Vehicle body steady state control system and method of motor caravan

Abstract

The invention relates to the technical field of vehicle control, in particular to a motor caravan body steady state control system and method. Multi-dimensional dynamic parameters of the motor caravan are collected in real time through the Internet of Things multi-dimensional parameter sensing module. And the multi-dimensional dynamic parameters are input into a data processing unit, real-time cooperative calculation is carried out through a vehicle steady-state model in combination with coupling constraint conditions of suspension control and brake control, and suspension height adjustment amount and wheel target brake force data are generated. And the servo control unit performs collaborative conversion and conflict arbitration according to the suspension height adjustment amount and the target braking force data of the wheels, and constructs a suspension control signal set and a braking control signal set. And the brake execution mechanism responds to the suspension control signal set to synchronously adjust the active suspension height of each wheel to maintain the stable posture of the vehicle body, and responds to the brake control signal set to cooperatively adjust the braking force of each wheel to correct the driving track. And through a multi-system hardware multiplexing and integrated cooperative regulation and control mechanism, the vehicle body steady state control comprehensive effect of the motor caravan under the dynamic working condition is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a vehicle body stability control system and method for a recreational vehicle. Background Technology

[0002] Currently, with the continuous improvement of people's living standards and the increasing popularity of self-driving travel, the market demand for recreational vehicles (RVs) continues to grow. To meet the needs of long-distance travel, RVs typically combine living and driving functions. The interior needs to be equipped with living facilities, storage space, and water tanks, resulting in frequent changes in the vehicle's load condition due to various factors such as the number of passengers, the amount of luggage and items loaded, and the water tank's capacity. The vehicle's center of gravity is generally high, and the load distribution of the four wheels is constantly changing during driving.

[0003] Traditional vehicle control systems are mostly based on fixed or preset parameters, making them unable to adaptively adjust to the real-time changes in load and significant center of gravity shifts characteristic of motorhomes. Therefore, under complex driving conditions, this can easily lead to safety hazards such as vehicle instability, braking dive, steering deviation, excessive roll, and even rollover. Especially in complex road conditions such as curves, slopes, and slippery conditions like rain or snow, the dynamic load distribution coupled with external road conditions further amplifies the safety risks during driving and braking. Existing general-purpose vehicle control systems are insufficient to fully meet the specific needs of motorhomes for posture stability and driving safety. Summary of the Invention

[0004] This invention provides a vehicle body steady-state control system and method for motorhomes, which solves the technical problem that existing motorhome vehicle body steady-state control methods lack a mechanism for reusing and integrating multi-system hardware, resulting in poor overall control performance of the system under dynamic operating conditions.

[0005] The first aspect of the present invention provides a vehicle body stability control system for a recreational vehicle, comprising: an Internet of Things multi-dimensional parameter sensing module, a data processing unit, a servo control unit, and a braking actuator;

[0006] The IoT multi-dimensional parameter sensing module is used to collect real-time data on the RV's water tank level, throttle travel, four-wheel rotational angular velocity, four-wheel load, vehicle roll angle, vehicle pitch angle, steering wheel angle, vehicle speed, longitudinal acceleration, lateral acceleration, and forward road condition information, and generate multi-dimensional dynamic parameters.

[0007] The data processing unit is communicatively connected to the IoT multi-dimensional parameter sensing module. It is used to perform real-time collaborative calculations using the multi-dimensional dynamic parameters and the coupling constraints of suspension control and braking control through the vehicle steady-state model corresponding to the recreational vehicle, and output suspension height adjustment amount and wheel target braking force data at a preset frequency.

[0008] The servo control unit is connected to the data processing unit and is used to perform coordinated conversion and conflict arbitration of the suspension height adjustment amount and the wheel target braking force data to generate corresponding suspension control signals and braking control signals.

[0009] The braking actuator, electrically connected to the servo control unit, includes an active suspension unit and a braking unit. It is used to synchronously adjust the active suspension height of each wheel in accordance with the suspension control signal to maintain vehicle body stability, and to coordinately adjust the braking force of each wheel in response to the braking control signal to correct the driving trajectory.

[0010] Optionally, the data processing unit performs the following steps:

[0011] The multidimensional dynamic parameters are subjected to noise suppression and outlier removal to generate valid dynamic parameter data.

[0012] Extract the real-time center of gravity position, rate of change of center of gravity, rate of change of roll angle, and rate of change of pitch angle corresponding to the recreational vehicle from the effective dynamic parameter data to generate a high-order dynamic feature dataset.

[0013] The road adhesion coefficient contained in the vehicle speed, steering wheel angle and forward road condition information in the high-order dynamic feature data and the effective dynamic parameter data is fused to generate steady-state model input data.

[0014] Based on the high center of gravity and large inertial mass characteristics of the motorhome, a joint optimization function is constructed with minimizing the vehicle attitude tracking error as the primary objective and the weighted sum of energy consumption of the suspension and braking systems as the secondary objective. The travel and response constraints of the suspension system and the distribution and anti-lock braking constraints of the braking system are defined together as a set of coupled constraint conditions.

[0015] Based on the historical attitude and center of gravity data of the RV within a preset historical time period, the steady-state model input data is subjected to feedforward prediction optimization processing to generate optimized steady-state model input data.

[0016] The rolling time-domain collaborative solution is performed based on the joint optimization function and the set of coupled constraints, and the suspension height adjustment amount and wheel target braking force data are output at a preset frequency.

[0017] Optionally, the step of performing feedforward prediction optimization processing on the steady-state model input data based on the historical attitude and center of gravity data of the recreational vehicle within a preset historical time period to generate optimized steady-state model input data includes:

[0018] Based on the historical attitude and center of gravity data of the motorhome within a preset historical period, the linear extrapolation method is used to predict the attitude change trend of the motorhome in the future preset period, and the predicted roll angle change rate, the predicted pitch angle change rate, and the predicted center of gravity change rate are obtained.

[0019] The predicted roll angle change rate, the predicted pitch angle change rate, and the predicted center of gravity change rate are weighted and fused with the roll angle change rate, pitch angle change rate, and center of gravity change rate, respectively, to generate feedforward optimized change rate data;

[0020] The feedforward optimized rate of change data is used to update the corresponding rate of change parameters in the steady-state model input data to generate optimized steady-state model input data.

[0021] Optionally, the data processing unit further performs the following steps:

[0022] According to the preset travel trajectory range, the millimeter-wave radar detection data and camera image data in the forward road condition information are used to identify obstacles and generate obstacle identification data.

[0023] Determine whether the obstacle recognition data intersects with the travel trajectory of the recreational vehicle;

[0024] If so, risk assessment and decision-making are performed based on the vehicle speed, vehicle posture information, tire status and driving trajectory prediction data in the multi-dimensional dynamic parameters, and evasion operation command data are generated.

[0025] If not, then based on the aforementioned multidimensional dynamic parameters, steady-state control command data is constructed.

[0026] Optionally, the step of performing risk assessment and decision-making based on the vehicle speed, vehicle posture information, tire status, and driving trajectory prediction data from the multi-dimensional dynamic parameters, and generating evasive maneuver command data, includes:

[0027] Based on the vehicle speed and driving trajectory prediction data in the multi-dimensional dynamic parameters, the estimated collision time between the recreational vehicle and the object is calculated.

[0028] Based on the vehicle body posture information and tire status in the multi-dimensional dynamic parameters, the stability margin of the motorhome under the current and preset avoidance actions is evaluated.

[0029] If the estimated collision time is greater than the first preset collision threshold, then first evasion operation command data is generated to control the recreational vehicle to decelerate to the first preset safe speed and pass through.

[0030] If the estimated collision time is less than or equal to the first preset collision threshold, then the environmental data on the side of the motorhome is obtained, and combined with the stability margin, it is determined whether the motorhome meets the detour conditions.

[0031] If the conditions are met, then a second evasion operation command data is generated to control the recreational vehicle to decelerate to the second preset safe speed and turn around to avoid it;

[0032] If the conditions are not met, a third evasive maneuver command data is generated to control the motorhome to brake suddenly until it comes to a stop.

[0033] Optionally, the servo control unit performs the following steps:

[0034] Establish an arbitration rule base with vehicle trajectory stability as the highest priority, vehicle posture smoothness as the second priority, and system energy consumption as the optimization objective;

[0035] The system obtains the real-time load status of the vehicle power system of the motorhome and the temperature status of the key actuators in the active suspension unit and the braking unit, and generates system resource status data.

