AFS yaw stability control method based on improved superspiral sliding mode algorithm

Abstract

The invention relates to the field of electric vehicle transverse stability control, and discloses an AFS yaw stability control method based on an improved super-spiral sliding mode algorithm, and the method comprises the steps: a state parameter estimation step: outputting an estimated value of a state quantity based on a Kalman filtering unit; a stability determination step in which the target setting unit outputs an ideal value of the state quantity; the side slip angle judgment unit receives the road adhesion coefficient, the side slip angle and the side slip angle speed and outputs an instability judgment result; the yaw velocity judgment unit receives an ideal value and an actual value of the yaw velocity, and outputs an instability judgment result after judgment of a logic threshold value; an additional front wheel steering angle control output step, wherein a control mode selection unit receives the instability judgment result and outputs a corresponding control mode; and the control quantity calculation unit outputs the sliding mode control quantity according to the selected control mode. According to the method, yaw stability control with high rapidity and robustness can be realized only by using a common vehicle-mounted sensor, and the real-time precision of vehicle state parameter estimation is improved.

Description

Technical Field

[0001] The invention relates to the field of lateral stability control of electric vehicles, and in particular to an AFS yaw stability control method based on an improved super-helical sliding film algorithm. Background Art

[0002] At present, automobile driving safety has become the primary consideration in automobile design and development, and active safety control has been widely used with the development of electronic technology. From the early anti-lock braking system (ABS), direct yaw moment control (DYC) to active front steering system (AFS), vehicle stability assist system (VSA), electronic stability program (ESP), etc., various active safety control technologies have been applied to automobiles to improve the driving stability and safety of automobiles. Active front steering (AFS) and direct yaw moment control (DYC) are commonly used chassis active control systems, among which active front steering control can fully play a role in yaw stability control before the vehicle is about to lose stability; according to the error between the current vehicle state parameters and the expected state parameters, the active steering controller calculates the front wheel steering angle required to follow the ideal state parameters, and the steering actuator motor drives the steering actuator assembly to move.

[0003] In the design and research of active steering controller, the commonly used control algorithms include PID, model predictive control (MPC) and sliding mode control (SMC). Based on the unscented Kalman filter (UKF) theory, the vehicle's center of mass slip angle and yaw rate are estimated. According to the difference between the estimated value and the ideal value of the vehicle state, the LQR optimal controller is designed to achieve the best control of the front and rear steering; the simulation results show that the state feedback optimal control method has good lateral control stability. The active steering controller is designed with the vehicle's yaw rate and center of mass slip angle as the control targets, and the extended Kalman filter (EKF) is designed to estimate the center of mass slip angle. The simulation and experimental results show the effectiveness of the active steering control strategy and improve the handling stability of emergency rescue vehicles.

[0004] In summary, active steering stability control has been widely studied, but most studies only considered yaw rate feedback control and did not consider the design of the center of mass sideslip angle active steering controller. In addition, the state parameter estimation method based on Kalman filter selected by most studies has general real-time accuracy. Summary of the invention

[0005] The present invention aims to provide an AFS yaw stability control method based on an improved super-helical sliding film algorithm, which can achieve rapid and robust yaw stability control using only common vehicle-mounted sensors and improve the real-time accuracy of vehicle state parameter estimation.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme:

[0007] An AFS yaw stability control method based on an improved super-helical sliding film algorithm comprises:

[0008] In the state parameter estimation step, the prior estimation unit based on the Romberg observer receives the front wheel steering angle and lateral acceleration output by the sensor, and outputs the prior estimation value of the state quantity; the Kalman filter unit receives the prior estimation value of the state quantity and the lateral acceleration output by the sensor, and outputs the estimation value of the state quantity;

