A vehicle posture cooperative control method based on a distributed inertial measurement unit

Abstract

This invention relates to a vehicle attitude cooperative control method based on a distributed inertial measurement unit (IMU). The method includes setting a dynamic time synchronization interval based on unexpected vehicle operating conditions, and adjusting this interval by considering the error coupling characteristics of the IMU. A sampling timing sequence is determined based on the adjusted synchronization interval and node deployment locations. Vehicle motion and environmental perception information are collected in real time, and a state description vector is constructed by combining dynamic characteristic information and node deployment locations. A distributed extended Kalman filter (EDF) model is constructed, taking the state description vector as input and outputting preliminary state results. Cooperative corrections are performed to obtain control parameters. The risk of vehicle attitude instability is assessed to obtain an instability risk assessment result. A control adjustment strategy is then formulated based on the control parameters. This invention improves the measurement accuracy and synchronization stability of the distributed IMU, achieving global cooperative and precise control of vehicle attitude and accurate prediction and hierarchical regulation of attitude instability risk.

Description

Technical Field

[0001] This invention belongs to the field of cooperative control technology, specifically a vehicle attitude cooperative control method based on distributed inertial measurement units. Background Technology

[0002] With the development of intelligent connected vehicle technology, the accuracy of vehicle attitude control directly affects driving safety and ride comfort. Especially in unexpected situations such as high-speed emergency obstacle avoidance and off-road bumps, the requirements for the real-time performance and accuracy of attitude perception and control are becoming increasingly stringent. Distributed inertial measurement units (IMUs) have become the core solution for vehicle attitude perception because they can collect motion data from multiple points. However, their application faces problems such as low time synchronization accuracy and inaccurate attitude instability assessment, which restricts the improvement of control performance.

[0003] In existing technologies, distributed IMUs mostly use fixed-time synchronization intervals, which cannot adapt to complex operating conditions. High-frequency sampling is required to capture sudden attitude changes during high-speed emergency obstacle avoidance, but conventional intervals easily lead to data lag. Under off-road and bumpy conditions, IMU zero-bias drift is coupled with installation coordinate deviations, and excessively long synchronization intervals amplify errors. Simultaneously, the coupling effect of multi-node time synchronization deviations and measurement noise further reduces data consistency, leading to attitude perception deviations and making it difficult to meet control requirements.

[0004] In assessing vehicle attitude instability, traditional methods often rely on single motion parameters, such as judging instability solely by yaw rate, neglecting the influence of environmental factors. When skidding on icy or snowy roads, the correlation between the decrease in road adhesion coefficient and tire slip ratio is not quantified; under crosswind interference, the coupling relationship between aerodynamic loads and vehicle attitude is not included in the assessment model, leading to delayed or misjudged assessment results and failing to provide accurate basis for control strategies.

[0005] Some improvement schemes attempt to optimize the synchronization interval or evaluation indicators, but they have limitations: some only consider the error characteristics of the uncorrelated IMU under the operating conditions, and the synchronization adjustment lacks theoretical support; some evaluation models do not integrate multi-source environmental data, making it difficult to cover complex scenarios. Therefore, there is an urgent need for a vehicle attitude cooperative control method based on distributed inertial measurement units. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention proposes a vehicle attitude cooperative control method based on distributed inertial measurement units (IMUs). This invention primarily addresses the problems of low time synchronization accuracy of IMU nodes and inaccurate assessment of vehicle attitude instability.

[0007] The technical solution adopted by this invention to solve its technical problem is: a vehicle attitude cooperative control method based on a distributed inertial measurement unit provided by this invention, comprising:

[0008] The dynamic characteristics of the target vehicle and the node deployment location of the distributed inertial measurement unit are obtained. The dynamic time synchronization interval is set according to the unexpected operating conditions of the vehicle, and the adjustment synchronization interval is obtained by combining the error coupling characteristics of the distributed inertial measurement unit.

[0009] Based on adjusting the synchronization interval and node deployment location, the sampling timing is determined, and the vehicle's motion information and environmental perception information are collected in real time. The state description vector is constructed by combining the dynamic characteristic information and node deployment location.

[0010] Construct a distributed extended Kalman filter model, take the state description vector as input, and output the preliminary state results.

[0011] The control parameters are obtained by coordinating the preliminary state results using a multi-agent consensus method. The instability risk of the vehicle is assessed based on motion information and environmental perception information to obtain the instability risk assessment result. The control adjustment strategy is then formulated in conjunction with the control parameters.