[0036] Determine whether the suspension height adjustment amount and the target braking force data of the wheel meet the preset conflict conditions;

[0037] If satisfied, then according to the arbitration rule base, priority is given to ensuring the complete or partial execution of the target braking force data of the wheel to maintain the stability of the driving trajectory. At the same time, without causing secondary instability, the suspension height adjustment amount is dynamically adjusted to generate the target suspension adjustment amount and the target braking force.

[0038] If not satisfied, the suspension height adjustment amount and the target braking force data of the wheel will be used as the target suspension adjustment amount and target braking force.

[0039] Based on a preset hardware driver mapping relationship, the target suspension adjustment amount and the target braking force are respectively converted into suspension control signals that drive the active suspension unit and braking control signals that drive the braking unit.

[0040] Optionally, the step of converting the target suspension adjustment amount and the target braking force into a suspension control signal driving the active suspension unit and a braking control signal driving the braking unit, respectively, based on a preset hardware drive mapping relationship, includes:

[0041] Obtain suspension operation experimental data from real-vehicle tests of the motorhome under different loads and road conditions;

[0042] The initial big data model is trained and its parameters are adjusted online using the suspension actuation test data and the real vehicle operation data continuously collected during actual driving of the recreational vehicle, thereby generating the target big data model.

[0043] The target big data model is used to iteratively optimize the mapping relationship of the control parameters of the active suspension unit under different loads and road conditions, and a control parameter mapping table is constructed based on the optimized mapping relationship.

[0044] Pressure and current characteristic curves are constructed using the pressure and drive current data obtained from calibration tests of the electro-hydraulic brake master pump contained in the braking unit at multiple discrete pressure control points.

[0045] Based on the input pressure value and output braking force corresponding data obtained by calibrating each wheel brake pump in the braking unit at multiple discrete braking force control points, the braking force transfer function of each wheel brake pump is constructed.

[0046] The target suspension adjustment amount is input into the control parameter mapping table for query and mapping operations to generate a pulse width modulation signal set that drives the active suspension unit;

[0047] The target braking force is input into the pressure and current characteristic curve and the braking force transfer function for calculation and distribution operations, generating a pressure control signal for braking the electro-hydraulic master cylinder and a current control signal for each wheel brake cylinder.

[0048] The second aspect of this invention provides a method for steady-state control of a motorhome body, comprising:

[0049] Real-time data collection of the motorhome's water tank level, throttle travel, four-wheel rotational angular velocity, four-wheel load, body roll angle, body pitch angle, steering wheel angle, vehicle speed, longitudinal acceleration, lateral acceleration, and forward road condition information generates multi-dimensional dynamic parameters.

[0050] The vehicle steady-state model corresponding to the recreational vehicle is used to perform real-time collaborative calculations using the multi-dimensional dynamic parameters and the coupling constraints of suspension control and braking control, and the suspension height adjustment amount and wheel target braking force data are output at a preset frequency.

[0051] The suspension height adjustment amount and the wheel target braking force data are coordinated and arbitrated to generate corresponding suspension control signals and braking control signals.

[0052] In accordance with the integrated and coordinated timing response to the suspension control signal, the active suspension height of each wheel is adjusted synchronously to maintain vehicle body stability, and in accordance with the braking control signal, the braking force of each wheel is adjusted in coordination to correct the driving trajectory.

[0053] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the vehicle body stability control method for a motorhome as described above.

[0054] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the vehicle body steady-state control method for a motorhome as described above.

[0055] As can be seen from the above technical solutions, the present invention has the following advantages:

[0056] This invention utilizes an IoT multi-dimensional parameter sensing module to collect real-time multi-dimensional dynamic parameters such as four-wheel load, vehicle posture, motion state, and forward road conditions. A data processing unit performs collaborative calculations based on the vehicle's steady-state model and the coupling constraints of suspension and braking, outputting the suspension height adjustment and target wheel braking force. A servo control unit further performs collaborative conversion and conflict arbitration between these parameters, generating a coordinated control signal. Finally, a braking actuator integrating the active suspension and braking units synchronously adjusts the vehicle posture and wheel braking force according to an integrated, coordinated timing sequence. The core of this solution lies in establishing a hardware reuse and dynamic coordination mechanism between suspension control and braking control, effectively solving the problem of poor control performance under dynamic conditions caused by the isolated operation and lack of coordination of multiple systems in existing motorhome steady-state control systems.

[0057] Its beneficial effects are significant: by integrating suspension height adjustment and braking force distribution, the system can simultaneously achieve vehicle posture stability and trajectory correction under complex driving conditions such as steering, bumps, or braking, avoiding control conflicts and improving overall handling stability; through real-time calculation and arbitration under coupled constraints, it achieves unified scheduling and resource reuse of multiple execution hardware, improving system response speed and control accuracy, and enhancing adaptability in continuous dynamic conditions; at the same time, the integrated collaborative mechanism reduces redundant control actions, helping to reduce energy consumption and hardware wear, improving driving safety and comfort while also enhancing the overall reliability and economy of the system. Therefore, this solution not only breaks through the limitations of independent control of multiple systems in terms of methodology, but also achieves systematic optimization of the steady-state control performance of motorhomes in real driving environments through hardware collaboration and resource integration. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a structural block diagram of a vehicle body stability control system for a recreational vehicle provided in an embodiment of the present invention;

[0060] Figure 2 A flowchart illustrating the steps of a vehicle body steady-state control method provided in an embodiment of the present invention;

[0061] Figure 3 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0062] This invention provides a vehicle body stability control system and method for motorhomes, which addresses the technical problem that existing motorhome vehicle body stability control methods lack a mechanism for reusing and integrating multi-system hardware, resulting in poor overall control performance of the system under dynamic operating conditions.

[0063] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0064] Please see Figure 1 , Figure 1 This is a structural block diagram of a vehicle body stability control system for a recreational vehicle provided in an embodiment of the present invention.

[0065] The present invention provides a vehicle body stability control system for a recreational vehicle, comprising: an Internet of Things multi-dimensional parameter sensing module, a data processing unit, a servo control unit, and a braking actuator.

[0066] The IoT multi-dimensional parameter sensing module 101 is used to collect real-time information on the RV's water tank level, throttle travel, four-wheel rotational angular velocity, four-wheel load, vehicle roll angle, vehicle pitch angle, steering wheel angle, vehicle speed, longitudinal acceleration, lateral acceleration, and forward road conditions, generating multi-dimensional dynamic parameters.

[0067] In this embodiment of the invention, the IoT multi-dimensional parameter sensing module 101 is a comprehensive data acquisition system integrating multiple high-performance sensors. It acquires the dynamic parameters of the recreational vehicle in real time and accurately through the following process, obtaining the corresponding multi-dimensional dynamic parameters of the recreational vehicle.

[0068] The water tank level is collected by a water tank level sensor. This sensor uses ultrasonic technology and is embedded inside the water tank to measure the water level height with an accuracy of ±1cm and convert it into a load. The data delay is no more than 50ms, and its IP67 waterproof rating ensures stable operation in the humid environment of the RV.

[0069] The throttle travel is directly measured by the throttle travel sensor, which monitors the position changes of the accelerator pedal in real time and accurately reflects the driver's acceleration intention.

[0070] The angular velocity of the four wheels is obtained by a four-wheel angular velocity sensor installed on each wheel hub. This sensor is usually based on the Hall element principle and can accurately capture the instantaneous rotational speed of the wheel.

[0071] The four-wheel load data comes from four-wheel pressure sensors installed on the four suspensions. These high-precision strain gauge sensors can operate in a wide temperature range of -40°C to 85°C and measure the pressure on each wheel in real time with an accuracy of ±0.5%FS and a frequency of ≥100Hz.

[0072] The roll and pitch angles of the vehicle body are provided by an inertial measurement unit (IMU) installed in the center of gravity area of ​​the vehicle body. This MEMS-type IMU continuously outputs the attitude angle and angular velocity information of the vehicle body with a sampling frequency of 100Hz and an accuracy of ±0.1°.

[0073] The steering wheel angle is measured by a dedicated steering wheel angle sensor, which converts the steering wheel rotation angle into an electrical signal in real time, and is the key to judging the vehicle's steering status.

[0074] Vehicle speed information is obtained through a vehicle speed sensor. This Hall effect sensor supports CAN bus communication and can achieve a measurement accuracy of ±1 km / h within the range of 0-120 km / h, with an extremely fast response time.

[0075] Longitudinal and lateral accelerations are collected by longitudinal and lateral accelerometers deployed at corresponding locations on the vehicle body, respectively, to accurately sense changes in inertial forces during vehicle acceleration, braking, and cornering.

