Longitudinal control method and system for front-axle steering rear-wheel distributed driving vehicle

Abstract

The invention discloses a longitudinal speed control method and system for a front-axle steering rear-wheel distributed driving vehicle. Based on an integral longitudinal control framework, vehicle speed tracking control, driving anti-slip control and electronic differential control are integrated, and a unified state-space equation and an optimization control problem are established through inter-wheel speed coupling constraint modeling, slip rate dynamic control and a driving wheel dynamic model. And a model predictive control algorithm is adopted to solve the optimal driving torque in real time, so that high-precision tracking and dynamic stability control of the longitudinal speed of the vehicle are realized. The system comprises a vehicle state acquisition unit, an inter-wheel speed reference calculation unit, a driving anti-skid constraint generation unit, a driving wheel dynamics modeling unit, an overall optimization control unit and a motor execution unit, and closed-loop optimization of a control strategy is achieved. Compared with an existing decoupling control method, the longitudinal control precision and the stability and safety of the vehicle under the complex working condition are remarkably improved, and the high dynamic performance requirement of the automatic driving vehicle is met.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent driving vehicle control, and in particular relates to a longitudinal control method and system for a front-axle steering and rear-wheel distributed drive vehicle. Background Art

[0002] With the rapid development of electric vehicles and autonomous driving technology, distributed drive electric vehicles have become an important development direction in intelligent connected vehicles due to their flexible structure, high control accuracy and high energy utilization. Especially in terms of longitudinal control, the vehicle needs to have good speed tracking ability, drive anti-skid control ability and electronic differential control ability when turning to improve the dynamic stability and driving safety of the vehicle.

[0003] However, existing longitudinal control methods generally adopt a decoupled control architecture, that is, the speed tracking control is independently designed and implemented from the drive anti-skid control and electronic differential control functions. Specifically, traditional schemes usually use a PID control algorithm to track and control the longitudinal speed of the vehicle and output the main control torque; at the same time, an independent anti-skid controller is used to control the vehicle tire slip rate to suppress the drive wheel slip, and then output additional control torque. Finally, the control results of the two are merged in the execution link and output to the rear wheel motor through the drive system. Although this "decoupling first, then fusion" control architecture realizes the basic longitudinal speed control and drive anti-skid functions, it also has the following defects:

[0004] Due to the increase of control links and multi-level controller collaboration, system errors are difficult to avoid, which easily leads to a decrease in overall control performance. Real-time coordination and optimization between longitudinal speed tracking and drive anti-skid cannot be achieved, affecting control accuracy.

[0005] When the vehicle is turning, there is a significant speed difference between the inner and outer wheels. The traditional control method does not consider the dynamic coupling relationship between the wheel speeds and lacks modeling and control of the wheel coupling constraints. This can easily cause problems such as longitudinal speed tracking failure or drive slip when the vehicle is turning, affecting the stability and safety of the entire vehicle.

[0006] For example, the existing technical literature: Zhang Xubin. "Research on Trajectory Tracking Control of Dual-motor Independently Driven Unmanned Racing Car [D]. Liaoning University of Technology", 2020, proposed a trajectory tracking control method for a front-wheel steering and rear-wheel dual-motor driven vehicle. In terms of longitudinal control, the PID control algorithm is used to achieve the desired speed tracking control, and the yaw rate is adjusted based on the fuzzy control algorithm, and the differential torque is output. However, this scheme adopts a functional decoupling control method, which solves the speed tracking and electronic differential functions separately, and then redistributes and merges them, so there are the following problems:

[0007] 1. The control strategy is complex and hierarchical control leads to high computing resource usage;

[0008] 2. Due to functional decoupling and lack of overall optimized design, there is insufficient coordination between speed tracking and electronic differential, which affects control accuracy;

[0009] 3. The wheel slip phenomenon is not taken into account, and it is impossible to dynamically adapt to different road conditions. The anti-skid control effect is limited, and the algorithm generalization ability is insufficient.

[0010] Other technical documents: Zhao Yanbin. "Study on the Electro-hydraulic Control Characteristics of Electronic Limited Slip Differential and Its Drive Anti-skid Control" [D]. Wuhan University of Technology, 2019. For rear-wheel drive vehicles, an electronic limited slip differential model was established, and a drive anti-skid control algorithm was designed. The BP neural network was used to realize the self-tuning of PID control parameters to improve the anti-skid control performance. Although this scheme realizes the effective combination of electronic differential and drive anti-skid functions, its control target is limited to maintaining the optimal slip rate of the vehicle at a certain fixed speed, and does not involve speed tracking control tasks. Therefore, this scheme cannot achieve accurate tracking control of the vehicle according to the longitudinal desired speed target, and can only perform anti-skid and differential control at a given speed.

[0011] In summary, the prior art mainly has the following unresolved technical problems:

[0012] 1. Existing longitudinal control methods usually adopt a decoupling control strategy, lacking the overall coordinated design of speed tracking control, drive anti-slip control and electronic differential control, resulting in insufficient overall control accuracy of the system, making it difficult to meet the needs of autonomous driving vehicles for high-precision longitudinal control;

[0013] 2. The existing technology lacks dynamic modeling and control of the inter-wheel speed coupling relationship under vehicle steering conditions, and fails to effectively solve the adverse effects of the inner and outer wheel speed differences on the longitudinal control performance, which easily leads to a decrease in the longitudinal speed control and drive anti-skid effect when the vehicle is turning, and there are hidden dangers to driving stability;

[0014] 3. The existing methods for wheel slip rate control strategies lack dynamic adaptability to time-varying parameters and road surface changes, resulting in insufficient robustness and generalization of anti-skid control, and unable to ensure the safety and stability of autonomous driving vehicles in complex road conditions and changing environments.

