Multi-actuator full vector control method and related equipment

Abstract

The present invention discloses a multi-actuator full vector control method and related devices, including: obtaining vehicle motion state information and road surface adhesion information, wherein the vehicle motion state information is obtained based on in-vehicle sensors and / or a vehicle state estimation module, and the road surface adhesion information is obtained based on a road surface identification module; obtaining steering wheel angle information based on the vehicle motion state information to determine an expected yaw rate tracking sequence; determining upper-layer control information based on the vehicle motion state information, the road surface adhesion information, and the expected yaw rate tracking sequence, wherein the upper-layer control information includes additional yaw moment, additional roll moment, additional pitch moment, and rear wheel steering angle; determining lower-layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper-layer control information, wherein the lower-layer control information is used to allocate vehicle actuators, and the lower-layer control information includes additional driving torque and suspension active force.

Description

Technical Field

[0001] The present invention relates to the technical field of driving control, and particularly to a multi-actuator full vector control method and related devices. Background Art

[0002] During vehicle driving, affected by behaviors such as acceleration, deceleration, or lane change, the vehicle is prone to become unstable in multiple aspects, such as having a high lateral speed, excessive pitch and roll angles, and out-of-control yaw angular velocity. In order to reduce the instability during vehicle driving, many designs have been developed to ensure that the vehicle operates in a reasonable motion state and control the vehicle stability.

[0003] However, most of these solutions are passive solutions, with poor adaptability to real-time road conditions and low coupling between individual actuators. Summary of the Invention

[0004] In view of the above problems, the present invention provides a multi-actuator full vector control method and related devices, mainly aiming to improve the coupling between individual actuators and improve the operation efficiency.

[0005] To solve the above at least one technical problem, in a first aspect, the present invention provides a multi-actuator full vector control method, which includes:

[0006] Obtain vehicle motion state information and road surface adhesion information, where the vehicle motion state information is obtained based on in-vehicle sensors and / or a vehicle state estimation module, and the road surface adhesion information is obtained based on a road surface identification module;

[0007] Obtain steering wheel angle information based on the vehicle motion state information to determine an expected yaw angular velocity tracking sequence;

[0008] Determine upper layer control information based on the vehicle motion state information, the road surface adhesion information, and the expected yaw angular velocity tracking sequence, where the upper layer control information includes additional yaw moment, additional roll moment, additional pitch moment, and rear wheel steering angle;

[0009] Determine lower layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper layer control information, where the lower layer control information is used to allocate vehicle actuators, and the lower layer control information includes additional driving torque and suspension active force.

[0010] Optionally, obtaining steering wheel angle information based on the vehicle motion state information to determine an expected yaw angular velocity tracking sequence includes:

[0011] Determine an expected front wheel rotation angle based on the variable transmission ratio and the steering wheel angle information;

[0012] Determine the desired yaw rate based on the desired front wheel rotation angle and the second-order reference model;

[0013] Define the upper limit value of the desired yaw rate based on the road surface adhesion information;

[0014] Determine the desired yaw rate tracking sequence based on the desired yaw rate and the upper limit value of the desired yaw rate.

[0015] Optionally, the above method further includes:

[0016] Determine the sideslip angles of the front and rear wheels of the vehicle based on the vehicle motion state information;

[0017] Determine the tire force based on the sideslip angles of the front and rear wheels of the vehicle, the tire model, the road surface adhesion information, the vertical load, and the tire cornering stiffness;

[0018] Determine the nonlinear dynamic equation based on the tire force, the state vector and the input vector of the actuator, and the six-degree-of-freedom vehicle model;

[0019] Determine the item to be optimized based on the desired yaw rate tracking sequence, roll angle suppression, pitch angle suppression, and lateral velocity suppression;

[0020] Determine the control quantity constraint of the actuation energy;

[0021] Define the objective function based on the item to be optimized and the control quantity constraint;

[0022] Determine the incremental model based on the Taylor expansion at the operating point of the six-degree-of-freedom dynamic model of the vehicle and ignoring the high-order terms:

[0023]

[0024] where, are the Jacobian matrices of the nonlinear dynamic equation at the operating point respectively.

[0025] Optionally, determine the upper-layer control information based on the vehicle motion state information, the road surface adhesion information, and the desired yaw rate tracking sequence, including:

[0026] Perform Euler discretization on the incremental model to obtain the prediction equation within the prediction horizon :

[0027]

[0028] where, represents the state prediction value within the entire prediction horizon, represents the control quantity increment within the entire prediction horizon, , and is the corresponding matrix;

[0029] Determine the observation matrix within the prediction time domain based on the observation vector, where the observation matrix is used to calculate the observation variable, and the observation variable is used to calculate the objective function;

[0030] Construct the objective function based on the tracking expected observation value and the suppression control quantity;

[0031] Convert the six-degree-of-freedom dynamics model into the standard form of the QP problem based on the quadratic term matrix and the linear term matrix;

[0032] Solve the QP problem to determine the additional yaw moment, additional roll moment, additional pitch moment, and rear wheel steering angle.

