Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control(MPC) schemes and robust H_∞ state feedback control(RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers' performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.     

Recursive least square vehicle mass estimation based on acceleration partition

Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resistance that occur under different conditions. This paper proposes a vehicle mass estimator. The estimator incorporates road gradient information in the longitudinal accelerometer signal, and it removes the road grade from the longitudinal dynamics of the vehicle. Then, two different recursive least square method （RLSM） schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions. A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters. The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations. The modification of the algorithm is also discussed to improve the result of the mass estimation. Experiment data on asphalt road, plastic runway, and gravel road and on sloping roads are used to validate the estimation algorithm. The adaptability of the algorithm is improved by using data collected under several critical operating conditions. The experimental results show the error of the estimation process to be within 2.6%, which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications. This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.     

Characteristic verification and parameter optimization of airbags cushion system for airborne vehicle

Abstract： The major methods to investigate the airbags cushion system are experimental method, thermodynamic method and finite element method （FEM）. Airbags cushion systems are very complicated and very difficult to be investigated thoroughly by such methods For experimental method, it is nearly impossible to completely analyze and optimize the cushion characteristics of airbags of airborne vehicle because of charge issue, safety concern and time constraint. Thermodynamic method fails to take the non-linear effects of large airbag deformation and varied contact conditions into consideration. For finite element method, the FE model is usually complicated and the calculation takes tens of hours of CPU time. As a result, the optimization of the design based on a nonlinear model is very difficult by traditional iterative approach method. In this paper, a model based on FEM and control volume method is proposed to simulate landing cushion process of airborne vehicle with airbags cushion system in order to analyze and optimize the parameters in airbags cushion system. At first, the performance of airbags cushion system model is verified experimentally. In airdrop test, accelerometers are fixed in 4 test points distributed over engine mount, top, bottom and side armor plate of hull to obtain acceleration curves with time. The simulation results are obtained under the same conditions of the airdrop test and the simulation results agree very well with the experimental results, which indicate the established model is valid for further optimization. To optimize the parameters of airbags, equivalent response model based on Latin Hypercube DOE and radial basis function is employed instead of the complex finite element model. Then the optimal results based on equivalent response model are obtained using simulated annealing algorithm. After optimization, the maximal acceleration of airborne vehicle landing reduces 19.83%, while the energy absorption by airbags increases 7.85%. The performance of the airbags cushion system thus is largely improved through optimization, which indicates the proposed method has the capability of solving the parameter optimization problem of airbags cushion system for airborne vehicle.     

APPROACH TO IMPROVEMENT OF ROBOT TRAJECTORY ACCURACY BY DYNAMIC COMPENSATION

Some dynamic factors, such as inertial forces and friction, may affect the robot trajectory accuracy. But these effects are not taken into account in robot motion control schemes. Dynamic control methods, on the other hand, require the dynamic model of robot and the implementation of new type controller. A method to improve robot trajectory accuracy by dynamic compensation in robot motion control system is proposed. The dynamic compensation is applied as an additional velocity feedforward and a multilayer neural network is employed to realize the robot inverse dynamics. The complicated dynamic parameter identification problem becomes a learning process of neural network connecting weights under supervision. The finite Fourier series is used to activate each actuator of robot joints for obtaining training samples. Robot control system, consisting of an industrial computer and a digital motion controller, is implemented. The system is of open architecture with velocity feedforward function. The proposed m     

Load-sharing characteristic of multiple pinions driving in tunneling boring machine

The failure of the key parts, such as gears, in cutter head driving system of tunneling boring machine has not been properly solved under the interaction of driving motors asynchronously and wave tunneling torque load. A dynamic model of multi-gear driving system is established considering the inertia effects of driving mechanism and cutter head as well as the bending-torsional coupling. By taking into account the nonlinear coupling factors between ring gear and multiple pinions, the influence for meshing angle by bending-torsional coupling and the dynamic load-sharing characteristic of multiple pinions driving are analyzed. Load-sharing coefficients at different rotating cutter head speeds and input torques are presented. Numerical results indicate that the load-sharing coefficients can reach up to 1.2-1.3. A simulated experimental platform of the multiple pinions driving is carried out and the torque distributions under the step load in driving shaft of pinions are measured. The imbalance of torque distribution of pinions is verified and the load-sharing coefficients in each pinion can reach 1.262. The results of simulation and test are similar, which shows the correctness of theoretical model. A loop coupling control method is put forward based on current torque master slave control method. The imbalance of the multiple pinions driving in cutter head driving system of tunneling boring machine can be greatly decreased and the load-sharing coefficients can be reduced to 1.051 by using the loop coupling control method. The proposed research provides an effective solution to the imbalance of torque distribution and synchronous control method for multiple pinions driving of TBM.     