[0015] Therefore, how to design an integrated longitudinal control algorithm for speed tracking control, drive anti-skid control and electronic differential control for autonomous driving vehicles with front-axle steering and rear-wheel distributed drive structure has become a core technical problem that the industry urgently needs to solve. In response to the above problems, the present invention proposes a longitudinal speed control method and system based on an overall optimization control architecture, which can effectively improve the longitudinal control accuracy and stability of the vehicle and meet the comprehensive requirements of autonomous driving vehicles for high dynamic performance and high safety. Summary of the invention

[0016] The purpose of the present invention is to address the deficiencies in the prior art and to provide a longitudinal speed control method and system for front-axle steering and rear-wheel distributed drive vehicles based on an integrated longitudinal control architecture, integrating speed tracking, drive anti-skid and electronic differential control functions into a unified optimization control problem, achieving system coordinated control through a model predictive control algorithm, and improving longitudinal control performance and vehicle stability.

[0017] In order to solve the above technical problems, the technical method adopted by the present invention is as follows: the present invention discloses a longitudinal control method of a front-axle steering rear-wheel distributed drive vehicle, comprising the following steps:

[0018] S1. Based on the vehicle's center of mass speed and yaw rate information, a wheel-to-wheel speed coupling constraint model is constructed to calculate the reference wheel speeds of the left and right rear wheels. The wheel-to-wheel speed constraint model is used to meet the vehicle's longitudinal speed control requirements in straight-line and turning conditions.

[0019] S2. Construct a driving wheel anti-skid control constraint model, determine the target slip range of the left and right driving wheels based on the tire slip control strategy, form an anti-skid inequality constraint, and reduce the time-varying nature of the inequality constraint by fixing the boundary conditions, thereby reducing the complexity of the optimization solution;

[0020] S3. Establish a dynamic model of the drive motors of the left and right rear wheels, form a nonlinear state space dynamic equation based on the relationship between the motor moment of inertia, the output torque and the tire longitudinal force, and linearize the nonlinear dynamic model at the reference wheel speed point to obtain an error state space discrete model;

[0021] S4. Construct an overall optimization control problem model, reconstruct the drive anti-slip constraint inequality into the input constraint of the state space optimization problem, combine the error state space equation, establish the optimization control problem under multiple constraints, and obtain the optimal control torque of the left and right drive wheels;

[0022] S5. Input the optimal control torque to the rear-wheel motor drive system, implement closed-loop feedback control based on the vehicle state sensor and state estimation algorithm, dynamically adjust the longitudinal speed and the left and right rear wheel speeds, implement high-precision tracking control of the vehicle's longitudinal speed, drive anti-skid control and electronic differential control, and improve the longitudinal driving stability and power response performance of the vehicle.

[0023] Furthermore, the wheel speed coupling constraint model adopts dynamic coupling control, and the reference wheel speed of the right wheel is dynamically adjusted according to the actual wheel speed of the left wheel to satisfy the following formula:

[0024] In the formula, v l ,v r Represents the wheel speed of the left and right wheels respectively; reference center of mass speed v c,ref is the expected speed target that the vehicle hopes to achieve, which is a known quantity; v l,ref Left wheel reference speed, v r,ref Right wheel reference speed; v c,ref The reference speed at the center of mass of the vehicle; w c,ref Vehicle reference yaw rate, w c,ref =v c,ref c R , current road curvature c R .

[0025] Furthermore, the target slip ratio range is from 0 to an optimal slip ratio, wherein the optimal slip ratio is dynamically adjusted according to the road adhesion condition and is between 20% and 30%.

[0026] Furthermore, the driving anti-skid constraint is constructed by calculating the optimal slip rate range of the wheel according to the slip rate formula to form the driving anti-skid constraint.

[0027] In the formula, x is the state quantity, ω l ,ω r Respectively represent the rotation speed of the left and right wheels, λ l , r Respectively represent the slip rate of the left and right wheels, λ opt It represents the optimal slip rate under the current road conditions, and R represents the tire radius of each wheel.

[0028] Furthermore, the driving wheel dynamics modeling relies on the motor dynamics equation, and combines the magic formula tire model to model the tangential reaction force of the wheel, which is linearized to obtain the dynamics equation of the driving wheel.

[0029]

[0030] In the formula, the speed error e=[ω l -ω l,ref ,ω r-ω r,ref ] T , torque error is the new control quantity;

[0031]

[0032] In the formula, B x ,C x ,D x ,E x They represent stiffness factor, curve shape factor, curve peak factor, and curve curvature factor, respectively. h ,S v Represent the horizontal and vertical drift of the curve respectively.

[0033] Furthermore, the tire model is based on the magic formula, and the model parameters include stiffness factor, shape factor, peak factor and curvature factor, satisfying

[0034] Furthermore, the nonlinear dynamic model is expanded by the first order Taylor at the reference wheel speed points of the left and right wheels to obtain the linear discrete state space model of the control torque error and wheel speed error.

[0035] Furthermore, the closed-loop feedback control updates the longitudinal speed, yaw rate and left and right wheel speed information in real time based on the vehicle state sensor and state estimation algorithm, and dynamically adjusts and optimizes the control input and constraints.

