212 results about "Slip angle" patented technology

The invention discloses a vehicle instability active compensation method based on an angle module and related equipment, and the method comprises the steps: responding to a received steering instruction for a target vehicle, and determining a target yaw velocity and a target slip angle for the target vehicle according to the steering instruction; driving data and tire force constraint conditions of the target vehicle are obtained, and the tire force increment of the target wheel end of the target vehicle is determined according to the target yaw velocity, the target slip angle, the driving data and the tire force constraint conditions; wherein the tire force constraint condition is determined according to an adhesion coefficient estimation value of a road surface where the target vehicle is located; and controlling a target wheel end of the target vehicle to steer, brake or / and drive according to the tire force increment so as to correct a driving route of the target vehicle.

The application discloses a kind of distributed drive electric vehicle stability control method and system, desired centroid side slip angle and yaw angular velocity are calculated as control target according to vehicle dynamics model, actual centroid side slip angle and yaw angular velocity are estimated in real time by Kalman filtering.Further through stability judging law, vehicle state is identified using phase plane method.According to vehicle state, it is divided into: stable domain, coordination domain and non-stable domain.Further control target value and actual value are input into model reference adaptive controller, and the size of additional yaw moment and front wheel angle is respectively solved.The weight size of the two is calculated by the vehicle stability region determined in stability law.Additional front wheel angle can be directly input into vehicle actuator, if direct torque control is needed, additional yaw moment size needs to be input into torque distribution module for real-time solution, and finally left and right wheel torque value of front axle is output to vehicle actuator.

The invention provides a vehicle drifting control method, device and equipment, a computer readable storage medium and a computer program product. The method comprises the steps that based on target state information and actual state information of a vehicle, a first slip rate component and a second slip rate component are obtained, the first slip rate component is used for slip angle stability control, and the second slip rate component is used for yaw velocity control; determining a left rear wheel slip rate target and a right rear wheel slip rate target based on a basic slip rate target, the first slip rate component and the second slip rate component; and rotating speed calculation is conducted based on the actual state information, the left rear wheel slip rate target and the right rear wheel slip rate target, and the vehicle is controlled based on the obtained left rear wheel rotating speed target and the obtained right rear wheel rotating speed target. According to the invention, a driver can maintain stable drifting of the vehicle only by operating the steering wheel and the accelerator.

The invention discloses a vehicle kinetic parameter identification method based on constrained nonlinear optimization, which comprises the following steps: establishing a vehicle two-degree-of-freedom transverse kinetic model based on a small-slip-angle tire and a road cross slope, acquiring a vehicle historical driving data measurement value, acquiring a to-be-estimated vehicle kinetic parameter, and identifying the to-be-estimated vehicle kinetic parameter according to the to-be-estimated vehicle kinetic parameter. Constructing a constrained nonlinear optimization problem about the yaw velocity, solving to obtain an estimated value sequence of the kinetic parameters of the vehicle, carrying out statistics on the sequence, and taking a statistical median as a final estimated value; on one hand, a vehicle transverse dynamics model including a road cross slope is adopted; on the other hand, the constraint of vehicle parameters is considered, a nonlinear optimization problem is constructed, and the vehicle front axle distance (the longitudinal distance from a front axle to the vehicle mass center), the vehicle rear axle distance (the longitudinal distance from a rear axle to the vehicle mass center), the front wheel cornering stiffness, the rear wheel cornering stiffness and the vehicle total mass are identified at the same time.

The invention relates to the technical field of automobiles, and discloses a vehicle state estimation method and device, equipment and a storage medium. The method comprises the steps that vehicle state parameters of a vehicle under the current working condition are obtained; constructing a vehicle dynamics model according to the vehicle state parameters; performing transverse dynamic correction on the nonlinear dynamic model according to the tire force and the slip angle of the vehicle to obtain a corrected vehicle dynamic model; and carrying out adaptive Kalman filtering on the target state variable in the corrected vehicle dynamics model, and updating process noise information and measurement noise information by adopting a regularization method in the adaptive Kalman filtering process so as to obtain estimation information of the target state variable at the current moment. According to the embodiment of the invention, the vehicle state evaluation precision can be improved, and the filtering divergence in the vehicle state process can be suppressed.

