A vehicle stability control method based on a hybrid game theory-based corner module and an onboard computer

Abstract

This invention provides a method for stability control of corner module vehicles based on hybrid game theory and an on-board computer, relating to the field of vehicle dynamics control. This method constructs a hybrid game architecture consisting of a master-slave game and a non-cooperative game, establishing a master-slave relationship by setting the direct yaw moment control (DYC) as the leader and the active rear-wheel steering (ARS) as the follower. Then, a non-cooperative game is formed with the active suspension system (ASS). This ensures the decision-making priority of key systems under extreme conditions and achieves dynamic coordination among multiple systems. This invention enables more precise, efficient, and adaptive collaborative control between direct yaw moment control, active rear-wheel steering, and active suspension, ultimately comprehensively improving the dynamic stability of corner module vehicles in complex environments.

Description

Technical Field

[0001] This invention relates to the field of vehicle dynamics control, and in particular to a corner module vehicle stability control method and a vehicle computer based on hybrid game theory. Background Technology

[0002] With the rapid development of automotive electrification and intelligentization technologies, modular vehicles, with their ability for each wheel to drive and steer independently, have achieved unparalleled degrees of freedom and maneuverability compared to traditional vehicles, becoming a cutting-edge direction in chassis technology. However, this freedom also brings unprecedented control challenges. The dynamic model of modular vehicles exhibits high nonlinearity, strong coupling, and uncertainty. Under extreme conditions such as high speed and low adhesion, even minor misalignments in the movement of each wheel can easily lead to vehicle instability, placing extremely high demands on the accuracy and robustness of the stability control system.

[0003] To address this challenge, multi-system collaborative control has become an inevitable choice. Systems such as Direct Yaw Control (DYC), Active Rear Steering (ARS), and Active Suspension (ASS) are integrated on the corner module platform, with their control functions overlapping and potentially conflicting.

[0004] Existing collaborative control strategies are mostly based on rules or static weight allocation, which makes it difficult to dynamically evaluate the performance of each system in complex and ever-changing driving conditions. This may lead to control command conflicts and response delays, and fail to fully leverage the synergistic effect of multiple execution systems, thus limiting the safe release of the performance potential of corner module vehicles. Summary of the Invention

[0005] Objective: To propose a stability control method and vehicle computer for corner module vehicles based on hybrid game theory, constructing a novel control architecture through hybrid game theory. This architecture utilizes a master-slave game model and a non-cooperative game model to describe the cooperative yet competitive relationship among subsystems for a common stability goal. This enables more precise, efficient, and adaptive coordinated control between direct yaw moment control, active rear-wheel steering, and active suspension, ultimately comprehensively improving the dynamic stability of corner module vehicles in complex environments.

[0006] Firstly, a corner module vehicle stability control method based on hybrid game theory is proposed, with the following steps:

[0007] Considering the effects of additional yaw moment, rear wheel steering angle and roll moment, vehicle lateral, yaw and roll models are established. The vehicle dynamics control state space equation is obtained by combining the vehicle lateral, yaw and roll models. The vehicle dynamics control state space equation is then discretized.

[0008] The ideal yaw rate and ideal centroid sideslip angle of the vehicle are calculated under normal operating conditions and in inclined driving mode, respectively. The desired roll angle of the vehicle is defined in combination with the requirements of vehicle ride comfort and stability, so as to obtain the desired state of the vehicle under multiple operating conditions.

[0009] Based on hybrid game theory, in the corner module, the direct yaw moment control (DYC) is defined as the leader, the active rear wheel steering (ARS) as the follower, and the active suspension system (ASS) as the participant. A leader-follower game model between DYC and ARS is constructed. The leader-follower game model is combined with ASS to form a non-cooperative Nash game model. Cost functions for DYC, ARS, and ASS are established, and the system state equation for predicting future steps is established. The optimal control actions of DYC, ARS, and ASS are solved to obtain the optimal control actions of DYC, ARS, and ASS.