[0036] Furthermore, the overall optimization control problem is designed by combining all the above parts through the optimization framework, updating the drive anti-slip constraints, and forming an overall optimization control problem, that is,

[0037]

[0038] In the formula, e represents the speed error, Indicates the upper and lower limits of the error after speed constraint reconstruction.

[0039] The present invention also discloses a longitudinal control system of a front-axle steering and rear-wheel distributed drive vehicle, comprising:

[0040] The vehicle status acquisition unit is used to acquire key status information such as vehicle center of mass speed, yaw rate, left and right rear wheel speeds, and longitudinal acceleration, and output the status information in real time;

[0041] A wheel speed reference calculation unit, used to calculate the target reference wheel speeds of the left and right rear wheels based on the vehicle's center of mass speed and yaw rate information, wherein the wheel speed calculation satisfies a preset wheel speed coupling model to form a dynamic coordinated control reference for the left and right rear wheel speeds;

[0042] The driving anti-skid constraint unit is used to generate a driving wheel slip rate target interval according to the tire slip rate model, and determine the upper and lower limits of the anti-skid control to form dynamic constraint parameters of the left and right rear wheel speeds and slip rates;

[0043] A driving wheel dynamics modeling unit is used to establish a dynamics model of the left and right rear wheel driving motors. The model is based on the motor rotation inertia parameters and the tire longitudinal force model to form a nonlinear state space dynamics description of the left and right rear wheels;

[0044] An optimization control unit, used for inputting the state space model and the driving anti-slip constraint into an overall optimization control algorithm, performing model predictive control optimization calculation, and outputting the optimal control torque of the left and right rear wheels;

[0045] The motor execution unit is used to receive the control torque signal output by the optimization control unit and drive the independent motors of the left and right rear wheels to achieve differential control and longitudinal drive control of the left and right rear wheels;

[0046] The feedback control unit is used to correct the control parameters and control instructions in real time based on the feedback data from the vehicle status acquisition unit, so as to ensure high-precision tracking control of the longitudinal speed and wheel speed, as well as the longitudinal stability and dynamic response performance of the whole vehicle.

[0047] Beneficial effects:

[0048] 1) The integrated control architecture eliminates the control delay and precision loss caused by system decoupling, and improves the longitudinal control performance and the dynamic response capability of the whole vehicle. Specifically, the present invention integrates the three functions of vehicle speed tracking control, drive anti-skid control and electronic differential control into an overall control framework by constructing a unified longitudinal control optimization problem. Through the model predictive control algorithm, the wheel speed error state space model and the anti-skid inequality constraints are solved uniformly to achieve the coordination and optimization of multiple control objectives. This integrated control strategy avoids the calculation delay and control precision loss caused by hierarchical or decoupled control in the traditional control architecture, significantly improves the real-time and accuracy of the vehicle's longitudinal speed control, and is particularly suitable for the longitudinal control requirements of autonomous driving vehicles under complex working conditions.

[0049] 2) The dynamic coupling constraint of wheel speed realizes the coordinated control of the left and right wheels under steering conditions, effectively ensuring the longitudinal stability and driving balance of the vehicle when turning. Specifically, the present invention constructs a wheel speed coupling constraint model, dynamically calculates the reference wheel speeds of the left and right rear wheels according to the vehicle's center of mass speed and yaw rate, and introduces a coupling control relationship between the left and right wheel speeds. In addition, by dynamically selecting the wheel with poor driving performance as a reference, the wheel speed on the other side is dynamically adjusted to ensure that the inner and outer wheel speeds coordinate to meet the vehicle's yaw stability requirements. This method breaks through the defect of ignoring the dynamic coupling between wheels in the prior art, and effectively solves the problems of vehicle steering instability and driving imbalance caused by uncoordinated wheel speeds in distributed drive vehicles under conditions such as sharp turns and low adhesion.

[0050] 3) The dynamic constraint of slip rate is combined with the optimization algorithm to improve the anti-skid performance and longitudinal traction control ability of the vehicle in complex road environments. Specifically, the present invention designs a dynamic inequality constraint model for slip rate, calculates the wheel slip rate in real time, and dynamically sets the wheel speed control boundary in combination with the reference wheel speed and the dynamic bias correction term. The nonlinear relationship between the longitudinal force and the slip rate of the tire is accurately modeled through the tire magic formula to ensure that the slip rate control under different road adhesion conditions is always close to the optimal value. In addition, the overall optimization control unit solves the optimal control torque in real time through the MPC algorithm to achieve dynamic coordinated control of the slip rate and longitudinal force. This method significantly improves the vehicle's driving anti-skid ability and traction management ability in low-adhesion environments such as wet, slippery, ice and snow, and improves the safety and stability of the vehicle's driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is the overall control architecture diagram of the present invention;

[0052] Figure 2 It is a front-wheel steering and rear-wheel drive vehicle configuration of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0054] Embodiment 1: Longitudinal speed control method for a front-axle steering and rear-wheel distributed drive vehicle

[0055] A longitudinal control method for a front-axle steering and rear-wheel distributed drive vehicle comprises the following steps:

[0056] S1. Based on the vehicle's center of mass speed and yaw rate information, a wheel-to-wheel speed coupling constraint model is constructed to calculate the reference wheel speeds of the left and right rear wheels. The wheel-to-wheel speed constraint model is used to meet the vehicle's longitudinal speed control requirements in straight-line and turning conditions.