The invention discloses an active steering and braking integrated control system based on roll angle feedback, and belongs to the technical field of vehicle dynamics control. In order to solve the problems of slow control response and multi-actuator action conflict of the vehicle under the limit working condition, the method quantifies the comprehensive instability risk of the whole vehicle by constructing a dynamic virtual potential field. The potential field is based on key state variables such as a roll angle and a side slip angle, and the topological structure (namely the weight of each variable) of the potential field is remodeled in real time by a dynamic risk factor reflecting the intention urgency degree of a driver and the environmental danger level. The system evaluates the instantaneous control efficiency by calculating the contribution degree of each actuator to the reduction of the potential field gradient in a distributed manner. According to the method, the complex control problem is converted into the process of finding the minimum potential energy point, prospective suppression of the instability trend and conflict-free efficient cooperation between the actuators are achieved, and the limit stability and safety of the vehicle are remarkably improved.

The invention relates to a vehicle steering control method and device, vehicle-mounted equipment, a vehicle, a medium and a product. The method comprises the following steps: acquiring an ideal Ackerman corner of each wheel in a vehicle in a current driving state; according to the ideal Ackerman rotation angle of each wheel and the current driving state, estimating the side slip angle of each wheel to obtain an estimated side slip angle of each wheel; determining whether the vehicle is in a high-speed steering state according to the current driving state; determining a target steering angle according to whether the vehicle is in a high-speed steering state, the estimated slip angle of each wheel and the ideal Ackerman steering angle of each wheel; and controlling each wheel of the vehicle according to the target steering angle. By adopting the method, the deviation of the ideal Ackerman angle in the steering process can be compensated by estimating the slip angle, so that the handling stability and the steering precision of the vehicle can be improved, and the stability and the safety of the vehicle in the running process are further improved.

The invention discloses a distributed steering control method, and belongs to the technical field of vehicle steering. The control method comprises the following steps: acquiring real-time driving data of a vehicle, wherein the real-time driving data comprises a steering wheel angle, a vehicle speed, a road adhesion coefficient and a side slip angle; determining a target Ackerman rate according to the real-time driving data; and controlling the wheels of the vehicle according to the target Ackerman rate and the real-time driving data. According to the real-time driving data of the vehicle, the Ackerman rate is determined, the vehicle turning angle control value is determined according to the Ackerman rate determined in real time and the real-time driving data, and then vehicle wheels are controlled according to the vehicle turning angle control value. Namely, the Ackerman rate of the vehicle in the driving process can adapt to different real-time driving data of the vehicle, the real-time driving data of the vehicle is fully considered, the control stability of the vehicle in key scenes such as high speed, low attachment and low speed is effectively improved, the driving burden of a driver is relieved, and the risk that the vehicle is unstable or even out of control is reduced.

The invention provides a side slip angle estimation method based on an intelligent tire and related equipment, and relates to the field of vehicle motion state estimation.The method comprises the steps that an on-line circulation estimation process is carried out, and the one-time process comprises the steps that the friction coefficient dynamic adjusting amount in the last estimation process is obtained; acquiring a current first friction coefficient output by the intelligent tire system and a corresponding confidence coefficient; outputting a second friction coefficient at the current time according to a second friction coefficient, the friction coefficient dynamic adjusting quantity, the first friction coefficient and the corresponding confidence coefficient which are output by the self-adaptive Kalman filtering last time through the self-adaptive Kalman filtering; and inputting the chassis signal and the second friction coefficient into a nonlinear observer to obtain an estimated value of the side slip angle of the vehicle. A new friction coefficient information source is provided for side slip angle estimation, the side slip angle is estimated through friction coefficients of multiple sources, and decoupling estimation is achieved.

The invention relates to a low-adhesion road surface automobile instability risk quantification method and device and a vehicle, and the method comprises the steps: calculating the longitudinal force and lateral force of the vehicle based on a preset Fiala tire model and a double-track two-degree-of-freedom vehicle model, so as to determine a front and rear slip angle phase plane graph of the vehicle; based on the front and rear slip angle phase plane graphs, determining stable areas and stable boundaries of front and rear wheel slip angle states of the vehicle under different vehicle states and different attachment coefficients so as to determine a low attachment road surface reference stable area and a reference stable boundary of the vehicle; on the basis of the influence of the reference stable boundary and the road adhesion coefficient on the stable boundary of the vehicle and the influence of the vehicle speed and the front wheel rotation angle on the stable boundary, the distance between the state of the vehicle and the original point is scaled to obtain the scaled vehicle state, and the scaled vehicle state is compared with the reference stable boundary to generate the vehicle stability margin index of the low adhesion road. Therefore, the problems that the state of the vehicle on the low-adhesion road surface and the change of the adhesion coefficient of the ice and snow covered road surface are difficult to accurately describe due to the limitation of a linear two-degree-of-freedom model, the vehicle model parameters have time-varying characteristics, the instability risk is influenced, and the instability risk quantification method is complicated and difficult to generalize in the related technology are solved.