[0076] Forward road condition information is comprehensively perceived through multi-sensor fusion: millimeter-wave radar uses 24GHz / 77GHz dual-band to detect vehicles and pedestrians within a range of 5-500 meters ahead; high-definition AI cameras perform real-time visual analysis through built-in ISP chips, intelligently identifying up to 32 types of targets, including lanes, vehicles, and pedestrians; simultaneously, road surface perception sensors can determine whether the road surface is dry, wet, or icy / snowy, with an accuracy rate of ≥90%. These three sensors work together to form a three-dimensional perception of the environment and road conditions ahead. Data from all sensors is aggregated through an IoT gateway and transmitted at high speed and low latency via a 5G communication module to the core processing unit, ultimately fusing to generate a multi-dimensional parameter set for vehicle dynamic control.

[0077] The data processing unit 102 is connected to the IoT multi-dimensional parameter sensing module. It is used to perform real-time collaborative calculations using multi-dimensional dynamic parameters and coupling constraints of suspension control and braking control through the vehicle steady-state model corresponding to the recreational vehicle, and output suspension height adjustment amount and wheel target braking force data according to a preset frequency.

[0078] In this embodiment of the invention, the data processing unit 102, as the core computing hub of the system, is equipped with an ARM Cortex-A9 architecture core processor (such as the NXP i.MX6 series), with a main operating frequency of ≥1.5GHz, supporting multi-threaded parallel processing, data processing accuracy of ≥95%, cache of ≥2MB, and low power consumption characteristics suitable for motorhome vehicle power systems. This unit achieves data interaction with the IoT multi-dimensional parameter sensing module through a 5G / Bluetooth dual-mode communication module. The 5G module (such as the Huawei ME909s-821) supports a data transmission rate of ≥100Mbps and a latency of ≤50ms; the Bluetooth 5.0 module (such as the TI CC2652R) has a communication distance of ≥10m, suitable for short-range sensor data retransmission scenarios.

[0079] Further, the data processing unit 102 performs the following steps:

[0080] S11. Perform noise suppression and outlier removal on the multidimensional dynamic parameters to generate valid dynamic parameter data.

[0081] In this embodiment of the invention, the multidimensional dynamic parameters collected by the original sensors contain two types of interference: high-frequency noise generated by the continuous vibration of the vehicle body and the road surface, and instantaneous abnormal signals caused by a single wheel encountering a pothole or other obstacle. A dedicated filtering and outlier processing algorithm is implemented through programming: For high-frequency noise, a Kalman filter algorithm (noise suppression ratio ≥40dB) is used, combined with the main vehicle body sway frequency (below 2Hz) determined by actual vehicle testing. The cutoff frequency of the digital low-pass filter is set to 2Hz, effectively filtering out high-frequency noise caused by tire vibration and engine vibration while preserving the true motion signal of the vehicle body. For instantaneous outliers, a 3σ criterion outlier detection algorithm is used to process the four-wheel load signals. The system maintains a 0.2-second sliding window in real time. When each new load data arrives, it is checked whether it is within the reasonable range of historical data within the window. If the load of the left front wheel suddenly shows a peak much higher than the trend due to running over a stone, this point is identified as an anomaly and removed, and then interpolated with adjacent normal data to complete the data. After the above processing, clean, continuous, and effective dynamic parameter data is obtained.

[0082] S12. Extract the real-time center of gravity position, rate of change of center of gravity, rate of change of roll angle, and rate of change of pitch angle corresponding to the recreational vehicle from the effective dynamic parameter data, and generate a high-order dynamic feature dataset.

[0083] In this embodiment of the invention, based on the readings of four four-wheel load sensors and their suspension mounting positions, the lateral and longitudinal coordinates of the vehicle's center of gravity are calculated in real time using a torque balance formula. Using the raw data of the vehicle's roll and pitch angles, along with the center of gravity coordinates calculated in the previous step, the roll angle change rate, pitch angle change rate, and center of gravity change rate are obtained through differential calculations. All calculation results are packaged into a high-order dynamic feature dataset and output at a frequency of 100Hz.

[0084] S13. The road surface adhesion coefficient contained in the vehicle speed, steering wheel angle and forward road condition information in the high-order dynamic feature data and effective dynamic parameter data are fused to generate steady-state model input data.

[0085] In this embodiment of the invention, the vehicle's internal state and external environmental information are integrated. The high-order dynamic feature dataset generated by S12, the vehicle speed and steering wheel angle from the effective dynamic parameter data obtained by S11, and the current road surface adhesion coefficient estimated by the fusion perception of the forward-looking camera and millimeter-wave radar are given a unified timestamp, and then assembled into a structured data packet to obtain the steady-state model input data.

[0086] S14. Based on the high center of gravity and large inertial mass characteristics of motorhomes, a joint optimization function is constructed with minimizing the vehicle posture tracking error as the primary objective and the weighted sum of energy consumption of the suspension and braking systems as the secondary objective. The travel and response constraints of the suspension system and the distribution and anti-lock braking constraints of the braking system are defined together as a set of coupled constraint conditions.

[0087] In this embodiment of the invention, this step aims to transform the vehicle steady-state control problem into a mathematical optimization problem that can be solved in real time. The core of this problem is a multi-objective optimization function specifically tailored to the dynamic characteristics of motorhomes: its primary objective is to minimize the tracking error between the actual attitudes such as body roll angle and pitch angle and the ideal steady-state reference values, so as to prioritize rollover safety; the secondary objective is to minimize the weighted sum of the combined energy consumption of the active suspension actuators and braking system actuators, while satisfying the primary objective, so as to improve energy efficiency.

[0088] Meanwhile, to ensure the physical feasibility and safety of control commands, a set of coupling constraints is defined. This set mainly includes: 1) Suspension travel constraint: The height adjustment of each wheel's active suspension must be within its maximum mechanical design travel range; 2) Suspension response rate constraint: The adjustment speed of the suspension height must not exceed the maximum operating capacity of its motor or hydraulic drive unit; 3) Braking force distribution constraint: The braking force distributed to each wheel must not exceed the maximum adhesion limit determined based on the wheel's real-time vertical load and road surface adhesion coefficient; 4) Anti-lock braking constraint: During braking, the slip ratio of each wheel must be dynamically controlled within the optimal range set according to the real-time road surface adhesion coefficient to avoid wheel lock-up and maintain steering control.

[0089] These constraints are coupled with each other. For example, adjusting the suspension height will instantly change the vertical load on the wheels, thus affecting the boundary of the braking force distribution constraint. Under the combined effect of the above joint objective and coupled constraints, the optimizer will solve for the optimal suspension and braking control commands in each control cycle.

[0090] S15. Based on the historical data of the RV's attitude and center of gravity within a preset historical time period, perform feedforward prediction optimization on the steady-state model input data to generate optimized steady-state model input data.

[0091] Furthermore, S15 includes the following sub-steps:

[0092] S151. Based on the historical data of the RV's attitude and center of gravity within a preset historical period, the linear extrapolation method is used to predict the attitude change trend of the RV in the future preset period, and the predicted roll angle change rate, predicted pitch angle change rate, and predicted center of gravity change rate are obtained.

[0093] It should be noted that linear extrapolation is a mathematical method for short-term prediction based on historical data trends. It has low computational complexity and meets the requirements of real-time control.

[0094] In this embodiment of the invention, the system maintains a circular data buffer with a preset historical duration (e.g., 1.0 second) to continuously store historical attitude and center of gravity data generated by S12, including historical roll rate of change, pitch rate of change, and center of gravity rate of change. When prediction is needed, the most recent 0.5-second data sequence is extracted from the buffer. For each rate of change parameter (e.g., roll rate of change), a straight line is fitted using the least squares method to describe its recent trend. Subsequently, this fitted line is extended into the future for a preset future duration (e.g., 0.3 seconds), and the trend value at that future moment is calculated, thus obtaining the predicted roll rate of change, predicted pitch rate of change, and predicted center of gravity rate of change. This process is performed independently and in parallel at a frequency of 100Hz to ensure the real-time nature of the prediction.

[0095] S152. The predicted roll angle change rate, predicted pitch angle change rate, and predicted center of gravity change rate are weighted and fused with the roll angle change rate, pitch angle change rate, and center of gravity change rate, respectively, to generate feedforward optimized change rate data.

[0096] In this embodiment of the invention, each rate of change parameter is calculated separately using a fusion method. Taking the rate of change of roll angle as an example, let the measured value at the current moment be... The predicted value is The system calculates recent data in real time. variance As a stability indicator, the fusion weights are dynamically allocated based on this indicator: if A smaller value (e.g., less than a set threshold) indicates stable vehicle movement and a reliable trend, thus assigning a higher weight to the predicted value. (e.g., 0.7), current value weight It is 0.3; if A larger value indicates the vehicle is in a severe transient state, so the prediction weight is reduced (e.g., 0.3), and the current value weight is increased (0.7). Finally, the formula is used to... The optimized rate of change is calculated. The same operation is performed on the pitch angle rate of change and the center of gravity rate of change, and finally the feedforward optimized rate of change data is generated.