[0033] Optionally, the above method further includes:

[0034] Determine the force equation during tire rotation:

[0035]

[0036] where the state variable of the tire rotation system is the tire rotation speed , the moment of inertia of the tire is , the effective rolling radius of the tire is , the longitudinal force received by the tire is , the total torque acting on the tire is ;

[0037] Determine the tire slip ratio based on the longitudinal speeds of the four tires;

[0038] Determine the desired wheel speed of the tire based on the vehicle center of mass speed and the vector sum of the actual yaw angular velocity generated at the tire, where the desired wheel speed is used to construct the objective function for the driving torque distribution of the actuator;

[0039] Determine the composite brush model based on the total tire force, the composite tire slip amount, and the composite slip amount threshold, where the composite tire slip amount includes the longitudinal slip amount and the lateral slip amount;

[0040] Determine the tire force data based on the force equation during tire rotation, the tire slip ratio, and the composite brush model.

[0041] Optionally, determine the lower layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper layer control information, including:

[0042] Determine the objective function for the driving torque distribution of the actuator based on the tire force data, reducing the additional torque energy consumption, the additional yaw moment, and the desired wheel speed:

[0043]

[0044] Among them, , and are the weights of the corresponding items respectively, and are the lower and upper bounds of the control quantity respectively, and are the lower and upper bounds of the slip ratio respectively, , , ;

[0045] Solve the objective function of the driving torque distribution of the actuator to determine the additional driving torque.

[0046] Optionally, determine the lower-layer control information based on the vehicle motion state information, road surface adhesion information, and upper-layer control information, including:

[0047] Determine the expression of the sprung mass of the suspension and the state vector and control vector of the suspension actuator;

[0048] Determine the additional roll moment and additional pitch moment;

[0049] Determine the objective function of the suspension actuator based on the expression of the sprung mass of the suspension and the state vector and control vector of the suspension actuator, the additional roll moment and the additional pitch moment;

[0050] Solve the objective function of the suspension actuator to determine the active force of the suspension.

[0051] In a second aspect, an embodiment of the present invention further provides a multi-actuator full-vector control device, including:

[0052] A first acquisition unit, configured to acquire vehicle motion state information and road surface adhesion information, wherein the vehicle motion state information is acquired based on in-vehicle sensors and / or a vehicle state estimation module, and the road surface adhesion information is acquired based on a road surface identification module;

[0053] A second acquisition unit, configured to acquire steering wheel angle information based on the vehicle motion state information to determine an expected yaw rate tracking sequence;

[0054] A first determination unit, configured to determine upper-layer control information based on the vehicle motion state information, road surface adhesion information, and the expected yaw rate tracking sequence, wherein the upper-layer control information includes an additional yaw moment, an additional roll moment, an additional pitch moment, and a rear wheel steering angle;

[0055] A second determination unit, configured to determine lower-layer control information based on the vehicle motion state information, road surface adhesion information, and upper-layer control information, wherein the lower-layer control information is used to allocate vehicle actuators, and the lower-layer control information includes an additional driving torque and an active force of the suspension.

[0056] To achieve the above object, according to the third aspect of the present invention, there is provided a computer-readable storage medium, which includes a stored program. When the program is executed by a processor, the steps of the above multi-actuator full vector control method are implemented.

[0057] To achieve the above object, according to the fourth aspect of the present invention, there is provided an electronic device, including at least one processor and at least one memory connected to the processor. The processor is used to call program instructions in the memory to execute the steps of the above multi-actuator full vector control method.

[0058] By means of the above technical solution, for the problem that the coupling between actuators in the multi-actuator full vector control method and related devices provided by the present invention is relatively low and there is a lack of a better implementation and deployment method, the present invention obtains vehicle motion state information and road surface adhesion information. The vehicle motion state information is obtained based on in-vehicle sensors and / or a vehicle state estimation module, and the road surface adhesion information is obtained based on a road surface identification module. Steering wheel angle information is obtained based on the vehicle motion state information to determine an expected yaw rate tracking sequence. Upper layer control information is determined based on the vehicle motion state information, the road surface adhesion information, and the expected yaw rate tracking sequence. The upper layer control information includes additional yaw moment, additional roll moment, additional pitch moment, and rear wheel steering angle. Lower layer control information is determined based on the vehicle motion state information, the road surface adhesion information, and the upper layer control information. The lower layer control information is used to deploy vehicle actuators, and the lower layer control information includes additional driving torque and suspension active force. In the above solution, through an active solution, the power situation is adjusted instantaneously according to different power system situations during vehicle driving. It can adapt to common driving situations, and considering the coupling between various systems, multiple actuators are coordinated to optimize the stability control of the vehicle. The present application optimizes the stability and smoothness of the vehicle in the transverse, longitudinal, and vertical directions by adjusting actuators such as the rear wheel rotation angle of the vehicle, additional rotational torques of the four tires, and suspension active force, thereby improving the safety and comfort during vehicle driving.

[0059] Correspondingly, the multi-actuator full vector control device, equipment, and computer-readable storage medium provided by the embodiments of the present invention also have the above technical effects.

