Parameter adaptive commercial vehicle steering control method and system based on roll index

Abstract

The application discloses a parameter adaptive commercial vehicle steering control method and system based on a roll index, relates to the technical field of vehicle steering control, and comprises the following steps: acquiring vehicle sensor data to obtain current state variables of a vehicle and estimating vehicle parameters in real time; the roll index is re-expressed as a linear combination of a roll angle and a roll rate; a three-degree-of-freedom dynamics model of the vehicle is established, and a target function is designed based on the model predictive control principle to track and control a desired vehicle trajectory; a barrier function is introduced into the target function, the danger degree of the vehicle is determined by dynamically calculating the barrier function, and the weight of the roll index in the target function is adjusted; the optimal solution sequence is solved based on the improved target function and state constraints, and the optimal solution sequence is sent to an executing mechanism as a control signal to output an ideal front wheel steering angle. The above scheme greatly improves the steering stability and safety of an intelligent commercial vehicle under a sharp turning condition or in the case of driver operation failure.

Description

Technical Field

[0001] This invention relates to the field of vehicle steering control technology, specifically to a parametric adaptive commercial vehicle steering control method and system based on rollover index. Background Technology

[0002] With the rapid development of the logistics and transportation industry, the risk of rollovers faced by commercial vehicles in complex road conditions, under heavy loads, and over long distances is becoming increasingly prominent. Statistics show that although commercial vehicle rollover accidents account for a small percentage of traffic accidents, their fatality rate is as high as 30%, seriously threatening public safety. Traditional rollover prevention control methods mainly rely on the Static Stability Factor (SSF) or a single dynamic indicator (such as lateral acceleration or roll angle), but these have significant limitations in dynamic driving scenarios. For example, SSF is calculated based solely on vehicle geometric parameters (center of gravity height, wheelbase), and cannot reflect the impact of real-time load distribution, road gradient, and tire nonlinear characteristics on rollovers; while threshold control based on lateral acceleration is easily affected by uneven road surfaces or emergency lane changes, leading to false triggering or delayed response.

[0003] In recent years, the dynamic rollover index (RI) has gradually become a research hotspot. The lateral load transfer ratio (LTR) is a commonly used indicator, but it relies on real-time measurement of vertical tire forces. However, commercial vehicles experience large load fluctuations and sensors are expensive, making high-precision monitoring difficult in practical applications. Some improved methods indirectly estimate LTR through suspension parameters or tire deformation, but these still suffer from problems such as oversimplification of the model and poor environmental adaptability. Furthermore, existing steering control strategies mostly employ fixed-parameter PID or fuzzy control, which struggles to cope with dynamic parameter drift caused by changes in commercial vehicle load and suspension aging, especially in emergency obstacle avoidance or continuous curves, where control lag or over-intervention is likely to occur.

[0004] At the control algorithm level, Model Predictive Control (MPC) has been introduced into the rollover prevention field due to its multi-objective optimization and constraint handling capabilities. However, existing MPC solutions are mostly based on linear vehicle models, neglecting the coupling effects of tire force nonlinearity, road gradient, and lateral load transfer, resulting in insufficient prediction accuracy. For example, patent CN111703413B proposes a lateral control safety monitoring method for autonomous vehicles, but it does not consider the dynamic impact of load changes on the center of gravity position. In addition, the fixed weight parameters of traditional MPC cannot adapt to complex operating condition switching, and may cause control conflicts on slippery roads or during emergency braking.

[0005] To address the aforementioned issues, there is an urgent need for a steering control method and system that integrates high-precision rollover risk assessment with a parameter adaptive mechanism. This invention proposes a parameter adaptive steering control method for commercial vehicles based on the rollover index. By constructing a Dynamic Rollover Index (DRI), it integrates multi-source information such as roll angle, roll rate, road slope, and load distribution to quantify rollover risk in real time. Combined with an adaptive MPC framework, it identifies vehicle parameters online and dynamically adjusts control weights and prediction models to improve control robustness under complex operating conditions. This method effectively solves core problems in traditional technologies such as model mismatch, poor environmental adaptability, and rigid control parameters, providing an innovative solution for the field of active safety in commercial vehicles. Summary of the Invention