[0010] The optimal ARS control action is sent to the rear wheel actuator as the desired rear wheel angle; the optimal DYC control action and the optimal ASS control action are used as the desired control quantity for the current cycle, and the longitudinal force of the vehicle is optimized and allocated by combining the tire utilization cost function, the drive motor torque distribution cost function and the constraint conditions, and the active suspension force is allocated by combining the active suspension force distribution rules.

[0011] In a further embodiment of the first aspect, the expressions for the vehicle's lateral, yaw, and roll models are as follows:

[0012] ;

[0013] ;

[0014] ;

[0015] In the formula, m is the total vehicle mass; Let be the sprung mass of the vehicle; a and b are the distances from the center of gravity to the front and rear axles, respectively. The distance from the center of mass to the center of roll; Let X be the moment of inertia of the sprung mass of the vehicle about the X-axis. Let Z be the moment of inertia of the entire vehicle about the Z-axis; , These are the front and rear axle lateral stiffness, respectively. This refers to the lateral force acting on the front axle. This refers to the lateral force acting on the rear axle. For axial tilt stiffness; For roll damping; This refers to the vehicle body roll angle; ω is the yaw rate; u is the longitudinal velocity; v is the lateral velocity; It is the centroid sideslip angle; , These are the steering angles of the front and rear wheels of the car, respectively. , These are the front and rear wheel slip angles, respectively. This refers to the vehicle's lateral acceleration. Differentiate for lateral velocity; This is the yaw acceleration; The angular velocity of the center of mass deflection; This refers to the roll angular velocity; This refers to the roll acceleration; To add yaw moment; This is the tilting moment.

[0016] In a further embodiment of the first aspect, the vehicle dynamics control state-space equation is: Where x is a state variable, ; ; ; ; .

[0017] In a further embodiment of the first aspect, the ideal yaw rate under the conventional operating conditions Side slip angle relative to the ideal center of mass Calculated using the following formula:

[0018] ;

[0019] ;

[0020] In the formula, K is the vehicle stability factor. L is the wheelbase. u represents the longitudinal velocity; The steering angle of the car's front wheels;

[0021] The ideal yaw rate in the oblique mode Ideal centroid sideslip angle .

[0022] In a further embodiment of the first aspect, the desired state of the vehicle under multiple operating conditions is defined as follows:

[0023] ;

[0024] ;

[0025] ;

[0026] In the formula, The desired yaw rate of the vehicle under multiple operating conditions; The expected centroid sideslip angle under various operating conditions of the vehicle; This represents the desired roll angle of the vehicle under various operating conditions.

[0027] In a further embodiment of the first aspect, the master-slave game model is a Stackelberg master-slave game model in which DYC is the leader and ARS is the follower;

[0028] In the master-slave game model, the following output is defined:

[0029] ;

[0030] ;

[0031] ;

[0032] In the formula, DYC output represents the leader's output; Represents the ARS output of the follower; Representative participant's ASS output;

[0033] Define the desired control state of the leader DYC as follows: The follower ARS expects the control state to be... The desired control state of the participant ASS is .

[0034] In a further embodiment of the first aspect, a cost function for the leader DYC is established. as follows:

[0035] ;

[0036] In the formula, The weights for the sideslip angle and yaw rate under DYC conditions; This represents the desired control state for DYC. This represents the output of DYC in the master-slave game model. DYC control weights; To predict the step size;

[0037] Cost function The optimization problem is constructed into a standard quadratic programming form:

[0038] ;

[0039] ;

[0040] In the formula, This is the control input sequence vector of DYC at the current sampling time k; and Let represent the Hessian matrix and linear coefficient vector of the leader's DYC quadratic programming, respectively; This is the matrix transpose. and Let represent the constraint matrix and the right-hand vector of the leader's DYC quadratic programming, respectively;

[0041] Establish a cost function that follows the ARS. as follows:

[0042] ;

[0043] In the formula, The weights for the sideslip angle and yaw rate under ARS; The desired control state for ARS; This represents the output of ARS in the master-slave game model. Control weights for ARS; To predict the step size;

[0044] Cost function The optimization problem is constructed into a standard quadratic programming form:

[0045] ;

[0046] ;