[0060] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other objects, features, and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0062] Figure 1 A flowchart of a multi-actuator full-vector control method provided by an embodiment of the present invention is shown;

[0063] Figure 2 A schematic diagram of the principle of a multi-actuator full-vector parameter control process provided by an embodiment of the present invention is shown;

[0064] Figure 3 A schematic diagram of multi-actuator full-vector parameters of a vehicle provided by an embodiment of the present invention is shown;

[0065] Figure 4 Another schematic diagram of multi-actuator full-vector parameters of a vehicle provided by an embodiment of the present invention is shown;

[0066] Figure 5 A schematic block diagram of the composition of a multi-actuator full-vector control device provided by an embodiment of the present invention is shown;

[0067] Figure 6 A schematic block diagram of the composition of a multi-actuator full-vector control electronic device provided by an embodiment of the present invention is shown. Detailed Embodiments

[0068] The exemplary embodiments of the present invention will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0069] In order to solve the problem that the coupling between actuators is relatively low and there is a lack of a better implementation and coordination method, an embodiment of the present invention provides a multi-actuator full-vector control method, as Figure 1-2 shown, the method includes:

[0070] S101. Obtain vehicle motion state information and road surface adhesion information, where the vehicle motion state information is obtained based on in-vehicle sensors and / or a vehicle state estimation module, and the road surface adhesion information is obtained based on a road surface identification module;

[0071] Exemplarily, the above vehicle motion states include, but are not limited to, a stationary state, a starting state, a driving state, a decelerating state, a braking state, a reversing state, an emergency braking state, a bumpy state, a drifting state, and a rollover state. The above road surface adhesion information is the ratio of the adhesion force to the wheel normal force (the direction perpendicular to the road surface). It is determined by the road surface and the tires. The larger this coefficient, the greater the available adhesion force, and the less likely the vehicle is to skid. The above road surface adhesion information is affected by rainy and snowy weather.

[0072] S102. Obtain the steering wheel angle information based on the vehicle motion state information to determine the desired yaw rate tracking sequence;

[0073] Exemplarily, according to the vehicle driving requirements input by the user, such as vehicle handling and vehicle stability and other vehicle driving requirements, the present application establishes an original control target, that is, the steering wheel angle information, and generates a desired yaw rate tracking sequence for the torque vector actuator.

[0074] S103. Determine the upper-layer control information based on the vehicle motion state information, the road surface adhesion information, and the desired yaw rate tracking sequence, wherein the upper-layer control information includes an additional yaw moment, an additional roll moment, an additional pitch moment, and a rear-wheel steering angle;

[0075] Exemplarily, according to the current vehicle motion state information, the road surface adhesion information, and the desired yaw rate tracking sequence of the vehicle, the present application optimizes the overall control torque and the rear-wheel steering angle of the vehicle to obtain a control torque and a rear-wheel steering angle that can meet the vehicle handling, stability, and comfort objectives, and uses the desired control torque and the rear-wheel steering angle as the reference input of the lower-layer actuator and sends them to the lower-layer actuator.

[0076] It can be understood that the desired yaw rate tracking sequence is determined based on the desired state information generated by the desired reference value module and the control requirement analysis information generated by the control requirement analysis module.

[0077] S104. Determine the lower-layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper-layer control information, wherein the lower-layer control information is used to allocate the vehicle actuators, and the lower-layer control information includes an additional driving torque and a suspension active force.

[0078] Exemplarily, according to the current driving state of the vehicle and the desired control input generated by the upper-layer actuator, including an additional yaw moment, an additional roll moment, an additional pitch moment, and a rear-wheel steering angle, considering the coupling relationship of each vehicle actuator and the system constraints, the present application comprehensively coordinates the control amounts of the drive, steering, suspension and other actuators to achieve the longitudinal-lateral-vertical coordinated control of the vehicle.

[0079] Thus, the longitudinal-lateral-vertical coordinated stability control of the vehicle is achieved by adjusting the additional torques of the four wheels of the distributed-drive vehicle and the active suspension control amount.

[0080] In summary, this application is for the longitudinal-lateral-vertical coordinated stability control of a distributed-drive vehicle. By receiving the vehicle motion state information collected from in-vehicle sensors and output by the vehicle state estimation module and the road surface adhesion information from the road surface identification module, through the optimization calculation of the coordinated motion control strategy inside the module, the control amounts of multiple actuators such as drive, steering, and suspension are finally output to adjust the vehicle motion posture under different working conditions and ensure the stable driving of the vehicle. This application instantaneously adjusts the power according to the different conditions of the power system during the vehicle driving process. It can adapt to relatively common driving conditions, and considering the coupling between various systems, multiple actuators are coordinated to optimize the stability control of the vehicle. This application optimizes the stability and smoothness of the vehicle in the transverse, longitudinal, and vertical directions by adjusting actuators such as the rear wheel rotation angle of the vehicle, the additional driving torques of the four tires, and the active suspension force, thereby improving the safety and comfort during vehicle driving.

