Direct yaw moment control and active steering integrated control strategy for multi-axle vehicle

Abstract

The invention discloses a direct yawing moment control and active steering integrated control strategy for a multi-axle vehicle, and aims to realize efficient vehicle stability control of the multi-axle vehicle under different road conditions and improve the driving safety, maneuverability and comfort of the multi-axle vehicle. The method specifically comprises the following steps: (1) establishing a two-degree-of-freedom multi-axle vehicle dynamics model considering transverse and yawing motions; (2) considering the influence of the road adhesion coefficient on the coordination effect, and designing a fuzzy controller for coordinating direct yawing moment control and active front wheel steering; and (3) taking a coordination result of the fuzzy controller as input, designing a yaw stability integrated controller based on adaptive model predictive control, and calculating an additional front wheel steering angle and a direct yaw moment for ensuring stable driving of the vehicle. According to the method, integrated control over the yaw stability of the multi-axle vehicle is achieved by coordinating the DYC and the AFS, the influence of road adhesion on the coordinating effect is considered, and finally stable and safe driving of the multi-axle vehicle under different road conditions is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of chassis stability control of multi-axle vehicles, and in particular is a direct yaw moment control and active steering integrated control strategy for multi-axle vehicles. Background Art

[0002] With the continuous progress of the automobile industry and the increasing complexity of the road traffic environment, ensuring the safety and stability of vehicles has become a core issue in the industry. Especially for multi-axle vehicles, their long wheelbase, large mass and large turning radius have put forward more stringent requirements on vehicle stability. In view of this, researchers have actively explored and developed a variety of active safety control strategies to improve the stability performance of vehicles during driving. Among the many strategies, the direct yaw moment control system (DYC) and the active front steering system (AFS) have attracted much attention due to their significant effects in improving the lateral stability of vehicles. The DYC system directly intervenes in the yaw motion of the vehicle by adjusting the wheel braking force distribution, but this intervention mechanism will affect the longitudinal dynamic parameters of the vehicle, such as changes in braking deceleration, which may affect driving comfort and safety. In contrast, the AFS system can ensure vehicle stability without sacrificing vehicle driving performance, but under high-speed driving or complex and changeable road conditions, relying solely on AFS may not fully meet the needs of vehicle stability.

[0003] It is worth noting that most of the current research on vehicle stability control systems focuses on single-axle or dual-axle vehicle designs. For multi-axle vehicles, their control efficiency is subject to certain limitations. In addition, traditional methods often adopt a single DYC or AFS control strategy, which is difficult to take into account both vehicle stability and maneuverability at the same time. In addition, when faced with complex and changeable driving conditions and parameter changes, the robustness and adaptability of the system are obviously insufficient. Therefore, in view of the particularity of multi-axle vehicles and the needs of complex road environments, it is particularly important to design an integrated control strategy of direct yaw moment control and active front wheel steering. This strategy aims to achieve more sophisticated and efficient vehicle stability control by comprehensively utilizing the advantages of DYC and AFS. It can not only effectively cope with the challenges brought by the long wheelbase and large mass of multi-axle vehicles, but also improve the flexibility of control and driving comfort while ensuring driving safety. In addition, this integrated control strategy has strong robustness and adaptability, can cope with complex road conditions and parameter changes, and ensure that multi-axle vehicles can achieve stable and safe driving under various road conditions.

[0004] Some existing patents, such as the invention patent with patent number CN117885717A, propose a vehicle stability control method and system for coordinating AFS and DYC. It coordinates AFS and DYC, combines the PI controller to generate the steering wheel angle, the linear vehicle model to generate the reference value, and the sliding membrane surface to calculate the total additional yaw moment. Based on the stability level division result of the vehicle operation data, the AFS and DYS control torques are calculated respectively to achieve precise adjustment of the additional front wheel angle and braking torque. However, this patent is designed for two-axle vehicles. For multi-axle vehicles, its control efficiency is subject to certain limitations, and it does not take into account the influence of the changeable road conditions of multi-axle vehicles on the coordination effect of the AFS and DYC systems. Summary of the invention

[0005] The present invention aims to achieve efficient vehicle stability control of a multi-axle vehicle under different road conditions, improve the driving safety, maneuverability and comfort of the multi-axle vehicle, and proposes a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle. The method first establishes a two-degree-of-freedom multi-axle vehicle dynamics model considering lateral and yaw motions, then uses the established multi-axle vehicle dynamics model as a lateral stability control reference model, considers the influence of the road adhesion coefficient on the coordination effect, and designs a fuzzy controller for coordinating direct yaw moment control and active front wheel steering. Finally, the coordination result of the fuzzy controller is used as input to design a yaw stability integrated controller based on adaptive model predictive control, and calculates the additional front wheel steering angle and direct yaw moment to ensure stable driving of the vehicle, so as to achieve stable and safe driving of the multi-axle vehicle under different road conditions.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0007] A direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle comprises the following steps:

[0008] S1: Assuming that the tire is in the linear range, the two-degree-of-freedom multi-axis vehicle dynamics model considering lateral and yaw motion is established as follows:

[0009]

[0010] Where m represents the vehicle mass; v x represents the longitudinal speed; β represents the sideslip angle of the center of mass; ω r represents the yaw angular velocity of the center of mass; C f , C m and C r Represent the cornering stiffness of the front, middle and rear axles of the vehicle respectively; θ represents the sum of the front wheel turning angle and the additional front wheel turning angle; L f , L m and L r Represents the distance from the center of mass to the front, middle and rear axles respectively; Iz Represents the moment of inertia of the vehicle around the z-axis; M c is the additional yaw moment required;

[0011] S2: Based on the two-degree-of-freedom multi-axis vehicle dynamics model established in step S1, when the vehicle is in a steady-state condition, the steady-state yaw angular velocity of the vehicle is expressed as:

[0012]

[0013] When the vehicle is in extreme conditions, the calculated expected yaw rate will be replaced by the critical value, so the vehicle's expected yaw rate is expressed as:

[0014]

[0015] Among them, μ represents the road adhesion coefficient; g represents the acceleration of gravity;

[0016] Based on this model, the expected sideslip angle is β d =0;

[0017] S3: The two-degree-of-freedom multi-axis vehicle dynamics model established in step S1 is used as a reference model for lateral stability control, and the influence of the road adhesion coefficient on the coordination effect is considered. A fuzzy controller for coordinating direct yaw moment control and active front wheel steering is designed. The process is as follows: Phase plane analysis calculates the critical state of the vehicle during driving and divides the stable boundary area and the unstable boundary area in the phase plane; the activation index X of direct yaw moment control is defined as:

[0018]

[0019] When the vehicle enters the unstable boundary area, direct yaw moment control is activated; the coefficient correction table is obtained according to the change of the road adhesion coefficient, as shown in Table 1:

[0020] Table 1 DYC activation index coefficient correction table

[0021] <![CDATA[λ 1 ]]> <![CDATA[λ 2 ]]> μ≥0.75 0.07 0.2 0.75 ≥ μ ≥ 0.50 0.08 0.24 0.50≥μ≥0.35 0.095 0.3 0.35>μ 0.15 0.4

[0022] Correction coefficient λ online by looking up the table 1 and λ 2, and then change the size of the stable area, so that the vehicle considers a smaller stable area on the road with a lower adhesion coefficient; the obtained direct yaw moment control activation index is used as the input of the fuzzy system, and according to the size of the activation index value, its fuzzy set is defined as 5 levels, namely {S, MS, M, MB, B}, corresponding to small, second small, medium, second large and large, respectively, and the domain range is [0,1]. The detected activation index value is converted into a fuzzy number using a triangular and Z-shaped mixed membership function curve; the output variables of the fuzzy system are the direct yaw moment control gain and the active front wheel steering gain. The fuzzy set of the direct yaw moment control gain is defined as 3 levels, namely {S, M, B}, corresponding to small, medium and large, respectively, and the domain range is [0,1]. The range is [0, 1], and the decision made according to the rule base is defuzzified by using triangular and Z-shaped mixed membership function curves. The center average method is used to perform weighted averaging on the center of the fuzzy set, and the weight is set to the height of the corresponding fuzzy set. The calculated fuzzy output is the direct yaw moment control gain; the fuzzy set of the active front wheel steering gain is defined as three levels, {S, M, B}, corresponding to small, medium and large, respectively, and the domain range is [0, 1]. The Gaussian membership function curve is used to defuzzify the decision made according to the rule base. The center average method is used to perform weighted averaging on the center of the fuzzy set, and the weight is set to the height of the corresponding fuzzy set. The calculated fuzzy output is the active front wheel steering gain;

[0023] S4: Taking the coordination result of the fuzzy controller in step S3 as input, design a yaw stability integrated controller based on adaptive model predictive control, and calculate the additional front wheel steering angle and direct yaw moment to ensure stable driving of the vehicle. The process is as follows: select the state quantity x = [ω r ,β] T ; Select the control quantity as u=[θ,M c ] Τ , the prediction model of the system is:

[0024]

[0025] in,

[0026] Assuming that the current moment is moment k, perform forward Euler discretization on equation (13), and combine the control quantity and state quantity into a new state matrix ξ(k) = [x(k)u(k-1)] T , the system state equation and output equation at the next moment can be expressed as:

[0027]

[0028] in, Δu(k)=u(k)-u(k-1); T represents the sampling period;

[0029] By iterative calculation, the prediction time domain N p and control time domain N c The system output is predicted, and the prediction equation of the system is obtained as follows:

[0030] Y(k)=Ψξ(k|k)+ΘΔU(k) ​​(7)

[0031] in,

[0032] ΔU(k)=[Δu(k|k),Δu(k+1|k),Δu(k+2|k),…,Δu(k+N c -1|k)] T ,

[0033] Y(k)=[η(k+1|k), η(k+2|k), η(k+3|k),…, η(k+N c |k),…,η(k+N p |k)] T ,

[0034]

[0035] The optimization objective function is designed as:

[0036]

[0037] Among them, Q and R represent the weight matrices of output error and control increment respectively; η r (k+ik) represents the expected output value of the system at time (k+i); K is the gain matrix obtained according to the output of the upper fuzzy controller, and

[0038] Based on the structural limitations and safety factors of the vehicle, constraints are imposed on the above control quantities, control increments and output quantities:

[0039]

[0040] Compared with the prior art, the advantages of the present invention are:

[0041] 1. The integrated control strategy of direct yaw moment control and active steering for a multi-axle vehicle described in the present invention adopts the multi-axle vehicle dynamics model as the reference model for lateral stability control, and realizes the coordination of direct yaw moment control and active front wheel steering based on phase plane analysis and fuzzy control to obtain the optimal coordination effect of AFS and DYC systems. The influence of the road adhesion coefficient on the coordination effect is considered in the fuzzy control, and the correction coefficient is adaptively adjusted according to the change of the road adhesion coefficient, so that the vehicle considers a smaller stable area on the road with a lower adhesion coefficient, thereby improving the robustness and adaptability of the coordinated control.

[0042] 2. The direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle described in the present invention takes the coordination result of the fuzzy controller as input, designs a yaw stability integrated controller based on adaptive model predictive control, and realizes precise control of the vehicle's yaw moment and active steering by predicting the future state and solving the optimal control input in each control cycle. While ensuring driving safety, the maneuverability and driving comfort are improved. The designed yaw stability integrated controller can automatically adjust the control strategy according to the real-time state of the multi-axle vehicle and the fuzzy control coordination result, effectively cope with complex road conditions and parameter changes, thereby improving the robustness and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0044] Figure 1 It is a flow chart of a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle according to the present invention;

[0045] Figure 2 It is a schematic diagram of a multi-axle vehicle dynamics model in a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle according to the present invention;

[0046] Figure 3 A schematic diagram of a triangle and Z-shaped mixed membership function curve of an input variable direct yaw moment control activation index corresponding to fuzzy control in a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle according to the present invention;

[0047] Figure 4 It is a schematic diagram of a triangle and Z-shaped mixed membership function curve of an output variable direct yaw moment control gain corresponding to fuzzy control in a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle according to the present invention;

[0048] Figure 5It is a schematic diagram of a Gaussian membership function curve of an output variable active front wheel steering gain corresponding to fuzzy control in a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle described in the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] See also Figure 1 The present invention provides a direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle, which specifically includes the following steps:

[0052] S1: See Figure 2 , assuming that the tire is in the linear range, the two-degree-of-freedom multi-axis vehicle dynamics model considering lateral and yaw motion is established as:

[0053]

[0054] Where m represents the vehicle mass; v x represents the longitudinal speed; β represents the sideslip angle of the center of mass; ω r represents the yaw angular velocity of the center of mass; C f , C m and C r Represent the cornering stiffness of the front, middle and rear axles of the vehicle respectively; θ represents the sum of the front wheel turning angle and the additional front wheel turning angle; L f , L m and L r Represents the distance from the center of mass to the front, middle and rear axles respectively; I z Represents the moment of inertia of the vehicle around the z-axis; M c is the additional yaw moment required;

[0055] S2: Based on the two-degree-of-freedom multi-axis vehicle dynamics model established in step S1, when the vehicle is in a steady-state condition, the steady-state yaw angular velocity of the vehicle is expressed as:

[0056]

[0057] When the vehicle is in extreme conditions, the calculated expected yaw rate will be replaced by the critical value, so the vehicle's expected yaw rate is expressed as:

[0058]

[0059] Among them, μ represents the road adhesion coefficient; g represents the acceleration of gravity;

[0060] Based on this model, the expected sideslip angle is β d =0;