[0057] S2. Construct a driving wheel anti-skid control constraint model, determine the target slip range of the left and right driving wheels based on the tire slip control strategy, form an anti-skid inequality constraint, and reduce the time-varying nature of the inequality constraint by fixing the boundary conditions, thereby reducing the complexity of the optimization solution;

[0058] S3. Establish a dynamic model of the drive motors of the left and right rear wheels, form a nonlinear state space dynamic equation based on the relationship between the motor moment of inertia, the output torque and the tire longitudinal force, and linearize the nonlinear dynamic model at the reference wheel speed point to obtain an error state space discrete model;

[0059] S4. Construct an overall optimization control problem model, reconstruct the drive anti-slip constraint inequality into the input constraint of the state space optimization problem, combine the error state space equation, establish the optimization control problem under multiple constraints, and obtain the optimal control torque of the left and right drive wheels;

[0060] S5. Input the optimal control torque to the rear-wheel motor drive system, implement closed-loop feedback control based on the vehicle state sensor and state estimation algorithm, dynamically adjust the longitudinal speed and the left and right rear wheel speeds, implement high-precision tracking control of the vehicle's longitudinal speed, drive anti-skid control and electronic differential control, and improve the longitudinal driving stability and power response performance of the vehicle.

[0061] Specifically, the longitudinal speed control method involved in the present invention mainly consists of four parts: left and right wheel constraint construction, drive anti-skid constraint construction, drive wheel dynamics modeling and overall optimization control problem design. The overall control architecture is shown in the figure: Figure 1 shown.

[0062] In the inter-wheel constraint construction part, the expected values ​​of the left and right wheel speeds are calculated based on the known actual vehicle speed and actual yaw rate information, and reconstructed into an inter-wheel speed constraint relationship with coupling. In addition, in order to meet the speed tracking task, the existing coupling constraint relationship is improved to obtain the inter-wheel coupling constraint based on the reference speed.

[0063] In the construction of the drive anti-slip constraint, the optimal wheel slip ratio range is obtained through the slip ratio formula, that is, the wheel drive anti-slip inequality constraint. At the same time, considering the difficulty in solving the problem caused by the time-varying parameter of the actual wheel speed in the upper and lower limits of the constraint, it is changed to a non-time-varying parameter consisting of the reference wheel speed plus a known bias term to simplify the calculation.

[0064] In the driving wheel dynamics modeling part, the dynamics equation of the controlled object (i.e., driving wheel) is constructed based on the motor dynamics equation, and the ground tangential reaction force is calculated in combination with the magic formula tire model to obtain a nonlinear dynamics equation with the driving wheel speed as the state quantity. Furthermore, based on the left and right wheel reference speeds obtained in the inter-wheel constraint construction part, the reference speeds of the left and right driving wheels are calculated, and the nonlinear equation is linearized at the reference speed point. Finally, the error state space equation with the driving wheel speed error as the state quantity and the control torque error as the control quantity is obtained, and discretized to construct the driving wheel dynamics model.

[0065] In the design of the overall optimization control problem, the drive anti-slip constraint is first reconstructed to solve the mismatch problem between the established speed error state space equation and the drive anti-slip constraint state quantity. Secondly, the optimization control problem is designed by combining the existing driving wheel dynamics model and the drive anti-slip reconstruction constraint. Among them, the wheel speed error state space equation and the reconstructed drive anti-slip constraint are used as the equality and inequality constraints of the optimization control problem respectively.

[0066] Finally, the driving torque obtained by solving the optimization control problem is input to the actual front-wheel steering and rear-wheel drive vehicle, and the actual vehicle state quantity obtained is used as the feedback input of the controller to form the entire closed-loop control system.

[0067] 1. Construction of inter-wheel speed constraints

[0068] A schematic diagram of a front-wheel steering and rear-wheel drive vehicle according to the present invention is shown in FIG. Figure 2 As shown, CG represents the center of mass of the vehicle, B represents the wheelbase of the left and right wheels, and l f ,l r Respectively represent the distance from the front axle and rear axle of the vehicle to the center of mass, δ f represents the front axle steering angle of the vehicle, v c represents the velocity at the center of mass of the vehicle, w c represents the vehicle yaw rate, v l ,v r Represents the wheel speeds of the left and right wheels respectively.

[0069] Vehicle center of mass velocity v c and vehicle yaw rate w c It can be actually obtained by the state estimation / sensor measurement method. Based on the two known quantities, the expected wheel speed expression of the left and right wheels can be obtained as follows:

[0070]

[0071] The wheel speed relationship is further expressed as an inter-wheel coupling constraint:

[0072]

[0073] Among them, the expression of the desired wheel speed of the left wheel remains unchanged and is still calculated based on the vehicle's center of mass speed and the vehicle's yaw rate. The expression of the desired wheel speed of the right wheel is expressed as a function of the left wheel speed, and the left wheel speed is a measurable. This means that the right wheel considers the influence of the left wheel speed when rotating. Compared with the left and right wheels rotating independently at the desired wheel speed, this method of considering the coupling relationship between the left and right wheel rotations can better meet the left and right wheel speed requirements of the vehicle when turning, and effectively ensure the lateral and longitudinal stability of the vehicle during the turning process.

[0074] Preferably, in the coupling constraint (2), the desired wheel speed of the right wheel is formulated based on the actual wheel speed of the left wheel rather than the desired wheel speed. The advantage of such formulation is that even when the left wheel fails to reach the desired rotation speed, the right wheel can still satisfy the wheel speed coupling relationship during the steering process based on the actual wheel speed of the left wheel. Of course, the desired wheel speed of the right wheel can also be expressed as a function of the center of mass speed and the yaw rate, and the desired wheel speed of the left wheel can be expressed as a function of the actual wheel speed of the right wheel, depending on which of the left and right wheels has a worse driving performance, that is, the wheel with good driving performance ensures the stability of the vehicle during the steering process by "accommodating" the wheel with poor driving performance. Preferably, the driving performance of the left wheel is inferior to that of the right wheel.