[0097] S153. Update the corresponding rate of change parameters in the steady-state model input data using feedforward optimized rate of change data to generate optimized steady-state model input data.

[0098] In this embodiment of the invention, the original, unoptimized rate of change parameters in the data packet are replaced with feedforward optimized rate of change data. Specifically, the original "roll angle rate of change" field is replaced with the optimized "roll angle rate of change," and the same applies to the pitch angle and center of gravity rate of change. Other information in the data packet, such as vehicle speed, steering wheel angle, and road adhesion coefficient, remains unchanged. After the replacement is completed, optimized steady-state model input data is generated.

[0099] S16. Perform rolling time-domain collaborative solution based on joint optimization function and coupled constraint set, and output suspension height adjustment amount and wheel target braking force data according to preset frequency.

[0100] It should be noted that the preset frequency is a fixed core control parameter in this system, specifically referring to the periodic frequency at which the data processing unit 102 outputs control commands to the servo control unit 103. This frequency is set to 100Hz, meaning the control period is 10 milliseconds. This means that the system calculates and outputs a new set of control commands every 10 milliseconds based on the latest vehicle status.

[0101] In this embodiment of the invention, the steady-state model input data, optimized by feedforward, is transformed into directly executable collaborative control commands through rolling time-domain collaborative calculation. This step operates at a fixed preset frequency. At the beginning of each cycle, based on the joint optimization function and the set of coupling constraints (specifically: the travel change of the four-wheel active suspension must be within ±50mm, and the actuation rate must be less than 30mm / s mechanical limits; the target braking force of each wheel must meet the adhesion circle limit based on the real-time road adhesion coefficient and the four-wheel load, and the slip ratio must meet the dynamic threshold of the anti-lock braking system), a nonlinear optimization problem covering the prediction time domain of the next 0.5 seconds is constructed and solved using algorithms such as sequential quadratic programming, thereby simultaneously planning the optimal sequence of four-wheel active suspension height and wheel target braking force data sequence for the future period. Finally, strictly following the rolling time-domain principle, only the first control quantity corresponding to the current moment in the sequence, i.e., the suspension height adjustment and wheel target braking force data expected to be executed this week, is output in real time and transmitted to the servo control unit 103. This process repeats every 10 milliseconds to provide feedforward compensation for the dynamic response lag caused by the high center of gravity and large inertial mass of the motorhome, and to synchronously and collaboratively optimize the suspension control and braking control in order to continuously generate control commands that adapt to the real-time state of the vehicle.

[0102] Furthermore, the data processing unit 102 also performs the following steps:

[0103] S21. Based on the preset travel trajectory range, perform obstacle identification on the millimeter-wave radar detection data and camera image data in the forward road condition information, and generate obstacle identification data.

[0104] In this embodiment of the invention, the preset travel trajectory range refers to a rectangular detection area, 0.8m to the left and right and 0-200m in front, pre-defined based on the width of the motorhome body, steering system characteristics, and safety redundancy, with the vehicle's centerline as the reference. This range is adapted to the characteristics of the motorhome's large turning radius and high vehicle inertia, aiming to cover its normal driving trajectory while reserving a safe distance.

[0105] The system uses Kalman filtering to smooth millimeter-wave radar detection data from forward road condition information (filter window 0.1 seconds, confidence threshold 0.8) to eliminate false targets caused by road bumps. Simultaneously, it maps the target area detected by the radar to the corresponding pixel coordinates of the image data captured by a high-definition AI camera. The system then utilizes the camera's built-in ISP chip and target detection algorithms (such as the YOLO algorithm, classification confidence threshold ≥ 0.7) to accurately classify and identify the contour of this area, fully leveraging its real-time multi-target identification capabilities. Finally, by correlating the precise target location and velocity information provided by the radar with the target category and contour size information provided by the camera, the system performs data fusion to generate structured obstacle recognition data. This data format includes fields such as target ID, relative distance, relative speed, azimuth angle, category, and contour size.

[0106] S22. Determine whether there is an object that intersects with the travel trajectory of the motorhome in the obstacle recognition data. If yes, proceed to S23; otherwise, proceed to S24.

[0107] It should be noted that the intersecting object refers to the object whose static position or dynamic trajectory overlaps with the future trajectory of the RV predicted based on its current driving status within a preset time window.

[0108] In this embodiment of the invention, the RV's trajectory prediction is based on a vehicle dynamics model: using the current vehicle speed, steering wheel angle, longitudinal acceleration, and lateral acceleration as inputs, combined with the RV's inherent parameters such as wheelbase, track width, and steering ratio, the trajectory is predicted within the next 0.5 seconds. This trajectory is output as discrete coordinate points (one point every 10ms).

[0109] For each valid target in the obstacle recognition data, the following judgment is made: If the target is a static obstacle (relative speed ≤ 0.5 km / h), its position coordinates are directly judged to see if they fall within the coordinate range of the predicted trajectory of the RV; if it is a dynamic obstacle (relative speed > 0.5 km / h), its trajectory in the next 0.5 seconds is predicted under the short-time prediction assumption (assuming it maintains its current relative speed vector and moves in a uniform straight line), and then it is judged whether the two trajectories overlap spatially. If there is an overlap, and the area of ​​the overlap region is ≥ 0.5 m² (this threshold is set based on the RV body projection and safety margin to filter out minor interference and reliably identify potential collision risks), it is determined that there is an intersecting object, and the system enters the risk avoidance decision process, i.e., S23; otherwise, if all obstacles do not meet the above overlap conditions, it is determined that there is no direct risk, and the system continues to execute steady-state control, i.e., S24.

[0110] S23. Based on the vehicle speed, body posture information, tire status and driving trajectory prediction data in the multi-dimensional dynamic parameters, risk assessment and decision-making are carried out to generate evasive operation command data.

[0111] Furthermore, S23 includes the following steps:

[0112] S231. Calculate the estimated collision time between the motorhome and the object to be encountered based on the vehicle speed and trajectory prediction data in the multi-dimensional dynamic parameters.

[0113] In this embodiment of the invention, the system acquires the real-time vehicle speed v of the motorhome and the predicted driving trajectory based on the dynamic model, while also acquiring the relative positions of intersecting objects. and relative velocity The shortest distance is obtained by solving the problem of minimizing the relative distance between the two in future time intervals. and the corresponding time .like Less than the safe distance threshold ( Where R is the radius of the circumscribed circle, and 1.5 meters is a fixed safety margin for the size of the motorhome and system errors), then... The estimated collision time is determined to be T.

[0114] S232. Based on the vehicle body attitude information and tire status in the multi-dimensional dynamic parameters, evaluate the stability margin of the motorhome under the current and preset avoidance actions.

[0115] In this embodiment of the invention, stability margin is a comprehensive indicator for evaluating the resilience of a motorhome under emergency conditions. The evaluation is divided into two levels:

[0116] 1. Current State Stability Assessment: Directly utilizes vehicle body attitude information, such as roll angle and roll angle change rate, combined with the condition of each tire (vertical load). ), calculate the lateral load transfer rate And the static stability coefficient based on the centroid height.

[0117] 2. Predictive Stability Assessment under Avoidance Maneuvers: The system pre-stores dynamic models of preset avoidance maneuvers such as emergency braking and single-lane changes. The current vehicle state and the intensity parameters of the planned maneuver (such as target deceleration) are then considered. Target lateral acceleration Input the model to predict the vehicle's maximum roll angle within the next 0.8 seconds. yaw rate and the coefficient of adhesion of each tire The stability margin S is ultimately quantized as a scalar of 0-1: .in, For rollover margin, threshold Based on real-vehicle testing This represents the sideslip margin. When... A value <0.3 indicates severely insufficient stability. A value greater than 0.6 indicates good stability.

[0118] S233. If the expected collision time is greater than the first preset collision threshold, generate first evasive maneuver command data to control the motorhome to decelerate to the first preset safe speed and pass through.

[0119] In this embodiment of the invention, the first preset collision threshold is set to 3.0 seconds. When the estimated collision time... The system determines the risk to be at the "warning level." At this point, the system generates the first evasive maneuver instruction data, the core of which is gradual deceleration. The first preset safe speed is the smaller of the following two values: 80% of the current legal speed limit, and the speed based on the current road surface adhesion coefficient. Road curvature radius R and gravitational acceleration constant Calculated safe passage speed The first evasive maneuver instruction data contains a smooth target deceleration curve, the maximum value of which typically does not exceed -2.0. To ensure passenger comfort, the instructions also require the vehicle to remain in its original lane.