The invention belongs to the field of vehicle trajectory tracking control, and discloses an intelligent vehicle trajectory tracking control method and system based on differential programming, and the method comprises the steps: constructing a vehicle dynamics model; a reference trajectory time-varying curvature is introduced, delay of a steering execution mechanism is expressed as pure delay plus a first-order inertia link, and construction of a road tracking model is completed; assuming that the system is in a steady state, calculating reference values of a state quantity and a control quantity; constructing an MPC optimization problem, converting the MPC optimization problem into a QP problem, and calculating to obtain an MPC loss value; constructing a differential programming training calculation network diagram; a hypercube is used for sampling the state quantity of a vehicle, the driving speed and the time-varying reference items of a road, and construction of a network training initial state data set is completed; carrying out MPC trajectory tracking task integration, and constructing a physical model in network training; using a full-link layer network, adding tire slip angle soft constraints, completing the construction of a strategy network, carrying out end-to-end network training, and completing the control of a turning angle instruction. According to the method, the MPC online solving optimization problem can be avoided.

The invention relates to the technical field of active safety control of intelligent automobiles, in particular to a multi-source fusion estimation method and system for a side slip angle and a road adhesion coefficient, and aims to realize step-by-step estimation of the side slip angle and the road adhesion coefficient through a cascade nonlinear observer architecture, solve the problem of joint estimation caused by dynamic coupling of the side slip angle and the road adhesion coefficient, and improve the safety of the side slip angle and the road adhesion coefficient. Based on an estimation result of a cascade nonlinear observer, a joint estimation method for the side slip angle and the road adhesion coefficient based on an exogenous Kalman filtering framework is provided, the problem that stability and optimality / suboptimum are difficult to consider in a traditional method is solved, lane line information is recognized through visual recognition, and a vehicle kinematics model is combined, so that the road adhesion coefficient is estimated. According to the method, a Kalman filter of the vehicle side slip angle is constructed to output expectation and variance, so that the uncertainty of visual estimation of the side slip angle is quantified, meanwhile, after visual-based road surface classification, the expectation and variance of a visual estimation road surface adhesion coefficient are obtained by adopting a Bayesian method, and subsequent multi-source information fusion is facilitated.

The invention provides a vehicle dynamic stability control method based on reinforcement learning and variable pole assignment, and relates to the field of vehicle dynamics control. According to the method, the stability state and the energy evolution trend of the vehicle are recognized in real time by constructing a multi-dimensional criterion of a side slip angle phase plane and an energy phase plane; a reinforcement learning agent is utilized to make an expected closed-loop pole position of a system according to a current state online decision, aggressive poles are configured under a stable working condition to improve the flexibility, and conservative poles are configured under an unstable working condition to guarantee the stability. Besides, a safety monitoring mechanism containing pole physical domain constraints is further designed, the dynamic characteristics of the vehicle are remodeled through feedback control on the premise that the steering intention of a driver is not changed, and the problem of real-time balance of flexibility and safety in intelligent chassis control is effectively solved.

The invention provides a lateral movement sensing method and a vehicle, the method is applied to the technical field of vehicle control, and the method comprises the steps that a tire slip angle value at the current moment is obtained in response to a target calculation instruction of the vehicle; calculating an actual lateral force value at the current moment based on the tire slip angle value and the current wheel slip rate of the vehicle; and calculating the actual lateral motion acceleration of the vehicle according to the actual lateral force value, and evaluating the axle load transfer amount in the vehicle steering process according to the actual lateral motion acceleration. According to the method, the actual lateral force can be calculated in real time according to the tire slip angle and the wheel slip rate in various running states of the vehicle, then the actual lateral motion acceleration of the vehicle is accurately output, different working conditions can be adapted, and the real-time performance and accuracy of vehicle dynamics control are improved.