[0009] In the stability determination step, the target setting unit receives the longitudinal vehicle speed and the front wheel turning angle, and outputs an ideal value of a state quantity; the state quantity includes a yaw rate and a center of mass sideslip angle; a center of mass sideslip angle determination unit receives the road adhesion coefficient, the center of mass sideslip angle and the center of mass sideslip rate, and outputs an instability determination result 1 according to an instability determination model; a yaw rate determination unit receives an ideal value and an actual value of a yaw rate, and outputs an instability determination result 2 after determining the deviation between the actual yaw rate and the ideal yaw rate through a logic threshold value;

[0010] In an additional front wheel steering angle control output step, a control mode selection unit receives instability determination result one and instability determination result two, and outputs a corresponding control mode; a control quantity calculation unit receives a yaw rate deviation and a sideslip angle deviation of a center of mass, and outputs a sliding membrane control quantity according to the selected control mode.

[0011] The principles and advantages of this scheme are: in actual application, this scheme combines the active front wheel steering high-order sliding film control method of the Lumberg observer and the Kalman filter, calculates the additional front wheel steering angle based on the improved super-helical sliding film algorithm, reduces the vibration of the system response while ensuring robustness and rapidity, and based on the stability judgment strategy, selects yaw rate control or center of mass sideslip angle control according to the real-time state of the vehicle, and the control target is clear. In addition, it can also improve the estimation error of the traditional Kalman filter algorithm caused by the large deviation of the set initial value, and improves the real-time accuracy of the state parameter estimation, especially for the non-asymptotically stable system in the open-loop condition. The improvement effect is more significant.

[0012] Preferably, as an improvement, the ideal value calculation model of the yaw angular velocity is:

[0013]

[0014] Among them, γ d is the ideal value of yaw angular velocity, v x represents the longitudinal speed, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, K is the stability factor of the vehicle, μ represents the road adhesion coefficient, g represents the acceleration of gravity, τ r It represents the time constant of the delay link, s is the variable of the transfer function, and sign() is the sign function.

[0015] Technical effect: The above formula can comprehensively consider the driver's steering intention, road adhesion limit and pure lag characteristics of the mechanical system.

[0016] Preferably, as an improvement, the calculation model of the stability factor of the vehicle is:

[0017]

[0018] Among them, m represents the mass of the vehicle, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, k1 represents the equivalent cornering stiffness of the front axle, and k2 represents the equivalent cornering stiffness of the rear axle.

[0019] Technical effect: Facilitates the evaluation of operational stability.

[0020] Preferably, as an improvement, the ideal value calculation model of the center of mass sideslip angle is:

[0021] β d =0

[0022] Among them, β d is the ideal value of the sideslip angle at the center of mass.

[0023] Technical effect: It is easy to consider the vehicle trajectory tracking and maintaining performance.

[0024] Preferably, as an improvement, the instability judgment model is:

[0025]

[0026] Among them, B1 and B2 are stability boundary constants, and β is the sideslip angle of the center of mass.

[0027] Technical effect: Facilitates instability judgment.

[0028] Preferably, as an improvement, in the additional front wheel steering angle control output step, when the first instability determination result is no instability and the second instability determination result is instability, the system control quantity model is as follows:

[0029]

[0030] Among them, I z represents the moment of inertia of the vehicle around the z-axis, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, k1 represents the equivalent lateral stiffness of the front axle, k2 represents the equivalent lateral stiffness of the rear axle, and v x represents the longitudinal speed, γ represents the yaw rate, β represents the sideslip angle of the center of mass, k a , k b , k cis the parameter to be designed, s1 is the intermediate state variable of the synovial control, τ is 0.5, k is the boundary control parameter, c1 and c2 are the parameters to be designed, q1 and q2 are positive odd numbers, is the derivative of the ideal yaw rate, δ f is the front wheel steering angle given by the driver through the steering wheel.

[0031] Technical effect: Improves the speed and robustness of the vehicle in following the ideal yaw rate and reduces vibration.