[0120] S234. If the expected collision time is less than or equal to the first preset collision threshold, obtain the environmental data on the side of the motorhome and, in conjunction with the stability margin, determine whether the motorhome meets the detour conditions. If it does, execute S235; otherwise, execute S236.

[0121] In an embodiment of the present invention, when At this point, the risk level escalates to "emergency." The system immediately acquires environmental data from the side of the RV using lateral millimeter-wave radar and surround-view cameras, including the position and speed of vehicles in adjacent lanes, and the width and type of road shoulders. Determining detour conditions requires the simultaneous fulfillment of the following three conditions:

[0122] Spatial conditions: There is a continuous safe space gap between adjacent lanes of the target vehicle, with a length greater than (vehicle length + 5 meters).

[0123] Road surface conditions: The target area (lane or shoulder) is firm, flat, and free of non-fixed obstacles.

[0124] Stability condition: The stability margin currently being assessed. (This threshold is 0.3 higher than the basic safety threshold, providing an additional safety margin for emergency steering.)

[0125] If all conditions are met, it is determined that the route can be bypassed, and S235 is executed; otherwise, it is determined that the route cannot be bypassed, and S236 is executed.

[0126] S235, Generate second evasive maneuver command data to control the motorhome to decelerate to the second preset safe speed and turn around to avoid it.

[0127] In this embodiment of the invention, when the detour conditions are met, the system generates second evasion operation command data, which is a coordinated action sequence of "deceleration-steering-correction". The second preset safe speed is set as follows: The aim is to reduce the collision kinetic energy to an acceptable level. The instructions include:

[0128] Longitudinal control: Apply stronger target deceleration, peak value can reach To quickly reduce vehicle speed.

[0129] Lateral control: A trajectory is planned to smoothly transition to a safe lateral zone, with maximum lateral acceleration limited to 0.3g to accommodate the high center of gravity of the motorhome and prevent rollover. The target yaw rate curve during steering is predefined to ensure smooth steering.

[0130] S236, Generate third evasive maneuver command data to control the motorhome to brake suddenly until it stops.

[0131] In this embodiment of the invention, when the detour conditions are not met, the system generates a third evasion operation command data, that is, executes the safest full braking strategy. The target deceleration is based on the current road surface adhesion coefficient. according to Calculations are performed (reserving a 10% adhesion margin). The system, through coordinated control of the braking actuators, applies maximum braking force to all four wheels, provided the anti-lock braking system is activated. The braking command continues until the vehicle speed drops below 2 km / h (considered a stop). This command also includes maintaining the current steering wheel angle or making minor adjustments to maintain straight-line driving and prevent brake pull.

[0132] S24. Construct steady-state control command data based on multi-dimensional dynamic parameters.

[0133] In this embodiment of the invention, when S22 determines that there is no intersecting object, the system determines that there is no immediate risk of collision and therefore does not trigger the active avoidance decision process. In this case, the system enters the normal steady-state control mode. At this time, the data processing unit directly inputs the multi-dimensional dynamic parameters collected in real time into the steady-state control algorithm process (S11 to S16), calculates and outputs the suspension height adjustment amount and wheel target braking force data used to maintain the vehicle's attitude and driving trajectory. These data are directly encapsulated into steady-state control command data and sent to the servo control unit for execution, thereby ensuring smooth and stable driving of the vehicle in the absence of external risks.

[0134] The servo control unit 103, connected to the data processing unit, is used to coordinate and arbitrate the suspension height adjustment amount and wheel target braking force data to generate corresponding suspension control signals and braking control signals.

[0135] Furthermore, the servo control unit 103 performs the following steps:

[0136] S31. Establish an arbitration rule base with vehicle trajectory stability as the highest priority, vehicle posture smoothness as the second priority, and system energy consumption as the optimization objective.

[0137] In this embodiment of the invention, the establishment and invocation process of the arbitration rule base is as follows: First, based on a large amount of real-vehicle road test data, typical conflict scenarios are summarized, such as braking combined with steering, attitude maintenance and trajectory tracking under continuous bumpy road surfaces, etc. For each scenario, according to safety regulations and driving experience evaluation, an initial priority weight matrix is ​​set offline. For example, when a risk of sideslip is identified, the decision weight of trajectory stability is instantly increased to above 0.8. This rule base is embedded in the firmware of the servo control unit in the form of a queryable data structure. During real-time operation, the system also fine-tunes the weights based on the current driving mode (such as "Comfort" or "Sport" mode selected through the central control screen) and periodically obtains rule optimization data packets from the manufacturer's cloud server through the vehicle network to achieve self-optimization of the arbitration rule base.

[0138] S32. Obtain the real-time load status of the vehicle power system of the motorhome, the temperature status of key actuators in the active suspension unit and braking unit, and generate system resource status data.

[0139] It should be noted that both the active suspension unit and the braking unit are high-power, high-thermal-load actuators. Their control performance depends not only on the algorithm instructions but also on the real-time power supply capabilities of the vehicle's physical system. Ignoring the power supply capacity limit or the thermal accumulation effect of the actuators and forcibly executing high-load control commands may lead to a sudden drop in system voltage, actuator overheating protection, or even permanent hardware damage, thus introducing safety hazards. Therefore, this invention collects real-time load status of the vehicle's power system and temperature status of key actuators, and integrates them to generate unified system resource status data. This provides crucial physical constraints for upper-level control decisions, solving the problem of the disconnect between control algorithms and hardware platform capabilities.

[0140] In this embodiment of the invention, the system uses an independent power management unit chip to sample the terminal voltage and total output current of the main battery at a frequency of 500Hz, and calculates the real-time load rate (current total power / battery nominal maximum continuous output power). Simultaneously, temperature sensors are pre-installed in the drivers of each motor in the active suspension unit, and high-temperature resistant thermistors are mounted on the electro-hydraulic control valve block of the braking unit, collecting temperature data at a frequency of 100Hz. A dedicated resource monitoring microcontroller collects the above raw data via a CAN FD bus, performs filtering and validity verification, and packages it into a standard data frame, i.e., system resource status data. This data frame includes fields such as timestamp, power supply voltage, load rate, temperatures of the four suspension motors, and brake master cylinder temperature.

[0141] S33. Determine whether the suspension height adjustment amount and the target braking force data of the wheels meet the preset conflict conditions. If they meet, execute S34; otherwise, execute S35.

[0142] It should be noted that the preset conflict condition is a criterion for identifying whether the control commands of the suspension and braking subsystems are mutually exclusive in terms of resources, timing, or physical effects.

[0143] In this embodiment of the invention, the system receives suspension height adjustment and target braking force data from the data processing unit in real time. First, a resource conflict check is performed: the latest system resource status data is queried. If the estimated instantaneous power required to execute these two sets of commands simultaneously would cause the power load rate to exceed 85%, or the temperature of any critical actuator has exceeded the derating threshold (e.g., 105°C for motor windings), a conflict is identified. Second, a "dynamic conflict" check is performed: based on a simplified vehicle model, real-time calculations are performed to determine whether executing the current command simultaneously would cause the slip ratio or sideslip angle of any wheel to exceed the stability threshold in the predicted next control cycle. If either condition is met, an arbitration process is triggered.

[0144] S34. Based on the arbitration rule base, priority is given to ensuring the complete or partial execution of the target braking force data of the wheels to maintain the stability of the driving trajectory. At the same time, without causing secondary instability, the suspension height adjustment amount is dynamically adjusted to generate the target suspension adjustment amount and target braking force.

[0145] In this embodiment of the invention, after S33 determines that the conflict condition is met, the arbitration module immediately calls the arbitration rule base (which prioritizes vehicle trajectory stability). The arbitration process is executed according to the conflict type:

[0146] For resource conflicts (such as power overload or actuator overheating): the system prioritizes ensuring the complete execution of target braking force data for the wheels, as it is directly related to the highest priority trajectory stability. For suspension height adjustments, execution is reduced or delayed in descending order based on their contribution to attitude stability, until the system resource state satisfies the constraints. Only in extreme cases will braking commands be reduced proportionally according to the minimum necessary principle.

[0147] For dynamic conflicts (such as suspension actions that might weaken tire grip): the system retains the full target braking force data for each wheel. Simultaneously, a command reshaping algorithm is initiated. This algorithm takes the current vehicle attitude (roll angle, pitch angle) and rate of change, along with the suspension system travel and response constraints defined in S14, as input to calculate a transient "safe adjustment corridor." This corridor defines the allowable adjustment range and rate limit for each wheel's suspension height adjustment under the current dynamic state. Its core purpose is to prevent any suspension actions that might exacerbate roll, pitch, or cause drastic load transfer. The original suspension height adjustment will be strictly constrained within this corridor, thereby generating a target suspension adjustment that will not trigger secondary instability.