[0081] In one embodiment, obtaining the steering wheel angle information based on the vehicle motion state information to determine the desired yaw rate tracking sequence includes:

[0082] Determining the desired front wheel rotation angle based on the variable transmission ratio and the steering wheel angle information;

[0083] Determining the desired yaw rate based on the desired front wheel rotation angle and a second-order reference model;

[0084] Defining the upper limit value of the desired yaw rate based on the road surface adhesion information;

[0085] Determining the desired yaw rate tracking sequence based on the desired yaw rate and the upper limit value of the desired yaw rate.

[0086] Specifically, assuming that the externally input variable transmission ratio is , then according to the steering wheel angle information input by the driver, the corresponding desired front wheel rotation angle can be calculated as :

[0087]

[0088] Since the driver operates the steering wheel, the desired yaw rate of the vehicle during driving is calculated from the desired front wheel rotation angle.

[0089] In the reference model, a second-order reference model is adopted. According to the current desired front wheel rotation angle and the corresponding transfer function to calculate the desired yaw rate:

[0090]

[0091] wherein, is the steady-state gain value of the yaw rate, is the differential coefficient of the yaw rate, is the oscillation frequency, is the damping coefficient.

[0092] When the road surface adhesion coefficient is low, the maximum value of the tire force that the tire can generate is not sufficient to support the required large yaw rate. Define the upper limit value of the desired yaw rate as:

[0093]

[0094] wherein, is the road surface adhesion coefficient, is the gravitational acceleration, is the longitudinal speed of the vehicle.

[0095] The reference value of the yaw rate should be:

[0096]

[0097] Based on the above scheme, the present application establishes an original control target according to vehicle driving requirements, such as vehicle driving requirements for improving vehicle maneuverability and vehicle stability, etc., establishes a mapping from the desired control target to the desired yaw rate based on the vehicle six-degree-of-freedom dynamics model, and generates a desired yaw rate tracking sequence for the torque vector actuator.

[0098] In one embodiment, the above method further includes:

[0099] Determine the sideslip angles of the front and rear wheels of the vehicle based on the vehicle motion state information;

[0100] Determine the tire force conditions based on the sideslip angles of the front and rear wheels of the vehicle, the tire model, the road surface adhesion information, the vertical load and the tire sideslip stiffness;

[0101] Determine the nonlinear dynamics equation based on the tire force conditions, the state vector and the input vector of the actuator in combination with the vehicle six-degree-of-freedom model;

[0102] Determine the items to be optimized based on the desired yaw rate tracking sequence, roll angle suppression, pitch angle suppression and lateral speed suppression;

[0103] Determine the control quantity constraint of the actuation energy;

[0104] Define the objective function based on the items to be optimized and the control quantity constraint

[0105] Taylor expansion at the operating point based on the six - degree - of - freedom dynamic model of the vehicle and determination of the incremental model by neglecting high - order terms

[0106]

[0107] where, are respectively the Jacobian matrices of the non - linear dynamic equation at the operating point .

[0108] It can be understood that the motion states of each degree of freedom of the vehicle are mutually coupled. Therefore, in this application, a high - order coupled vehicle dynamic equation is established for the overall vehicle motion characteristics. The present invention uses a six - degree - of - freedom model of the vehicle to describe the lateral motion, yaw motion, pitch motion, and roll motion of the vehicle. The vehicle state is predicted at the current operating point. In a very short time, the longitudinal speed of the vehicle can be considered approximately constant. At this time, the longitudinal speed of the vehicle is introduced as a variable parameter into the six - degree - of - freedom model of the vehicle and updated during each calculation to ensure a high degree of accuracy. The formula of the six - degree - of - freedom model of the vehicle is as follows, and the schematic diagram is shown in Figure 3 .

[0109]

[0110] where, is the distance from the sprung mass to the coordinate system, and are the angles of the vehicle rotating around the x - axis and y - axis respectively, respectively represent the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel. and respectively represent the distances from the front and rear axles to the vehicle's center of mass, is the mass of the vehicle, is the sprung mass of the vehicle, is the moment of inertia of the vehicle rotating around the axis, represents the longitudinal speed, represents the lateral speed, represents the yaw angular velocity, represents the front - wheel rotation angle, represents the left - and - right wheel track, are respectively the additional torques on the x - axis, y - axis, and z - axis.

[0111] Denote as the rear - wheel rotation angle, then the sideslip angles of the front and rear axles can be calculated as follows:

[0112]

[0113] The lateral force of the tire is described using the Fiala tire model. When the tire sideslip angle is very small, there is , and then this tire model can be approximated as:

[0114]

[0115] where is the tire sideslip force, is the tire sideslip angle, is the road surface adhesion coefficient, is the vertical load, and the tire cornering stiffness is divided into the front wheel cornering stiffness and the rear wheel cornering stiffness .

[0116] The tire force condition is thus determined.

[0117] For the actuator design, its state vector is defined as:

[0118]

[0119] Its control input vector is defined as:

[0120]

[0121] Combined with the vehicle's six-degree-of-freedom dynamic model, the vehicle's non-linear dynamic equation can be obtained as:

[0122]

[0123] In predictive control, it is necessary to define an objective function to describe the optimization problem. Considering the yaw rate tracking, roll angle suppression, pitch angle suppression, and lateral velocity suppression of the vehicle comprehensively, the objective term to be optimized can be defined as:

[0124]

[0125] where, , , and are the reference values of the yaw rate, roll angle, pitch angle, and lateral velocity respectively.

