A human-machine collaborative motion control method for a distributed drive and rear-wheel steering electric vehicle

Abstract

The present application relates to a kind of man-machine collaborative motion control methods for distributed drive and rear wheel steering electric vehicle, comprising the following steps: the input steering information of driver is identified as steering intent result;Vehicle reference model is designed, and expected reference state is designed;Driving-steering collaborative controller for distributed drive and rear wheel steering electric vehicle is designed, including upper controller and lower controller;By solving upper controller, the optimal additional rear wheel angle and additional yaw moment are obtained;The optimal additional yaw moment is distributed by the lower controller, and four-wheel additional torque is obtained;The four-wheel additional torque is sent to electric vehicle four-wheel hub motor as control command, and the collaborative control to vehicle is realized.Compared with prior art, the present application has the advantages of matching vehicle control target with driver operation, improving man-machine collaborative motion control performance for driver-vehicle closed loop, etc.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle chassis control, and in particular to a human-machine cooperative motion control method for electric vehicles with distributed drive and rear-wheel steering. Background Technology

[0002] With the development of new energy and electronic and electrical technologies, the chassis motion control of electric vehicles equipped with multiple types of actuators has received widespread attention from academia and industry. On the one hand, the introduction of multiple types of actuators, such as four-wheel distributed drive and rear-wheel steering, brings more degrees of control freedom to the chassis motion control system, thereby further improving the vehicle's dynamic response performance. On the other hand, active chassis motion control strategies can effectively assist drivers in driving the vehicle, improving the vehicle's handling performance and driving stability under extreme conditions, thus ensuring driver safety. Therefore, chassis motion control methods are particularly important for the development of electric vehicles. However, existing chassis motion control methods for electric vehicles with distributed drive and rear-wheel steering have the following problems:

[0003] 1. In the prior art, Chinese patent CN116729136A discloses an electric four-wheel drive steering stability control method. This method determines whether the vehicle has entered a preset steering stability control zone based on the vehicle speed and steering wheel angle, without considering the influence of driver input on vehicle motion control. In reality, vehicle motion control is the result of the coupling effect between the active chassis control system and the driver. Driver steering control and rear-wheel steering control adjust the tire lateral force by regulating the front and rear wheel angles, respectively, thus affecting the vehicle's lateral and yaw motion. Distributed drive, on the other hand, adjusts the tire longitudinal force to give the vehicle an additional yaw moment, thereby regulating the vehicle's lateral and yaw motion. Therefore, when developing drive and steering control methods, the influence of driver input should be considered to improve the performance of human-machine collaborative motion control in a driver-vehicle closed-loop system. Most existing drive and steering control strategies do not consider the influence of driver operation on vehicle motion, resulting in a mismatch between the overall vehicle control effect and driver operation, and in severe cases, control effect conflicts.

[0004] 2. Chassis motion control systems assist drivers in adjusting vehicle posture; therefore, human-machine collaborative control research on chassis motion control should focus on driver expectations. However, existing chassis motion control research typically assumes that the driver expects the vehicle to maintain understeer characteristics. For electric vehicles with rear-wheel steering, the rear wheel angle is usually aligned with the driver's steering wheel angle to increase the turning radius and maintain vehicle stability. This makes the vehicle steering conservative and reduces its agility, which contradicts the driver's steering intentions and negatively impacts the driving experience. Therefore, it is necessary to design a chassis motion control method that meets driver needs, balancing vehicle stability and handling agility to improve the driver's experience while ensuring vehicle safety. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a human-machine cooperative motion control method for electric vehicles with distributed drive and rear-wheel steering.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] On one hand, the present invention provides a human-machine cooperative motion control method for electric vehicles with distributed drive and rear-wheel steering, comprising the following steps:

[0008] Step S1: Identify the driver's input steering information as a steering intention result;

[0009] Step S2: Design a vehicle reference model, design a stability factor based on the steering intention result, construct a transfer function for the front wheel steering angle based on the front wheel steering angle of the vehicle reference model and the stability factor, and calculate the vehicle's expected yaw rate through the transfer function.

[0010] Step S3: Design a drive-steering cooperative controller for distributed drive and rear-wheel steering electric vehicles. The drive-steering cooperative controller includes an upper-level controller and a lower-level controller. The upper-level controller is designed to track the desired yaw rate. By solving the upper-level controller, the optimal additional rear wheel angle and additional yaw moment are obtained. The optimal additional rear wheel angle is sent to the rear wheel steering actuator to participate in steering control. The lower-level controller distributes the optimal additional yaw moment to obtain the additional torque for all four wheels.