[0047] In the formula, This is the control input sequence vector of the ARS at the current sampling time k; and Let represent the Hessian matrix and linear coefficient vector of the follower ARS quadratic programming problem, respectively. This is the matrix transpose. and Let represent the constraint matrix and the constraint right-hand vector of the follower ARS quadratic programming, respectively;

[0048] Establish the cost function of the participant ASS as follows:

[0049] ;

[0050] In the formula, The weight of the ASS underpitch angle; The desired control state for the ASS; This represents the output of ASS in the master-slave game model. Control weights for ARS; To predict the step size;

[0051] Cost function The optimization problem is constructed into a standard quadratic programming form:

[0052] ;

[0053] ;

[0054] In the formula, This is the control input sequence vector of the ASS at the current sampling time k; and Let represent the Hessian matrix and linear coefficient vector of the participants in the ASS quadratic programming, respectively; This is the matrix transpose. and Let represent the constraint matrix and the right-hand vector of the ASS quadratic programming for the participants, respectively.

[0055] In a further embodiment of the first aspect, based on distributed model predictive control theory, the control step size is set to... Predicting at sampling time k The system state equation for step is:

[0056] ;

[0057] In the formula, This is the state sequence at the current sampling time k; This is the predicted sequence of vehicle states at the current sampling time k; The state matrix of the prediction equation; , , , These are the control input matrices for the leader (DYC), followers (ASS), participants (ASS), and perturbations, respectively. , , These are the control input sequence vectors for the leader DYC, the follower ASS, and the participant ASS at the current sampling time k; is the perturbation of the input sequence vector.

[0058] The prediction system output is:

[0059] ;

[0060] ;

[0061] ;

[0062] In the formula, Forecast output for leader DYC; Predict the output for the follower DYC; ASS predicts output for participants; The leader's DYC state observation matrix, For the follower ARS state observation matrix, This is the ASS state observation matrix for the participants.

[0063] In a further embodiment of the first aspect, the tire utilization cost function The definition is as follows:

[0064] ;

[0065] In the formula, This refers to the longitudinal force of the tire; This refers to the lateral force of the tire; The vertical force of the tire; w is taken as... Any one of them, Indicates the left front wheel. Indicates the right front wheel. Indicates the left rear wheel. Indicates the right rear wheel; The road adhesion coefficient for a single tire;

[0066] Cost function for maximum utilization of a single tire The definition is as follows:

[0067] ;

[0068] The torque distribution cost function J of the drive motor is defined as follows:

[0069] ;

[0070] In the formula, , For weights.

[0071] In a further embodiment of the first aspect, considering the constraints of the motor peak torque, the desired additional yaw moment, the total desired longitudinal force, and the road adhesion coefficient, the vehicle longitudinal force optimization allocation function is constructed as follows:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] In the formula, 'r' represents the peak torque of the motor; 'r' represents the tire radius. The total longitudinal force required by the driver; This represents the desired torque of the motor.

[0077] The active suspension force distribution rules are as follows:

[0078] ;

[0079] In the formula, B is the wheelbase; For the active suspension force of the left front wheel; The active suspension force is for the right front wheel; For the left rear wheel active suspension force; This is the active suspension force for the right rear wheel.

[0080] In a second aspect, the present invention provides a vehicle computer including a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the corner module vehicle stability control method based on hybrid game theory disclosed in the first aspect and its further embodiments.

[0081] Beneficial Effects: This invention constructs a hybrid game architecture of "master-slave game + non-cooperative game," establishing a master-slave relationship by setting Direct Yaw Torque Control (DYC) as the leader and Active Rear Steering (ARS) as the follower, and then forming a non-cooperative game with the Active Suspension System (ASS). This approach ensures the decision-making priority of key systems under extreme conditions while achieving dynamic coordination among multiple systems. This hierarchical hybrid strategy significantly improves the solution efficiency of the control system and is more consistent with vehicle dynamics, providing more accurate and robust stability assurance for corner module vehicles in complex driving environments. Attached Figure Description

[0082] Figure 1 This is a flowchart of the vehicle stability control method based on hybrid game theory of the present invention. Detailed Implementation