[0075] Furthermore, the center of mass velocity and yaw rate in equation (2) are replaced by the reference center of mass velocity and reference yaw rate, and the expected speed target of the tracking vehicle in the speed tracking task is combined with the inter-wheel coupling constraint:

[0076]

[0077] Where, the reference center of mass velocity v c,ref is the expected speed target that the vehicle wants to achieve, which is a known quantity. The reference yaw rate can be obtained by the reference center of mass speed and the current road curvature c R Calculated:

[0078] w c,ref =v c,ref c R . (4)

[0079] 2. Driving Anti-slip Constraint Construction

[0080] During vehicle driving, the main goal of drive anti-skid control is to keep the slip rate of the vehicle tires close to the optimal slip rate to obtain better longitudinal control performance. The slip rate formula of the wheel is expressed as:

[0081]

[0082] Among them, λ i represents the slip rate of each wheel, R represents the tire radius of each wheel (preferably, the tire radius of all wheels is equal), ω i Indicates the rotation speed of the wheel.

[0083] During the operation of the vehicle, it is difficult for the tire slip rate to be always near the optimal slip rate. If the slip rate is kept near the optimal slip rate, a large control cost is required. Therefore, preferably, the control requirements of the drive anti-skid control are appropriately relaxed. The modified drive anti-skid control requirements require the slip rates of the left and right wheels to be between 0 and the optimal slip rate, that is:

[0084]

[0085] It is further transformed into the state quantity x = [ω l ,ω l ] T The driving anti-slip inequality constraint is:

[0086]

[0087] Among them, λ opt represents the optimal slip rate under the current road conditions, usually 20%-30%. In addition, note that the upper and lower limits of the state quantity x here include the actual wheel speeds of the left and right wheels, making the constraint a time-varying constraint, which may be difficult to solve in the actual calculation and execution process. Therefore, the time-varying terms in the upper and lower limits of the inequality constraint are changed to the reference speed plus a pre-set deviation term:

[0088]

[0089] According to formula (8), formula (7) can be rewritten as:

[0090]

[0091] Preferably, the expected deviation term is 0.1 times the reference speed, which means that the actual speed cannot exceed 1.1 times the reference speed at most and cannot be less than 0.9 times the reference speed at least. The size of the deviation term is selected taking into account the fact that the actual speed and the reference speed of the autonomous driving vehicle will not differ too much in speed tracking. The wheel speed reference value here is not based on the coupling constraint (3) because there is also a time-varying term v in the coupling constraint. l , if this constraint is adopted, it will go against the original intention of changing the upper and lower limits of the constraints.

[0092] 3. Driving wheel dynamics modeling

[0093] When the vehicle is driving, the torque required for wheel rotation is provided by the drive motor. The dynamic equation of the drive wheel can be written based on the motor equation as follows:

[0094]

[0095] Among them, T eiRepresents the output torque of the motor, that is, the torque required by the wheels, J i Indicates the motor's moment of inertia, B mi Represents the viscous friction resistance coefficient of the motor, T Li Represents the load torque, including the torque formed by the tangential reaction force of the ground and the rolling resistance torque;

[0096] The motor dynamics equation expressed in the above formula can be written in the form of the following equation:

[0097]

[0098] Among them, F xl ,F xr They represent the ground tangential reaction force on the left and right driving wheels, R represents the wheel radius, f represents the rolling resistance coefficient, and F zl ,F zr They represent the vertical loads on the tires. Ignoring the lateral load transfer of the vehicle and assuming that the vertical loads on the four wheels are equal, the vertical loads on the two driving wheels can be written as:

[0099]

[0100] Among them, m is the mass of the vehicle and g is the gravity constant.

[0101] The tangential reaction force on the driving wheel is related to the wheel slip rate and various tire parameters. The magic formula tire model can be used to fit the tangential reaction force curve under different slip rates. The formula can be expressed as follows (taking the left driving wheel as an example):

[0102]

[0103] In the formula, B x ,C x ,D x ,E x They represent stiffness factor, curve shape factor, curve peak factor, and curve curvature factor, respectively. h ,S v Represent the horizontal and vertical drift of the curve respectively. The specific values ​​of each parameter can be found in the literature: Wu Xiaoshuai. Research on the Stability Control Strategy of Four-Wheel Steering-Drive Electric Vehicles[D]. Northeastern University, 2022. The derivation of the right drive wheel is the same as that of the left drive wheel, which will not be repeated here.

