28 results about "Slip angle" patented technology

A vehicle stability control method and device, vehicle, equipment and computer readable storage medium. In the method, vehicle operating parameters are obtained, including vehicle steering wheel angle signal, vehicle speed, lateral and longitudinal acceleration signals and yaw rate signal; According to the steering wheel angle signal, lateral and longitudinal acceleration signals and yaw rate signal, the mass center side slip angle offset is obtained; According to the mass center side slip angle offset, lateral and longitudinal acceleration signals and yaw rate signal, the target lateral correction torque is determined; According to the target lateral correction torque, the target active force is determined, and then the target active force is sent to the active suspension, so that the active suspension adjusts the wheel vertical load based on the target active force, which can quickly correct the vehicle driving posture, suppress the instability trend, and effectively improve the vehicle driving stability and safety.

A steering angle estimation device of an electric steering system. A first operator calculates a yaw rate. a second operator calculates an average value of rear wheel speeds. A Kalman filter predicts a yaw acceleration by filtering the yaw rate. A third operator estimates a steering angle also taking into account a slip angle by using the yaw rate and the yaw acceleration. A weight adjuster adjusts weights of the wheel speeds by averaging rear right and left wheel speeds calculated by the second operator. A multiplication operator multiplies weight values output by the weight adjuster with the steering angle. A steering angle conversion switch outputs a final steering angle by selecting the estimated steering angle output from the multiplication operator or a motor steering angle measured from a motor of a motorized power steering system by the weight values.

Disclosed in processing circuitry for a vehicle. The processing circuitry is configured to obtain, for at least two wheels of the vehicle, a slip ratio and a slip angle. The slip ratio has a non-linear relationship to the wheel rotational speed and the wheel centre point velocity. The slip angle has a non-linear relationship to the wheel centre point velocity. The processing circuitry is configured to obtain, for each wheel, a wheel longitudinal force and a wheel lateral force. The wheel longitudinal force has a linear dependence on the slip ratio. The wheel lateral force has a linear dependence on the slip angle. The processing circuitry is configured to determine a target yaw rate of the vehicle based on the slip ratio, the slip angle, the wheel longitudinal force, and the wheel lateral force for each wheel.

A four-wheel independent steering dynamic control method and system belong to the field of automobile chassis control, including obtaining vehicle running state parameters and tire state parameters of each wheel; determining the working area of each wheel tire based on the tire magic formula, the working area at least includes linear area, nonlinear transition area and saturation area; the product of tire side slip angle absolute value and road adhesion coefficient and vertical load is taken as an input variable, input to a preset fuzzy controller to obtain a steering switching weight coefficient; based on the steering switching weight coefficient, the control parameters of the steady-state steering mode and the control parameters of the oblique steering mode are weighted and fused to obtain target steering control parameters; based on the target steering control parameters, the four-wheel independent steering actuator is controlled to steer. The present application can switch steering mode in real time according to tire side slip state, ensure that the tire is always in the linear area, realize smooth dynamic switching of steering mode, avoid the wheel reaching the side slip limit, and improve the driving stability and safety of the vehicle.

This invention relates to the field of steer-by-wire technology, and more particularly to a steer-by-wire control method and redundant braking device compatible with braking function. The control method includes the following steps: S1: Obtain vehicle current operating status information and braking demand information. S2: If the longitudinal braking force provided by the failed wheel does not meet the braking expectation. S3: Obtain the lateral force of the failed wheel based on the current wheel load, the previous moment's wheel angle of the failed wheel, and the tire slip angle information. S4: Solve for the minimum wheel angle of the failed wheel. S5: Construct an optimization objective function and set constraints to calculate the target wheel angles. S6: Send the target wheel angles to the steering actuators of each wheel to achieve toe-in braking control under braking failure conditions. The advantage of this invention is that it fully utilizes the four-wheel independent steering and large-angle steering capabilities of the corner module vehicle, reconstructs the vehicle's braking capability through steering control, and improves the system redundancy utilization rate.