[0083] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0084] This embodiment discloses a corner module vehicle stability control method based on hybrid game theory, the process architecture of which is shown below. Figure 1 The specific process is as follows:

[0085] S1: Vehicle Model Building: Considering the effects of additional yaw moment, rear wheel steering angle, and roll moment, first establish the vehicle's lateral, yaw, and roll models:

[0086]

[0087] Where: m is the total vehicle mass; Let be the sprung mass of the vehicle; a and b are the distances from the center of gravity to the front and rear axles, respectively. The distance from the center of mass to the center of roll; Let X be the moment of inertia of the sprung mass of the vehicle about the X-axis. Let Z be the moment of inertia of the entire vehicle about the Z-axis; , These are the front and rear axle lateral stiffness, respectively. This refers to the lateral force acting on the front axle. This refers to the lateral force acting on the rear axle. For axial tilt stiffness; For roll damping; This refers to the vehicle body roll angle; ω is the yaw rate; u is the longitudinal velocity; v is the lateral velocity; It is the centroid sideslip angle; , These are the steering angles of the front and rear wheels of the car, respectively. , These are the front and rear wheel slip angles, respectively. This refers to the vehicle's lateral acceleration. Differentiate for lateral velocity; This is the yaw acceleration; The angular velocity of the center of mass deflection; This refers to the roll angular velocity; This refers to the roll acceleration; To add yaw moment; This is the tilting moment.

[0088] Considering that the vehicle roll angle is generally small during vehicle movement, to simplify the calculation, let:

[0089]

[0090]

[0091] Equation (1) can be rewritten as:

[0092]

[0093] Based on the vehicle dynamics model (4), the vehicle dynamics control state space equations expressed in the form of state space equations are established:

[0094]

[0095] In the formula, x is the state variable. ; ; ; ; ;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] Equation (5) is discretized using an approximate discretization method:

[0102]

[0103] In the formula: ; ; ; ; .

[0104] S2: Multi-mode vehicle state reference value calculation: Due to the diverse steering modes of the angular module, the ideal yaw rate is first calculated using a linear two-degree-of-freedom vehicle dynamics model under normal operating conditions. Side slip angle relative to the ideal center of mass :

[0105]

[0106]

[0107] In the formula, K is the vehicle stability factor. L is the wheelbase. .

[0108] Meanwhile, considering road surface adhesion limitations, the desired yaw rate is constrained:

[0109]

[0110] In the formula This is the road surface adhesion coefficient.

[0111] Ideal yaw rate under normal operating conditions for:

[0112]

[0113] Considering the independent steering of four wheels in a modular vehicle configuration, when all four wheels rotate at the same angle, the vehicle enters a yaw mode. The ideal yaw rate in yaw mode is... Side slip angle relative to the ideal center of mass :

[0114]

[0115]

[0116] Finally, considering the requirements for vehicle ride comfort and stability, the desired vehicle roll angle is defined as follows:

[0117]

[0118] In summary, the desired state of a vehicle under multiple operating conditions is defined as follows:

[0119]

[0120] S3: Stability control strategy for DYC, ARS, and ASS based on hybrid game theory: Define DYC as participant 1, ARS as participant 2, and ASS as participant 3. First, construct a master-slave game model between DYC and ARS. Then, construct a non-cooperative Nash game model between DYC-ARS and ASS to determine the rear wheel steering angle, additional yaw moment, and suspension force.

[0121] Considering that the longitudinal force working range is larger and less prone to saturation under the extreme state of the vehicle, we first construct a Stackelberg master-follower game model with DYC as the leader and ARS as the follower.

[0122] In the master-slave game, the system output is defined as follows:

[0123]

[0124] In the formula: ; ; ; ; DYC output represents the leader's output; Represents the ARS output of the follower; 3ASS outputs representing participants;

[0125] Define the desired control state of the leader DYC as follows: The follower ARS expects the control state to be... The desired control state of participant 3ASS is :

[0126]

[0127]

[0128]

[0129] Based on distributed model predictive control theory, predictive equations are established to predict the future output of the system, while also providing a basis for information exchange among game players. Based on model predictive control theory, the control step size is set... Predicting at sampling time k The system state equation for step is:

[0130]

[0131] In the formula:

[0132] ; ; ; ; ; ; ;

[0133] ; ; ; is the prediction step size; k is the current sampling time.