[0148] Ultimately, the system outputs a set of coordinated and arbitrated target suspension adjustments and target braking forces. This set of instructions eliminates direct conflicts in terms of resources or dynamics, and ensures that the stability of the RV's trajectory is prioritized during execution, while adjustments to the vehicle's attitude are limited to a safe and controllable range.

[0149] S35. Use the suspension height adjustment amount and the target braking force data of the wheels as the target suspension adjustment amount and the target braking force.

[0150] In this embodiment of the invention, when S33 determines that the preset conflict condition is not met, it indicates that the suspension height adjustment amount from the data processing unit 102 and the wheel target braking force data are compatible and do not require arbitration adjustment. At this time, the servo control unit 103 will perform a pass-through assignment operation: that is, the received suspension height adjustment amount is directly used as the target suspension adjustment amount to be executed, and the wheel target braking force data is directly used as the target braking force to be executed.

[0151] S36. Based on the preset hardware drive mapping relationship, the target suspension adjustment amount and the target braking force are converted into suspension control signals for driving the active suspension unit and braking control signals for driving the braking unit, respectively.

[0152] Furthermore, S36 includes the following steps:

[0153] S361. Obtain suspension operation test data from real vehicle tests of motorhomes under different loads and road conditions.

[0154] In this embodiment of the invention, a motorhome equipped with the prototype system is used at a dedicated test track to systematically change the test variables. Regarding load, three states are set: unloaded, half-loaded (with counterweight configured at 50% of the maximum gross weight), and fully loaded. Regarding road conditions, typical conditions are covered, including high-adhesion asphalt straight roads, low-adhesion concrete slab roads, Belgian cobblestone roads, and sinusoidal wave roads. Under each load-road condition combination, a sinusoidal sweep or step command signal of different frequencies (0.1Hz to 5Hz) and amplitudes covering the entire travel range (e.g., -50mm to +50mm) is injected into the suspension system via a test computer. Simultaneously, high-precision sensors synchronously collect: 1) the injected command voltage or PWM signal; 2) the actual displacement of the four suspension actuators (measured by laser displacement sensors); 3) the actuator current; and 4) the acceleration of relevant points on the vehicle body. All data are synchronously recorded at a 1kHz sampling rate, forming a multi-dimensional, timestamped suspension actuation experimental dataset.

[0155] S362. The initial big data model is trained and its parameters are adjusted online using suspension actuation test data and real vehicle operation data continuously collected during actual driving of the motorhome, thereby generating the target big data model.

[0156] It should be noted that the initial big data model refers to an initial suspension control parameter prediction model trained using the suspension actuation experimental data obtained by S361 during the system development phase.

[0157] In this embodiment of the invention, during actual vehicle operation, the system continuously collects real-vehicle operation data via background tasks, including vehicle status (such as load and speed), issued suspension height adjustment commands, corresponding suspension control signals, and the actual vehicle body attitude response fed back by sensors such as the inertial measurement unit. This data constitutes "command-response" sample pairs. After processing, this sample data, along with historical experimental data, can be used for online training and calibration of the initial model. The core objective of training is to minimize the deviation between the model's predicted suspension action and the actual vehicle body attitude improvement, enabling the model to more accurately predict the optimal control parameters required to achieve the adjustment amount under the current specific load and road conditions, based on the suspension height adjustment amount issued in S16. This process continuously corrects the model's adaptability to new operating conditions (such as road surface excitation at specific frequencies) and compensates for performance changes in the actuators due to use. Model optimization can be performed on the vehicle end or by uploading data to the manufacturer's cloud server via the vehicle connectivity module for larger-scale computation and training. The optimized model parameters can be updated to the vehicle via OTA and other means to replace the original model, thereby iteratively generating a target big data model with continuously improving performance, providing a better model foundation for the next step of building a high-precision control parameter mapping table for S363.

[0158] S363. The mapping relationship of control parameters of the active suspension unit under different loads and road conditions is iteratively optimized through the target big data model, and a control parameter mapping table is constructed based on the optimized mapping relationship.

[0159] In this embodiment of the invention, to ensure that the target big data model generated by S362 meets the stringent requirements of the vehicle data processing unit 102 for real-time performance and determinism, the system adopts an "offline optimization, online table lookup" strategy. Specifically, during the vehicle software release or update phase, the system development tool utilizes the pre-trained target big data model to perform a one-time offline calculation to generate a control parameter mapping table. This process first defines a discretized query space covering all typical operating conditions of the motorhome, whose dimensions typically include vehicle load status, road surface roughness level, and target suspension adjustment amount. For each discrete node in this multi-dimensional space (representing a specific combination of operating conditions), the tool calls the target big data model, inputs the conditional parameters corresponding to that node, and the model outputs a set of suspension control parameters optimized to accurately achieve the target adjustment amount under this operating condition. The input conditions of all nodes and the corresponding optimized output parameters are systematically recorded and stored, thus constructing a multi-dimensional control parameter mapping table. After generation, this table undergoes smoothness and validity verification and is ultimately compiled into the firmware and integrated into the storage space of the data processing unit 102. This table serves as a solidified carrier of model knowledge, replacing complex online model reasoning.

[0160] S364. Using the pressure and drive current values ​​obtained from calibration tests of the electro-hydraulic brake master pump contained in the braking unit at multiple discrete pressure control points, pressure and current characteristic curves are constructed.

[0161] In this embodiment of the invention, the electro-hydraulic brake master cylinder, along with its controller, is mounted on a hydraulic test bench. Static and dynamic calibrations are performed at multiple different oil temperature points (e.g., -40℃, -20℃, 0℃, 25℃, 80℃, 120℃). Static calibration: A series of discrete pressure control point commands (e.g., 0, 2, 4, ..., 14MPa) are sent to the master cylinder controller. After the pressure stabilizes, the actual average drive current value output by the controller is recorded. Dynamic calibration: Pressure ramp or step commands are sent, and the transient response curves of current and pressure are recorded. The static corresponding data at all temperature points are collected, and a surface fitting algorithm (e.g., polynomial regression or spline interpolation) is used to generate a target pressure... and oil temperature For input, with the required drive current The output binary function That is, the pressure and current characteristic curve.

[0162] S365. Based on the input pressure value and output braking force corresponding data obtained from the calibration test of each wheel brake pump in the braking unit at multiple discrete braking force control points, construct the braking force transfer function of each wheel brake pump.

[0163] It should be noted that the ultimate effectiveness of the braking system is reflected in the braking force between the tires and the ground. The hydraulic pressure output from the master cylinder is transmitted through pipelines and brake calipers, ultimately converting into clamping force and forming braking torque. This transmission process is not an ideal linear proportional relationship, but is affected by various factors such as the piston area of ​​the caliper, the wear condition of the friction pads, and the coefficient of friction. Due to manufacturing tolerances and uneven wear, the characteristics of each wheel's brake caliper will also differ. Therefore, this invention performs individual bench calibration tests on each brake caliper to obtain the corresponding data from the input pressure value to the actual output braking force, thereby constructing an independent and precise braking force transmission function for each wheel. This allows the upper-level controller to accurately know "how much hydraulic pressure is applied and how much braking force is generated," which is a prerequisite for achieving precise braking force distribution in vehicle stability control.

[0164] In this embodiment of the invention, a single wheel's brake caliper (brake cylinder) is mounted on a high-precision brake dynamometer. The dynamometer can simulate wheel inertia and accurately measure braking torque. During calibration, a series of discrete braking force control points corresponding to input pressure values ​​(e.g., from 1 MPa to 10 MPa, one point per 1 MPa) are applied to the caliper via an external hydraulic source. At each pressure point, after the braking force stabilizes, the input pressure value and the output braking force measured by the dynamometer (which needs to be converted into wheel tangential force) are recorded. To cover the differences in friction pads from cold to hot states, calibration is repeated at different initial temperatures. Finally, the obtained dataset is used to fit an estimation function of braking force F, which includes pressure P and temperature T as inputs, for each brake cylinder, for example... This function is the braking force transmission function of the brake pump, and the function parameters of all four wheels are calibrated and stored.

[0165] S366. Input the target suspension adjustment amount into the control parameter mapping table for query and mapping operations to generate a pulse width modulation signal set to drive the active suspension unit.

