditional independent closed-loop control architecture (such as the suspension and AFS system not being aware of each other in the background technology). By constructing a cross-dimensional information acquisition network, it realizes full-dimensional perception of vehicle status, road environment and driving behavior, and provides underlying data support for multi-system collaborative control.

[0048] 2) The pre-processing of anticipation information in this invention addresses the shortcomings of traditional feedback control, which relies on delayed attitude feedback (such as the "passive chasing control" in the background art, which leads to uncontrollable peak roll angle). It acquires anticipation information on the curvature of the road ahead (such as abrupt curvature changes, continuous curves, etc.) and uses a weighted fusion algorithm, with weights dynamically determined by sensor confidence, to achieve advanced analysis of road features. Combined with anticipation time parameters, environmental information is transformed into pre-control inputs, enabling the system to initiate pre-control strategies before the vehicle enters complex operating conditions, thus shortening response delay. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a vehicle anti-roll cooperative control method provided in an embodiment of the present invention;

[0050] Figure 2 This is a system operation principle diagram of a vehicle anti-roll cooperative control method provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram showing the comparison of roll suppression effects provided in an embodiment of the present invention;

[0052] Figure 4 A diagram illustrating the roll angle suppression rate provided in an embodiment of the present invention;

[0053] Figure 5 This is a structural block diagram of a vehicle anti-roll cooperative control device provided in an embodiment of the present invention; Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] This invention provides a vehicle anti-roll cooperative control method based on multi-source signal fusion, such as... Figure 1 As shown, it includes:

[0056] Acquire multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, road curvature information ahead, vehicle speed, and front wheel steering angle;

[0057] In a specific embodiment, a vehicle anti-roll cooperative control method is implemented by constructing a vehicle anti-roll cooperative control system, such as... Figure 2 As shown, the system includes a signal input layer, a signal processing layer, a signal control layer, a signal execution layer, and a vehicle dynamics system. The signal input layer specifically includes an IMU (Inertial Measurement Unit), a forward-facing camera, a V2X (Vehicle-to-Everything) communication module, a vehicle speed sensor, and a front wheel steering angle sensor. The IMU measures the vehicle's three-axis acceleration and angular velocity, while the forward-facing camera and V2X communication module simultaneously acquire information about the curvature of the road ahead. The vehicle speed sensor is used to measure vehicle speed signals.

[0058] After fusing the multi-source signals, the vehicle roll state is estimated using an extended Kalman filter.

[0059] In a specific embodiment, the fusion of the multi-source signals and the estimation of the vehicle roll state through the extended Kalman filter can be achieved through signal preprocessing of the signal processing layer of the vehicle anti-roll cooperative control system and the EKF state observer. The signal preprocessing mainly includes temperature compensation of the IMU and fusion calculation of the predicted road curvature to improve the reliability of the data. Specifically:

[0060] IMUs exhibit drift due to temperature changes, therefore the system requires preprocessing of the output signal. This system employs a linear compensation method.

[0061]

[0062] In the formula, They are respectively Shaft acceleration signal values ​​and their preprocessed values; , They are respectively around The angular velocity of the shaft and its preprocessed value; Represent Three axes; , These are the temperature drift coefficient matrices for the accelerometer and the gyroscope, respectively. The difference between the current actual temperature and the reference temperature, where the reference temperature is taken as 25°C. Specifically... ， . , The parameters in the matrix represent coefficients on the corresponding axes, and their actual values ​​are calibrated by temperature gradient experiments in the laboratory.

[0063] The forward-facing camera and V2X communication module collect information on the curvature of the road ahead, respectively. This allows the actual curvature of the road to be determined. . The size is determined based on the confidence level of the forward-facing camera and the V2X communication module. Specifically, , , This represents the noise variance of the front-view camera data. This represents the variance of data noise in the V2X communication module.

[0064] The EKF state observer described above primarily functions to fuse data from different types of sensors and provide the sliding mode controller with roll status information in the presence of external interference and measurement noise. and A reliable estimate is obtained. Simultaneously, the impact of disturbances is indirectly reflected through state estimation, assisting in controller design and thus significantly improving the overall system performance.