[0032] Preferably, as an improvement, in the additional front wheel steering angle control output step, when the first instability determination result is instability, the system control quantity model is as follows:

[0033]

[0034] Where m is the vehicle mass, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, k1 is the equivalent cornering stiffness of the front axle, k2 is the equivalent cornering stiffness of the rear axle, and v is x represents the longitudinal speed, γ represents the yaw rate, β represents the sideslip angle of the center of mass, k a , k b , k c is the parameter to be designed, s2 is the intermediate state variable of the sliding film control, k is the boundary control parameter, c3 and c4 are the parameters to be designed, q3 and q4 are positive odd numbers, is the derivative of the ideal value of the sideslip angle at the center of mass.

[0035] Technical effect: Improves the speed and robustness of the vehicle following the ideal center of mass sideslip angle and reduces vibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of an AFS yaw stability control method based on an improved super-helical sliding film algorithm;

[0037] Figure 2 It is a control block diagram of an AFS yaw stability control method based on an improved super-helical sliding film algorithm;

[0038] Figure 3 The figure is a schematic diagram of the calculation flow of the Kalman filter algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following is further described in detail through specific implementation methods:

[0040] The embodiment is basically as shown in the attached Figure 1 , Attachment Figure 2 As shown:

[0041] An AFS yaw stability control method based on an improved super-helical sliding film algorithm comprises:

[0042] The state parameter estimation step is based on the prior estimation unit of the Romberg observer receiving the front wheel steering angle δ output by the sensor and the lateral acceleration a output by the sensor. y , and outputs the prior estimation value of the state quantity. The Kalman filter unit receives the prior estimation value of the state quantity and the lateral acceleration output by the sensor, and outputs the estimated value of the state quantity. Since the value measured by the yaw rate sensor has an error, the center of mass sideslip angle cannot be directly obtained by the common vehicle-mounted center of mass sideslip angle sensor. In this embodiment, the estimated value of the state quantity is used as the actual value for calculation.

[0043] The research object in this embodiment is a distributed drive electric vehicle. The simulation vehicle model is a C-class hatchback with flexible control in CarSim. The driving force of the four wheels is input by an external Simulink four-wheel hub motor model. Due to the large weight of the hub motor, the unsprung mass of the vehicle is appropriately increased. The parameters are shown in Table 1:

[0044] Table 1

[0045]

[0046]

[0047] Specifically, first introduce the output equation of the controlled system:

[0048]

[0049] Among them, a y is the lateral acceleration of the vehicle, m is the mass of the vehicle, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, k1 is the equivalent cornering stiffness of the front axle, k2 is the equivalent cornering stiffness of the rear axle, and v x represents the longitudinal vehicle speed, γ represents the yaw rate, β represents the sideslip angle of the center of mass, and δ represents the front wheel slip angle of the vehicle.

[0050] Arrange the state space equation in matrix form:

[0051]

[0052] in,

[0053] Y=a y , u=δ,

[0054]

[0055] Research system observability matrix The rank of is verified by simulation. Under various vehicle speeds, there is always a full rank, so the system can be observed. The steps of designing the observer are as follows:

[0056] Design of a standard Luenberger Observer:

[0057]

[0058] in is the estimated value of state X, and L is the two-dimensional column vector to be designed.

[0059] set up Easy to know new status x The equilibrium point is zero, the coefficient matrix L is designed, and the poles of its state matrix (A-LC) are configured to increase the speed at which the observed value converges to the true value.

[0060] In this embodiment, the state of the Lumberg observer output is used as the prior estimate of the Kalman filter, which can improve the estimation error of the traditional Kalman filter algorithm caused by the large deviation of the set initial value, especially for the non-asymptotically stable system in the open-loop condition.

[0061] The changed continuous-time state space equation is:

[0062]

[0063] in,

[0064] Taking the sampling period as T, discretizing it and considering the Gaussian noise in the system, we get the state equation and measurement equation required by the following Kalman filter:

[0065]

[0066] in And W k-1 、V k are the process noise of the k-1th step and the measurement noise of the kth step, respectively, satisfying W k-1 ~N(0,Q),V k ~N(0,R), where Q is the process noise covariance matrix, R is the measurement noise covariance matrix, and Q = E[WW T ],R=E[VV T ].