[0126] In addition, the actuation energy of the control quantity also needs to be constrained:

[0127]

[0128] Since the actuation amplitude of the actuator is limited, the control quantity itself also needs to be constrained:

[0129]

[0130] Among them, and are the lower bound and upper bound of the control quantity respectively.

[0131] Combining the above items to be optimized and the corresponding control quantity constraints, the objective function of the upper actuator can be defined as:

[0132]

[0133] Among them, , , , , , , and are the weights of the corresponding items respectively.

[0134] This optimization problem is a nonlinear optimization problem, and the interior point method or the sequential quadratic programming method can be used to solve it. Since this problem is a highly coupled and highly nonlinear optimization problem, if the traditional interior point method or sequential quadratic programming method is used to solve it, it will require a large amount of computing resources and is difficult to solve in real time. To solve this problem, the original optimization problem is transformed and solved. Assuming that its current operating point is , the above nonlinear model is Taylor-expanded at its operating point and high-order terms are ignored to obtain the following incremental model:

[0135]

[0136] Among them, are the Jacobian matrices of the nonlinear system equations at the operating point respectively.

[0137] In one embodiment, the determining of the upper layer control information based on the vehicle motion state information, the road surface adhesion information, and the desired yaw rate tracking sequence includes:

[0138] Performing Euler discretization on the incremental model to obtain a prediction equation within the prediction horizon :

[0139]

[0140] Among them, represents the state prediction value within the entire prediction horizon, represents the control quantity increment within the entire prediction horizon, , and are the corresponding matrices;

[0141] Determine the observation matrix within the prediction horizon based on the observation vector, where the observation matrix is used to calculate the observation variables, and the observation variables are used to calculate the objective function;

[0142] Construct the objective function based on the tracking desired observation value and the suppression control amount;

[0143] Convert the six-degree-of-freedom dynamics model into the standard form of a QP problem based on the quadratic term matrix and the linear term matrix;

[0144] Solve the QP problem to determine the additional yaw moment, additional roll moment, additional pitch moment, and rear-wheel steering angle.

[0145] Specifically, in this application, the above equations are discretized by Euler with the sampling time to obtain the prediction equations within the prediction horizon as:

[0146]

[0147] where represents the state prediction value within the entire prediction horizon, represents the control amount increment within the entire prediction horizon, , and are the corresponding matrices.

[0148] Let be the observation matrix, then the observation vector within the prediction horizon is:

[0149]

[0150] Let the desired observation value to be tracked be , and the first term in the objective function is to track the desired observation value, and the second term is the magnitude of the suppression control amount:

[0151]

[0152] where and are the expected value tracking weight matrix and the control amount weight matrix respectively, and are the lower and upper bounds of the observation vector respectively, and are the lower and upper bounds of the control amount respectively.

[0153] For the standard form of the QP problem:

[0154]

[0155] The quadratic term matrix can be obtained and the linear term matrix can be separately expressed as:

[0156]

[0157] wherein, is the reference value matrix, .

[0158] For the constraints of the system, the upper and lower limits of the control quantity are defined as and respectively. Then, the upper and lower limits of the control quantity within the entire prediction time domain are:

[0159]

[0160] After that, QP can be used to solve the MPC problem to determine the additional yaw moment, additional roll moment, additional pitch moment, and rear wheel steering angle.

[0161] In one embodiment, the above method further includes::

[0162] Determine the force equation during the rotation of the tire:

[0163]

[0164] wherein, the state variable of the tire rotation system is the tire rotation speed , the moment of inertia of the tire is , the effective rolling radius of the tire is , the longitudinal force received by the tire is , and the total torque acting on the tire is ;

[0165] Determine the slip ratio of the tire based on the longitudinal speeds of the four tires;

[0166] Based on the vehicle center of mass speed and the vector sum of the actual yaw angular velocity generated at the tire and the desired wheel speed of the tire, wherein the desired wheel speed is used to construct the objective function for the driving torque distribution of the actuator;

[0167] Determine the composite brush model based on the total tire force, composite tire slip amount, and composite slip amount threshold, wherein the composite tire slip amount includes the longitudinal slip amount and the lateral slip amount;

[0168] Determine the tire force data based on the force equation during the rotation of the tire, the slip ratio of the tire, and the composite brush model.

[0169] The lower actuator is mainly responsible for coordinating the control quantities of multiple actuators such as driving, steering, and suspension to achieve longitudinal-lateral-vertical coordinated control of the vehicle. The forces acting on the tire during rotation are as follows:

[0170]

[0171] Among them, the state variable of the tire rotation system is the tire rotation speed , the moment of inertia of the tire is , the effective rolling radius of the tire is , the longitudinal force acting on the tire is , and the total torque acting on the tire is .

[0172] The total torque acting on the tire is the sum of the torque desired by the driver and the additional torque calculated by the actuator , that is . The slip ratio of the tire can be calculated as follows:

[0173]

[0174] Among them, respectively represent the longitudinal speeds of the four tires.