[0166] In this embodiment of the invention, during each control cycle, the servo control unit reads the target suspension adjustment amount (an array containing the target heights of the four wheels). Simultaneously, it obtains the current estimated load state (e.g., "fully loaded") and road surface grade (e.g., "rough road surface") from the sensor network. Using these three pieces of information (load, road condition, target height) as a joint index, a lookup is performed in a three-dimensional control parameter mapping table. The lookup algorithm employs nearest-neighbor interpolation or trilinear interpolation to balance speed and accuracy. The lookup output is a complete set of control parameters for each of the four suspension actuators, with the target PWM duty cycle being the most crucial. The timer / PWM generation module inside the servo control unit immediately generates four synchronous pulse width modulation signals with specific duty cycles based on these duty cycle values. These four signals constitute the pulse width modulation signal set driving the active suspension unit and are applied to the four suspension motors through the power drive circuit.

[0167] S367. Calculate and distribute the target braking force input pressure and current characteristic curves and braking force transfer function to generate the pressure control signal of the master cylinder of the electronic hydraulic brake and the current control signal corresponding to each wheel brake cylinder.

[0168] In this embodiment of the invention, for each wheel, the system calls the pre-calibrated braking force transfer function for that wheel. Given the current caliper temperature and the desired braking force for that wheel, the system calculates the target braking pressure required to achieve this braking force through function inversion. Subsequently, the system selects the maximum value from the target braking pressure values ​​calculated for the four wheels and determines it as the target system pressure that the electro-hydraulic brake master cylinder needs to establish. Next, the system uses the target system pressure and the real-time monitored brake fluid temperature as inputs, queries the pre-stored pressure and current characteristic curves, and calculates the pressure control signal required to drive the master cylinder. This signal is typically represented by a target current value. Finally, based on the ratio of the target braking pressure to the target system pressure calculated independently for each wheel, and combined with the pre-calibrated characteristics of the pressure regulating valves of each wheel's brake cylinder, the system calculates the current control signal required to control the pressure regulating valve of each wheel cylinder. After the entire calculation process is completed, the system synchronously outputs a pressure control signal (target current command) for driving the master cylinder and current control signals for the four wheel cylinders. These commands work together to ultimately achieve precise closed-loop control of the independent braking force of each wheel. For clarity, the core calculation formula is as follows:

[0169] 1. Calculation of target braking pressure for each wheel:

[0170] For each wheel Based on its braking force transfer function Given the current caliper temperature and target braking force Under these conditions, the required target braking pressure can be obtained by inverse solving. .

[0171] 2. Determination of system target pressure:

[0172] The maximum value among the four target braking pressures is selected as the target system pressure for the electro-hydraulic brake master pump. :

[0173]

[0174] 3. Pressure control signal calculation:

[0175] Will With brake fluid temperature Substitute the pressure and current characteristic curves Calculate the pressure control signal driving the master pump. .

[0176] 4. Calculation of current control signal:

[0177] Based on the pressure ratio of each wheel, the characteristic function of each wheel's brake caliper valve is used. Calculate the current control signals for each wheel. :

[0178] ;

[0179] Through the above calculations and allocation operations based on the calibration function and characteristic curve, the system ultimately generates and outputs the pressure control signal that drives the electro-hydraulic brake master pump. and the current control signal for driving the brake calipers of each wheel This allows for the precise execution of the target braking force distribution requirements.

[0180] The braking actuator 104 is electrically connected to the servo control unit and includes an active suspension unit and a braking unit. It is used to synchronously adjust the active suspension height of each wheel in accordance with the suspension control signal in an integrated and coordinated timing manner to maintain the stability of the vehicle body posture, and to coordinately adjust the braking force of each wheel in response to the braking control signal to correct the driving trajectory.

[0181] In this embodiment of the invention, the braking actuator is physically integrated with an active suspension unit and a braking unit. The active suspension unit includes four independently controllable or hydraulically driven suspension actuators (i.e., four-wheel active suspension), each capable of rapidly adjusting the vehicle's height and stiffness at the wheels. The braking unit includes an electro-hydraulic master cylinder, four independent wheel brake cylinders and their control valve assemblies, and is integrated with the hydraulic modules of the vehicle's anti-lock braking system (ABS) and electronic stability program (ESP), forming a braking execution platform with high precision and independent four-channel pressure regulation capabilities.

[0182] The braking actuator receives suspension control signals from the servo control unit in real time. The integrated coordinated timing controller analyzes these signals and drives the actuators of the four active suspension units. In each control cycle, the four actuators act synchronously according to instructions, adjusting the suspension height of their respective wheels. For example, during cornering, the outer wheel suspension is raised, while the inner wheel is lowered or held, generating a righting torque to counteract body roll and maintain vehicle stability. This process continues at a frequency of 100Hz, enabling rapid tracking and dynamic compensation of vehicle pitch and roll angles. Simultaneously, the braking actuator receives and analyzes braking control signals (including master cylinder pressure control signals and current control signals for each wheel cylinder). The integrated coordinated timing ensures precise alignment of braking force adjustment and suspension adjustment on the time axis. Based on the signals, the braking unit controls the electro-hydraulic brake master cylinder to establish base system pressure, and applies precisely differentiated braking forces to the four wheels through independent valve control of each wheel's brake cylinder. For example, when understeer is detected, the system applies appropriate braking force to the inner rear wheel to generate a corrective yaw moment and correct the driving trajectory.

[0183] It's worth noting that the integrated coordinated timing is not simply parallel execution, but rather a deep coupling based on the vehicle dynamics model. Within a control cycle, the system might instruct the active suspension unit to fine-tune the height to optimize the vertical load on a particular wheel, followed immediately by the braking unit applying a precise braking force calculated based on the new load to that wheel. Alternatively, during emergency avoidance, the suspension might be instructed to act rapidly to preload the vehicle's attitude, while the braking system applies maximum efficiency deceleration; both are initiated sequentially and their effects are superimposed within a very short timeframe. This coordination based on a unified time base and coupled dynamics predictions allows the desired mechanical effects (stabilized attitude, corrected trajectory) of the suspension control signal and braking control signal to be achieved efficiently and smoothly, avoiding transient vehicle disturbances caused by actuator timing conflicts or amplitude mismatches.

[0184] Please see Figure 2 , Figure 2 A flowchart illustrating the steps of a vehicle body steady-state control method provided in an embodiment of the present invention.

[0185] This invention provides a method for steady-state control of a motorhome body, comprising:

[0186] Step 201: Collect real-time data on the RV's water tank level, throttle travel, four-wheel rotational angular velocity, four-wheel load, vehicle roll angle, vehicle pitch angle, steering wheel angle, vehicle speed, longitudinal acceleration, lateral acceleration, and forward road condition information to generate multi-dimensional dynamic parameters;

[0187] Step 202: Real-time collaborative calculation is performed using multi-dimensional dynamic parameters and coupling constraints of suspension control and braking control through the vehicle steady-state model corresponding to the motorhome, and the suspension height adjustment amount and wheel target braking force data are output according to the preset frequency.

[0188] Step 203: Perform coordinated conversion and conflict arbitration on the suspension height adjustment amount and wheel target braking force data to generate corresponding suspension control signals and braking control signals;

[0189] Step 204: Synchronously adjust the active suspension height of each wheel according to the integrated coordinated timing response suspension control signal to maintain vehicle body posture stability, and coordinately adjust the braking force of each wheel in response to the braking control signal to correct the driving trajectory.

[0190] The specific implementation method of the vehicle body steady-state control method of this motorhome is basically the same as the specific implementation of the vehicle body steady-state control system of the motorhome described above, and will not be repeated here.

[0191] Please see Figure 3 , Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention.

[0192] An electronic device according to an embodiment of the present invention includes: a memory 301 and a processor 302. The memory 301 stores a computer program. When the computer program is executed by the processor 302, the processor 302 performs a vehicle body steady-state control method as described in any embodiment.

[0193] Memory 301 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 301 has storage space 303 for program code 313 for performing any of the method steps described above. For example, storage space 303 for program code may include various program codes 313 for implementing the various steps in the methods described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in the vehicle body steady-state control method for motorhomes described above.

[0194] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the vehicle body steady-state control method of the recreational vehicle as described in the above embodiments.