[0134] Define the output of the prediction system:

[0135]

[0136]

[0137]

[0138] In the formula For the leader DYC predictive output, For the follower DYC, predict the output. 3ASS prediction output for participants ; ; .

[0139] For the DYC, ARS, and ASS systems, their actuators have saturation constraints. The constraints on the actions of these three systems are shown in the following equation:

[0140] ;

[0141] ;

[0142] ;

[0143] In the formula, This is the minimum value of the DYC action. This represents the maximum value of the DYC action. This is the minimum value of the ARS action. This represents the maximum value of the ARS action. This is the minimum value of the ASS action. This represents the maximum value of the ASS action.

[0144] S3.1 Establish the leader DYC cost function:

[0145]

[0146] In the formula The weights are the sideslip angle and the yaw rate. The leader, DYC, controls the weight.

[0147] From equation (20), we can obtain:

[0148]

[0149] In the formula This refers to the actions of the follower ARS in the previous cycle; The actions of the participants in the previous cycle, 3ASS.

[0150] Define the expected output vector:

[0151]

[0152] In the formula, I is the identity matrix; This represents the Kroc inner product.

[0153] The leader DYC cost function can be rewritten as:

[0154]

[0155] In the formula ; .

[0156] Define the error term:

[0157]

[0158] The output error is then:

[0159]

[0160] Therefore, equation (26) can be expressed as:

[0161]

[0162] The cost function (29) can be expanded into matrix operation form:

[0163]

[0164] Expanding the above equation, we get:

[0165]

[0166] Expanding the above equation and ignoring the constant term, the optimization problem of the leader DYC can be constructed as a standard quadratic programming (QP) problem:

[0167]

[0168] The coefficient matrix of the QP problem is defined as follows:

[0169] ;

[0170] ;

[0171] constraint matrix with vector According to actuator amplitude constraints Build:

[0172]

[0173] By solving the above QP problem using the interior point method, the control actions of the leader DYC can be obtained.

[0174] S3.2 After obtaining the control actions of the leader DYC, it is necessary to solve for the actions of the follower ARS and establish the cost function for following ARS:

[0175]

[0176] In the formula The weights are the sideslip angle and the yaw rate. The weights are controlled by the ARS of the followers.

[0177] From equation (21), we can obtain the follower prediction equation:

[0178]

[0179] Rewrite the predicted output as:

[0180]

[0181] make:

[0182]

[0183] The expected output vector of the follower ARS is:

[0184]

[0185] Define the error vector:

[0186]

[0187] The output error is then:

[0188]

[0189] The follower ARS cost function can be described as follows:

[0190]

[0191] The cost function of the follower ARS is expanded into matrix form using the weighted Euclidean norm definition:

[0192]

[0193] Expanding the above equation, we get:

[0194]

[0195] To handle actuator constraints, the optimization problem of ARS is constructed into a standard quadratic programming (QP) form:

[0196]

[0197] The coefficient matrix of the QP problem is defined as follows:

[0198] ;

[0199] ;

[0200] constraint matrix with vector According to actuator amplitude constraints Build:

[0201]

[0202] By solving the above QP problem, the control actions of the follower ARS can be obtained.

[0203] S3.3, Define the cost function for participant 3ASS:

[0204]

[0205] In the formula The weight of the roll angle; ASS control weights for participants.

[0206] From equation (22), we can obtain the follower prediction equation:

[0207]

[0208] Rewrite the predicted output as:

[0209]

[0210] Define the expected output vector:

[0211]

[0212] In the formula, I is the identity matrix; This represents the Kroc inner product.