[0012] This invention provides a vehicle attitude cooperative control method based on distributed inertial measurement units (IMUs). The dynamic characteristics include vehicle center of gravity height, moment of inertia, suspension stiffness, and tire adhesion coefficient. Node deployment location information includes the three-dimensional coordinates and installation angles of each IMU in the vehicle's body coordinate system. Unexpected operating conditions include high-speed emergency obstacle avoidance, off-road bumpy conditions, heavy-load sharp turning conditions, and icy / slippery road conditions. Error coupling characteristics include the coupling between zero-bias drift of each IMU and installation coordinate deviation, and the coupling between multi-node time synchronization deviation and measurement noise.

[0013] The present invention provides a vehicle attitude cooperative control method based on a distributed inertial measurement unit, wherein the step of adjusting the synchronization interval includes:

[0014] Determine the basic time synchronization interval requirements and operating condition correction coefficients under different unexpected operating conditions, determine the error coupling type based on the error coupling characteristics, and construct a coupling error evaluation function.

[0015] Based on real-time data from onboard sensors, the type of unexpected operating condition the vehicle is currently in is identified by a threshold comparison method. Combined with a preset base interval and a real-time operating condition correction coefficient, the dynamic time synchronization interval is calculated.

[0016] The gradient value of the coupling strength as a function of dynamic time synchronization interval is obtained by taking the derivative of the coupling error evaluation function, and the adjustment synchronization interval is calculated by introducing the error coupling correction coefficient.

[0017] This invention provides a vehicle attitude cooperative control method based on a distributed inertial measurement unit, the steps of which include determining the sampling timing sequence as follows:

[0018] Based on the correlation between node deployment location and vehicle vibration characteristics, nodes are divided into key area group and auxiliary area group, and the reference duration of sampling period is determined by adjusting the synchronization interval.

[0019] Based on the spatial distance of node deployment locations, the signal transmission delay between each node in the regional group and auxiliary regional group and the central controller is calculated, and the local clock phase of each node is calibrated.

[0020] Based on the contribution of node deployment location to vehicle attitude perception, sampling priority is set, and sampling phase difference is set within the same adjustment synchronization interval period in combination with the spatial distribution of each node.

[0021] This invention provides a vehicle attitude cooperative control method based on distributed inertial measurement units, the steps of constructing the state description vector include:

[0022] According to the sampling sequence, three-axis angular velocity, three-axis acceleration data and node status are collected at the trigger time as local motion data. Based on the node deployment position, the local motion data is converted into the vehicle body coordinate system to obtain motion information.

[0023] Within the unified adjustment and synchronization interval of sampling, the vehicle-mounted multi-source environmental sensors are synchronously triggered to collect environmental perception information.

[0024] The dynamic feature information, node deployment location, motion information, and real-time environmental perception information are concatenated according to a preset dimensional order to form a state description vector.

[0025] This invention provides a vehicle attitude cooperative control method based on distributed inertial measurement units, the steps of which include constructing a distributed extended Kalman filter model:

[0026] Based on preset goals and actual usage requirements, constraint assumptions are set, and for multiple inertial measurement unit nodes, a local state vector is defined that includes the vehicle attitude state and the inertial measurement unit error state.

[0027] Based on the node deployment location, the local state vectors of all nodes are transformed into the vehicle centroid coordinate system, and the global state vector is obtained by aggregation. The local observation vector is defined using the sampled data of each node as the observation value.

[0028] Based on the vehicle rigid body attitude dynamics equations, a state prediction model for nodes is established, and the posterior state estimate is obtained by correcting the model based on the nonlinear mapping relationship between the observed values ​​and the local state vector.

[0029] The neighborhood state fusion value is calculated based on the posterior state estimate and covariance matrix of each node. The local posterior state of the node is corrected to obtain the interactive corrected state. The global state vector is then combined to apply a consistency constraint to the interactive corrected state of all nodes.

[0030] The present invention provides a vehicle attitude cooperative control method based on distributed inertial measurement units, the steps for obtaining preliminary state results include:

[0031] The integrity of the state description vector is verified by splitting it into system input components, observation input components, and topological constraint components according to the model input requirements.

[0032] For each node, filter initialization configuration is performed, an error penalty term parameter is added, and prior state prediction of local nodes is performed based on system input components.

[0033] The prior state prediction is corrected based on the observed input components to obtain the posterior state. The posterior state is then corrected based on the error penalty term parameter to obtain the local node state estimate.