[0104] Since the tangential reaction force is a nonlinear function of the slip rate, and the slip rate is related to the wheel speed, combined with the specific expression, it is easy to get that the tangential reaction force is a nonlinear function of the wheel speed. l ,ω r ] T is the state quantity, u=[T el,T er ] T is the control quantity, and the reference value x of the state quantity ref =[ω l,ref ,ω r,ref ] T The Taylor expansion method is used to linearize the obtained:

[0105]

[0106] Reference value x of the state quantity ref =[ω l,ref ,ω r,ref ] T The slip rate formula can be used to calculate the optimal slip rate based on the reference vehicle speed and the left and right drive wheels:

[0107]

[0108] The reference quantity x ref ,u ref Substituting into (15) and subtracting from (16), we obtain:

[0109]

[0110] in:

[0111]

[0112] Among them, a 11 ,a 22 The specific expression is:

[0113]

[0114] In addition, we additionally introduce the derivative of the reference speed in equation (18): Since we hope that the vehicle can maintain a stable and uniform speed during driving, we set both of them to 0. The reference value of the input torque T el,ref ,T er,ref It can be expressed as the ideal torque at the optimal slip rate, that is, the optimal slip rate λ opt Substituting into (14), the reference speed ω l,ref ,ω r,ref and the derivative of the reference speed Substitute into (11) and combine with (12) to calculate.

[0115] Furthermore, the speed error e = [ω l -ω l,ref ,ω t -ω r,ref ] T is the new state quantity, torque error is the new control variable, simplifying (18) into the standard form of the state space equation:

[0116]

[0117] in:

[0118]

[0119] The forward Euler method is used to discretize equation (19), and the discretization is performed at intervals of time T, and the discrete time state space equation is obtained as follows:

[0120]

[0121] in:

[0122]

[0123] 4. Design of Optimal Control Problem

[0124] In the optimization problem design stage, the calculation results of the previous stages are integrated, and the driving wheel dynamics model, inter-wheel coupling constraints, and driving anti-slip constraints are taken into consideration at the same time to form an overall optimization control solution.

[0125] First, since a non-time-varying inequality constraint on wheel speed is established in the construction of the driving anti-skid constraint, but a dynamic equation with wheel speed error as the state is established in the driving wheel dynamics modeling part, it is necessary to transform the driving anti-skid constraint in one step. Combining constraint (9) and reference wheel speed calculation formula (17), the reconstructed constraint is:

[0126]

[0127] Secondly, the control algorithm is designed using the optimization control framework, and the cost function including the state error and control input is designed:

[0128]

[0129] Since we hope that the final state error converges to 0, we design e ref (k+i) = 0. Q and R are weight matrices, indicating the degree of bias of the cost function towards the two components.

[0130] Finally, combining the calculated wheel coupling constraints, drive anti-slip constraints and motor model, the overall optimization control problem is obtained as follows:

[0131]

[0132] In the formula, e represents the speed error, Indicates the upper and lower limits of the error after speed constraint reconstruction.

[0133] The optimization control problem expressed in formula (25) includes three sub-problems: speed tracking, drive anti-skid, and inter-wheel coupling constraint. It is an integrated control method. Among them, the speed tracking problem is converted into the speed tracking problem of the left and right wheels through the three-step conversion process of the center of mass reference speed-the left and right wheel reference wheel speed-the left and right wheel reference speed. In the conversion process of the center of mass reference speed-the left and right wheel reference wheel speed, the inter-wheel coupling constraint is introduced to realize the combination of the speed tracking problem and the inter-wheel coupling constraint. The drive anti-skid control is expressed as the inequality constraint in formula (25), so that the slip rate of the left and right drive wheels is controlled within an ideal range to avoid slipping during vehicle driving.

[0134] Example 2: Longitudinal control system of a front-axle steering and rear-wheel distributed drive vehicle

[0135] Based on Embodiment 1: A longitudinal speed control method for a front-axle steering and rear-wheel distributed drive vehicle, this embodiment provides a longitudinal control system for a front-axle steering and rear-wheel distributed drive vehicle, the system structure comprising:

[0136] Vehicle status acquisition unit: uses high-precision vehicle speed sensor, yaw rate sensor and wheel speed sensor to collect vehicle center of mass speed, yaw rate and left and right rear wheel speed in real time; the collected information is transmitted to the controller via the CAN bus.

[0137] Preferably, wheel speed sensors are respectively arranged on the left and right rear wheels and the front wheel, and the yaw rate sensor collects the yaw angular velocity of the vehicle.

[0138] It is further preferred to equip the system with a high-speed A / D conversion module and a CAN bus communication module to ensure low-latency transmission of collected data. The collection frequency is not less than 1kHz to ensure the real-time and accuracy of the data.

[0139] Wheel speed reference calculation unit: inputs vehicle state information and calculates the expected wheel speeds of the left and right driving wheels based on formula (1), formula (2) and formula (3);

[0140] This module includes a speed reference generator and an inter-wheel coupling constraint modeler, which dynamically updates the wheel speed reference value based on the current steering angle and road curvature. The inter-wheel coupling coordination module monitors the difference in wheel speeds of the left and right rear wheels in real time, and dynamically adjusts the reference wheel speed of the other side according to the actual wheel speed of the wheel with poor driving performance, specifically satisfying formulas (2) and (3).

[0141] Preferably, if the left wheel performance is poor, the right wheel speed reference value is dynamically adjusted according to the left wheel actual wheel speed, and vice versa.

[0142] Drive anti-skid constraint generation unit: used to establish the dynamic model of the left and right rear wheel drive motors. The model is based on the motor rotation inertia parameters and the tire longitudinal force model to form the nonlinear state space dynamics of the left and right rear wheels, describe the input wheel speed information and tire model parameters, calculate the wheel slip rate, and satisfy formula (5);

[0143] The slip rate control range (Formulas (6) to (9)) is dynamically set to achieve multi-level constraints on slip rate and wheel speed.

[0144] Driving wheel dynamics modeling unit: Based on the vehicle dynamics model, the state space model of the left and right rear wheel driving motors is established;

[0145] The motor dynamics formulas (10) to (13) and the tire magic formula (14) are used to describe the relationship between the tire longitudinal force and the wheel speed error;

[0146] The error state space model (Formulas (18), (19), (20)) is obtained through linearization processing.