Fuzzy chaos control for vehicle lateral dynamics based on active suspension system

The existing research of the active suspension system（ASS） mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.     

Parallel Distributed Compensation/H_∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model

Current research on lane-keeping systems ignores the e ect of the driver and external resistance on the accuracy of tracking the lane centerline. To reduce the lateral deviation of the vehicle, a lane-keeping control method based on the fuzzy Takagi-Sugeno(T-S) model is proposed. The method adopts a driver model based on near and far visual angles, and a driver-road-vehicle closed-loop model based on longitudinal nonlinear velocity variation, obtaining the expected assist torque with a robust H_∞ controller which is designed based on parallel distributed compensation and linear matrix inequality. Considering the external influences of tire adhesion and aligning torque when the vehicle is steering, a feedforward compensation control is designed. The electric power steering system is adopted as the actuator for lane-keeping, and active steering redressing is realized by a control motor. Simulation results based on Carsim/Simulink and real vehicle test results demonstrate that the method helps to maintain the vehicle in the lane centerline and ensures driving safety.     

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.     

Torque-based optimal acceleration control for electric vehicle

The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin＇s maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle（EV） has not been reported. In this paper, the permanent magnet synchronous motor（PMSM） is controlled by the field oriented control（FOC） method and the electric drive system for the EV（including the PMSM, the inverter and the battery） is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming（DP）. This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV＇s acceleration process and the analytical algorithm is easy to be implemented in real-time control.     

Integrated control of lateral and vertical vehicle dynamics based on multi-agent system

The existing research of the integrated chassis control mainly focuses on the different evaluation indexes and control strategy. Among the different evaluation indexes, the comprehensive properties are usually not considered based on the non-linear superposition principle. But, the control strategy has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, based on belief, desire and intention（BDI）-agent model framework, the TYRE agent, electric power steering（EPS） agent and active suspension system（ASS） agent are proposed. In the system（SYS） agent, the coordination mechanism is employed to manage interdependences and conflicts among other agents, so as to improve the flexibility, adaptability, and robustness of the global control system. Due to the existence of the simulation demand of dynamic performance, the vehicle multi-body dynamics model is established by SIMPACK. And then the co-simulation analysis is conducted to evaluate the proposed multi-agent system（MAS） controller. The simulation results demonstrate that the MAS has good effect on the performance of EPS and ASS. Meantime, the better road feeling for the driver is provided considering the multiple and complex driving traffic. Finally, the MAS rapid control prototyping is built to conduct the real vehicle test. The test results are consistent to the simulation results, which verifies the correctness of simulation. The proposed research ensures the driving safety, enhances the handling stability, and improves the ride comfort.     

Dynamic Flow Control Strategies of Vehicle SCR Urea Dosing System

Selective Catalyst Reduction(SCR)Urea Dosing System(UDS)directly affects the system accuracy and the dynamic response performance of a vehicle.However,the UDS dynamic response is hard to keep up with the changes of the engine's operating conditions.That will lead to low NO_χconversion efficiency or NH_3 slip.In order to optimize the injection accuracy and the response speed of the UDS in dynamic conditions,an advanced control strategy based on an air-assisted volumetric UDS is presented.It covers the methods of flow compensation and switching working conditions.The strategy is authenticated on an UDS and tested in different dynamic conditions.The result shows that the control strategy discussed results in higher dynamic accuracy and faster dynamic response speed of UDS.The inject deviation range is improved from being between-8%and 10%to-4%and 2%and became more stable than before,and the dynamic response time was shortened from 200 ms to 150 ms.The ETC cycle result shows that after using the new strategy the NH_3 emission is reduced by 60%,and the NO_χemission remains almost unchanged.The trade-off between NO_χconversion efficiency and NH_3 slip is mitigated.The studied flow compensation and switching working conditions can improve the dynamic performance of the UDS significantly and make the UDS dynamic response keep up with the changes of the engine's operating conditions quickly.     