[0034] The neighborhood relationship is defined based on topological constraint components, data interaction between nodes is executed, and the neighborhood fusion state is calculated according to the principle that the smaller the covariance, the higher the weight.

[0035] The final local fusion state is calculated by combining the current state of the node with the fusion state of its neighborhood. The final local fusion states of all points are then aggregated by combining the weights of different node positions based on their deployment locations to obtain a preliminary state result.

[0036] The present invention provides a vehicle attitude cooperative control method based on a distributed inertial measurement unit, wherein the steps for obtaining control parameters include:

[0037] Each node's local controller is defined as an agent. An undirected connected topology graph is constructed based on the node's deployment location, and an adjacency matrix is ​​defined.

[0038] Through the interaction and iteration of state information between different intelligent agents, the state variables of all intelligent agents converge to the globally optimal state that matches the target posture of the vehicle attitude cooperative control.

[0039] To address the nonlinear characteristics of vehicle attitude states, an iterative formula for the intelligent agent is defined.

[0040] Initialize the state of each agent to the state quantity of the corresponding node in the preliminary state result, trigger iteration according to the adjusted synchronization interval until the preset number of iterations, and calculate the globally consistent state vector.

[0041] The target attitude corresponding to the globally consistent state vector is decomposed into sub-targets of different actuators to obtain the corresponding control parameters.

[0042] The present invention provides a vehicle attitude cooperative control method based on distributed inertial measurement units, the steps of which include obtaining instability risk assessment results are as follows:

[0043] By adjusting the synchronization interval, motion information and environmental perception information are synchronized in time and aligned in space, and key feature quantities related to attitude instability are extracted.

[0044] The intensity of the disturbance currently faced by the vehicle is calculated using a weighted summation method based on key feature quantities. Based on dynamic feature information, an ideal attitude reference model under different working conditions is constructed to output the ideal attitude of the vehicle.

[0045] The deviation between the real-time acquired actual attitude and the real-time ideal attitude of the vehicle is calculated. Combined with the interference intensity, linear fitting is used to extrapolate and predict the attitude change trend within a preset number of adjustment synchronization intervals.

[0046] By combining the intensity of disturbance and the trend of attitude change, the instability risk is divided into multiple levels to obtain the instability risk assessment results.

[0047] This invention provides a vehicle attitude cooperative control method based on distributed inertial measurement units, the steps of which include formulating a control adjustment strategy:

[0048] Based on the results of the instability risk assessment, the priority of control authority corresponding to different levels is determined, and the output limit values ​​of each implementing agency are retrieved as boundary constraints for control adjustments.

[0049] Based on the control authority priority and control parameters, a graded adjustment strategy is formulated for different levels. Within each adjustment synchronization interval, the attitude stability index after control adjustment is calculated, and the control parameters are adjusted according to the preset index threshold.

[0050] The beneficial effects of this invention are as follows:

[0051] 1. This invention classifies unexpected operating conditions such as high-speed emergency obstacle avoidance and calibrates operating condition correction coefficients; it constructs an error coupling evaluation function, and obtains the adjustment synchronization interval for the appropriate operating conditions through gradient solving and correction coefficient iteration, thus suppressing coupling errors. It divides key / auxiliary regions according to the vibration sensitivity of IMU nodes, calculates the transmission delay between nodes and the central controller, and performs clock phase calibration; it sets sampling priority and phase difference based on the node's contribution to attitude perception, achieving staggered sampling. This improves the measurement accuracy and synchronization stability of the distributed inertial measurement unit.

[0052] 2. This invention defines local / global state vectors containing vehicle attitude and inertial measurement unit (IMU) errors, and establishes a state prediction-observation update model. An IMU error penalty term is added, and high-precision aggregation of the global state is achieved through neighbor node information interaction and weighted fusion. The local controller of the IMU is defined as an agent, and an undirected connected topology graph is constructed. A consensus iteration protocol with constraint penalty terms is designed to ensure that the states of each agent converge to the global optimum. The target attitude is decomposed into sub-objectives of each actuator, and the optimal control parameters are calculated. Key motion and environmental features are extracted, the intensity of three types of disturbances (road surface, aerodynamics, and control) is quantified, and the comprehensive disturbance value is calculated. An ideal attitude reference model is constructed, the deviation between actual and ideal attitudes is compared, the trend is predicted, and instability risks are classified. This improves the robustness and accuracy of vehicle attitude state estimation. It achieves global collaborative precise control of vehicle attitude and accurate prediction and hierarchical control of attitude instability risks. Attached Figure Description

[0053] The invention will now be further described with reference to the accompanying drawings.