[0011] Step S4: Send the additional torque of the four wheels as a control command to the four wheel hub motors of the electric vehicle to achieve coordinated control of the vehicle.

[0012] Furthermore, the input steering information includes: steering wheel angle, steering wheel angular velocity, vehicle lateral velocity, and vehicle lateral acceleration;

[0013] The steering intention result is expressed by the following formula:

[0014]

[0015] Where Γ represents the turning intention result; δ f Steering wheel angle; A is the angular velocity of the steering wheel. x The vehicle's lateral speed; To accelerate the vehicle laterally.

[0016] Furthermore, the steering intention results are divided into three types, using the following formula:

[0017]

[0018] Where -1 represents the driver's expectation of turning when entering a curve, 0 represents the driver's expectation of driving in a straight line, and 1 represents the driver's expectation of straightening out of a curve. δ sw This refers to the angle at which the driver turns the steering wheel.

[0019] Furthermore, the stability factor is expressed by the following formula:

[0020]

[0021] Where K is the stability factor; ε + and ε - All are scaling factors; m is the vehicle mass; C f C r These are the front and rear wheel lateral stiffness, respectively; L f ,L r These are the distances from the vehicle's center of gravity to the front and rear axles, respectively, and L = L0. f +L r Γ represents the result of the intended turn.

[0022] Furthermore, the transfer function is expressed by the following formula:

[0023]

[0024] Where, γ ref For the desired yaw rate, δ f w is the steering angle of the vehicle's front wheels. n Let ζ be the natural frequency of the vehicle, ζ be the damping coefficient of the vehicle, and K be the natural frequency of the vehicle. γ For steady-state yaw rate gain, τ γ is the differential coefficient of the yaw rate, and s is the independent variable of the transfer function.

[0025] Furthermore, the desired yaw rate being less than or equal to the upper limit of the desired yaw rate reference is expressed by the following formula:

[0026] |γ ref |≤γ up

[0027] Where, γ up This is the upper limit of the desired yaw rate reference. Where μ is the road surface adhesion coefficient and g is the gravity constant.

[0028] Furthermore, the optimal additional yaw torque is distributed by the lower-level controller in an average distribution manner to obtain the additional torque for all four wheels.

[0029] Furthermore, the vehicle status information of the distributed drive and rear-wheel steering electric vehicles is fed back to steps S1, S2 and S3 respectively.

[0030] In a second aspect, the present invention provides a human-machine cooperative motion control for electric vehicles with distributed drive and rear-wheel steering, including a steering intention recognition module, a desired state design module, and a drive-steering cooperative control design module, wherein the system is used to execute the method described in any one of the above.

[0031] Thirdly, the present invention provides an in-vehicle controller, including a processor and a memory, wherein the memory is used to store execution instructions of the processor, the execution instructions performing the method described in any of the preceding claims.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This invention integrates driver input information into the vehicle chassis motion control architecture, considers the improvement in vehicle motion capability brought about by the coupling effect between the active chassis control system and the driver, and improves the human-machine collaborative motion control performance for driver-vehicle closed loop by taking into account the driver's driving input.

[0034] 2. This invention combines the driver's desired steering intention with the chassis motion control method, so that the controlled vehicle response has different degrees of driving stability and handling agility according to the driver's changing expectations, thereby improving the driver's driving experience while ensuring vehicle driving safety. Attached Figure Description

[0035] Figure 1 This is an architecture diagram of the human-machine cooperative motion control method for distributed drive and rear-wheel steering electric vehicles according to the present invention;

[0036] Figure 2This is a schematic diagram of the two-degree-of-freedom vehicle model of the present invention;

[0037] Figure 3 This is a yaw rate tracking curve of the vehicle obtained during the simulation process of this invention;

[0038] Figure 4 This is a curve of the vehicle's center of gravity sideslip angle obtained during the simulation process of this invention;

[0039] Figure 5 This is a driver intent recognition curve obtained during the simulation process of this invention;

[0040] Figure 6 This is a graph showing the front wheel steering angle and rear wheel steering angle curves of the vehicle obtained during the simulation process of this invention;

[0041] Figure 7 This is a graph of the additional yaw moment obtained during the simulation process of this invention. Detailed Implementation