[0067] Based on the above state equation and measurement equation, iterative calculation is performed in two steps: prediction and correction:

[0068] The prediction includes: calculating the prior estimate, the calculation model is as follows:

[0069]

[0070] Calculate the prior error covariance matrix, and the calculation model is as follows:

[0071]

[0072] The correction includes: calculating the Kalman gain, the calculation model is as follows:

[0073]

[0074] Calculate the posterior estimate and the calculation model is as follows:

[0075]

[0076] To prepare for the next calculation, update the error covariance matrix as follows:

[0077]

[0078] The calculation process of the Kalman filter algorithm is as follows: Figure 3 shown.

[0079] In the stability determination step, a target setting unit receives the longitudinal vehicle speed and the front wheel turning angle, and outputs an ideal value of a state quantity; the state quantity includes a yaw rate and a sideslip angle of the center of mass.

[0080] Specifically, considering the driver's steering intention, this embodiment takes the product of the steady-state yaw rate gain and the front wheel angle as the yaw stability control target. In addition, the road adhesion limit and the pure hysteresis characteristics of the mechanical system are also considered. Therefore, the ideal value calculation model of the yaw rate is:

[0081]

[0082] Among them, γ d is the ideal value of yaw angular velocity, v x represents the longitudinal speed, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, K is the stability factor of the vehicle, μ represents the road adhesion coefficient, g represents the acceleration of gravity, τ r It represents the time constant of the delay link, s is the variable of the transfer function, and sign() is the sign function.

[0083] Where K is the stability factor of the vehicle, expressed as follows:

[0084]

[0085] Among them, m represents the mass of the vehicle, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, k1 represents the equivalent cornering stiffness of the front axle, and k2 represents the equivalent cornering stiffness of the rear axle.

[0086] Considering the vehicle trajectory tracking performance, the most ideal situation of the center of mass sideslip angle during vehicle steering is that any front wheel steering angle input satisfies the ideal value of the center of mass sideslip angle. The ideal value calculation model of the center of mass sideslip angle is:

[0087] β d =0

[0088] Among them, β d is the ideal value of the sideslip angle at the center of mass.

[0089] Considering the parameter disturbance and external disturbance of the two-degree-of-freedom model, a high-order sliding film controller with strong robustness is designed, and an improved super-helical algorithm is used to weaken the sliding film jitter while maintaining strong tracking performance.

[0090] The center of mass slip angle determination unit receives the road adhesion coefficient, the center of mass slip angle and the center of mass slip angular velocity, and outputs the instability determination result 1 according to the instability determination model. The yaw rate determination unit receives the ideal value and the actual value of the yaw rate, and outputs the instability determination result 2 after judging the deviation between the actual yaw rate and the ideal yaw rate through the logic threshold value.

[0091] Specifically, the process of instability judgment includes: first, according to the given vehicle speed, steering wheel angle and vehicle running state, the deviation Δγ between the actual yaw rate and the ideal yaw rate, the actual center of mass sideslip angle and center of mass sideslip rate are obtained, and according to the road adhesion coefficient, the corresponding B1 and B2 values ​​are selected according to the table (Table 2), and the Table 2 is as follows:

[0092] Table 2

[0093]

[0094]

[0095] Whether the vehicle is unstable is determined according to the instability judgment model, and the instability judgment model is:

[0096]

[0097] Among them, B1 and B2 are stability boundary constants, and β is the center of mass sideslip angle. If the instability judgment model is not satisfied, adjustment control is required; if the instability judgment model is satisfied, it is determined whether the deviation Δγ between the actual yaw rate and the ideal yaw rate exceeds the threshold value. If it exceeds, the vehicle is unstable and adjustment control is required. If it does not exceed, the vehicle is not unstable at this time. The threshold value of the yaw rate deviation is shown in Table 3:

[0098] Table 3

[0099]

[0100] The size of the yaw rate deviation threshold is related to factors such as the road adhesion coefficient and vehicle speed. When the vehicle speed is high, the actual yaw rate lags behind the driver's steering wheel input. At this time, the yaw rate deviation is also large, and the corresponding yaw rate deviation threshold is also selected to be large.