[0054] Figure 1 This is one of the flowcharts illustrating a vehicle attitude cooperative control method based on a distributed inertial measurement unit provided in an embodiment of the present invention;

[0055] Figure 2 This is the second flowchart illustrating a vehicle attitude cooperative control method based on a distributed inertial measurement unit provided in this embodiment of the invention.

[0056] Figure 3 This is the third flowchart of a vehicle attitude cooperative control method based on a distributed inertial measurement unit provided in this embodiment of the invention. Detailed Implementation

[0057] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0058] like Figures 1 to 3 As shown in the figure, an embodiment of the present invention provides a vehicle attitude cooperative control method based on a distributed inertial measurement unit, the method comprising:

[0059] The dynamic characteristics of the target vehicle and the node deployment location of the distributed inertial measurement unit are obtained. The dynamic time synchronization interval is set according to the unexpected operating conditions of the vehicle, and the adjustment synchronization interval is obtained by combining the error coupling characteristics of the distributed inertial measurement unit.

[0060] Dynamic characteristics include vehicle center of gravity height, moment of inertia, suspension stiffness, and tire adhesion coefficient. Node deployment location information includes the three-dimensional coordinates and installation angles of each inertial measurement unit (IMU) in the vehicle's body coordinate system. Unexpected operating conditions include high-speed emergency obstacle avoidance, off-road bumpy conditions, heavy-load sharp turning conditions, and icy / slippery road conditions. Error coupling characteristics include the coupling between zero-bias drift of each IMU and installation coordinate deviation, and the coupling between multi-node time synchronization deviation and measurement noise.

[0061] The steps to adjust the synchronization interval include:

[0062] Determine the basic time synchronization interval requirements and condition correction coefficients under different unexpected operating conditions, determine the error coupling type based on the error coupling characteristics, and construct a coupling error evaluation function, expressed by the formula:

[0063] ;

[0064] In the formula, The angular velocity error is caused by the coupling of zero-bias drift and installation deviation. The angular velocity error is caused by the coupling of time synchronization deviation and measurement noise. , These are weighting coefficients. It is the coupling error evaluation function.

[0065] Based on real-time data from onboard sensors, the type of unexpected operating condition the vehicle is currently in is identified by a threshold comparison method. Combined with a preset base interval and a real-time operating condition correction coefficient, the dynamic time synchronization interval is calculated.

[0066] The gradient value of the coupling strength as a function of dynamic time synchronization interval is obtained by differentiating the coupling error evaluation function. The adjusted synchronization interval is then calculated by introducing the error coupling correction coefficient. The formula is as follows:

[0067]

[0068] In the formula, It's about adjusting the synchronization interval. It is a dynamic time synchronization interval. It is the gradient value. It is the error coupling correction coefficient.

[0069] Based on adjusting the synchronization interval and node deployment location, the sampling timing is determined, and the vehicle's motion information and environmental perception information are collected in real time. The state description vector is constructed by combining the dynamic characteristic information and node deployment location.

[0070] The steps for determining the sampling timing include:

[0071] Based on the correlation between node deployment location and vehicle vibration characteristics, nodes are divided into key area group and auxiliary area group, and the reference duration of sampling period is determined by adjusting the synchronization interval.

[0072] Key Area Group: This group includes inertial measurement unit (IMU) nodes in vibration-sensitive areas such as suspension tower tops and the front and rear ends of the vehicle frame. Data from these nodes directly reflects changes in vehicle attitude.

[0073] Auxiliary region group: This group includes IMU nodes in vibration-stable areas such as the middle section of the chassis. Data from these nodes is used for redundancy verification and error compensation.

[0074] Based on the spatial distance of node deployment locations, the signal transmission delay between each node in the regional group and auxiliary regional group and the central controller is calculated, and the local clock phase of each node is calibrated.

[0075] The formula for calculating signal transmission delay is expressed as:

[0076] ;

[0077] In the formula, It is signal transmission delay. It is the bus communication rate. It is the square of the transmission distance.

[0078] The formula for calibrating the local clock phase of each node is expressed as follows:

[0079] ;

[0080] In the formula, It is the trigger time of the global reference clock. It is the first The actual sampling trigger time of each node.