[0042] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0043] This embodiment provides a human-machine cooperative motion control method for distributed drive and rear-wheel steering electric vehicles, and its architecture diagram is shown below. Figure 1 As shown in the diagram, the driver steering intention recognition module identifies the driver's steering intention based on the driver's steering information and outputs the recognition result Γ. In the desired reference state design module, a stability factor K is first designed based on the steering intention recognition result to adjust the vehicle's steering characteristics to better match the driver's driving expectations. The calculated stability factor is used to solve for the desired yaw rate γ. ref This is used as the tracking reference target for the drive-steering cooperative controller. The drive-steering cooperative control design module consists of two sub-modules. The first is a model predictive control-based sub-module for optimizing the additional yaw moment and additional rear wheel angle, which calculates the optimal additional rear wheel angle and additional yaw moment. The second is a sub-module for designing a four-wheel additional torque vector distribution strategy, which obtains the four-wheel additional torque by vector distribution of the additional yaw moment. Finally, the additional rear wheel angle and the four-wheel additional torque output by the controller are sent as control commands to the corresponding actuators of the electric vehicle.

[0044] The specific process of the human-machine cooperative high-agility motion control method for distributed drive and rear-wheel steering electric vehicles provided in this embodiment is as follows:

[0045] Step S1: Identify the driver's input steering information as a steering intention result.

[0046] Specifically, in this embodiment, the driver's steering intention is identified based on the driver's input steering information, wherein the driver's steering information input includes the driver's steering wheel angle δ. f and steering wheel angular velocity Right now:

[0047]

[0048] In the above formula, Γ represents the driver's steering intention recognition result; δ f Steering wheel angle; A is the angular velocity of the steering wheel. x The vehicle's lateral speed; To accelerate the vehicle laterally.

[0049]

[0050] In the above formula, Γ represents the driver's steering intention recognition result, where -1 indicates that the driver has a driving expectation of turning into the curve, 0 indicates that the driver has a driving expectation of driving straight, and 1 indicates that the driver has a driving expectation of straightening out of the curve.

[0051] Step S2: Design a second-order reference model of the vehicle, design a stability factor based on the steering intention result, construct a transfer function for the front wheel steering angle based on the front wheel steering angle of the vehicle reference model and the stability factor, and calculate the desired yaw rate of the vehicle through the transfer function.

[0052] Yaw rate can effectively characterize the turning motion of a vehicle, so it is selected as the desired reference state.

[0053] Specifically, the following steps are taken: Constructing the vehicle dynamics model:

[0054] (1) Construct a two-degree-of-freedom model of the vehicle

[0055] In this embodiment, a two-degree-of-freedom vehicle model is used to describe the vehicle's lateral and yaw motions, such as... Figure 2 As shown in the schematic diagram of this model, F yf ,F yr These represent the lateral forces of the front and rear axle tires, respectively; F xf ,F xr These represent the longitudinal forces of the front and rear axle tires, respectively; α f ,α r These represent the tire slip angles of the front and rear axles, respectively; β represents the center-of-gravity slip angle; γ represents the yaw rate; δ f Indicates the front wheel steering angle; δ r Indicates the rear wheel steering angle.

[0056] according to Figure 2The two-degree-of-freedom model of the vehicle is established as follows:

[0057]

[0058] (2) Constructing a tire model:

[0059] In this embodiment, a linear tire model is constructed:

[0060]

[0061] The front and rear axle tire slip angles can be calculated using the following formula:

[0062]

[0063] For the rear wheel steering angle δ r Then, the rear wheel steering angle δ that already exists at the current moment is determined. r0 The optimal additional rear wheel steering angle Δδ calculated by the controller r Composition, therefore δ r =δ r0 +Δδ r Therefore, by controlling the increase in rear wheel steering angle, the vehicle's attitude can be adjusted.

[0064] Let the vehicle's state be yaw rate γ and sideslip angle β, denoted as x = [β, γ]. T The control input is the additional rear wheel steering angle Δδ r and additional yaw moment ΔM z denoted as u=[Δδ r ,ΔM z ] T By simultaneously solving the two-degree-of-freedom vehicle model and the tire model, the system state equation can be obtained as follows:

[0065]

[0066] in

[0067]

[0068] Let the current time be k, and the discrete time be T. s The vehicle dynamics model is discretized using the Euler discretization method, resulting in the following discrete state-space equations:

[0069]

[0070] in,

[0071] The desired yaw rate is calculated using a second-order reference model of the vehicle. In the reference model, the current front wheel steering angle δ is used. fAnd the corresponding transfer function is used to calculate the desired state, where the front wheel steering angle δ f The desired yaw rate γ of the vehicle ref The transfer function is:

[0072]