[0147] Overall optimization control unit: executes the model predictive control (MPC) algorithm, constructs the objective function using formula (24), and solves the optimal control torque through the optimization problem (formula (25));

[0148] Dynamically adjust the optimization window and control constraints to optimize longitudinal speed and slip rate in real time.

[0149] Motor execution unit: receives the control torque signal output by the control unit and drives the left and right rear wheel independent motors;

[0150] The motor controller adjusts the output torque in real time to ensure precise control of the vehicle's longitudinal speed and electronic differential.

[0151] Preferably, the motor controller interface of the motor execution unit supports real-time PWM modulation to achieve closed-loop control of the motor current; the control frequency reaches 10kHz to ensure that the execution torque is synchronized with the control instruction in real time.

[0152] Further preferably, a redundant safety management module is included, which includes functions such as motor redundant current protection, abnormal signal cutoff, over-temperature protection, etc., to ensure system stability and safety.

[0153] Closed-loop feedback unit: collects the current driving status of the vehicle, feeds it back to the optimization control unit, corrects the control instructions in real time, and realizes closed-loop control.

[0154] Preferably, the closed-loop feedback unit includes a state observation and estimation module and a dynamic feedback adjustment module.

[0155] Among them, the state observation and estimation module collects the vehicle's current longitudinal speed, left and right wheel speeds and yaw rate in real time, and estimates the state quantity in combination with the Kalman filter algorithm to suppress the influence of sensor noise.

[0156] The dynamic feedback adjustment module adjusts the controller model parameters and optimization algorithm weight matrix in real time based on the deviation between the observed state and the system reference target. It supports dynamic adjustment of the control mode under different working conditions and improves the system adaptability.

[0157] Through the above system, the integrated design of vehicle longitudinal speed control, drive anti-skid control and electronic differential control can be achieved, which can significantly improve the longitudinal control performance and dynamic response capability of the autonomous driving vehicle.

[0158] In summary, the integrated control architecture of the present invention eliminates the control delay and precision loss caused by system decoupling, and improves the longitudinal control performance and the dynamic response capability of the whole vehicle. The present invention integrates the three functions of vehicle speed tracking control, drive anti-skid control and electronic differential control into an overall control framework by constructing a unified longitudinal control optimization problem (see S4 and formulas (24) and (25)). Through the model predictive control (MPC) algorithm, the wheel speed error state space model (formulas (18) to (20)) and the anti-skid inequality constraints (formulas (6) to (9)) are uniformly solved to achieve coordination and optimization of multiple control objectives. This integrated control strategy avoids the calculation delay and control precision loss caused by hierarchical or decoupled control in the traditional control architecture, significantly improves the real-time and accuracy of the vehicle longitudinal speed control, and is particularly suitable for the longitudinal control requirements of autonomous driving vehicles under complex working conditions.

[0159] Secondly, the dynamic coupling constraint of wheel speed realizes the coordinated control of the left and right wheels under steering conditions, effectively ensuring the longitudinal stability and driving balance of the vehicle when turning. The present invention constructs a wheel speed coupling constraint model (see step S1 and formulas (1) to (3)), dynamically calculates the reference wheel speeds of the left and right rear wheels according to the vehicle's center of mass speed and yaw rate, and introduces the coupling control relationship between the left and right wheel speeds. In addition, by dynamically selecting the wheel with poor driving performance as the reference, the wheel speed on the other side is dynamically adjusted to ensure that the inner and outer wheel speeds coordinate to meet the vehicle's yaw stability requirements. This method breaks through the defect of ignoring the dynamic coupling between wheels in the prior art, and effectively solves the problem of vehicle steering instability and driving imbalance caused by uncoordinated wheel speeds in distributed drive vehicles under conditions such as sharp turns and low adhesion.

[0160] Finally, the slip rate dynamic constraint is combined with the optimization algorithm to improve the anti-skid performance and longitudinal traction control ability of the vehicle in complex road environments. The present invention designs a slip rate dynamic inequality constraint model (see step S2 and formulas (5) to (9)), calculates the wheel slip rate in real time, and dynamically sets the wheel speed control boundary in combination with the reference wheel speed and the dynamic bias correction term. The nonlinear relationship between the tire longitudinal force and the slip rate is accurately modeled through the tire magic formula (formula (14)), ensuring that the slip rate control under different road adhesion conditions is always close to the optimal value. In addition, the overall optimization control unit solves the optimal control torque in real time through the MPC algorithm to achieve dynamic coordinated control of the slip rate and longitudinal force. This method significantly improves the vehicle's driving anti-skid ability and traction management ability in low-adhesion environments such as wet, slippery, ice and snow, and improves the safety and stability of the vehicle's driving.