The application relates to the technical field of vehicle control, and discloses a control method and system for an active front wheel steering system based on an LQR algorithm, wherein the control method estimates a vehicle mass center side slip angle in real time through a Kalman filter observer, combines a two-degree-of-freedom vehicle dynamics model to construct a state space equation, and adopts an LQR controller to calculate an optimal front wheel steering angle compensation amount; the system compares the deviation of actual and ideal yaw angular velocities and mass center side slip angles, dynamically adjusts the front wheel steering angle, and realizes high-precision trajectory tracking and stable control of the vehicle under complex dynamic working conditions. The application has the advantages of high control precision, fast response, strong anti-interference capability and low energy consumption, is suitable for improving the vehicle control stability and driving safety, and is especially suitable for intelligent driving and an advanced auxiliary driving system.

The application discloses an AFS, ARS and DYC cooperative control method of a distributed electric drive vehicle based on DDPG. The method utilizes a cooperative control method and a deep reinforcement learning method, designs and develops a vehicle stability control method based on a centroid side slip angle and a yaw angular velocity, takes driving signals provided by a driver and measured or estimated vehicle state information as input, and judges the stability of the vehicle. When the active safety system needs to intervene, a DDPG-AAD model trained offline by a strategy deep gradient algorithm is used to coordinately control AFS, ARS and DYC systems, precise and independent control of each wheel is realized, and thus the active safety driving stability of the vehicle is improved.

The application relates to the technical field of vehicle dynamics control, and discloses a data-driven deep learning vehicle motion state estimation method and system, which comprises the following steps: inputting collected vehicle multi-source sensor data into a pre-constructed TCN-SA-GRU model; and using a whale optimization algorithm to optimize the hyperparameters of the TCN-SA-GRU model, wherein the TCN-SA-GRU model integrates the parallel computing and multi-scale time feature extraction capability of a time convolution network (TCN), the feature extraction capability of a self-attention (SA) mechanism and the time sequence processing capability of a gated recurrent unit (GRU), processes the vehicle sensor data, and obtains vehicle motion state estimation results. The application can realize dynamic and accurate estimation of key motion states such as a vehicle side slip angle and a yaw angular velocity.

Processing circuitry for a vehicle configured to: obtain for at least two wheels a slip ratio 202 and a slip angle 204. Obtain for each wheel, a wheel longitudinal force 206 and a wheel lateral force

The application discloses a vehicle lateral velocity estimation method and device. A fusion weight is determined according to a tire side slip angle of a vehicle. The fusion weight comprises a first weight corresponding to a tire model-based dynamics method and a second weight corresponding to a sensor-based kinematics method. The lateral velocity of the vehicle is estimated according to the fusion weight and motion parameters of the vehicle by using the tire model-based dynamics method and / or the sensor-based kinematics method, and a final estimation result of the lateral velocity is obtained. According to the embodiment of the application, the fusion of the tire model-based dynamics method and the sensor-based kinematics method is realized, the problem that the result is inaccurate due to errors of a tire model and intermediate estimation quantity when only the tire model-based dynamics method is used for estimation under a large vehicle load change and a combined working condition is solved, and more accurate lateral velocity estimation is realized.

The present application relates to the technical field of formation control, and discloses a method and system for cooperative obstacle avoidance of electric motorcycle formation driving, the method comprising: extracting the risk characteristics of the electric motorcycle formation after non-Euclidean space reconstruction in a dynamic risk field, to obtain a characteristic state set; taking a leading electric motorcycle as a leader and a member electric motorcycle as a follower, performing non-cooperative game on the electric motorcycle formation, and obtaining a formation cooperative obstacle avoidance decision scheme by solving Nash equilibrium; the leading electric motorcycle performs time stamp pre-compensation on the formation cooperative obstacle avoidance decision scheme according to the packet loss rate prediction value of the communication channel; performing bidirectional verification on the electric motorcycle formation according to the compensation scheme, automatically triggering a local control emergency stabilization program when the actual side slip angle exceeds the failure protection threshold, and returning the imbalance information to the game constraint boundary; the present application can improve the efficiency of cooperative obstacle avoidance of electric motorcycle formation driving.

The application discloses a variable prediction time vehicle path tracking control method, system, device and medium, and relates to the technical field of path tracking. The method comprises the following steps: determining the current optimal prediction time according to the reference path curvature, the vehicle longitudinal speed and the road adhesion coefficient at the current time; determining the current discrete time step and the prediction time domain according to the current optimal prediction time; inputting the vehicle state at the current time into a vehicle state prediction model to output a vehicle state prediction time sequence; the vehicle state prediction time sequence comprises the vehicle state of each current discrete time step within the current prediction time domain; constructing a target function of path tracking; the constraint condition of the target function is determined according to the physical limit constraint of the front wheel steering angle, the front wheel steering angle increment constraint and the adaptive soft constraint of the front and rear wheel side slip angle within the control time domain; and the target function is optimized to obtain an optimized control time sequence. The application improves the accuracy of path tracking.