[0006] This invention primarily addresses the difficulties in measuring traditional rollover indices (such as LTR) in practical applications and the technical challenges of timely controller parameter adjustment due to constantly changing environmental and load conditions during vehicle operation. It provides a parameter-adaptive commercial vehicle steering control method and system based on the rollover index. By implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to adapt to constantly changing environmental and load conditions. Based on sensor data of the roll angle and roll rate of the intelligent commercial vehicle, vehicle parameters such as sprung mass and center of gravity height are estimated in real time, and the proposed novel rollover index is updated in real time. Combining the three-degree-of-freedom dynamic equations of the commercial vehicle, a model predictive control-based active steering system is designed to track and control the desired vehicle trajectory. A barrier function is introduced into the objective function of the MPC (Multi-Purpose Control Controller) to restrict vehicle movement within a safe area without affecting vehicle handling under normal driving conditions. This invention, through real-time dynamic adjustment of the rollover index and dynamic constraints of the control system's barrier function, ensures the controller's dynamic adaptability and high operational accuracy, thereby significantly improving the handling stability and safety of intelligent commercial vehicles in sharp turns or situations of driver error.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Firstly, this invention proposes a parametric adaptive steering control method for commercial vehicles based on the rollover index, the key of which includes the following steps: Step 1: Acquire vehicle sensor data to obtain the vehicle's current state and estimate vehicle parameters in real time; Step 2: Using the roll dynamics equation and vehicle parameters, the rollover index is re-expressed as a linear combination of roll angle and roll rate; Step 3: Establish a three-degree-of-freedom dynamic model of the vehicle and design an objective function based on the model predictive control principle to track and control the desired vehicle trajectory; Step 4: Introduce a barrier function into the objective function, determine the degree of vehicle danger by dynamically calculating the barrier function, and adjust the weight of the rollover index in the objective function; Step 5: Based on the improved objective function and state constraints from Step 4, the optimal solution sequence is obtained through rolling solution, and it is sent as a control signal to the actuator to output the ideal front wheel steering angle.

[0008] Furthermore, the process of acquiring vehicle sensor data to obtain the vehicle's current state and estimating vehicle parameters in real time, as described in step 1, includes: Acquire data from onboard sensors to obtain the vehicle's roll angle, roll rate, and sprung mass; The vehicle roll dynamics equations are rewritten in linear parameterized form; Vehicle parameters are estimated using recursive least squares.

[0009] Furthermore, the vehicle parameters include the equivalent roll damping of the suspension system, the equivalent roll stiffness of the suspension system, and the height from the sprung mass center to the roll center.

[0010] Furthermore, the rollover index mentioned in step 2 is now expressed as a mathematical expression of a linear combination of the roll angle and roll rate: , in, RI The rollover index; This refers to the vehicle body roll angle; This refers to the roll angular velocity; and The coefficient is determined by the vehicle parameters;

[0011] Furthermore, step 3, which involves establishing a three-degree-of-freedom dynamic model of the vehicle and designing an objective function based on model predictive control principles to track and control the desired vehicle trajectory, includes: Establish a three-degree-of-freedom dynamic model of the vehicle, encompassing lateral, yaw, and roll maneuvers. Discretize the continuous-time vehicle dynamics model to obtain the discrete-time state-space equation; Design the objective function and its constraints based on the principle of model predictive control.

[0012] Furthermore, the mathematical expression of the objective function after introducing the barrier function in step 4 is as follows: , in, It is the prediction of all control sequences to be optimized in the time domain. , It is the prediction of the first in the time domain The reference state at each sampling time. It is the prediction of the first in the time domain The rollover index at each sampling time. It is the prediction of the first in the time domain The control input vector at each sampling time. It is the prediction of the first in the time domain The state vector at each sampling time. These are state tracking weight coefficients; These are the dynamic weighting coefficients of the barrier function. These are the weighting coefficients that control the input. N It is the number of steps in the prediction range. .

[0013] Furthermore, the process of dynamically calculating the barrier function to determine the vehicle's hazard level and adjusting the weight of the rollover index in the objective function, as described in step 4, includes: The barrier function is designed in segments to distinguish between low-risk, medium-risk, and high-risk situations: , in, This is the critical value. To intervene in advance, All are constants; After obtaining the current parameters through parameter identification, update To reflect the actual rollover risk, the updated formula is as follows: , in, This is the nominal critical value; These are coefficients calculated based on nominal parameters; The current coefficients are calculated based on the identification parameters.