[0161] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A longitudinal control method for a front-axle steering and rear-wheel distributed drive vehicle, characterized in that: The steps include: S1. Based on the vehicle's center of mass speed and yaw rate information, a wheel-to-wheel speed coupling constraint model is constructed to calculate the reference wheel speeds of the left and right rear wheels. The wheel-to-wheel speed constraint model is used to meet the vehicle's longitudinal speed control requirements in straight-line and turning conditions. S2. Construct a driving wheel anti-skid control constraint model, determine the target slip range of the left and right driving wheels based on the tire slip control strategy, form an anti-skid inequality constraint, and reduce the time-varying nature of the inequality constraint by fixing the boundary conditions, thereby reducing the complexity of the optimization solution; S3. Establish a dynamic model of the drive motors of the left and right rear wheels, form a nonlinear state space dynamic equation based on the relationship between the motor moment of inertia, the output torque and the tire longitudinal force, and linearize the nonlinear dynamic model at the reference wheel speed point to obtain an error state space discrete model; S4. Construct an overall optimization control problem model, reconstruct the drive anti-slip constraint inequality into the input constraint of the state space optimization problem, combine the error state space equation, establish the optimization control problem under multiple constraints, and obtain the optimal control torque of the left and right drive wheels; S5. Input the optimal control torque to the rear-wheel motor drive system, implement closed-loop feedback control based on the vehicle state sensor and state estimation algorithm, dynamically adjust the longitudinal speed and the left and right rear wheel speeds, implement high-precision tracking control of the vehicle's longitudinal speed, drive anti-skid control and electronic differential control, and improve the longitudinal driving stability and power response performance of the vehicle.

2. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 1, characterized in that: The wheel speed coupling constraint model adopts dynamic coupling control, and the reference wheel speed of the right wheel is dynamically adjusted according to the actual wheel speed of the left wheel, satisfying the following formula In the formula, v l ,v r Represents the wheel speed of the left and right wheels respectively; reference center of mass speed v c,ref is the expected speed target that the vehicle hopes to achieve, which is a known quantity; v l,ref Left wheel reference speed, v r,ref Right wheel reference speed; v c,ref The reference speed at the center of mass of the vehicle; w c,ref Vehicle reference yaw rate, w c,ref =v c,ref c R , current road curvature c R .

3. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 1 or 2, characterized in that: The target slip rate range is from 0 to the optimal slip rate, where the optimal slip rate is dynamically adjusted based on the road adhesion conditions and is between 20% and 30%.

4. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 3, characterized in that: The driving anti-skid constraint is constructed by calculating the optimal slip rate range of the wheel according to the slip rate formula to form the driving anti-skid constraint. In the formula, x is the state quantity, ω l ,ω r Represents the rotation speed of the left and right wheels respectively, λ l , r Respectively represent the slip rate of the left and right wheels, λ opt It represents the optimal slip rate under the current road conditions, and R represents the tire radius of each wheel.

5. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 2, characterized in that: The driving wheel dynamics modeling is based on the motor dynamics equation, and combined with the magic formula tire model to model the tangential reaction force of the wheel, which is linearized to obtain the dynamics equation of the driving wheel. In the formula, the speed error e=[ω l -ω l,ref ,ω r -ω r,ref ] T , torque error is the new control quantity; In the formula, B x ,C x ,D x ,E x They represent stiffness factor, curve shape factor, curve peak factor, and curve curvature factor, respectively. h ,S v Represent the horizontal and vertical drift of the curve respectively.

6. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 5, characterized in that: The tire model is based on the magic formula, and the model parameters include stiffness factor, shape factor, peak factor and curvature factor, which meet 7. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 6, characterized in that: The nonlinear dynamic model is expanded by the first order Taylor at the reference wheel speed points of the left and right wheels to obtain the linear discrete state space model of the control torque error and wheel speed error.

8. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 1, characterized in that: The closed-loop feedback control updates the longitudinal speed, yaw rate and left and right wheel speed information in real time based on the vehicle state sensor and state estimation algorithm, and dynamically adjusts and optimizes the control input and constraints.

9. The longitudinal control method of a front-axle steering and rear-wheel distributed drive vehicle according to claim 8, characterized in that: The overall optimization control problem design combines all the above parts through the optimization framework, updates the drive anti-slip constraints, and forms an overall optimization control problem, that is, In the formula, e represents the speed error, Indicates the upper and lower limits of the error after speed constraint reconstruction.

10. A longitudinal control system for a front-axle steering and rear-wheel distributed drive vehicle, characterized in that: include: The vehicle status acquisition unit is used to acquire key status information such as vehicle center of mass speed, yaw rate, left and right rear wheel speeds, and longitudinal acceleration, and output the status information in real time; A wheel speed reference calculation unit, used to calculate the target reference wheel speeds of the left and right rear wheels based on the vehicle's center of mass speed and yaw rate information, wherein the wheel speed calculation satisfies a preset wheel speed coupling model to form a dynamic coordinated control reference for the left and right rear wheel speeds; The driving anti-skid constraint unit is used to generate a driving wheel slip rate target interval according to the tire slip rate model, and determine the upper and lower limits of the anti-skid control to form dynamic constraint parameters of the left and right rear wheel speeds and slip rates; A driving wheel dynamics modeling unit is used to establish a dynamics model of the left and right rear wheel driving motors. The model is based on the motor rotation inertia parameters and the tire longitudinal force model to form a nonlinear state space dynamics description of the left and right rear wheels; An optimization control unit, used for inputting the state space model and the driving anti-slip constraint into an overall optimization control algorithm, performing model predictive control optimization calculation, and outputting the optimal control torque of the left and right rear wheels; The motor execution unit is used to receive the control torque signal output by the optimization control unit and drive the independent motors of the left and right rear wheels to achieve differential control and longitudinal drive control of the left and right rear wheels; The feedback control unit is used to correct the control parameters and control instructions in real time based on the feedback data from the vehicle status acquisition unit, so as to ensure high-precision tracking control of the longitudinal speed and wheel speed, as well as the longitudinal stability and dynamic response performance of the whole vehicle.

Images