[0065] Based on the vehicle roll state and multi-source signals, the vehicle control quantity is output by the sliding mode controller that enhances the sliding surface;

[0066] In a specific embodiment, the vehicle control quantity is output through a sliding mode controller based on pre-aiming information in the system control layer;

[0067] The vehicle control variables are decomposed into suspension roll moment and active front wheel steering correction using the QP optimization algorithm.

[0068] In a specific embodiment, the vehicle control quantity is decomposed into suspension roll moment and active front wheel steering correction quantity by the tire force distributor in the system control layer;

[0069] The suspension stiffness and damping are adjusted according to the suspension roll moment, and the front wheel steering angle is controlled according to the active front wheel steering correction amount to complete the coordinated control.

[0070] In a specific embodiment, coordinated control is achieved through the active suspension actuator and the AFS actuator in the actuator layer.

[0071] In a further embodiment, the vehicle roll state is estimated using an extended Kalman filter, including:

[0072] In the prediction phase of the extended Kalman filter, a discretized vehicle dynamics model and the previous time-state are utilized. Combined with control input Calculate the state prediction value Covariance Matrix ,Right now:

[0073]

[0074]

[0075] In the formula, Sampling time, for Discretized form, This is process noise. The process noise covariance matrix can be calibrated offline using the maximum likelihood estimation method based on real vehicle data. The state transition Jacobian matrix is ​​also relevant. The expression is:

[0076]

[0077] During the update phase of the extended Kalman filter, the multi-source signals are fused to construct a measurement vector, and the vehicle roll state is obtained by correcting the state estimation through Kalman gain.

[0078] In a specific embodiment, IMU data is fused ( , , Vehicle speed data from vehicle speed sensor Construct measurement vectors .exist When smaller, The measurement equation is:

[0079]

[0080] The measurement Jacobian matrix is = :

[0081]

[0082] Describe the mapping relationship between states and measurements (e.g.) Yes Partial derivatives (including tilt coupling terms), through Kalman gain The state estimate is revised to ensure robustness against multi-source noise. That is:

[0083]

[0084]

[0085]

[0086] In the formula, R To measure the noise covariance, data needs to be collected from actual vehicles, and the maximum likelihood estimation method should be used for offline calibration.

[0087] In a further embodiment, the discretized vehicle dynamics model is obtained by discretizing the vehicle dynamics model. Considering system disturbances, a coupled nonlinear vehicle dynamics model of AFS steering angle and active anti-suspension roll moment is established as follows:

[0088]

[0089] In the formula, Represents the vehicle dynamics model; Represents state variables, Indicates the centroid sideslip angle. Indicates yaw rate. Indicates the roll angle. Indicates the roll rate. It indicates the longitudinal vehicle speed, covering the key states of the vehicle's longitudinal, yaw, and roll movements; Indicates control input, Indicates the front wheel steering angle. This represents the anti-roll moment of the active suspension; based on the core objective of vehicle anti-roll, it is assumed that the longitudinal speed remains constant, i.e. Meanwhile, assume the roll angle Small, that is , ; , These represent the lateral forces of the front wheels and the rear wheels, respectively. , , , These represent the tire lateral stiffness of the front and rear wheels, respectively. 、 These represent the front and rear tire slip angles, respectively. , ; Indicates the overall vehicle weight; , These represent the roll moment of inertia of the vehicle about its longitudinal axis and its roll moment of inertia about its vertical axis, respectively. , These represent the longitudinal distances from the front wheels to the vehicle's center of gravity and the rear wheels to the vehicle's center of gravity, respectively. This represents the equivalent roll stiffness of the suspension system. This represents the equivalent roll damping of the suspension system; Represents gravitational acceleration; Indicates the height of the vehicle's center of gravity; The unmodeled disturbance term representing the roll direction is assumed to be bounded: .