[0081] Based on the contribution of node deployment location to vehicle attitude perception, sampling priority is set, and sampling phase difference is set within the same adjustment synchronization interval period in combination with the spatial distribution of each node.

[0082] Sampling priorities can include: High-priority sampling layer: assigned to key area nodes at the top of the suspension towers and the front and rear ends of the chassis, with a priority weight of 0.7. These nodes must participate in sampling 100% within each adjustment synchronization interval to ensure real-time capture of attitude changes.

[0083] Low-priority sampling layer: Auxiliary area group nodes assigned to the middle section of the chassis, with a priority weight of 0.3. These nodes adopt an interval sampling strategy, that is, they participate in sampling once every 2 adjustment synchronization intervals for data redundancy verification.

[0084] The steps for setting the sampling phase difference may include:

[0085] The synchronization interval period is divided into multiple phase windows of equal length, and the formula for calculating the window duration is as follows:

[0086] ;

[0087] In the formula, It is the window duration. It represents the number of phase windows.

[0088] The sampling phase difference is calculated by sorting the nodes according to their x-axis coordinates in the vehicle body coordinate system from smallest to largest, and the formula is expressed as:

[0089] ;

[0090] In the formula, It is the first The sampling phase difference of each node.

[0091] Example: If =10ms =5 nodes, then the phase window =2ms, the front-end node triggers sampling at t=0ms and the tail node triggers sampling at t=8ms, thus achieving a staggered distribution of sampling timing.

[0092] The steps to construct a state description vector include:

[0093] According to the sampling sequence, three-axis angular velocity, three-axis acceleration data and node status are collected at the trigger time as local motion data. Based on the node deployment position, the local motion data is converted into the vehicle body coordinate system to obtain motion information.

[0094] Within a unified adjustment synchronization interval for sampling, the onboard multi-source environmental sensors are synchronously triggered to collect environmental perception information. Environmental perception information may include distance to obstacles ahead, relative speed, road profile curvature, lane offset, real-time tire pressure, front and rear axle loads, road surface type, crosswind intensity, and ambient temperature, etc.

[0095] The dynamic feature information, node deployment location, motion information, and real-time environmental perception information are concatenated according to a preset dimensional order to form a state description vector.

[0096] Construct a distributed extended Kalman filter model, take the state description vector as input, and output the preliminary state results.

[0097] The steps to construct a distributed extended Kalman filter model include:

[0098] Based on preset goals and actual usage requirements, constraint assumptions are set, and for multiple inertial measurement unit nodes, a local state vector is defined that includes the vehicle attitude state and the inertial measurement unit error state.

[0099] Constraint assumptions may include: the vehicle's attitude motion satisfies the rigid body dynamics assumption, and the influence of the frame's elastic deformation on the attitude is ignored.

[0100] The communication between distributed IMU nodes is an undirected topology, with only adjacent nodes able to exchange data, and the communication latency is less than 10% of the adjustment synchronization interval.

[0101] The main source of error in an IMU is zero-bias drift, while higher-order nonlinear errors are ignored.

[0102] Both the system process noise and the observation noise are Gaussian white noise.

[0103] Based on the node deployment location, the local state vectors of all nodes are transformed into the vehicle centroid coordinate system, and the global state vector is obtained by aggregation. The local observation vector is defined using the sampled data of each node as the observation value.

[0104] Based on the vehicle rigid body attitude dynamics equations, a state prediction model for the nodes is established. The posterior state estimate is obtained by correcting the model based on the nonlinear mapping relationship between the observed values ​​and the local state vector. The formula is expressed as:

[0105] ;

[0106] In the formula, It is the first The Kalman gain matrix of the node, It is a nonlinear observation function. yes Jacobian matrix, It is the observation noise covariance matrix. It is the identity matrix. It is the first node The posterior state estimate at time t. It is the first node The posterior state covariance matrix at time t. It is the first node The prior state covariance matrix at time t. It is the first node The prior state estimate at time t.

[0107] The neighborhood state fusion value is calculated based on the posterior state estimate and covariance matrix of each node. The local posterior state of the node is corrected to obtain the interactive corrected state. The global state vector is then combined to apply a consistency constraint to the interactive corrected state of all nodes.

[0108] The steps to obtain preliminary state results include:

[0109] The integrity of the state description vector is verified by splitting it into system input components, observation input components, and topological constraint components according to the model input requirements.