A lateral–vertical dynamics coordinated control method for a distributed drive electric vehicle, and a related device. The lateral–vertical dynamics coordinated control method for a distributed drive electric vehicle comprises the following steps: creating a fourteen-degree-of-freedom full vehicle model which considers unbalanced magnetic force in an in-wheel motor, so as to accurately represent a state response of a vehicle under different inputs; a three-dimensional piecewise affine tire model performing piecewise linearization of a lateral force of a tire under different side slip angles and vertical loads, so that nonlinear features of the tire can be described under extreme operating conditions; creating an active front-wheel steering controller on the basis of hybrid model predictive control, obtaining a reference center of mass side slip angle and a yaw rate by means of a bicycle model, and then calculating an additional steering angle of the vehicle, thereby ensuring vehicle stability; and designing a multi-constraint input and multi-constraint output active suspension controller based on dual-model predictive control by using roll stability and ride comfort, that is, a tire dynamic load, a vehicle body acceleration, and an in-wheel motor stator-rotor eccentricity, as suspension design control targets.

A traveling control apparatus for a vehicle that travels by transmitting force of a driving motor to tires is provided, including: a relaxation length calculation unit that calculates a relaxation length of the tires in a longitudinal direction as a moving distance required until a lateral force reaches a steady-state value when a slip angle is present in the tires; and a slip rate calculation unit that divides a deviation length of a deformation of the tires in the longitudinal direction, by the relaxation length calculated at the relaxation length calculation unit, thereby calculating a slip rate indicating a degree of slip between the tires and a road surface.

The application provides a robust stability control method for a steer-by-wire vehicle, which comprises the following steps: firstly, a two-degree-of-freedom vehicle dynamics model is established, and the lateral velocity and the mass center side slip angle of the vehicle under a steady state condition are solved; then, the control target of the vehicle stability control is further modified, and a reference model for designing a robust controller is established based on the two-degree-of-freedom vehicle dynamics model; finally, a steer-by-wire vehicle robust stability controller based on a hybrid sensitivity control is designed and solved, and is used for performing the steer-by-wire vehicle robust stability control. The method can effectively improve the robust stability and the trajectory tracking performance of the steer-by-wire vehicle.

The invention relates to the technical field of vehicle braking control, and discloses a turning braking force distribution control method and system for a vehicle carrying an electronic mechanical braking system. The method comprises the following steps: acquiring real-time state information of a vehicle; initial braking force distribution is conducted on the vehicle; calculating an expected yaw velocity and an expected side slip angle of the vehicle by adopting a linear two-degree-of-freedom vehicle steady-state model; a torque adjusting controller is adopted to calculate the torque adjusting quantity of the yaw velocity and the torque adjusting quantity of the side slip angle; dynamically adjusting the torque adjusting quantity of the yaw velocity and the torque adjusting quantity of the side slip angle by adopting an error weight proportion distribution method to obtain an expected braking torque needing to be applied to a wheel; a compensation torque controller is adopted to calculate the slip rate compensation torque of each wheel; and secondary braking force distribution is conducted on all the wheels of the vehicle according to the expected braking torque of the wheels and the slip rate compensation torque of all the wheels. According to the method, yaw stability improvement, sideslip suppression and braking efficiency optimization of the vehicle in the turning braking process can be achieved.

The invention relates to a vehicle torque vector control method and device, a vehicle and a storage medium, and the method comprises the steps that the target yaw velocity of the vehicle is calculated; when the vehicle meets the preset rear axle torque vector control condition, the differential torque limiting range of the vehicle is determined by using the control parameter boundary constraint of the vehicle, and the yaw velocity closed-loop control differential torque is calculated in combination with the target yaw velocity; whether the vehicle meets a preset side slip angle limiting activation condition or not is judged through the side slip angle, the side slip angle closed-loop control differential torque of the vehicle is obtained in combination with the judgment result and the differential torque limiting range, then the target differential torque of the vehicle is calculated, and the vehicle is controlled based on the target differential torque. And the torque vector control of the vehicle meets the preset smooth control condition. Therefore, the technical problems that in the related technology, it is difficult to achieve the expected yaw velocity of a driver by distributing the driving force according to the adhesion capacity of all the wheels, the smoothness is poor during steering, and the driving experience of the driver cannot be improved easily are solved.