[0073] The meanings and calculation methods of each parameter in the above formula are as follows:

[0074] Vehicle natural frequency w n for:

[0075]

[0076] The vehicle damping coefficient ζ is:

[0077]

[0078] Steady-state yaw rate gain K γ for:

[0079]

[0080] The differential coefficient τ of the yaw rate γ for:

[0081]

[0082] In equations (9)-(13), C f C r These are the front and rear wheel lateral stiffness, respectively; L f ,L r These are the distances from the vehicle's center of gravity to the front and rear axles, respectively, and L = L0. f +L r V x The vehicle's longitudinal velocity is represented by m, and the total vehicle mass is represented by I. z Let Z be the moment of inertia of the vehicle about the Z-axis.

[0083] In addition, K is a stability factor used to characterize the vehicle's steady-state steering characteristics. When K = 0, the vehicle has neutral steering characteristics; when K < 0, the vehicle has oversteer characteristics; and when K > 0, the vehicle has understeer characteristics.

[0084] In this embodiment, the driver's steering intention and stability factor design are combined to achieve a closed-loop human-machine collaborative high-agility motion control between the driver and the vehicle. When the driver intends to control the vehicle to enter a curve using the steering wheel, the vehicle should have oversteer characteristics, with the rear wheel steering angle in the opposite direction to the front wheel steering angle. This reduces the vehicle's turning radius, improves its handling agility, and allows the driver to more easily steer the vehicle, facilitating quick entry into the curve. When the driver intends to control the vehicle to exit a curve and return to a straight position using the steering wheel, the vehicle should have understeer characteristics, with the rear wheel steering angle in the same direction as the front wheel steering angle. This increases the vehicle's turning radius, thereby improving vehicle stability, enhancing driver safety, and reducing driver workload.

[0085] Based on the above design concept, the stability factor is designed as follows:

[0086]

[0087] Where K is the stability factor; ε + and ε - All are scaling factors; m is the vehicle mass; C f C r These are the front and rear wheel lateral stiffness, respectively; L f ,L r These are the distances from the vehicle's center of gravity to the front and rear axles, respectively, and L = L0. f +L r Γ represents the result of the intended turn.

[0088] During vehicle operation, tire force is limited by the road adhesion limit. Therefore, when calculating the desired yaw rate reference, it should be limited to a certain extent to accommodate the road adhesion limit. The upper limit of the desired yaw rate reference is defined as γ. up Then we have:

[0089]

[0090] The yaw rate reference value should be limited to a boundary range, namely:

[0091] |γ ref |≤γ up (16)

[0092] Where μ is the road surface adhesion coefficient and g is the gravity constant.

[0093] Step S3: Design a drive-steering cooperative controller for distributed drive and rear-wheel steering electric vehicles. The drive-steering cooperative controller includes an upper controller and a lower controller. The upper controller is designed to track the desired yaw rate. By solving the upper controller, the optimal additional rear wheel angle and additional yaw moment are obtained. The optimal additional rear wheel angle is sent to the rear wheel steering actuator to participate in steering control. The lower controller distributes the optimal additional yaw moment to obtain the four-wheel additional torque.

[0094] Specifically, it includes the following steps:

[0095] Step S3.1, Design the upper-level controller:

[0096] The upper-level controller design is based on predictive control theory. The control objective function is constructed to consider tracking the desired yaw rate, suppressing the vehicle's sideslip angle, and suppressing the system's kinetic energy. Assuming the prediction time domain is N, and the control time domain is consistent with the prediction time domain, the optimization problem can be described as follows:

[0097]

[0098] In the above formula, All are control weight parameters, which remain constant throughout the prediction time domain; Δδ r_min ,Δδ r_max Additional upper and lower boundary constraints for the rear wheel steering angle; ΔM z_min ,ΔM z_max The upper and lower boundary constraints are for the additional yaw moment.

[0099] Since the vehicle dynamics model is a linear model, the designed drive-steering coordination controller is a linear model predictive controller, and the optimal additional rear wheel steering angle can be obtained through quadratic programming. and additional yaw moment The optimal additional rear wheel steering angle is directly sent to the rear wheel steering actuator to participate in steering control, while the optimal additional yaw moment is distributed through the lower-level controller to obtain the additional driving moment for all four wheels.

[0100] Step S3.2, Design the lower-level controller:

[0101] The optimal additional yaw moment is distributed to obtain the additional torque for all four wheels. Preferably, this embodiment uses an average distribution method to distribute the additional yaw moment, resulting in the following additional torque for all four wheels:

[0102]

[0103] In the above formula, fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; Re d is the tire rolling radius; d is the vehicle track width.