[0195] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0196] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0199] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0200] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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. Such 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 body stability control system for a motorhome, characterized in that, include: The Internet of Things (IoT) includes a multi-dimensional parameter sensing module, a data processing unit, a servo control unit, and a braking actuator. The IoT multi-dimensional parameter sensing module is used to collect real-time data on the RV's water tank level, throttle travel, four-wheel rotational angular velocity, four-wheel load, vehicle roll angle, vehicle pitch angle, steering wheel angle, vehicle speed, longitudinal acceleration, lateral acceleration, and forward road condition information, and generate multi-dimensional dynamic parameters. The data processing unit is communicatively connected to the IoT multi-dimensional parameter sensing module. It is used to perform real-time collaborative calculations using the multi-dimensional dynamic parameters and the coupling constraints of suspension control and braking control through the vehicle steady-state model corresponding to the recreational vehicle, and output suspension height adjustment amount and wheel target braking force data at a preset frequency. The servo control unit is connected to the data processing unit and is used to perform coordinated conversion and conflict arbitration of the suspension height adjustment amount and the wheel target braking force data to generate corresponding suspension control signals and braking control signals. The braking actuator, electrically connected to the servo control unit, includes an active suspension unit and a braking unit. It is used to synchronously adjust the active suspension height of each wheel in accordance with the suspension control signal to maintain vehicle body stability, and to coordinately adjust the braking force of each wheel in response to the braking control signal to correct the driving trajectory.

2. The vehicle body stability control system for a motorhome according to claim 1, characterized in that, The data processing unit performs the following steps: The multidimensional dynamic parameters are subjected to noise suppression and outlier removal to generate valid dynamic parameter data. Extract the real-time center of gravity position, rate of change of center of gravity, rate of change of roll angle, and rate of change of pitch angle corresponding to the recreational vehicle from the effective dynamic parameter data to generate a high-order dynamic feature dataset. The road adhesion coefficient contained in the vehicle speed, steering wheel angle and forward road condition information in the high-order dynamic feature data and the effective dynamic parameter data is fused to generate steady-state model input data. Based on the high center of gravity and large inertial mass characteristics of the motorhome, a joint optimization function is constructed with minimizing the vehicle attitude tracking error as the primary objective and the weighted sum of energy consumption of the suspension and braking systems as the secondary objective. The travel and response constraints of the suspension system and the distribution and anti-lock braking constraints of the braking system are defined together as a set of coupled constraint conditions. Based on the historical attitude and center of gravity data of the RV within a preset historical time period, the steady-state model input data is subjected to feedforward prediction optimization processing to generate optimized steady-state model input data. The rolling time-domain collaborative solution is performed based on the joint optimization function and the set of coupled constraints, and the suspension height adjustment amount and wheel target braking force data are output at a preset frequency.

3. The vehicle body stability control system for a motorhome according to claim 2, characterized in that, The step of performing feedforward prediction optimization processing on the steady-state model input data based on the historical attitude and center of gravity data of the recreational vehicle within a preset historical time period to generate optimized steady-state model input data includes: Based on the historical attitude and center of gravity data of the motorhome within a preset historical period, the linear extrapolation method is used to predict the attitude change trend of the motorhome in the future preset period, and the predicted roll angle change rate, the predicted pitch angle change rate, and the predicted center of gravity change rate are obtained. The predicted roll angle change rate, the predicted pitch angle change rate, and the predicted center of gravity change rate are weighted and fused with the roll angle change rate, pitch angle change rate, and center of gravity change rate, respectively, to generate feedforward optimized change rate data; The feedforward optimized rate of change data is used to update the corresponding rate of change parameters in the steady-state model input data to generate optimized steady-state model input data.

4. The vehicle body stability control system for a motorhome according to claim 1, characterized in that, The data processing unit also performs the following steps: According to the preset travel trajectory range, the millimeter-wave radar detection data and camera image data in the forward road condition information are used to identify obstacles and generate obstacle identification data. Determine whether the obstacle recognition data intersects with the travel trajectory of the recreational vehicle; If so, risk assessment and decision-making are performed based on the vehicle speed, vehicle posture information, tire status and driving trajectory prediction data in the multi-dimensional dynamic parameters, and evasion operation command data are generated. If not, then based on the aforementioned multidimensional dynamic parameters, steady-state control command data is constructed.

5. The vehicle body stability control system for a motorhome according to claim 4, characterized in that, The step of performing risk assessment and decision-making based on the vehicle speed, vehicle posture information, tire status, and driving trajectory prediction data from the multi-dimensional dynamic parameters, and generating evasive maneuver command data includes: Based on the vehicle speed and driving trajectory prediction data in the multi-dimensional dynamic parameters, the estimated collision time between the recreational vehicle and the object is calculated. Based on the vehicle body posture information and tire status in the multi-dimensional dynamic parameters, the stability margin of the motorhome under the current and preset avoidance actions is evaluated. If the estimated collision time is greater than the first preset collision threshold, then first evasion operation command data is generated to control the recreational vehicle to decelerate to the first preset safe speed and pass through. If the estimated collision time is less than or equal to the first preset collision threshold, then the environmental data on the side of the motorhome is obtained, and combined with the stability margin, it is determined whether the motorhome meets the detour conditions. If the conditions are met, then a second evasion operation command data is generated to control the recreational vehicle to decelerate to the second preset safe speed and turn around to avoid it; If the conditions are not met, a third evasive maneuver command data is generated to control the motorhome to brake suddenly until it comes to a stop.

6. The vehicle body stability control system for a motorhome according to claim 1, characterized in that, The servo control unit performs the following steps: Establish an arbitration rule base with vehicle trajectory stability as the highest priority, vehicle posture smoothness as the second priority, and system energy consumption as the optimization objective; The system obtains the real-time load status of the vehicle power system of the motorhome and the temperature status of the key actuators in the active suspension unit and the braking unit, and generates system resource status data. Determine whether the suspension height adjustment amount and the target braking force data of the wheel meet the preset conflict conditions; If satisfied, then according to the arbitration rule base, priority is given to ensuring the complete or partial execution of the target braking force data of the wheel to maintain the stability of the driving trajectory. At the same time, without causing secondary instability, the suspension height adjustment amount is dynamically adjusted to generate the target suspension adjustment amount and the target braking force. If not satisfied, the suspension height adjustment amount and the target braking force data of the wheel will be used as the target suspension adjustment amount and target braking force. Based on a preset hardware driver mapping relationship, the target suspension adjustment amount and the target braking force are respectively converted into suspension control signals that drive the active suspension unit and braking control signals that drive the braking unit.

7. The vehicle body stability control system for a motorhome according to claim 6, characterized in that, The step of converting the target suspension adjustment amount and the target braking force into suspension control signals for driving the active suspension unit and braking control signals for driving the braking unit, respectively, based on a preset hardware drive mapping relationship, includes: Obtain suspension operation experimental data from real-vehicle tests of the motorhome under different loads and road conditions; The initial big data model is trained and its parameters are adjusted online using the suspension actuation test data and the real vehicle operation data continuously collected during actual driving of the recreational vehicle, thereby generating the target big data model. The target big data model is used to iteratively optimize the mapping relationship of the control parameters of the active suspension unit under different loads and road conditions, and a control parameter mapping table is constructed based on the optimized mapping relationship. Pressure and current characteristic curves are constructed using the pressure and drive current data obtained from calibration tests of the electro-hydraulic brake master pump contained in the braking unit at multiple discrete pressure control points. Based on the input pressure value and output braking force corresponding data obtained by calibrating each wheel brake pump in the braking unit at multiple discrete braking force control points, the braking force transfer function of each wheel brake pump is constructed. The target suspension adjustment amount is input into the control parameter mapping table for query and mapping operations to generate a pulse width modulation signal set that drives the active suspension unit; The target braking force is input into the pressure and current characteristic curve and the braking force transfer function for calculation and distribution operations, generating a pressure control signal for braking the electro-hydraulic master cylinder and a current control signal for each wheel brake cylinder.

8. A method for steady-state control of a motorhome body, characterized in that, include: Real-time data collection of the motorhome's water tank level, throttle travel, four-wheel rotational angular velocity, four-wheel load, body roll angle, body pitch angle, steering wheel angle, vehicle speed, longitudinal acceleration, lateral acceleration, and forward road condition information generates multi-dimensional dynamic parameters. The vehicle steady-state model corresponding to the recreational vehicle is used to perform real-time collaborative calculations using the multi-dimensional dynamic parameters and the coupling constraints of suspension control and braking control, and the suspension height adjustment amount and wheel target braking force data are output at a preset frequency. The suspension height adjustment amount and the wheel target braking force data are coordinated and arbitrated to generate corresponding suspension control signals and braking control signals. In accordance with the integrated and coordinated timing response to the suspension control signal, the active suspension height of each wheel is adjusted synchronously to maintain vehicle body stability, and in accordance with the braking control signal, the braking force of each wheel is adjusted in coordination to correct the driving trajectory.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the vehicle body stability control method of the recreational vehicle as described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the vehicle body steady-state control method for a recreational vehicle as described in claim 8.

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