[0175] Specifically, the speed at the tire center consists of two parts. The first part is the speed of the vehicle's center of mass, and the second part is the speed generated at the tire due to the yaw angular velocity. The speed at the tire center is the vector sum of these two parts and can be calculated as follows.

[0176]

[0177] Therefore, the desired wheel speed can be expressed as:

[0178]

[0179] This application introduces a composite brush tire model to calculate the tire force for predicting the future rotation state of the tire. The composite brush model can be expressed as:

[0180]

[0181] Among them, . 、 and respectively represent the total tire force, the composite tire slip, and the threshold of the composite slip. The composite tire slip can be calculated as follows:

[0182]

[0183] Among them, the longitudinal slip and lateral slip The calculation is as follows:

[0184]

[0185] The longitudinal force and lateral force of the tire are calculated respectively in the following ways:

[0186]

[0187] Discretize the rotational equation of the tire, and the sampling time is , then we can get:

[0188]

[0189] Define the state vector of the system as , and define the control vector as , where represents the additional torque of the motor, represents the relaxation variable.

[0190] According to vehicle dynamics, the additional yaw moment generated by the differential torque can be expressed as:

[0191]

[0192] where .

[0193] In one embodiment, determining the lower-layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper-layer control information includes:

[0194] Based on the tire force data, reducing the additional torque energy consumption, the additional yaw moment, and the desired wheel speed to determine the objective function of the driving torque distribution of the actuator:

[0195]

[0196] where, , and are the weights of the corresponding items respectively, and are the lower and upper bounds of the control quantity respectively, and are the lower and upper bounds of the slip ratio respectively, , , ;

[0197] Solve the objective function of the driving torque distribution of the actuator to determine the additional driving torque.

[0198] In the lower-layer driving torque distribution actuator, it is necessary to simultaneously consider tracking the expected additional yaw moment output by the upper-layer additional yaw moment actuator and the expected wheel speed , and reducing the energy consumption required to generate the additional torque. Considering the constraints simultaneously, the objective function in the lower-layer driving torque distribution actuator is defined as:

[0199]

[0200] where , and are the weights of the corresponding terms respectively, and are the lower and upper bounds of the control quantity respectively, and are the lower and upper bounds of the slip ratio respectively, , , .

[0201] After that, QP can be used to solve this MPC problem to determine the additional driving torque.

[0202] In one embodiment, determining the lower-layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper-layer control information includes:

[0203] Determine the expression of the sprung mass of the suspension and the state vector and control vector of the suspension actuator;

[0204] Determine the additional roll moment and additional pitch moment;

[0205] Based on the expression of the sprung mass of the suspension and the state vector and control vector of the suspension actuator, the additional roll moment and additional pitch moment, determine the objective function of the suspension actuator;

[0206] Solve the objective function of the suspension actuator to determine the active suspension force.

[0207] Assume that the sprung masses corresponding to the four suspensions are respectively, the elastic stiffness coefficient of the suspension is , the damping coefficient of the suspension is , let the compression stroke of each suspension spring be expressed as , define the positive direction as downward compression and upward tension as negative. As Figure 4 shown, define the active suspension force as , the positive direction of the suspension force is upward as positive and downward as negative. At this time, the equations corresponding to each sprung mass can be expressed as:

[0208]

[0209] Define the state vector of the suspension actuator and the control vector respectively as:

[0210]

[0211] The additional roll moment and additional pitch moment generated by the active suspension force can be expressed as:

[0212]

[0213] where .

[0214] In the suspension force actuator of the active suspension, it is necessary to simultaneously consider tracking the additional roll moment and additional pitch moment generated by the upper actuator, and also consider the energy consumption required to generate the additional moment. Therefore, the objective function in the lower-layer active suspension actuator is defined as:

[0215]

[0216] where , and are the weights of the corresponding terms respectively, and are the lower and upper bounds of the control quantity respectively, and are the lower and upper bounds of the slip ratio respectively.

[0217] After that, QP can be used to solve this MPC problem to determine the active suspension force.

[0218] Based on the above scheme, by actively adjusting mechanisms such as the rear wheel rotation angle, tire additional torque, and active suspension force, the stability of the vehicle in the transverse, longitudinal, and vertical directions during operation is improved, and it is ensured as much as possible that the lateral speed, pitch angle, and roll angle of the vehicle are 0, while making the yaw angular velocity close to the value of the reference yaw angular velocity. The above full-vector control technology can effectively ensure the stability of the vehicle, avoid dangers such as rollover and out-of-control of the vehicle, thereby improving the safety during driving. For drivers and passengers, reducing the body shake can also enhance the comfort and convenience of driving and riding. In addition, the energy consumption is optimized in the algorithm, which can improve the energy use efficiency and reduce the wear of the vehicle, etc.