[0014] Secondly, the present invention proposes a parameter-adaptive commercial vehicle steering control system based on the rollover index, applicable to the control method described in the first aspect, comprising: The sensor unit is used to perceive the status of the intelligent commercial vehicle in real time; The parameter estimation unit is used to estimate vehicle parameters in real time based on the current vehicle state variables obtained by the sensor unit. The control unit is used to re-express the rollover index RI as a linear combination of roll angle and roll rate using the roll dynamics equation and vehicle parameters; it is also used to establish a three-degree-of-freedom dynamic model of the vehicle and design an objective function based on model predictive control principles to track and control the desired vehicle trajectory; it is also used to introduce a barrier function into the objective function, determine the degree of vehicle danger by dynamically calculating the barrier function, and adjust the weight of the rollover index in the objective function; and it is also used to solve for the optimal solution sequence based on the designed objective function and state constraints, and send it as a control signal to the actuator. The actuator is used to drive the steering motor to control the wheel steering according to the ideal front wheel steering angle in the control signal, and to measure the actual steering angle of the wheel in real time and feed it back to the control unit for closed-loop control.

[0015] Thirdly, the present invention provides a computer device comprising: One or more processors; A memory configured to store one or more programs that, when executed by one or more processors, cause the one or more processors to perform the method as described in the first aspect.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0017] The beneficial effects of this invention are: This invention, through the implementation of model predictive control and adaptive control strategies, allows the system to adjust its control parameters in real time to adapt to constantly changing environmental and load conditions, significantly improving the handling stability and safety of intelligent commercial vehicles in complex environments. Simultaneously, the application of multi-sensor data fusion technology and least squares parameter estimation enables the system to more accurately estimate the vehicle's state, enhancing the robustness of the control strategy. Furthermore, by adding a barrier function to the objective function to determine the vehicle's hazard level and adjusting the weight of the rollover index in model predictive control, control intervention is increased when the RI approaches a critical value, preserving vehicle handling during normal driving. This significantly improves driver comfort and safety during commercial vehicle steering, reducing accidents caused by erroneous operation. These improvements provide intelligent commercial vehicles with a significant competitive advantage in the fields of intelligent driving and logistics transportation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method described in this invention; Figure 2 This is a schematic diagram of the system described in this invention; Figure 3 This is a schematic diagram of the structure of the sensor unit described in this invention; Figure 4 This is a schematic diagram of the structure of the device described in this invention. Detailed Implementation

[0019] The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

[0020] Example 1

[0021] like Figure 1As shown in the figure, this embodiment of the invention provides a parameter-adaptive commercial vehicle steering control method based on the rollover index, and the specific steps are as follows: Step 1: Obtain vehicle sensor data to obtain the vehicle's current state parameters, and use the recursive least squares (RLS) method to estimate vehicle parameters such as center of gravity height, suspension stiffness, and damping in real time. Step 2: Propose a new rollover index (RI), and express the rollover index as a linear combination of roll angle and roll rate through roll dynamics equations and vehicle parameters to dynamically adapt to changes in vehicle parameters; Step 3: Establish a three-degree-of-freedom dynamic model of the vehicle and design an objective function based on the model predictive control principle to track and control the desired vehicle trajectory; Step 4: Introduce a barrier function into the objective function. Calculate the barrier function dynamically to determine the degree of vehicle hazard, and adjust the weight of the rollover index (RI) in the model predictive control so that control intervention can be increased when the RI approaches the critical value. Step 5: Based on the improved objective function and state constraints from Step 4, the optimal solution sequence is obtained through rolling solution, and it is sent as a control signal to the actuator to output the ideal front wheel steering angle.