[0101] In an additional front wheel steering angle control output step, a control mode selection unit receives instability determination result one and instability determination result two, and outputs a corresponding control mode; a control quantity calculation unit receives a yaw rate deviation and a sideslip angle deviation of a center of mass, and outputs a sliding membrane control quantity according to the selected control mode.

[0102] Specifically, in order to facilitate the development of yaw stability control based on active front wheel steering, roll and pitch motion are not considered, and it is assumed that the longitudinal speed of the vehicle remains unchanged when turning, the front wheel turning angle is small, and only lateral and yaw motions are considered, that is, the linear two-degree-of-freedom dynamic model of the vehicle is as follows:

[0103]

[0104] Where γ represents the yaw rate, β represents the sideslip angle of the center of mass, m represents the vehicle mass, δ = (δ f +Δδ) indicates the front wheel turning angle, δ f is the front wheel steering angle given by the driver’s steering wheel, Δδ is the additional front wheel steering angle given by the steer-by-wire system, and v x represents the longitudinal vehicle speed, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, k1 represents the equivalent cornering stiffness of the front axle, k2 represents the equivalent cornering stiffness of the rear axle, I z Represents the vehicle's moment of inertia around the z-axis.

[0105] If the first instability determination result is not instability and the second instability determination result is instability, then:

[0106] The control objective is equivalent to the actual value of the yaw rate minus the ideal value. The control objective function is as follows:

[0107] e1=γ-γ d =0

[0108] Among them, γ is the actual value of the yaw angular velocity, γ d is the ideal value of the yaw angular velocity.

[0109] The synovial function selects the global fast terminal sliding mode, and the synovial function is as follows:

[0110]

[0111] That is, the sliding membrane surface is s1=0, in the above formula, c1 and c2 are parameters to be designed, q1 and q2 are positive odd numbers, and q1<q2.

[0112] The superhelix algorithm model improved by reaching law selection is as follows:

[0113]

[0114] Among them, k a , k b , k c is the parameter to be designed, which is related to the system disturbance; tanh() is the hyperbolic tangent function, τ is 0.5, k is the boundary control parameter, which is 0.005 in this embodiment, which helps to reduce the synovial jitter phenomenon, s1 is the intermediate state variable of the synovial control, and v1 is the intermediate variable.

[0115] The vehicle linear two-degree-of-freedom dynamics model, the sliding film function and the improved superhelical algorithm model are combined to obtain the system control quantity model as follows:

[0116]

[0117] If the instability judgment result is instability, then:

[0118] The control objective is equivalent to the actual value of the center of mass sideslip angle minus the ideal value. The control objective function is:

[0119] e2=β-β d =0

[0120] Among them, β is the actual value of the center of mass sideslip angle, β d is the ideal value of the sideslip angle at the center of mass.

[0121] The synovial function selects the global fast terminal sliding mode, and the synovial function is:

[0122]

[0123] That is, the sliding membrane surface is s2=0, in the above formula, c3 and c4 are parameters to be designed, q3 and q4 are positive odd numbers, and q3<q4.

[0124] The convergence law selects the improved superhelix algorithm, and the improved superhelix algorithm model is:

[0125]

[0126] where k a , k b , k c is the parameter to be designed, which is related to the system disturbance, tanh() is the hyperbolic tangent function, τ is 0.5, k is the boundary control parameter, and in this embodiment it is 0.005, which helps to reduce the synovial vibration phenomenon, s2 is the intermediate state variable of the synovial control, and v2 is the intermediate variable.