[0219] Furthermore, as an implementation of the method shown above Figure 1 , an embodiment of the present invention further provides a multi-actuator full-vector control device for the above Figure 1The method shown is implemented. The device embodiment corresponds to the foregoing method embodiment. For ease of reading, the details in the foregoing method embodiment will not be elaborated one by one in this device embodiment. However, it should be clear that the device in this embodiment can correspondingly implement all the contents in the foregoing method embodiment. As Figure 5 shown, the device includes: a first acquisition unit 21, a determination unit 22, a second acquisition unit 23, and a generation unit 24, where

[0220] The first acquisition unit 21 is configured to acquire vehicle motion state information and road surface adhesion information. Among them, the vehicle motion state information is acquired based on in-vehicle sensors and / or a vehicle state estimation module, and the road surface adhesion information is acquired based on a road surface identification module;

[0221] The second acquisition unit 22 is configured to acquire steering wheel angle information based on the vehicle motion state information to determine an expected yaw rate tracking sequence;

[0222] The first determination unit 23 is configured to determine upper-layer control information based on the vehicle motion state information, the road surface adhesion information, and the expected yaw rate tracking sequence. Among them, the upper-layer control information includes additional yaw moment, additional roll moment, additional pitch moment, and rear wheel steering angle;

[0223] The second determination unit 24 is configured to determine lower-layer control information based on the vehicle motion state information, the road surface adhesion information, and the upper-layer control information. Among them, the lower-layer control information is used to allocate vehicle actuators, and the lower-layer control information includes additional driving torque and suspension active force.

[0224] The processor contains a kernel, and the kernel retrieves corresponding program units from the memory. One or more kernels can be set, and by adjusting the kernel parameters, a multi-actuator full vector control method can be implemented, which can solve the problem that the coupling between each actuator is relatively low and there is a lack of a better implementation and allocation method.

[0225] An embodiment of the present invention provides a computer-readable storage medium. The above computer-readable storage medium includes a stored program, and when the program is executed by a processor, the multi-actuator full vector control method is implemented.

[0226] An embodiment of the present invention provides a processor. The processor is used to run a program, and when the program runs, the multi-actuator full vector control method is executed.

[0227] An embodiment of the present invention provides an electronic device. The above electronic device includes at least one processor and at least one memory connected to the processor; among them, the above processor is used to call program instructions in the above memory to execute the multi-actuator full vector control method as described above

[0228] An embodiment of the present invention provides an electronic device 30, as Figure 6 shown. The electronic device includes at least one processor 301, at least one memory 302 connected to the processor, and a bus 303. Among them, the processor 301 and the memory 302 complete mutual communication through the bus 303. The processor 301 is used to call program instructions in the memory to execute the above multi-actuator full vector control method.

[0229] The intelligent electronic device herein may be a PC, a PAD, a mobile phone, etc.

[0230] This application also provides a computer program product, which is suitable for executing a program initialized with the steps of the above multi-actuator full vector control method when executed on a process management electronic device.

[0231] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0232] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0233] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0234] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in the process Figure 1one or more processes and / or blocks Figure 1 the functions specified in one or more blocks.

[0235] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes and / or blocks Figure 1 one or more processes and / or blocks Figure 1 the steps of the functions specified in one or more blocks.

[0236] The embodiments of the present application also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the process of controlling the memory in the corresponding embodiment Figure 1 as

[0237] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0238] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0239] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0240] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0241] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0242] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0243] The above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A multi-actuator full vector control method, characterized in that: include: Acquire vehicle motion state information and road adhesion information, wherein the vehicle motion state information is acquired based on a vehicle-mounted sensor and / or a vehicle state estimation module, and the road adhesion information is acquired based on a road recognition module; Based on the vehicle motion state information, obtaining steering wheel angle information to determine a desired yaw rate tracking sequence; Determining upper-level control information based on the vehicle motion state information, the road adhesion information and the desired yaw rate tracking sequence, wherein the upper-level control information includes an additional yaw moment, an additional roll moment, an additional pitch moment and a rear wheel steering angle; Determining lower-level control information based on the vehicle motion state information, the road adhesion information and the upper-level control information, wherein the lower-level control information is used to allocate vehicle actuators, and the lower-level control information includes additional driving torque and suspension active force; The acquiring steering wheel angle information based on the vehicle motion state information to determine an expected yaw rate tracking sequence includes: determining a desired front wheel turning angle based on the variable transmission ratio and the steering wheel angle information; Determining a desired yaw rate based on the desired front wheel turning angle and a second-order reference model; defining an upper limit value of a desired yaw rate based on the road adhesion information; determining the desired yaw rate tracking sequence based on the desired yaw rate and the upper limit value of the desired yaw rate; Based on the vehicle motion state information, determining the sideslip angles of the front and rear wheels of the vehicle; Determining tire stress conditions based on the side slip angles of the front and rear wheels of the vehicle, the tire model, road adhesion information, vertical load, and tire cornering stiffness; Determine a nonlinear dynamic equation based on the tire force condition, the state vector and the input vector of the actuator in combination with a six-degree-of-freedom model of the vehicle; Determining an item to be optimized based on the desired yaw rate tracking sequence, roll angle suppression, pitch angle suppression, and lateral velocity suppression; Determine the control quantity constraints of the actuation energy; Defining an objective function based on the item to be optimized and the control amount constraint; Based on the Taylor expansion at the working point of the vehicle's six-degree-of-freedom dynamic model and ignoring high-order terms, the incremental model is determined: in, The nonlinear dynamic equations at the working point are The Jacobian matrix at .