The invention discloses a steering stability control method and system of an electric scooter, electronic equipment and a medium, and relates to the technical field of electric scooter control. The method comprises the following steps: acquiring real-time operation parameters of the electric scooter and a road adhesion coefficient under a current road condition; determining the maximum available adhesive force; a predicted slip angle is calculated based on the steering angle and the vehicle speed, an actual slip angle is determined according to the axial force and the transverse force, and when the deviation angle exceeds a stable threshold value, the electric scooter is controlled to enter a steering stable control mode; calculating a distribution proportion according to the change sequence of the vehicle speed and the steering angle; detecting the center-of-gravity position of the passenger, and correcting the distribution proportion based on the center-of-gravity position of the passenger and the maximum available adhesive force; and respectively adjusting the output torque of the inner side motor and the output torque of the outer side motor according to the corrected target distribution proportion until the deviation angle is smaller than a stable threshold value. By implementing the technical scheme provided by the invention, the steering stability of the electric scooter can be improved.

The invention provides a tire cornering stiffness estimation method and device based on double-extended Kalman filtering and a vehicle, and relates to the technical field of chassis control, and the method comprises the steps: building a state estimator and a parameter estimator which run in parallel, and achieving the combined online estimation of a state variable and a tire cornering stiffness parameter; according to the method, high-precision estimation can be completed only by using a conventional vehicle sensor signal without depending on a side slip angle or tire lateral force information which is difficult to obtain, an estimation result is continuously optimized through a closed-loop iteration mechanism, and stable and reliable cornering stiffness parameters are output for a vehicle stability controller to use after smooth filtering, so that the stability of the vehicle is improved. Therefore, the adaptability, robustness and safety of the control system under the non-linear working condition are remarkably improved.

The invention discloses a composite steering unmanned platform stability control method based on an extension theory. According to the method, an upper-layer hierarchical control framework, a middle-layer hierarchical control framework and a lower-layer hierarchical control framework are constructed for a distributed driving composite steering unmanned platform. The upper-layer trajectory tracking controller adopts model predictive control (MPC) to output an expected yawing moment, and adjusts the longitudinal driving moment of each wheel in combination with PID to realize path tracking control; the middle layer adopts a Gaussian mixture model (GMM) based on a nonlinear tire model, solves an extension boundary threshold value on a phase plane through a maximum likelihood estimation algorithm, introduces an extension set correlation function, adaptively distributes Ackerman steering and differential steering weights according to a side slip angle and a change rate thereof, and realizes smooth switching and fusion of multi-mode steering; and the lower layer decouples and distributes the tire force, and realizes coordination and satisfaction of the yawing moment and the driving demand by combining the driving torque and the Ackerman steering angle inverse solution.

The invention discloses a vehicle curve driving control method and system, and belongs to the technical field of vehicle control, and the method comprises the steps: obtaining vehicle driving state data and a road surface adhesion coefficient of a road surface where a vehicle is located; according to the vehicle driving state data, the road adhesion coefficient of the road where the vehicle is located and a steering control model of the vehicle, the rear wheel turning angle of the vehicle and the driving torque and driving force of each wheel are calculated and determined; wherein the steering control model comprises a seven-degree-of-freedom model of the vehicle, a kinetic model of wheel movement, a target yaw velocity calculation model, a target side slip angle calculation model, a target driving force calculation model, a yaw moment calculation model and a target optimization model with minimum vehicle yaw moment error and energy consumption as targets; and controlling the vehicle according to the rear wheel turning angle of the vehicle and the driving torque and the driving force of each wheel. On the premise of ensuring the control stability, the abrupt change probability of rear wheel steering and yawing moment fluctuation can be reduced.

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.

The invention discloses a driving stability domain division method and system of an unmanned mine vehicle and electronic equipment. The driving stability domain division method of the unmanned mine vehicle comprises the steps that a nonlinear dynamic model of the unmanned mine vehicle is established; based on the nonlinear kinetic model, generating a phase plane graph taking a side slip angle and a yaw velocity as state vectors; dividing a yaw velocity steady-state boundary based on a vehicle sideslip instability condition by using the phase plane graph, and dividing a side slip angle steady-state boundary based on a saturated tire side slip angle; according to the steady-state boundary of the yaw velocity and the steady-state boundary of the side slip angle, dividing an initial driving stability domain of the unmanned mine vehicle; and on the basis of the real-time state parameters of the unmanned mine vehicle and in combination with the instantaneous yaw-side offset dynamic characteristics of the unmanned mine vehicle, calculating an expansion vertex of the initial driving stability domain to obtain an expanded vehicle real-time driving stability domain.