The application discloses a tire model construction method based on total shear force direction angle, and belongs to the technical field of tire model construction. The method first acquires tire total shear force direction angle test data, represents the variation law of the direction angle with slip rate in the form of inverse cosine trigonometric function, and constructs a combined trigonometric function as a basic expression. Secondly, the influence of the side slip angle, load and roll angle on the variation law of the total shear force direction angle is studied, the function relationship of each parameter with the working condition input is established, the total shear force direction angle modeling method based on symbol transformation is proposed, and the total shear force direction angle parameter identification of the full composite working condition is completed. Then, the pure working condition parameter identification and total shear force size prediction are completed based on the UniTire tire model, and finally the prediction of the tire longitudinal force and lateral force under the composite working condition is realized. The application effectively reduces the identification error caused by the data fluctuation of the composite working condition longitudinal force and lateral force, significantly improves the modeling precision and working condition adaptability, and provides key technical support for the virtual matching of the tire and the whole vehicle.

This invention discloses a vehicle control method, device, electronic device, and storage medium. The method includes: acquiring vehicle driving information corresponding to a target vehicle; the vehicle driving information includes tire side stiffness and tire side slip angle; determining a first adhesion coefficient based on the vehicle driving information and road surface parameters of at least one reference road; and determining the forces to be applied between the target vehicle and the current driving road surface based on the first adhesion coefficient, tire side stiffness, and tire side slip angle, so as to control the target vehicle's movement on the current driving road surface based on the forces to be applied. This solves the problem of poor control accuracy and low safety in existing technologies that rely on predictive control of vehicles based on the adhesion conditions of the current driving road surface, thereby improving the accuracy of vehicle predictive control and enhancing driving safety.

The application relates to the technical field of driving parameter estimation, in particular to a road adhesion coefficient estimation method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: collecting state information of a driving vehicle and obtaining a water film thickness; combining a three-degree-of-freedom vehicle model and a Dugoff tire model to obtain a slip rate, a tire side slip angle and a vertical load; adopting a double-layer adaptive cubature Kalman filter to obtain a first estimation value; inputting the water film thickness into a support vector regression model to output a second estimation value; then time-aligning the first estimation value and the second estimation value; calculating a residual error, a double-layer adaptive cubature Kalman filter confidence and a support vector regression model confidence; and obtaining a final road adhesion coefficient. Thus, the problem that related technologies do not effectively fuse the advantages of model-driven and data-driven methods, resulting in low precision, slow response and poor robustness in complex environments when the tire-road adhesion condition suddenly changes is solved.

The application discloses a vehicle driving control method and device and a vehicle, and applies to the technical field of vehicle control. The method comprises the following steps: in the case that the real-time vehicle speed of a target vehicle is greater than or equal to a vehicle speed threshold, acquiring the real-time yaw angular velocity and the real-time center of mass side slip angle of the target vehicle at the real-time vehicle speed; determining the proportional integral differential control parameter of the target vehicle according to the real-time yaw angular velocity and the real-time center of mass side slip angle; determining the target longitudinal force difference value of the target vehicle according to the proportional integral differential control parameter; and performing driving control on the target vehicle according to the target longitudinal force difference value. The application fully considers the two key factors of the yaw angular velocity and the center of mass side slip angle of the vehicle, realizes the driving control of the target vehicle under lateral disturbance, can comprehensively reflect the actual driving condition of the vehicle under lateral disturbance, reduces the yaw influence of the vehicle caused by lateral disturbance, and further effectively improves the control precision of the vehicle under lateral disturbance.