[0110] The system input components may include: real-time centroid height and tire adhesion coefficient extracted from vehicle dynamics features, and road surface curvature and crosswind intensity extracted from environmental data, as external inputs to the model.

[0111] The observation input components may include: extracting the angular velocity and linear acceleration of each IMU node transformed to the body coordinate system, as the observation value of the i-th node.

[0112] The topology constraint component may include: extracting the three-dimensional coordinates of the IMU node and the neighborhood topology relationship, which is used to define the information interaction rules between nodes.

[0113] For each node, filter initialization configuration is performed, an error penalty term parameter is added, and prior state prediction of local nodes is performed based on system input components.

[0114] The prior state prediction is corrected based on the observed input components to obtain the posterior state. The posterior state is then corrected based on the error penalty term parameter to obtain the local node state estimate.

[0115] The neighborhood relationship is defined based on topological constraint components, data interaction between nodes is executed, and the neighborhood fusion state is calculated according to the principle that the smaller the covariance, the higher the weight.

[0116] The final local fusion state is calculated by combining the current state of a node with the fusion state of its neighboring area. The final local fusion states of all points are then aggregated based on the weights of different node deployment locations to obtain a preliminary state result. The preliminary state structure may include the vehicle's three-axis attitude angles, vehicle's three-axis angular velocities, and node synthesis errors, etc.

[0117] The control parameters are obtained by coordinating the preliminary state results using a multi-agent consensus method. The instability risk of the vehicle is assessed based on motion information and environmental perception information to obtain the instability risk assessment result. The control adjustment strategy is then formulated in conjunction with the control parameters.

[0118] The steps to obtain control parameters include:

[0119] Each node's local controller is defined as an agent. An undirected connected topology graph is constructed based on the node's deployment location, and an adjacency matrix is ​​defined.

[0120] Through the interaction and iteration of state information between different intelligent agents, the state variables of all intelligent agents converge to the globally optimal state that matches the target posture of the vehicle attitude cooperative control.

[0121] To address the nonlinear characteristics of the vehicle's attitude state, an iterative formula for the agent is defined, expressed as follows:

[0122] ;

[0123] In the formula, It is the set of neighboring nodes of the intelligent agent. It is the consistency step size factor. It is the constraint penalty coefficient. It is the gradient vector of the coupled motion constraint function. It is the gradient vector of the collaborative constraint function. It is the first An intelligent agent in The state vector at time t, It is the first An intelligent agent in The state vector at time t, It is an intelligent agent With intelligent agents The adjacency matrix between them It is the state deviation vector.

[0124] Initialize the state of each agent to the state variables of the corresponding nodes in the preliminary state results, trigger iterations according to the adjusted synchronization interval until the preset number of iterations, and calculate the globally consistent state vector, expressed by the formula:

[0125] ;

[0126] In the formula, It is a globally consistent state vector. These are the agent weight coefficients.

[0127] The target attitude corresponding to the globally consistent state vector is decomposed into sub-targets of different actuators to obtain the corresponding control parameters.

[0128] The steps to obtain the instability risk assessment results include:

[0129] By adjusting the synchronization interval, motion information and environmental perception information are synchronized in time and aligned in space, and key feature quantities related to attitude instability are extracted.

[0130] Key characteristic quantities may include vehicle roll angle change rate, peak yaw rate, root mean square value of vertical acceleration, tire load transfer rate, road adhesion coefficient, crosswind intensity level, road slope angle, and tire slip ratio.

[0131] The intensity of the disturbance currently faced by the vehicle is calculated using a weighted summation method based on key feature quantities. Based on dynamic feature information, an ideal attitude reference model under different working conditions is constructed to output the ideal attitude of the vehicle.

[0132] The deviation between the real-time acquired actual attitude and the real-time ideal attitude of the vehicle is calculated. Combined with the interference intensity, linear fitting is used to extrapolate and predict the attitude change trend within a preset number of adjustment synchronization intervals.

[0133] By combining the intensity of disturbance with the trend of attitude change, the instability risk is divided into multiple levels to obtain the instability risk assessment results.

[0134] The results of the instability risk assessment may include: risk level: level 0 / level 1 / level 2.

[0135] Main risk factors: such as high road surface interference (icy and snowy road surface) + excessive yaw deviation.

[0136] Posture deviation details: Comparison of each deviation value with the threshold.

[0137] Trend forecast: The trend of posture changes over the next 1-3 cycles.