The application provides a four-wheel vertical load dynamic estimation system and simulation method of a variable load working condition commercial vehicle, relates to the field of vehicle motion characteristic simulation, and is composed of a nonlinear three-degree-of-freedom vehicle body model, a tire model, a four-wheel side slip angle / slip rate calculation model, a four-wheel vertical load solving model and a mass center position and spring mass estimation model. The mass center position and spring mass estimation model can be combined with the vehicle static axle load to estimate the mass center and mass related information, and correct the parameters in the vehicle body model and the vertical load solving model. The four-wheel vertical load and load transfer rate of the commercial vehicle can be obtained under the premise that the wheels are not additionally modified.

The invention discloses a tire slip angle estimation method based on a BP neural network, and the method comprises the steps: building a tire finite element model, and verifying the precision of the model through a tire rigidity test; calculating the steady-state rolling angular velocity of the tire based on the tire finite element model; dividing the tire finite element model into a grounding area and a non-grounding area, configuring a fine grid for the grounding area, and configuring a coarsening grid for the non-grounding area; setting different side slip angles for simulation under a tire steady-state rolling working condition, and extracting a grounding area, a transverse stress maximum value, a transverse stress average value, a longitudinal stress maximum value and a longitudinal stress average value corresponding to each side slip angle as characteristic indexes; and by taking the characteristic indexes as input and the tire slip angle as output, constructing and training a BP neural network model to realize prediction of the tire slip angle. According to the method, the calculation efficiency is improved while the simulation precision of the key area is ensured, the selected characteristic indexes and the tire slip angle have clear mechanical association, and the interpretability of the model is enhanced.

The invention discloses a tractor sideslip angle real-time estimation system and method based on auxiliary navigation, and belongs to the technical field of agricultural machinery intelligent navigation and motion control. The method comprises the steps of obtaining a vehicle motion state vector; constructing an extended kinematics model and a two-degree-of-freedom dynamical model based on the vehicle motion state vector; obtaining the initial state of the tire sideslip angle based on an extended kinematics model and an extended Kalman filtering algorithm; on the basis of a two-degree-of-freedom dynamic model and a Luenberger state observer, carrying out online identification and correction on the initial state of the tire sideslip angle to obtain corrected estimated values of the front and rear tire sideslip angles; and obtaining a fusion weighting factor of the sideslip angles of the front and rear wheels based on the corrected estimated values of the sideslip angles of the front and rear tires, the vehicle speed and the vehicle steering angle, and obtaining a final estimated value of the sideslip angles of the front and rear wheels based on the fusion weighting factor. According to the method, the perception robustness of the navigation system to the vehicle sideslip state can be remarkably improved.

A vehicle travel control device for a vehicle (100) that travels by transmitting power from driving motors (89, 90) to tires (111, 112, 121, 122), the vehicle travel control device being provided with: a cushion length calculation unit (32) that calculates a cushion length in the front-rear direction of the tires, which is a movement distance required until a lateral force reaches a stable value when the tires have a slip angle; and a slip rate calculation unit (33) that calculates a slip rate indicating the degree of slip between the tire and the road surface by dividing the length of the deformation displacement in the front-rear direction of the tire by the buffer length calculated by the buffer length calculation unit.

A tire model is combined with linear time-varying model predictive control. An autonomous vehicle includes a system for operating the vehicle. A slip angle sensor measures a slip angle of a wheel of an autonomous vehicle. The accelerometer measures acceleration of the autonomous vehicle. The steering actuator controls a steering angle of the autonomous vehicle. The processor determines a road friction coefficient in a selected prediction step, calculates a normal force of the autonomous vehicle from the acceleration, determines a wheel stiffness of the wheel for the normal force at the slip angle using the road friction coefficient based on a tire model of the wheel, a steering command for the autonomous vehicle is calculated using model predictive control having wheel stiffness as an input, and a steering actuator is controlled using the steering command to steer the autonomous vehicle.