[0213] The participant 3ASS cost function can be described as follows:

[0214]

[0215] Define the error term:

[0216]

[0217] The output error is then:

[0218]

[0219] Therefore, equation (26) can be expressed as:

[0220]

[0221] The cost function of the participant ASS is expanded into matrix form using the weighted Euclidean norm definition:

[0222]

[0223] Expanding the above equation, we get:

[0224]

[0225] To handle actuator constraints, the optimization problem of ASS is constructed into a standard quadratic programming (QP) form:

[0226]

[0227] The coefficient matrix of the QP problem is defined as follows:

[0228] ;

[0229] ;

[0230] constraint matrix with vector According to actuator amplitude constraints Build:

[0231]

[0232] By solving the QP problem described above, the control actions of the participant ASS can be obtained.

[0233] S3.4. Based on steps S3.1, S3.2, and S3.3, a sequential iterative method is used to approximate the Nash equilibrium. The first solution yields... , and At this time and Substituting into equation (33) yields a new solution. After that and Substituting into equation (45) and solving, we get Then and Substituting into equation (57) yields .

[0234] S3.5 Definition:

[0235]

[0236]

[0237] When equation (59) is not satisfied, let:

[0238]

[0239] Then repeat step S3.5.

[0240] When equation (59) is satisfied, the optimal solution is considered to have been reached. Equation (60) is then executed, the iteration loop is exited, and the optimal solution for the leader DYC, follower ARS, and participant 3ASS is obtained. , and .

[0241] S4: Based on step S3 The first cycle, as the expected control value for the current cycle, serves as the expected rear wheel angle. It is sent directly to the rear wheel actuator; the result is... and Take the first period as the expected control value for the current period. and Then, torque is distributed to the lower actuators to achieve the desired additional yaw and roll torques.

[0242] Define the tire utilization cost function:

[0243]

[0244] In the formula, This refers to the longitudinal force of the tire; This refers to the lateral force of the tire; The vertical force of the tire; w is taken as... Any one of them, Indicates the left front wheel. Indicates the right front wheel. Indicates the left rear wheel. Indicates the right rear wheel; This represents the road adhesion coefficient for a single tire.

[0245] Define the cost function for maximizing the utilization of a single tire:

[0246]

[0247] Establish a drive motor torque distribution cost function that considers tire utilization rate and the maximum utilization rate of a single tire:

[0248]

[0249] In the formula and These are the weights for tire average and maximum utilization, respectively.

[0250] Finally, considering the peak torque of the motor, the desired additional yaw moment, the total desired longitudinal force, and the constraints of the road adhesion coefficient, the optimal distribution function for the vehicle's longitudinal force is designed as follows:

[0251]

[0252] In the formula, The peak torque of the motor is r; the tire radius is r. The total longitudinal force required by the driver; This represents the desired torque of the motor.

[0253] The active suspension force distribution rules are as follows:

[0254]

[0255] In the formula, B is the wheelbase. Left front wheel active suspension force, Right front wheel active suspension force, Left rear active suspension force, Right rear wheel active suspension force.

[0256] Furthermore, this embodiment also discloses a vehicle control unit (ECU), which includes a processor, a memory, a communication interface, and a communication bus. The processor, memory, and communication interface communicate with each other via the communication bus. The memory stores at least one executable instruction, which causes the processor to execute the vehicle energy-saving and stability coordination control method disclosed in the above embodiment. It should be understood that other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0257] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0258] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0259] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0260] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0261] In a typical configuration, a computing device includes one or more processors, input / output interfaces, network interfaces, and memory.

[0262] Memory may include non-persistent memory in computer-readable media, random access memory, and / or non-volatile memory, such as read-only memory or flash memory. Memory is an example of computer-readable media.

[0263] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, optical disc read-only memory, digital versatile optical disc or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media do not include temporary computer-readable media, such as modulated data signals and carrier waves.