[0104] Step S4: Send the additional torque of the four wheels as a control command to the four-wheel hub motor of the electric vehicle to achieve coordinated control of the vehicle, thereby improving the control of vehicle handling and driving stability.

[0105] To verify the effectiveness of the human-machine cooperative high-agility motion control method for distributed drive and rear-wheel steering electric vehicles mentioned in this embodiment, the method was verified under a double lane change driving condition on a high-friction surface. The specific process is as follows:

[0106] 1. Software Selection

[0107] The programming of the handling stability control algorithm and the automated parameter calibration algorithm proposed in this invention is implemented using the software Matlab / Simulink. The simulation model of the controlled object is implemented using the high-fidelity vehicle dynamics simulation software CarSim, with the software versions being Matlab R2021b and CarSim2019.1, respectively.

[0108] 2. Co-simulation settings

[0109] To achieve co-simulation between the two software programs, the input / output interface modules of CarSim were first configured, and the Simulink model path was added to CarSim to enable joint communication. Then, CarSim was compiled, and the corresponding S-Function module was generated in Simulink. Finally, the parameters of the S-Function were configured, and its input / output signal interfaces were brought out. The co-simulation step size was set to 0.001s. While the Simulink simulation model was running, the CarSim model was simultaneously performing calculations and solving. Data exchange between the two was continuous during the simulation. If the model structure or parameter settings in CarSim were modified, recompilation was required, followed by regenerating the S-Function module to update the CarSim software configuration information.

[0110] 3. Simulation Condition Settings

[0111] To verify the effectiveness of the human-machine cooperative high-agility motion control method for distributed drive and rear-wheel steering electric vehicles described in this embodiment, a double lane-change driving condition on a high-adhesion road surface is selected as the simulation verification condition. During the test, the road adhesion coefficient μ = 0.85, the vehicle speed is 70 km / h and remains constant during driving, and the vehicle steering wheel angle is implemented using the driver model built into Carsim. The vehicle model parameters used in the simulation test are the vehicle's moment of inertia I about the Z-axis. z =2059.2 kg·m2 The total vehicle mass m = 1430 kg; the distance from the vehicle's center of gravity to the front axle L f = 1.05m, distance L from the rear axle r =1.61m; Front tire lateral stiffness C f = 43082 N / rad, rear tire lateral stiffness C r = 59950N / rand, vehicle wheelbase d = 1.55m, discrete time T s =0.01. For the desired reference state design section, the scaling factor is set to ε. + =1,ε - =-0.1, for the drive-steering co-controller design, the prediction time domain is set to N=5, and the control weight parameters are set as follows: The upper and lower boundary constraints of the additional rear wheel steering angle are set to Δδ r_max =3deg,Δδ r_min = -3deg, with the additional yaw moment upper and lower boundary constraints set to ΔM z_max =5000Nm,ΔM z_min = -5000Nm.

[0112] The simulation results are shown in the figure. Figure 3 The vehicle yaw rate tracking curve obtained during the simulation process. Figure 4 The curve of the vehicle's center of gravity sideslip angle obtained during the simulation is shown. Analysis of the curve shows that the vehicle's yaw rate tracking accuracy is high, and the vehicle's center of gravity sideslip angle is maintained within a small range. This indicates that the high-agility human-machine cooperative motion control method proposed in this embodiment can effectively improve the vehicle's handling performance and driving stability. Figure 5 The driver intent recognition curve obtained during the simulation process. Figure 6 The front wheel steering angle and rear wheel steering angle curves of the vehicle obtained during the simulation process. Figure 7The curves show the additional yaw moment obtained during the simulation. Analysis of the curves reveals that during the time intervals of 0.8s-1.5s, 1.9s-2.3s, 3.5s-4.1s, and 4.5s-4.9s, the driver anticipates entering the curve. During these intervals, the rear wheel angles are opposite to the front wheel angles, reducing the vehicle's turning radius and improving handling agility. With the assistance of the controller, the driver can more easily steer the vehicle, facilitating quick entry into the curve. During the time intervals of 1.5s-1.9s, 2.3s-3.5s, 4.1s-4.5s, and 4.9s-7.5s, the driver anticipates exiting the curve and returning to center. During these intervals, the rear wheel angles are in the same direction as the front wheel angles, increasing the vehicle's turning radius and improving stability. With the assistance of the controller, the driver can more safely perform the steering return operation, effectively reducing the driver's workload. The additional yaw moment is distributed as additional drive torque to all four wheels, working together with the additional steering angle of the rear wheels to achieve a highly agile human-machine cooperative motion control effect. In summary, the method proposed in this embodiment can effectively achieve all the benefits described in this invention.