[0090] In a further embodiment, the sliding mode controller integrates preview information to achieve proactive control of vehicle roll motion, reducing the hysteresis of traditional feedback control and improving roll suppression during high-speed cornering. To this end, an enhanced sliding surface is designed, comprising both a basic sliding surface and a preview energy term. The enhanced sliding surface includes the basic sliding surface. and pre-aiming energy item :

[0091]

[0092]

[0093]

[0094] In the formula, Indicates reinforcement of the sliding surface. Indicates the target weight, and ; , These represent the weight coefficients; =0 represents the ideal roll angle, the goal of which is to suppress roll and make the actual roll angle track the zero reference. Indicates information about the curvature of the road ahead. The theoretical yaw rate of the vehicle; Indicates a future time window, , Pre-aiming distance; , It is based on dynamic models and road curvature. The extrapolated future roll state is calculated using a quasi-static model method; This is the roll rate; adding this term enhances the controller's sensitivity to roll rate. To better utilize advance information, enabling the control to respond earlier to changes in road curvature and reducing hysteresis, the EKF estimate is used. Value, design target energy term Embedded sliding surface Typically, the fourth-order Runge-Kutta method, the first-order lag model method, or the quasi-static model method can be used for calculation. This embodiment uses the quasi-static model method, that is:

[0095]

[0096] In the formula, , For the sprung mass;

[0097] In practical engineering, the fourth-order Runge-Kutta method can also be used to... Perform discretization prediction, such as:

[0098]

[0099] 10ms is acceptable. . The aiming time parameter represents a future time window used to predict the vehicle's roll state during that period, allowing the controller to respond in advance to changes in road curvature and solving the lag problem of traditional feedback control. Because , The aiming distance represents the range of road information the system needs to detect in advance. The higher the vehicle speed, the longer the aiming distance. Based on Table 1, this embodiment is designed. The possible values ​​are as follows:

[0100]

[0101] Table 1

[0102]

[0103] In a further embodiment, dynamic adjustment To avoid overshooting, such as based on the current rate of change of road curvature. Adaptive adjustment:

[0104]

[0105] In the formula, Based on the weighting coefficient, It is a curvature-sensitive adjustment factor, and , All are positive numbers, and their specific values ​​can be calibrated experimentally. This invention calibrates them through high-speed dual-track switching simulation. , This can reduce the roll overshoot by 15%. When the curvature changes drastically ( (increase) Dynamic attenuation to suppress control oscillations caused by excessively long aiming distances; on straight roads ( Maintain sufficient aiming capability when ≈0).

[0106] In a further embodiment, the Lyapunov function is defined as follows:

[0107]

[0108] right Differentiation yields: Substituting the derivative of the sliding surface:

[0109]

[0110] Combining the equations of roll motion We can obtain:

[0111]

[0112] To ensure the stability of the controller, the following must be met: ;

[0113] This is the roll angle weighting coefficient, used to adjust the roll angle deviation. The convergence speed. Increase It can accelerate the convergence of the roll angle, but may increase the load on the suspension actuators. In the roll motion equation, The term is a damping term, which hinders changes in the roll angle. To enable sliding mode control to effectively overcome the influence of the damping term, it is necessary to combine... The expression needs The item can offset The impact, namely It can be concluded that:

[0114]

[0115] This is the yaw rate weighting coefficient. Adjusting the yaw rate deviation. The response speed. Increase It can enhance the ability to track changes in road curvature, but may introduce the risk of oversteering. The value of can be determined based on the time constant of the yaw dynamics. To determine. . It reflects the inherent characteristics of the vehicle's yaw dynamics system, representing the rate of change of yaw angular velocity. The yaw response time constant is taken as... s ( The value is determined based on factors such as actual vehicle performance requirements and driving safety. Therefore, it can be determined that... The range of values ​​for:

[0116]

[0117] In the formula, The time constant is the desired response speed index of the control system. The smaller the value, the faster the system response is required (e.g., in emergency obstacle avoidance scenarios). The larger the value, the slower the system response (such as cruise mode).

[0118] Design Index Convergence Rate The control law of the sliding mode controller can be obtained as follows:

[0119]

[0120] In the formula, For the gain of the switching item, The thickness of the boundary layer of the sliding surface. and The value of is determined to ensure that the sliding surface converges to . At the same time, minimize the jitter amplitude of the controller.

[0121] Substituting the control law of the aforementioned sliding mode controller into... We can obtain:

[0122]

[0123] because Only ≥ ,ensure The system is asymptotically stable.