[0264] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A vehicle stability control method based on a hybrid game theory approach, characterized in that, Includes the following steps: Considering the effects of additional yaw moment, rear wheel steering angle and roll moment, vehicle lateral, yaw and roll models are established. The vehicle dynamics control state space equation is obtained by combining the vehicle lateral, yaw and roll models. The vehicle dynamics control state space equation is then discretized. The ideal yaw rate and ideal centroid sideslip angle of the vehicle are calculated under normal operating conditions and in inclined driving mode, respectively. The desired roll angle of the vehicle is defined in combination with the requirements of vehicle ride comfort and stability, so as to obtain the desired state of the vehicle under multiple operating conditions. Based on hybrid game theory, in the corner module, the direct yaw moment control (DYC) is defined as the leader, the active rear wheel steering (ARS) as the follower, and the active suspension system (ASS) as the participant. A leader-follower game model between DYC and ARS is constructed. The leader-follower game model is combined with ASS to form a non-cooperative Nash game model. Cost functions for DYC, ARS, and ASS are established, and the system state equation for predicting future steps is established. The optimal control actions of DYC, ARS, and ASS are solved to obtain the optimal control actions of DYC, ARS, and ASS. The optimal ARS control action is sent to the rear wheel actuator as the desired rear wheel angle; the optimal DYC control action and the optimal ASS control action are used as the desired control quantity for the current cycle, and the longitudinal force of the vehicle is optimized and allocated by combining the tire utilization cost function, the drive motor torque distribution cost function and the constraint conditions, and the active suspension force is allocated by combining the active suspension force distribution rules.

2. The corner module vehicle stability control method based on hybrid game theory according to claim 1, characterized in that, The expressions for the vehicle's lateral, yaw, and roll models are as follows: ； ； ； In the formula, m is the total vehicle mass; For vehicle sprung mass; a and b are the distances from the center of mass to the front and rear axles, respectively; The distance from the center of mass to the center of roll; Let X be the moment of inertia of the sprung mass of the vehicle about the X-axis. Let Z be the moment of inertia of the entire vehicle about the Z-axis; , These are the front and rear axle lateral stiffness, respectively. This refers to the lateral force acting on the front axle. This refers to the lateral force acting on the rear axle. For axial tilt stiffness; For roll damping; This refers to the vehicle body roll angle; ω is the yaw rate; u is the longitudinal velocity; v is the lateral velocity; It is the centroid sideslip angle; , These are the steering angles of the front and rear wheels of the car, respectively. , These are the front and rear wheel slip angles, respectively. This refers to the vehicle's lateral acceleration. Differentiate for lateral velocity; This is the yaw acceleration; The angular velocity of the center of mass deflection; This refers to the roll angular velocity; This refers to the roll acceleration; To add yaw moment; This is the tilting moment.

3. The corner module vehicle stability control method based on hybrid game theory according to claim 2, characterized in that, The vehicle dynamics control state space equation is: Where x is a state variable, ; ; ; ; ; , , , , These are the system matrix, DYC input matrix, ARS input matrix, ASS input matrix, and disturbance matrix.

4. The corner module vehicle stability control method based on hybrid game theory according to claim 2, characterized in that, The ideal yaw rate under normal operating conditions Side slip angle relative to the ideal center of mass Calculated using the following formula: ； ； In the formula, K is the vehicle stability factor. L is the wheelbase. u represents the longitudinal velocity; The steering angle of the car's front wheels; The ideal yaw rate in the oblique mode Ideal centroid sideslip angle .

5. The corner module vehicle stability control method based on hybrid game theory according to claim 4, characterized in that, The desired state of the vehicle under multiple operating conditions is defined as follows: ； ； ； In the formula, The desired yaw rate of the vehicle under multiple operating conditions; The expected centroid sideslip angle under various operating conditions of the vehicle; This represents the desired roll angle of the vehicle under various operating conditions.

6. The corner module vehicle stability control method based on hybrid game theory according to claim 1, characterized in that, The master-slave game model is the Stackelberg master-slave game model in which DYC is the leader and ARS is the follower. In the master-slave game model, the following output is defined: ； ； ； In the formula, DYC output represents the leader's output; Represents the ARS output of the follower; Representative participant's ASS output; Define the desired control state of the leader DYC as follows: The follower ARS expects the control state to be... The desired control state of the participant ASS is .