Precision balance method for cupped wave gyro based on cup-bottom trimming

The mechanical balance process is the key process to eliminate the quadrature error and improve the performance of the cupped wave gyro.The conventional mechanical balance method for cupped wave gyro based on cup-wall trimming requires high control accuracy of trimming quantity,which increases the production cost and decreases the fabrication efficiency in large extent.However,it is hard to reach the high balance accuracy with the natural frequency split of mHz grade by using the conventional method.In this paper,the lumped mass dynamic model of the cupped wave gyro is built by discretization method,and the effects of different position trimming on the natural frequency are analyzed.It is pointed out that trimming off a tiny quantity of material from cup-wall causes large variation of the natural frequency is the main reason for the low accuracy of the conventional mechanical balance method.Then,a precision balance method for cupped wave gyro based on cup-bottom trimming is presented and the entire procedures of this method are given.The static balance process and dynamic balance process of the precision balance method are simulated by the finite element software.The simulation result shows that the precision balance method based on cup-bottom trimming brings less additional natural frequency split in the static balance process,minimizes the natural frequency split to mHz grade and rectify the angle of mode offset to 0.1° grade in the dynamic balance process,furthermore,the method decreases the requirement for control accuracy of trimming quantity evidently.The research work provides references for structure optimization design and balance process plan of the cupped wave gyro.     

Dynamic Friction Control Using Dynamic Structured RFNN and Friction Parameter Estimator

A nonlinear dynamic friction control is dealt with using dynamic friction observer and intelligent control.The adaptive dynamic friction observer based on the LuGre friction is proposed to estimate the friction parameters and a directly immeasurable friction state variable.The dynamic structured Recurrent Fuzzy Neural Network(RFNN)is designed to give additional robustness to the control system under the presence of the friction model uncertainty.A proposed composite control scheme is applied to the position tracking control of the servo system.The performances of the proposed friction observer and the friction controller are demonstrated by simulation.     

Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.     

Multi-objective stability control algorithm of heavy tractor semi-trailer based on differential braking

Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety,and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents.However,currently tractor semi-trailer stability control is generally only a single hazardous condition (rollover or jack-knifing) control,it is difficult to ensure the vehicle comprehensive stability of various dangerous conditions.The main objective of this study is to introduce a multi-objective stability control algorithm which can improve the vehicle stability of a tractor semi-trailer by using differential braking.A vehicle controller is designed to minimize the likelihood of rollover and jack-knifing.First a linear vehicle model of tractor semi-trailer is constructed.Then an optimal yaw control for tractor using differential braking is applied to minimize the yaw rate and lateral acceleration deviation of tractor,as well as the hitch articulation angle of tractor semi-trailer,so as to improve the vehicle stability.Second a braking scheme and variable structure control with sliding mode control are introduced in order to achieve the best braking effect.Last Fishhook maneuver is introduced to the active safety simulation and the active control system effect verification.The simulation results show that multi-objective stability control algorithm of semi-trailer could improve the vehicle stability significantly during the transient maneuvers.The proposed multi-objective stability control algorithm is effective to prevent the vehicle rollover and jackknifing.     

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation(CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control(MC)problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control(NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control(TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lanechange(DLC) test results show that by using the optimal predictive controller, the root-mean-square(RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration(OPA) driver model with the non-preview speed-tracking method. Additionally,the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and collision-avoidance capabilities during autonomous driving.     

Novel application-oriented transient fuel model of a port fuel injection S. I. engine

Most researches on transient fuel control of port fuel injection S.I. engine are carried out from the perspective of advanced mathematical theories. When it comes to practical control, there exist many limitations although they are more intelligent. In order to overcome the fuel wetting effect of PFI engine, the application-oriented transient fuel control is studied by analyzing the key parameters which are closely related with the engine transient characteristics. Both validity and simplicity are taken into consideration. Based on the fuel wall-wetting theory and popular fuel compensation strategy, short-term transient fuel（STF） and long-term transient fuel（LTF）, as well as their individual decay approaches, are introduced. STF is to compensate the drastic fuel film loss caused by sudden throttle change, while the function of LTF is to compensate the fuel film loss by manifold air pressure（p） fluctuation. Each of them has their respective pros and cons. The engine fuel mass and air mass are also calculated for air-fuel ratio（AFR） according to ideal gas state equation and empirical equations. The vehicle acceleration test is designed for model validation. The engine experiences several mild and heavy accelerations corresponding to the gear change during vehicle acceleration. STF and LTF control are triggered reliably. The engine transient fuel control simulation adopts the same inputs as the test to ensure consistency. The logged test data are used to check the model output. The results show that the maximum fuel pulse width（FPW） error reaches 2 ms, and it only occurs under engine heavy acceleration condition. The average FPW error is 0.57 ms. The results of simulation and test are close overall, which indicates the accuracy of steady and transient fuel. The proposed research provides an efficient approach not only suitable for practical engineering application, but also for AFR prediction, fuel consumption calculation, and further studies on emission control.     