[0124] In a further embodiment, the QP optimization algorithm is used to decompose the vehicle control quantity into suspension roll moment and active front wheel steering correction, so that the two work together to meet the control requirements of vehicle yaw, roll and lateral motion, ensuring the stability and handling of the vehicle under complex conditions. The QP objective function and constraints are set to ensure reasonable and feasible allocation, and the distributor state vector is redesigned. The control vector is ,include:

[0125] Design the objective function for:

[0126]

[0127] In the formula, , , The weight of the centroid sideslip angle error. The range is generally , The weight of the yaw rate error is generally within the range of... , This represents the weight of the roll angle error, since roll suppression is the core objective, and its range is taken as... , The weight of the roll rate error is taken as the range of... , This indicates the weight of the front wheel steering angle, representing steering energy consumption and wear, with a value range of [value missing]. Avoid over-steering. This indicates the suspension torque weight, representing the suspension's kinetic energy consumption and lifespan, and its range is within... ; This is the terminal state weight matrix. , Indicates the time step number in the discretization. To predict the length of the time domain;

[0128] To protect the actuator's mechanical structure and prevent overload, the constraints are as follows:

[0129]

[0130] In the formula, express The maximum value, express The maximum value, express The maximum value, express The maximum value, Pick , Pick , Pick , Pick ;

[0131] Through the objective function Given the constraints, redesign the allocator state vector. The control vector is The suspension roll moment is obtained. and active front wheel steering correction .

[0132] In a further embodiment, adjusting the suspension stiffness and damping according to the suspension roll moment, and controlling the front wheel steering angle according to the active front wheel steering correction amount, includes:

[0133] The suspension roll moment is sent to the active suspension via the CAN bus, and the active suspension adjusts the suspension stiffness and damping according to the suspension roll moment;

[0134] The active front wheel steering correction is sent to the active front wheel steering actuator via the CAN bus, and the active front wheel steering actuator controls the front wheel steering angle according to the active front wheel steering correction.

[0135] The actual simulation results of the system are as follows Figure 3 and Figure 4 As shown in the figure, the roll suppression effect is significantly improved after using the vehicle anti-roll cooperative control method of this embodiment.

[0136] A second embodiment of the present invention also provides a vehicle anti-roll cooperative control device based on multi-source signal fusion, such as... Figure 5 As shown, it includes:

[0137] The acquisition module acquires multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, road curvature information ahead, vehicle speed, and front wheel steering angle;

[0138] The first calculation module fuses the multi-source signals and estimates the vehicle roll state using an extended Kalman filter.

[0139] The second calculation module outputs vehicle control quantities based on the vehicle roll state and multi-source signals through a sliding mode controller that enhances the sliding surface.

[0140] The third calculation module uses the QP optimization algorithm to decompose the vehicle control quantity into suspension roll moment and active front wheel steering correction quantity;

[0141] The control module adjusts the suspension stiffness and damping according to the suspension roll moment and controls the front wheel steering angle according to the active front wheel steering correction amount to complete the coordinated control.

[0142] A third embodiment of the present invention also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, it implements the steps of the vehicle anti-roll cooperative control method based on multi-source signal fusion.

[0143] The fourth embodiment of the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the vehicle anti-roll cooperative control method based on multi-source signal fusion.