The present application relates to the field of vehicle dynamics control, especially to a kind of distributed mine truck stability coordination control method based on dynamic envelope boundary. Including: establishing dynamics model to generate mass center side slip angle-yaw rate phase plane, combined with equivalent front and rear axle limit side slip angle to construct dynamic stability envelope boundary, actual state point is mapped to phase plane and is determined to be located inside or outside boundary according to intersection position, if inside, then adopt model predictive control to solve target additional yaw moment, if outside, then calculate the shortest distance and rate of change of state point to boundary and solve the moment through improved particle swarm self-tuning PID controller, finally, through quadratic programming, longitudinal tire force of each wheel is optimized and distributed and torque is output.The present application can quantify the stability boundary and deviation degree of double rear axle mine truck, and effectively balance the steering flexibility and limit stability.

A front wheel-differential cooperative steering control method and system of a multi-axle distributed drive unmanned vehicle, the main steps of which include: obtaining the vehicle state and the desired path; based on the quadratic differential game algorithm of Nash equilibrium theory, calculating the optimal front wheel steering angle and differential steering yaw moment in the path tracking process of the unmanned vehicle to reduce the turning radius and increase flexibility; identifying the stability of the vehicle based on the phase plane of the center of mass side slip angle; using a weight control strategy based on fuzzy rules, the differential steering yaw moment and stability control yaw moment are coordinated and distributed according to the current stability of the vehicle. This method can effectively solve the balanced control of front wheel steering-differential steering and the coordinated control of path tracking performance-stability, and is suitable for multi-axle distributed drive unmanned vehicles that need to consider path tracking accuracy and driving stability.

In a travel assist method, a target lateral position of a front target distance at which a driver looks ahead is decided and a target slip angle for making a vehicle body face the target lateral position is calculated (S3), a target braking / driving force of front wheels and rear wheels is calculated based on the target slip angle (S5), at least one of a slip ratio of the wheels or a wheel speed difference between the front wheels and the rear wheels is calculated as a tire state quantity (S4), an allowable limit of the tire state quantity is set (S6), the target braking / driving force is generated to the front wheels and the rear wheels in a case where the calculated tire state quantity does not exceed the set allowable limit (S11), a distribution ratio or a total of the target braking / driving force is corrected in a manner such that the tire state quantity becomes below the set allowable limit in a case where the calculated tire state quantity exceeds the set allowable limit (S12-S14), the corrected target braking / driving force is generated to the front wheels and the rear wheels, and the correction of the target braking / driving force is suppressed in a case where the vehicle is in a turn (S8).

This invention provides a method and apparatus for controlling vehicle torque, relating to the field of vehicle control technology. The method includes: acquiring a first state of the vehicle, the first state including a first lateral slip angle and a first vertical load for each of a plurality of wheels of the vehicle; and in each control cycle, performing the following steps: acquiring the road surface adhesion coefficient of the vehicle in the current control cycle; for each of the plurality of wheels, determining a first tire lateral force of the wheel based on the first lateral slip angle and the first vertical load using a segmented tire model; constructing a target cost function for a model predictive control strategy based on the first tire lateral force of each of the plurality of wheels and the road surface adhesion coefficient; and solving the target cost function to obtain the target torque of each of the plurality of wheels, so as to control the plurality of wheels to operate according to the target torque in the next control cycle.

The application discloses an auxiliary drift control method based on rear-wheel distributed drive and related equipment, and relates to the technical field of vehicles. The method comprises the following steps: when the drift intention of a driver is identified, the vehicle speed, the front-wheel steering angle, the mass center side slip angle and the yaw angular velocity of the vehicle under a current drift working condition are obtained; the reference value of the dynamic parameter of the vehicle under a drift equilibrium state is determined according to the vehicle speed and the front-wheel steering angle of the vehicle under the current drift working condition; the vehicle speed, the mass center side slip angle and the yaw angular velocity of the vehicle under the current drift working condition and the reference value of the dynamic parameter of the vehicle under the drift equilibrium state are input into a pre-designed model predictive controller for predictive optimization, so as to obtain the optimal rear axle longitudinal force control amount of the vehicle; and the left and right rear wheels of the vehicle are torque regulated according to the optimal rear axle longitudinal force control amount of the vehicle. The application realizes the stable control of the drift posture of the vehicle by adopting the rear-wheel distributed drive technology.