Dynamic Feedforward Control of a Novel 3-PSP 3-DOF Parallel Manipulator

Parallel manipulators with less than six degrees of freedom (DOF) have been increasingly used in high-speed hybrid machine tools. The structural features of parallel manipulators are dynamic, a characteristic that is particularly significant when these manipulators are used in high-speed machine tools. However, normal kinematic control method cannot satisfy the requirements of the control system. Many researchers use model-based dynamic control methods, such as the dynamic feedforward control method. However, these methods are rarely used in hybrid machine tools because of the complex dynamic model of the parallel manipulator. In order to study the dynamic control method of parallel manipulators, the dynamic feedforward control method is used in the dynamic control system of a 3-PSP (prismatic-spherical-prismatic) 3-DOF spatial parallel manipulator used as a spindle head in a high-speed hybrid machine tool. Using kinematic analysis as basis and the Newton-Euler method, we derive the dynamic model of the parallel manipulator. Furthermore, a model-based dynamic feedforward control system consisting of both kinematic control and dynamic control subsystems is established. The dynamic control subsystem consists of two modules. One is used to eliminate the influence of the dynamic characteristics of high-speed movement, and the other is used to eliminate the dynamic disturbances in the milling process. Finally, the simulation model of the dynamic feedforward control system of the 3-PSP parallel manipulator is constructed in Matlab/Simulink. The simulations of the control system eliminating the influence of the dynamic characteristics and dynamic disturbances are conducted. A comparative study between the simulations and the normal kinematic control method is also presented.The simulations prove that the dynamic feedforward control method effectively eliminates the influence of the dynamic disturbances and dynamic characteristics of the parallel manipulator on high-speed machine tools, and significantly improves the trajectory accuracy. This is the first attempt to introduce the dynamic feedfordward control method into the 3-PSP spatial parallel manipulator whose dynamic model is complex and provides a study basis for the real-time dynamic control of the high-speed hybrid machine tools.     

Online learning control for hybrid electric vehicle

Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS).However,the uncertainty of future driving conditions generally cannot be easily tackled in EMS design.Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions.Therefore,they usually fail to fully explore the potential of these advanced vehicles.In this paper,a novel EMS design procedure based on neural dynamic programming (NDP) is proposed.The NDP is a generic online learning algorithm,which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method.Instead of computing the utility function and optimal control actions through Bellman equations,the NDP algorithm uses two neural networks to approximate them.The weights of these neural networks are updated online by the TD method.It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation.The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions,and can self-tune with a wide variance in operating conditions.The NDP EMS has been applied to "Qianghua-I",a prototype of a parallel HEV,using a revolving drum test bench for verification.Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level.The proposed research ensures the optimality of NDP EMS,as well as real-time applicability.     

Study on modeling of vehicle dynamic stability and control technique

In order to solve the problem of enhancing the vehicle driving stability and safety,which has been the hot question researched by scientific and engineering in the vehicle industry,the new control method was investigated.After the analysis of tire moving characteristics and the vehicle stress analysis,the tire model based on the extension pacejka magic formula which combined longitudinal motion and lateral motion was developed and a nonlinear vehicle dynamical stability model with seven freedoms was made.A new model reference adaptive control project which made the slip angle and yaw rate of vehicle body as the output and feedback variable in adjusting the torque of vehicle body to control the vehicle stability was designed.A simulation model was also built in Matlab/Simulink to evaluate this control project.It was made up of many mathematical subsystem models mainly including the tire model module,the yaw moment calculation module,the center of mass parameter calculation module,tire parameter calculation module of multiple and so forth.The severe lane change simulation result shows that this vehicle model and the model reference adaptive control method have an excellent performance.