[0144] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A vehicle anti-roll cooperative control method based on multi-source signal fusion, characterized in that, The method comprises: acquiring multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, front road curvature information, vehicle speed and front wheel steering angle; estimating vehicle roll state through an extended Kalman filter after fusing the multi-source signals; outputting vehicle control quantity through a sliding mode controller based on the vehicle roll state and the multi-source signals; decomposing the vehicle control quantity into suspension roll torque and active front wheel steering correction quantity by using a QP optimization algorithm; adjusting suspension stiffness and damping according to the suspension roll torque and controlling front wheel steering angle according to the active front wheel steering correction quantity to complete cooperative control; estimating vehicle roll state through an extended Kalman filter, comprising: In the prediction phase of the extended Kalman filter, the discretized vehicle dynamics model and the state of the previous time instant are used in combination with the control input to compute the state prediction and the covariance matrix ; in the update stage of the extended Kalman filter, constructing a measurement vector by fusing the multi-source signals, correcting state estimation through Kalman gain and obtaining vehicle roll state; the discretized vehicle dynamics model is obtained by discretizing a vehicle dynamics model, and the vehicle dynamics model is: wherein denotes the vehicle dynamics model; denotes the state variable, denotes the side slip angle, denotes the yaw rate, denotes the roll angle, denotes the roll rate, denotes the longitudinal vehicle speed; denotes the control input, denotes the front wheel steering angle, denotes the active anti-roll moment of the suspension; it is assumed that the longitudinal vehicle speed is constant, i.e. , while the roll angle is assumed to be small, i.e. , ; , denote the front and rear wheel side forces, , , , denote the front and rear wheel tire cornering stiffness, 、 denote the front and rear tire side slip angle, , ; denotes the total vehicle mass; , denote the roll moment of inertia of the vehicle about the longitudinal axis and the roll moment of inertia about the vertical axis, respectively; , denote the longitudinal distance of the front and rear wheels to the vehicle center of mass; denotes the equivalent roll stiffness of the suspension system; denotes the equivalent roll damping of the suspension system; denotes the gravitational acceleration; denotes the height of the vehicle center of mass; denotes the unmodeled disturbance in roll direction, which is assumed to be bounded; The enhanced slip surface includes a base slip surface and a preview energy term : wherein represents the enhanced sliding surface, represents the preview weight, and ; , represent the weight coefficients, respectively, and , , represents the desired response speed index; = 0 represents the ideal roll angle; represents the theoretical yaw rate along the front road curvature information of the vehicle; represents the future time window, , is the preview distance; , is the future roll state extrapolated based on the dynamics model and the road curvature extrapolation, calculated using the quasi-static model method; Also in accordance with the current rate of change of road curvature To Adaptively adjust: wherein is a base weight coefficient, is a curvature change sensitivity adjustment factor, and , are positive numbers; decomposing the vehicle control quantity into suspension roll torque and active front wheel steering correction quantity by using a QP optimization algorithm, comprising: Design objective function is: wherein , , is a center of mass side slip angle error weight, is a yaw rate error weight, denotes a roll angle error weight, denotes a roll rate error weight, denotes a front wheel steering angle weight, denotes a suspension force moment weight; is a terminal state weight matrix, denotes a discretized time step number, is a prediction horizon length; the constraint condition is: wherein denotes the maximum value of denotes the maximum value of denotes the maximum value of denotes the maximum value of denotes the maximum value of denotes the maximum value of denotes the maximum value of denotes the maximum value of By the objective function and the constraints, the redesign allocator state vector is obtained, the control vector is obtained, the suspension roll moment and the active front wheel steering correction are obtained.

2. The vehicle anti-roll cooperative control method based on multi-source signal fusion according to claim 1, characterized in that, adjusting suspension stiffness and damping according to the suspension roll torque and controlling front wheel steering angle according to the active front wheel steering correction quantity, comprising: sending the suspension roll torque to the active suspension through a CAN bus, and adjusting suspension stiffness and damping according to the suspension roll torque by the active suspension; sending the active front wheel steering correction quantity to the active front wheel steering actuator through a CAN bus, and controlling front wheel steering angle according to the active front wheel steering correction quantity by the active front wheel steering actuator.

3. A vehicle anti-roll cooperative control device based on multi-source signal fusion, characterized in that, The cooperative control device applies the vehicle anti-roll cooperative control method based on multi-source signal fusion of claim 1 or 2, comprising: an acquisition module that acquires multi-source signals including vehicle three-axis acceleration, three-axis angular velocity, front road curvature information, vehicle speed and front wheel steering angle; a first calculation module that estimates vehicle roll state through an extended Kalman filter after fusing the multi-source signals; a second calculation module that outputs vehicle control quantity through a sliding mode controller based on the vehicle roll state and the multi-source signals; a third calculation module that decomposes the vehicle control quantity into suspension roll torque and active front wheel steering correction quantity by using a QP optimization algorithm; a control module that adjusts suspension stiffness and damping according to the suspension roll torque and controls front wheel steering angle according to the active front wheel steering correction quantity to complete cooperative control.

4. An electronic device, comprising: The method comprises: a processor, a memory and a program stored on the memory and executable on the processor, wherein the program is executed by the processor to implement the steps of the vehicle anti-roll cooperative control method based on multi-source signal fusion of claim 1 or 2.

5. A computer readable storage medium, characterized in that, The computer program is stored on the computer readable storage medium, and the computer program is executed by the processor to implement the steps of the vehicle anti-roll cooperative control method based on multi-source signal fusion of claim 1 or 2.

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