7. The corner module vehicle stability control method based on hybrid game theory according to claim 6, characterized in that, Establish the cost function of leader DYC as follows: ； In the formula, The weights for the sideslip angle and yaw rate under DYC conditions; This represents the desired control state for DYC. This represents the output of DYC in the master-slave game model. DYC control weights; To predict the step size; Cost function The optimization problem is constructed into a standard quadratic programming form: ； ； In the formula, This is the control input sequence vector of DYC at the current sampling time k; and Let represent the Hessian matrix and linear coefficient vector of the leader's DYC quadratic programming, respectively; This is the matrix transpose. and Let represent the constraint matrix and the right-hand vector of the leader's DYC quadratic programming, respectively; Establish a cost function that follows the ARS. as follows: ； In the formula, The weights for the sideslip angle and yaw rate under ARS; The desired control state for ARS; This represents the output of ARS in the master-slave game model. Control weights for ARS; To predict the step size; Cost function The optimization problem is constructed into a standard quadratic programming form: ； ； In the formula, This is the control input sequence vector of the ARS at the current sampling time k; and Let represent the Hessian matrix and linear coefficient vector of the follower ARS quadratic programming problem, respectively. This is the matrix transpose. and Let represent the constraint matrix and the constraint right-hand vector of the follower ARS quadratic programming, respectively; Establish the cost function of the participant ASS as follows: ； In the formula, The weight of the ASS underpitch angle; The desired control state for the ASS; This represents the output of ASS in the master-slave game model. Control weights for ARS; To predict the step size; Cost function The optimization problem is constructed into a standard quadratic programming form: ； ； In the formula, This is the control input sequence vector of the ASS at the current sampling time k; and Let represent the Hessian matrix and linear coefficient vector of the participants in the ASS quadratic programming, respectively; This is the matrix transpose. and Let represent the constraint matrix and the right-hand vector of the ASS quadratic programming for the participants, respectively.

8. The corner module vehicle stability control method based on hybrid game theory according to claim 1, characterized in that, Based on distributed model predictive control theory, let the control step size Predicting at sampling time k The system state equation for step is: ； In the formula, This is the state sequence at the current sampling time k; This is the predicted sequence of vehicle states at the current sampling time k; The state matrix of the prediction equation; , , , These are the control input matrices for the leader (DYC), followers (ASS), participants (ASS), and perturbations, respectively. , , These are the control input sequence vectors for the leader DYC, the follower ASS, and the participant ASS at the current sampling time k; The input sequence vector is the perturbation. The prediction system output is: ； ； ； In the formula, Forecast output for leader DYC; For follower ARS prediction output; ASS predicts output for participants; The leader's DYC state observation matrix, For the follower ARS state observation matrix, The ASS state observation matrix for participants.

9. The corner module vehicle stability control method based on hybrid game theory according to claim 1, characterized in that, The tire utilization cost function The definition is as follows: ； In the formula, This refers to the longitudinal force of the tire; This refers to the lateral force of the tire; The vertical force of the tire; w is taken as... Any one of them, Indicates the left front wheel. Indicates the right front wheel. Indicates the left rear wheel. Indicates the right rear wheel; The road adhesion coefficient for a single tire; Cost function for maximum utilization of a single tire The definition is as follows: ； The torque distribution cost function J of the drive motor is defined as follows: ； In the formula, , As weight.

10. The corner module vehicle stability control method based on hybrid game theory according to claim 9, characterized in that, Considering the constraints of the motor peak torque, the desired additional yaw moment, the total desired longitudinal force, and the road adhesion coefficient, the optimal allocation function for the vehicle's longitudinal force is constructed as follows: ； ； ； ； In the formula, 'r' represents the peak torque of the motor; 'r' represents the tire radius. The total longitudinal force required by the driver; This represents the desired torque of the motor. The active suspension force distribution rules are as follows: ； In the formula, B is the wheelbase; For the active suspension force of the left front wheel; The active suspension force is for the right front wheel; For the left rear wheel active suspension force; This is the active suspension force for the right rear wheel.

11. A vehicle computer, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the corner module vehicle stability control method based on hybrid game theory as described in any one of claims 1 to 10.

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