The application relates to a low-attachment road vehicle instability risk quantification method and device and a vehicle, wherein the method comprises the following steps: based on a preset Fiala tire model and a double-track two-degree-of-freedom vehicle model, calculating the longitudinal force and the lateral force of the vehicle to determine a front-rear side slip angle phase plane of the vehicle; based on the front-rear side slip angle phase plane, determining the stability region and the stability boundary of the front-rear wheel side slip angle state of the vehicle under different vehicle states and different attachment coefficients to determine the low-attachment road vehicle reference stability region and the reference stability boundary; based on the influence of the reference stability boundary and the road attachment coefficient on the stability boundary of the vehicle and the influence of the vehicle speed and the front wheel steering angle on the stability boundary, scaling the distance between the vehicle state and the origin to obtain a scaled vehicle state, and comparing the scaled vehicle state with the reference stability boundary to generate a low-attachment road vehicle stability margin index. Therefore, the problems in the prior art that the linear two-degree-of-freedom model has limitations, the state of the vehicle on the low-attachment road cannot be accurately described, the ice and snow road attachment coefficient changes, the vehicle model parameters have time variability, the instability risk size is affected, and the instability risk quantification method is complex and difficult to generalize are solved.

The application discloses a distributed drive electric vehicle driving stability control method, system and medium, the method is based on the control strategy of improved particle swarm optimization sliding mode control to coordinate distribution of the driving torque of the distributed drive electric vehicle, and then the vehicle reaches the expected yaw moment, and the vehicle is maintained to stably drive; further, the improved particle swarm optimization algorithm divides the particle population into regions, and proposes to take the regional weight as a reference index for dividing the particles, and each regional particle is updated and iterated at the same time, so that the convergence time of the fitness value is shortened; further, the two-degree-of-freedom vehicle model is used to calculate the expected yaw angular velocity and the mass center side slip angle when the vehicle drives, the error between the expected value and the actual value of the yaw angular velocity and the mass center side slip angle is collected respectively as the input of the stability controller, and through reasonable distribution of the driving torque of each wheel, the driving stability of the whole vehicle is improved.

Embodiments of the present application provide a vehicle trajectory tracking control method, device, equipment and medium under extreme conditions, the method comprising: when detecting that the vehicle enters the limit control mode, determining the reference trajectory information of the reference driving trajectory of the vehicle; optimizing the first reference information in the reference trajectory information according to the target optimization function constructed in advance, determining the optimized target trajectory information, the target optimization function comprising the constraint on the target control amount of the vehicle and the change of the center of mass side slip angle; determining the target control amount of the vehicle according to the target trajectory information and the second reference information in the reference trajectory information, in combination with the preset nonlinear dynamics model. By using the method, the improved nonlinear optimization method is used, that is, the constraint on the target control amount of the vehicle and the change of the center of mass side slip angle is used as the target optimization function to optimize the reference trajectory, so that the planned trajectory can be tracked as much as possible to meet the engineering needs on the premise of maintaining the stability of the vehicle body.

The application discloses a vehicle active front wheel steering and direct yaw moment cooperative control method, which firstly acquires vehicle state information and establishes an ideal yaw angular velocity and ideal mass center side slip angle reference model based on a two-degree-of-freedom vehicle model; secondly, a mass center side slip angle-mass center side slip angular velocity phase plane is established, and an instability index representing vehicle transient stability is calculated; then, a sliding mode control algorithm is adopted to calculate a total additional yaw moment required for maintaining vehicle stability; subsequently, according to the relationship between the instability index and a preset threshold, the total moment is decomposed into target moments of the active front wheel steering and the direct yaw moment through dynamic weight distribution; finally, the target moments are converted into front wheel active superimposed steering angles and four-wheel longitudinal force moments and are sent to corresponding actuators.

The present application belongs to the field of vehicle control technology, and relates to a kind of automobile motion control system based on angle module dynamics.System construction contains two degrees of freedom driver control system of steering wheel input and vehicle body posture lever input, for respectively representing path curvature tracking demand and vehicle body posture adjustment demand;Based on driver control input, ideal yaw rate control target and ideal mass side slip angle control target are constructed, and four-wheel steering angle feedforward control amount is determined through pure yaw motion and pure lateral motion vehicle kinematics;Through feedback optimization control, additional yaw torque and four-wheel steering angle control amount are carried out compound working condition coordinated control, based on working condition self-adaptive adjustment control weight and constraint, four-wheel drive / braking torque and four-wheel steering angle distribution control are realized.The present application realizes the decoupling control of vehicle yaw motion and lateral motion by constructing two degrees of freedom driver control system and combining feedforward kinematics and feedback dynamics control.