[0138] The steps involved in developing a control adjustment strategy include:

[0139] Based on the results of the instability risk assessment, the priority of control authority corresponding to different levels is determined, and the output limit values ​​of each implementing agency are retrieved as boundary constraints for control adjustments.

[0140] Based on the control authority priority and control parameters, a graded adjustment strategy is formulated for different levels. Within each adjustment synchronization interval, the attitude stability index after control adjustment is calculated, and the control parameters are adjusted according to the preset index threshold.

[0141] If the attitude stability index is <0.7, adjust the correction coefficient and re-optimize the control parameters. If the attitude stability index is >0.9, appropriately reduce the correction coefficient to balance control performance and comfort.

[0142] When the vehicle's operating conditions change, the control strategy's correction logic and parameter thresholds are updated in real time based on the new risk assessment results to ensure control adaptability under all operating conditions.

[0143] In summary, the vehicle attitude cooperative control method based on a distributed inertial measurement unit provided in this embodiment can detect attitude instability trends earlier and more accurately under extreme conditions such as icy skidding and emergency obstacle avoidance. Through cooperative control, it can more effectively restore or maintain vehicle stability, significantly expanding the safe operating boundaries of the vehicle. Through distributed fusion and error compensation, more accurate and reliable vehicle attitude, angular velocity, and other state information are obtained than traditional solutions. Dynamic synchronization intervals and time-division sampling timing save communication bandwidth and computing resources. The hierarchical control strategy avoids one-size-fits-all over-control, improving ride comfort and reducing energy consumption while ensuring safety. It can adapt to different loads, road surfaces, and driving styles, maintaining optimal control performance by updating model parameters and risk assessments in real time. This enhances the system's fault tolerance and adaptive capabilities.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle attitude cooperative control method based on distributed inertial measurement units, comprising: The dynamic characteristics of the target vehicle and the node deployment location of the distributed inertial measurement unit are obtained. The dynamic time synchronization interval is set according to the unexpected working conditions of the vehicle, and the adjustment synchronization interval is obtained by combining the error coupling characteristics of the distributed inertial measurement unit. Based on the adjustment synchronization interval and the node deployment location, a sampling timing sequence is determined, and vehicle motion information and environmental perception information are collected in real time. A state description vector is constructed by combining the dynamic characteristic information and the node deployment location. Construct a distributed extended Kalman filter model, input the state description vector, and output the preliminary state results; The control parameters are obtained by coordinating the preliminary state results using a multi-agent consensus method. The vehicle attitude instability risk is assessed based on the motion information and the environmental perception information to obtain the instability risk assessment result. A control adjustment strategy is then formulated in conjunction with the control parameters.

2. The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, characterized in that: The dynamic characteristic information includes vehicle center of gravity height, moment of inertia, suspension stiffness, and tire adhesion coefficient; the node deployment location information includes the three-dimensional coordinates and installation angle of each inertial measurement unit in the vehicle body coordinate system; the unexpected working conditions include high-speed emergency obstacle avoidance, off-road bumpy conditions, heavy-load sharp turning conditions, and icy and snowy road slipping conditions; the error coupling characteristics include the coupling between zero-bias drift of each inertial measurement unit and installation coordinate deviation, and the coupling between multi-node time synchronization deviation and measurement noise. 3.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps for adjusting the synchronization interval include: Determine the basic time synchronization interval requirements and operating condition correction coefficients under different unexpected operating conditions, determine the error coupling type based on the error coupling characteristics, and construct a coupling error evaluation function. Based on real-time data from onboard sensors, the type of unexpected operating condition currently experienced by the vehicle is identified using a threshold comparison method. The dynamic time synchronization interval is then calculated by combining a preset base interval with a real-time operating condition correction coefficient. The gradient value of the coupling strength as a function of the coupling error evaluation function is obtained by taking the derivative of the coupling error evaluation function. The adjustment synchronization interval is then calculated by introducing the error coupling correction coefficient. 4.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps for determining the sampling timing include: Based on the correlation between the node deployment location and the vehicle body vibration characteristics, the nodes are divided into a key area group and an auxiliary area group, and the reference duration of the sampling period is determined according to the adjustment synchronization interval. Based on the spatial distance of the node deployment locations, the signal transmission delay between each node in the regional group and the auxiliary regional group and the central controller is calculated, and the local clock phase of each node is calibrated. Based on the contribution of the node deployment location to vehicle attitude perception, a sampling priority is set, and the sampling phase difference is set within the same adjustment synchronization interval period in combination with the spatial distribution of each node. 5.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps for constructing the state description vector include: According to the sampling sequence, triaxial angular velocity, triaxial acceleration data and node status are collected at the trigger time as local motion data. The local motion data is converted into vehicle body coordinate system according to the node deployment position to obtain the motion information. Within the unified adjustment and synchronization interval of sampling, the vehicle-mounted multi-source environmental sensors are synchronously triggered to collect the environmental perception information; The dynamic feature information, the node deployment location, the motion information, and the real-time environmental perception information are concatenated according to a preset dimensional order to form the state description vector. 6.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps for constructing the distributed extended Kalman filter model include: Based on the preset goals and actual usage requirements, constraint assumptions are set, and for multiple inertial measurement unit nodes, a local state vector containing the vehicle attitude state and the inertial measurement unit error state is defined. Based on the node deployment location, the local state vectors of all nodes are transformed to the vehicle centroid coordinate system, and the global state vector is obtained by aggregation. The local observation vector is defined with the sampled data of each node as the observation value. Based on the vehicle rigid body attitude dynamics equation, a state prediction model for the node is established, and the posterior state estimate is obtained by correcting the nonlinear mapping relationship between the observed value and the local state vector. The neighborhood state fusion value is calculated based on the posterior state estimate and covariance matrix of each node. The local posterior state of the node is corrected to obtain the interactive corrected state. The global state vector is then combined to apply a consistency constraint to the interactive corrected state of all nodes.