[0127] The vehicle linear two-degree-of-freedom dynamics model, the sliding film function and the improved superhelical algorithm model are combined to obtain the system control quantity model as follows:

[0128]

[0129] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An AFS yaw stability control method based on an improved super-helical sliding film algorithm, characterized in that: include: In a state parameter estimation step, a priori estimation unit based on a Romberg observer receives the front wheel steering angle and lateral acceleration output by the sensor and outputs a priori estimation values ​​of the state quantities; The Kalman filter unit receives a priori estimated value of the state quantity and the lateral acceleration output by the sensor, and outputs an estimated value of the state quantity; In the stability determination step, the target setting unit receives the longitudinal vehicle speed and the front wheel turning angle, and outputs an ideal value of a state quantity; the state quantity includes a yaw rate and a sideslip angle of the center of mass; The center of mass slip angle determination unit receives the road adhesion coefficient, the center of mass slip angle and the center of mass slip angular velocity, and outputs the instability determination result 1 according to the instability determination model; the yaw rate determination unit receives the ideal value and the actual value of the yaw rate, and outputs the instability determination result 2 after judging the deviation between the actual yaw rate and the ideal yaw rate through the logic threshold value; In an additional front wheel steering angle control output step, a control mode selection unit receives instability determination result one and instability determination result two, and outputs a corresponding control mode; a control quantity calculation unit receives a yaw rate deviation and a sideslip angle deviation of a center of mass, and outputs a sliding membrane control quantity according to the selected control mode.

2. The AFS yaw stability control method based on the improved super-helical sliding film algorithm according to claim 1 is characterized in that: The ideal value calculation model of the yaw angular velocity is: Among them, γ d is the ideal value of yaw angular velocity, v x represents the longitudinal speed, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, K is the stability factor of the vehicle, μ represents the road adhesion coefficient, g represents the acceleration of gravity, τ r It represents the time constant of the delay link, s is the variable of the transfer function, and sign() is the sign function.

3. The AFS yaw stability control method based on the improved super-helical sliding film algorithm according to claim 2 is characterized in that: The calculation model of the stability factor of the vehicle is: Among them, m represents the mass of the vehicle, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, k1 represents the equivalent cornering stiffness of the front axle, and k2 represents the equivalent cornering stiffness of the rear axle.

4. The AFS yaw stability control method based on the improved super-helical sliding film algorithm according to claim 1 is characterized in that: The ideal value calculation model of the center of mass sideslip angle is: β d ＝0 Among them, β d is the ideal value of the sideslip angle at the center of mass.

5. The AFS yaw stability control method based on the improved super-helical sliding film algorithm according to claim 1 is characterized in that: The instability judgment model is: Among them, B1 and B2 are stability boundary constants, and β is the sideslip angle of the center of mass.

6. The AFS yaw stability control method based on the improved super-helical sliding film algorithm according to claim 1 is characterized in that: In the additional front wheel steering angle control output step, when the first instability determination result is no instability and the second instability determination result is instability, the system control quantity model is as follows: Among them, I z represents the moment of inertia of the vehicle around the z-axis, a represents the distance from the center of mass to the front axle, b represents the distance from the center of mass to the rear axle, k1 represents the equivalent lateral stiffness of the front axle, k2 represents the equivalent lateral stiffness of the rear axle, and v x represents the longitudinal speed, γ represents the yaw rate, β represents the sideslip angle of the center of mass, k a , k b , k c is the parameter to be designed, s1 is the intermediate state variable of synovial control, τ is 0.5, k is the boundary control parameter, c1 and c2 are the parameters to be designed, q1 and q2 are positive odd numbers.

7. The AFS yaw stability control method based on the improved super-helical sliding film algorithm according to claim 1 is characterized in that: In the additional front wheel steering angle control output step, when the instability determination result 1 is instability, the system control quantity model is as follows: Where m is the vehicle mass, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, k1 is the equivalent cornering stiffness of the front axle, k2 is the equivalent cornering stiffness of the rear axle, and v is x represents the longitudinal speed, γ represents the yaw rate, β represents the sideslip angle of the center of mass, k a , k b , k c is the parameter to be designed, s2 is the intermediate state variable of sliding film control, k is the boundary control parameter, c3 and c4 are the parameters to be designed, q3 and q4 are positive odd numbers.

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