[0061] S3: The two-degree-of-freedom multi-axis vehicle dynamics model established in step S1 is used as a reference model for lateral stability control, and the influence of the road adhesion coefficient on the coordination effect is considered. A fuzzy controller for coordinating direct yaw moment control and active front wheel steering is designed. The process is as follows: Phase plane analysis calculates the critical state of the vehicle during driving and divides the stable boundary area and the unstable boundary area in the phase plane; the activation index X of direct yaw moment control is defined as:

[0062]

[0063] When the vehicle enters the unstable boundary area, direct yaw moment control is activated; the coefficient correction table is obtained according to the change of the road adhesion coefficient, as shown in Table 1:

[0064] Table 1 DYC activation index coefficient correction table

[0065] <![CDATA[λ 1 ]]> <![CDATA[λ 2 ]]> μ≥0.75 0.07 0.2 0.75 ≥ μ ≥ 0.50 0.08 0.24 0.50≥μ≥0.35 0.095 0.3 0.35>μ 0.15 0.4

[0066] Correction coefficient λ online by looking up the table 1 and λ 2 , thereby changing the size of the stable region so that the vehicle considers a smaller stable region on roads with lower adhesion coefficients; see Figure 3 , the direct yaw moment control activation index is used as the input of the fuzzy system. According to the value of the activation index, its fuzzy set is defined into five levels, namely {S, MS, M, MB, B}, corresponding to small, medium, medium, large and large, respectively. The domain range is [0, 1]. The triangular and Z-shaped mixed membership function curves are used to convert the detected activation index value into a fuzzy number. Figure 4 and Figure 5The output variables of the fuzzy system are the direct yaw moment control gain and the active front wheel steering gain. The fuzzy set of the direct yaw moment control gain is defined as three levels, {S, M, B}, corresponding to small, medium and large, respectively, and the domain range is [0, 1]. The triangular and Z-shaped mixed membership function curves are used to defuzzify the decisions made according to the rule base. The center average method is used to perform weighted averaging on the centers of the fuzzy sets, and the weights are set to the heights of the corresponding fuzzy sets. The fuzzy output calculated is the direct yaw moment control gain; the fuzzy set of the active front wheel steering gain is defined as three levels, {S, M, B}, corresponding to small, medium and large, respectively, and the domain range is [0, 1]. The Gaussian membership function curve is used to defuzzify the decisions made according to the rule base. The center average method is used to perform weighted averaging on the centers of the fuzzy sets, and the weights are set to the heights of the corresponding fuzzy sets. The fuzzy output calculated is the active front wheel steering gain;

[0067] S4: Taking the coordination result of the fuzzy controller in step S3 as input, design a yaw stability integrated controller based on adaptive model predictive control, and calculate the additional front wheel steering angle and direct yaw moment to ensure stable driving of the vehicle. The process is as follows: select the state quantity x = [ω r ,β] T ; Select the control quantity as u=[θ,M c ] Τ , the prediction model of the system is:

[0068]

[0069] in,

[0070] Assuming that the current moment is moment k, perform forward Euler discretization on equation (13), and combine the control quantity and state quantity into a new state matrix ξ(k) = [x(k) u(k-1)] T , the system state equation and output equation at the next moment can be expressed as:

[0071]

[0072] in, Δu(k)=u(k)-u(k-1); T represents the sampling period;

[0073] By iterative calculation, the prediction time domain N p and control time domain N c The system output is predicted, and the prediction equation of the system is obtained as follows:

[0074] Y(k)=Ψξ(k|k)+ΘΔU(k) ​​(7)

[0075] in,

[0076] ΔU(k)=[Δu(k|k),Δu(k+1|k),Δu(k+2|k),…,Δu(k+N c -1|k)] T ,

[0077] Y(k)=[η(k+1|k), η(k+2|k), η(k+3|k),…, η(k+N c |k),…,η(k+N p |k)] T ,

[0078]

[0079] The optimization objective function is designed as:

[0080]

[0081] Among them, Q and R represent the weight matrices of output error and control increment respectively; η r (k+ik) represents the expected output value of the system at time (k+i); K is the gain matrix obtained according to the output of the upper fuzzy controller, and

[0082] Based on the structural limitations and safety factors of the vehicle, constraints are imposed on the above control quantities, control increments and output quantities:

[0083]