7. The distributed inertial measurement unit based vehicle cooperative attitude control method according to claim 1, wherein: The steps to obtain the preliminary state result include: The integrity of the state description vector is verified by splitting it into system input components, observation input components, and topological constraint components according to the model input requirements. For each node, a filter initialization configuration is performed, an error penalty term parameter is added, and based on the system input components, the prior state prediction of the local nodes is performed. The prior state prediction is corrected based on the observed input components to obtain the posterior state, and the posterior state is corrected based on the error penalty term parameter to obtain the local node state estimate. Based on the topological constraint components, neighborhood relationships are defined, data interaction between nodes is performed, and the neighborhood fusion state is calculated according to the principle that the smaller the covariance, the higher the weight. The final local fusion state is calculated by combining the current state of the node with the fusion state of the neighborhood. The final local fusion states of all points are then aggregated by combining the weights of the different node positions at the node deployment locations to obtain the preliminary state result. 8.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps to obtain the control parameters include: Define the local controller corresponding to each node as an agent, construct an undirected connected topology graph based on the node deployment location, and define an adjacency matrix; Through the interaction and iteration of state information between different intelligent agents, the state variables of all intelligent agents are converged to the global optimal state that matches the target attitude of the vehicle attitude cooperative control. To address the nonlinear characteristics of vehicle attitude states, an iterative formula for the intelligent agent is defined. Initialize the state of each agent to the state quantity of the corresponding node in the preliminary state result, trigger iteration according to the adjusted synchronization interval until the preset number of iterations, and calculate the globally consistent state vector. The target attitude corresponding to the globally consistent state vector is decomposed into sub-targets of different actuators to obtain the corresponding control parameters. 9.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps to obtain the instability risk assessment results include: Based on the adjusted synchronization interval, the motion information and the environmental perception information are synchronized in time and aligned in space to extract key feature quantities related to attitude instability. The intensity of the disturbance currently faced by the vehicle is calculated using a weighted summation method based on the key feature quantities. Based on the dynamic feature information, an ideal attitude reference model under different working conditions is constructed to output the ideal attitude of the vehicle. The deviation between the real-time acquired actual attitude and the real-time ideal attitude of the vehicle is calculated. Combined with the interference intensity, linear fitting is used to extrapolate and predict the attitude change trend within a preset number of adjustment synchronization intervals. By combining the intensity of the disturbance with the trend of attitude change, the instability risk is divided into multiple levels to obtain the instability risk assessment result. 10.The vehicle cooperative control method based on distributed inertial measurement units according to claim 1, wherein: The steps for developing the control adjustment strategy include: Based on the instability risk assessment results, the priority of control authority corresponding to different levels is determined, and the output limit of each actuator is retrieved as the boundary constraint for control adjustment. Based on the control authority priority and the control parameters, a graded adjustment strategy is formulated for different levels. Within each adjustment synchronization interval, the attitude stability index after control adjustment is calculated, and the control parameters are adjusted according to a preset index threshold.

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