[0113] This embodiment provides a human-machine cooperative motion control for electric vehicles with distributed drive and rear-wheel steering, including a steering intention recognition module, a desired state design module, and a drive-steering cooperative control design module. This system is used to execute the methods mentioned in the above embodiment.

[0114] This embodiment also provides an in-vehicle controller, including a processor and a memory. The memory stores execution instructions of the processor, which execute the methods mentioned in the above embodiments. The in-vehicle controller can be used in data transmission processes of various types of controllers within a vehicle. These controllers include, but are not limited to, vehicle controllers, gateway controllers, and instrument cluster controllers.

[0115] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A human-robot cooperative motion control method for a distributed drive and rear wheel steering electric vehicle, characterized in that, Includes the following steps: Step S1: Identify the driver's input steering information as a steering intention result; Step S2: Design a vehicle reference model, design a stability factor based on the steering intention result, construct a transfer function for the front wheel steering angle based on the front wheel steering angle of the vehicle reference model and the stability factor, and calculate the vehicle's desired yaw rate through the transfer function. Step S3: Design a drive-steering cooperative controller for distributed drive and rear-wheel steering electric vehicles. The drive-steering cooperative controller includes an upper-level controller and a lower-level controller. The upper-level controller is designed to track the desired yaw rate. By solving the upper-level controller, the optimal additional rear wheel angle and additional yaw moment are obtained. The optimal additional rear wheel angle is sent to the rear wheel steering actuator to participate in steering control. The lower-level controller distributes the optimal additional yaw moment to obtain the additional torque for all four wheels. Step S4: Send the additional torque of the four wheels as a control command to the four wheel hub motors of the electric vehicle to achieve coordinated control of the vehicle; The input steering information includes: steering wheel angle, steering wheel angular velocity, vehicle lateral velocity, and vehicle lateral acceleration; The steering intention result is expressed by the following formula: wherein, is a steering intention result; is a steering wheel angle; is a steering wheel angular velocity; is a vehicle lateral velocity; is a vehicle lateral jerk; The steering intention results are divided into three types, using the following formula: Here, -1 indicates that the driver expects to turn when entering a curve, 0 indicates that the driver expects to drive in a straight line, and 1 indicates that the driver expects to straighten out of the curve. The angle of the driver's steering wheel; The stability factor is expressed by the following formula: in, As a stability factor; and All are scaling factors; For the overall vehicle weight; These are the lateral stiffness of the front and rear wheels, respectively. These are the distances from the vehicle's center of gravity to the front and rear axles, respectively. ; This is the result of the intended reversal.

2. The human-machine cooperative motion control method for distributed drive and rear-wheel steering electric vehicles according to claim 1, characterized in that, The transfer function is expressed by the following formula: in, For the desired yaw rate, For the steering angle of the vehicle's front wheels, For the inherent frequency of the whole vehicle, This is the overall vehicle damping coefficient. For steady-state yaw rate gain, The differential coefficients of the yaw rate are... This is the argument of the transfer function.

3. The human-machine cooperative motion control method for distributed drive and rear-wheel steering electric vehicles according to claim 2, characterized in that, The desired yaw rate being less than or equal to the upper limit of the desired yaw rate reference is expressed by the following formula: in, This is the upper limit of the desired yaw rate reference. ,in, The road surface adhesion coefficient, is the gravitational constant.

4. The human-machine cooperative motion control method for distributed drive and rear-wheel steering electric vehicles according to claim 1, characterized in that, The optimal additional yaw moment is distributed by the lower-level controller in an average distribution manner to obtain the additional torque for all four wheels.

5. The human-machine cooperative motion control method for distributed drive and rear-wheel steering electric vehicles according to claim 1, characterized in that, The vehicle status information of the distributed drive and rear-wheel steering electric vehicles is fed back to steps S1, S2 and S3 respectively.

6. A human-machine cooperative motion control system for distributed drive and rear-wheel steering electric vehicles, characterized in that, The system includes a steering intention recognition module, a desired state design module, and a drive-steering cooperative control design module, and is used to execute the method of any one of claims 1-5.

7. A vehicle-mounted controller, characterized in that, It includes a processor and a memory, the memory being used to store execution instructions of the processor, the execution instructions being used to perform the method of any one of claims 1-5.

Images