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A direct yaw moment control and active steering integrated control strategy for a multi-axle vehicle, characterized in that: The following steps are involved: S1: Assuming that the tire is in the linear range, the two-degree-of-freedom multi-axis vehicle dynamics model considering lateral and yaw motion is established as follows: Where m represents the vehicle mass; v x represents the longitudinal speed; β represents the sideslip angle of the center of mass; ω r represents the yaw angular velocity of the center of mass; C f , C m and C r Represent the cornering stiffness of the front, middle and rear axles of the vehicle respectively; θ represents the sum of the front wheel turning angle and the additional front wheel turning angle; L f , L m and L r Represents the distance from the center of mass to the front, middle and rear axles respectively; I z Represents the moment of inertia of the vehicle around the z-axis; M c is the additional yaw moment required; S2: Based on the two-degree-of-freedom multi-axis vehicle dynamics model established in step S1, when the vehicle is in a steady-state condition, the steady-state yaw angular velocity of the vehicle is expressed as: When the vehicle is in extreme conditions, the calculated expected yaw rate will be replaced by the critical value, so the vehicle's expected yaw rate is expressed as: Among them, μ represents the road adhesion coefficient; g represents the acceleration of gravity; Based on this model, the expected sideslip angle of the center of mass is β d =0; S3: The two-degree-of-freedom multi-axis vehicle dynamics model established in step S1 is used as a reference model for lateral stability control, and the influence of the road adhesion coefficient on the coordination effect is considered. A fuzzy controller for coordinating direct yaw moment control and active front wheel steering is designed. The process is as follows: Phase plane analysis calculates the critical state of the vehicle during driving and divides the stable boundary area and the unstable boundary area in the phase plane; the activation index X of direct yaw moment control is defined as: When the vehicle enters the unstable boundary area, direct yaw moment control is activated; the coefficient correction table is obtained according to the change of the road adhesion coefficient, as shown in Table 1: Table 1 DYC activation index coefficient correction table The coefficients λ1 and λ2 are corrected online by looking up the table, and then the size of the stable area is changed, so that the vehicle considers a smaller stable area on the road with a lower adhesion coefficient; the obtained direct yaw moment control activation index is used as the input of the fuzzy system, and according to the size of the activation index value, its fuzzy set is defined as 5 levels, namely {S, MS, M, MB, B}, corresponding to small, second small, medium, second large and large, respectively, and the domain range is [0,1]. The detected activation index value is converted into a fuzzy number using a triangular and Z-shaped mixed membership function curve; the output variables of the fuzzy system are the direct yaw moment control gain and the active front wheel steering gain. The fuzzy set of the direct yaw moment control gain is defined as 3 levels, namely {S, M, B}, corresponding to small, second small, medium, second large and large, respectively. Medium and large, the domain range is [0, 1], and the triangular and Z-shaped mixed membership function curves are used to defuzzify the decisions made according to the rule base. The center average method is used to perform weighted averaging on the centers of the fuzzy sets, and the weights are set to the heights of the corresponding fuzzy sets. The calculated fuzzy output is the direct yaw moment control gain; the fuzzy set of the active front wheel steering gain is defined at three levels, {S, M, B}, corresponding to small, medium and large, respectively, and the domain range is [0, 1]. The Gaussian membership function curve is used to defuzzify the decisions made according to the rule base. The center average method is used to perform weighted averaging on the centers of the fuzzy sets, and the weights are set to the heights of the corresponding fuzzy sets. The calculated fuzzy output is the active front wheel steering gain; S4: Taking the coordination result of the fuzzy controller in step S3 as input, design a yaw stability integrated controller based on adaptive model predictive control, and calculate the additional front wheel steering angle and direct yaw moment to ensure stable driving of the vehicle. The process is as follows: select the state quantity x = [ω r ,β] T ; Select the control quantity as u=[θ,M c ] Τ , the prediction model of the system is: in, Assuming that the current moment is moment k, perform forward Euler discretization on equation (13), and combine the control quantity and state quantity into a new state matrix ξ(k) = [x(k) u(k-1)] T , the system state equation and output equation at the next moment can be expressed as: in, Δu(k)=u(k)-u(k-1); T represents the sampling period; By iterative calculation, the prediction time domain N p and control time domain N c The system output is predicted, and the prediction equation of the system is obtained as follows: Y(k)=Ψξ(k|k)+ΘΔU(k) ​​(7) in, ΔU(k)=[Δu(k|k),Δu(k+1|k),Δu(k+2|k),…,Δu(k+N c -1|k)] T , Y(k)=[η(k+1|k),η(k+2|k),η(k+3|k),…,η(k+N c |k),…,η(k+N p |k)] T , The optimization objective function is designed as: Among them, Q and R represent the weight matrices of output error and control increment respectively; η r (k+i|k) represents the expected output value of the system at time (k+i); K is the gain matrix obtained according to the output of the upper fuzzy controller, and Based on the structural limitations and safety factors of the vehicle, constraints are imposed on the above control quantities, control increments and output quantities:

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