2. The method according to claim 1, characterized in that The determining of upper-level control information based on the vehicle motion state information, the road adhesion information and the expected yaw rate tracking sequence includes: The incremental model is subjected to Euler discretization to obtain the prediction time domain The prediction equation inside: in, Represents the state prediction value in the entire prediction time domain, Represents the control quantity increment in the entire prediction time domain, , and is the corresponding matrix; Determining an observation matrix in a prediction time domain based on the observation vector, wherein the observation matrix is ​​used to calculate observation variables, and the observation variables are used to calculate an objective function; Constructing the objective function based on tracking expected observations and suppressing control amounts; The six-degree-of-freedom dynamics model is converted into a standard form of a QP problem based on a quadratic term matrix and a linear term matrix; The QP problem is solved to determine the additional yaw moment, the additional roll moment, the additional pitch moment and the rear wheel steering angle.

3. The method according to claim 1, characterized in that Also includes: Determine the force equation during tire rotation: Among them, the state variable of the tire rotation system is the tire speed The moment of inertia of the tire is , the effective rolling radius of the tire is The longitudinal force on the tire is The total torque acting on the tire is ; determining a tire slip ratio based on the longitudinal velocities of the four tires; Determining a desired wheel speed of the tire based on a vector sum of the vehicle center of mass velocity and an actual yaw rate generated at the tire, wherein the desired wheel speed is used to construct an objective function for driving torque distribution of the actuator; Determining a composite brush model based on the total tire force, the composite tire slip and the composite slip threshold, wherein the composite tire slip includes a longitudinal slip and a lateral slip; The tire force data is determined based on the tire rotation process force equation, the tire slip rate and the composite brush model.

4. The method according to claim 3, characterized in that The determining of the lower-layer control information based on the vehicle motion state information, the road adhesion information and the upper-layer control information includes: Based on tire force data, reducing additional torque energy consumption, additional yaw torque and expected wheel speed, determine the objective function of actuator drive torque distribution: in, , and are the weights of the corresponding items, and are the lower and upper bounds of the control volume, respectively. and are the lower and upper bounds of the slip rate, , , ; An objective function of the drive torque distribution of the actuator is solved to determine the additional drive torque.

5. The method according to claim 1, characterized in that The determining of the lower-layer control information based on the vehicle motion state information, the road adhesion information and the upper-layer control information includes: Determine the expression for the sprung mass of the suspension and the state and control vectors of the suspension actuator; Determine the additional rolling moment and the additional pitching moment; Determine the objective function of the suspension actuator based on the expression of the suspension sprung mass and the state vector and control vector of the suspension actuator, the additional roll moment and the additional pitch moment; The objective function of the suspension actuator is solved to determine the active suspension force.

6. A multi-actuator full vector control device, characterized in that: Also includes: A first acquisition unit, configured to acquire vehicle motion state information and road adhesion information, wherein the vehicle motion state information is acquired based on a vehicle-mounted sensor and / or a vehicle state estimation module, and the road adhesion information is acquired based on a road recognition module; a second acquisition unit, configured to acquire steering wheel angle information based on the vehicle motion state information to determine a desired yaw rate tracking sequence; The second acquisition unit is further used to determine the expected front wheel turning angle based on the variable transmission ratio and the steering wheel angle information; determine the expected yaw rate based on the expected front wheel turning angle and the second-order reference model; define the upper limit of the expected yaw rate based on the road adhesion information; determine the expected yaw rate tracking sequence based on the expected yaw rate and the upper limit of the expected yaw rate; Based on the vehicle motion state information, determining the sideslip angles of the front and rear wheels of the vehicle; Determining tire stress conditions based on the side slip angles of the front and rear wheels of the vehicle, the tire model, road adhesion information, vertical load, and tire cornering stiffness; Determine a nonlinear dynamic equation based on the tire force condition, the state vector and the input vector of the actuator in combination with a six-degree-of-freedom model of the vehicle; Determining an item to be optimized based on the desired yaw rate tracking sequence, roll angle suppression, pitch angle suppression, and lateral velocity suppression; Determine the control quantity constraints of the actuation energy; Defining an objective function based on the item to be optimized and the control amount constraint; Based on the Taylor expansion at the working point of the vehicle's six-degree-of-freedom dynamic model and ignoring high-order terms, the incremental model is determined: in, The nonlinear dynamic equations at the working point are The Jacobian matrix at ; a first determining unit, configured to determine upper-level control information based on the vehicle motion state information, the road adhesion information, and the desired yaw rate tracking sequence, wherein the upper-level control information includes an additional yaw moment, an additional roll moment, an additional pitch moment, and a rear wheel steering angle; The second determination unit is used to determine lower-level control information based on the vehicle motion state information, the road adhesion information and the upper-level control information, wherein the lower-level control information is used to allocate vehicle actuators, and the lower-level control information includes additional driving torque and suspension active force.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the multi-actuator full vector control method according to any one of claims 1 to 5 are implemented.

8. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the multi-actuator full vector control method as described in any one of claims 1 to 5.

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