[0022] In this example, the implementation method for obtaining vehicle sensor data to acquire the current vehicle state and estimating vehicle parameters in real time in step 1 is as follows: Acquire data from onboard sensors to obtain the vehicle's roll angle, roll rate, and sprung mass; Vehicle roll dynamics equations Rewrite in linear parameterized form, specifically: The first step is to rearrange the terms, leaving the part containing the second derivative term separately on the left side. ; The second step involves dividing both parts simultaneously. , Right now ; Define the left side as the output. Then the equation in linear parameterized form is: ;

[0023] in, Let be the roll inertia of the sprung mass about the vehicle's roll axis; For roll acceleration, This refers to the roll angular velocity; g It is gravitational acceleration. For the sprung mass, The height from the center of mass of the sprung mass to the center of tilt. For the parameters to be identified, p=ms h s ; The equivalent roll damping of the suspension system. This is the equivalent roll stiffness of the suspension system; This is the regression matrix; This is the transpose of the regression matrix; For noise or unmodeled dynamics, The lateral acceleration of the vehicle's center of gravity (centrifugal acceleration during cornering); This refers to the vehicle body roll angle, which is the angle of tilt around the roll axis.

[0024] Vehicle sensor data is discretely sampled, so continuous time needs to be processed. t Change to walking k : ; in, For a moment k Measured roll acceleration , For a moment k Data collected by sensors , For a moment k Random noise at that time; Vehicle parameters are estimated using recursive least squares method. Make an estimate , k For the current moment, k -1 represents the previous time step. The specific process is as follows: At each time step The parameter estimates are updated using the following formula: ; in, For the current moment k Vehicle parameter estimates, For the previous moment k -1 vehicle parameter estimate; Kalman gain Covariance Matrix The updated formula is:

[0025] Among them, Kalman gain Covariance Matrix The updated formula is: , , in, λThis is a forgetting factor (usually between 0.95 and 0.99), used to reduce the influence of old data; For the current moment k The corresponding regression matrix, This is the transpose of the regression matrix.

[0026] The specific implementation method of this embodiment is as follows: By combining the roll dynamics equations with vehicle parameters, Reexpressed as a linear combination of roll angle and roll rate: ; where the coefficient and From vehicle parameters (such as suspension stiffness) Damping Center of mass height )Decide. Responsive suspension stiffness The influence of center of gravity position on rollover Reflecting suspension damping The effects on rollover are expressed as follows: , , in, This is the equivalent roll stiffness of the suspension system; This is the equivalent roll stiffness of the suspension system; For the sprung mass; Unsprung mass; It refers to the overall vehicle quality; It is gravitational acceleration. It is the wheel track; The height from the center of mass of the sprung mass to the center of tilt. The height of the tilt center from the ground; The height of the center of mass of the unsprung mass.

[0027] In this embodiment, step 3, which involves establishing a three-degree-of-freedom dynamic model of the vehicle and designing an objective function based on the model predictive control principle to track and control the desired vehicle trajectory, includes: A three-degree-of-freedom dynamic model of the vehicle is established, encompassing lateral, yaw, and roll conditions. The state-space equations are as follows: , in, For the system matrix, , For state variables, u For the longitudinal speed of the vehicle, This is the equivalent roll damping of the suspension. v ( t ) represents the lateral velocity. For the horizontal angle, The body roll angle, This refers to the roll angular velocity; , , To control the variables, the continuous-time vehicle dynamics model is discretized, resulting in the discrete-time state-space equations: , in, The state matrix of the connected system is a continuous matrix. A Discretization yields, for k The vehicle state vector at any given moment includes lateral velocity, yaw angle, roll angle, and roll angular velocity. ; Discrete input matrix, composed of continuous matrix B Discretization yields the result; for k The control input for the timing, in this example, is the front wheel steering angle. ; The discretized constant perturbation vector corresponds to the continuous form. D This represents constant disturbances such as road surface side joints and slope. The objective function and its constraints are designed based on the principle of model predictive control, where the constraints on the control input are expressed as follows: , , in, For the i-th front wheel steering angle control value, This is the minimum steering angle for the front-wheel mechanical components. This is the maximum steering angle of the front-wheel machinery. At the current initial time k =0 front wheel steering angle; This represents the angle increment for the next moment; For the current moment k The rotation increment; The discrete sampling period of the model; This represents the maximum angular velocity of the front wheels during steering.

[0028] The control process is continuously optimized and solved. That is, at each moment, based on the error of the state quantity at the current moment, a quadratic programming problem is used to solve the optimal solution of the control quantity that minimizes the error between the system output and the reference quantity. The optimization process is repeated online.

[0029] At each sampling time, correction is performed through feedback, that is, the prediction result is corrected using the actual output, and then the next optimization process is carried out.

[0030] In practical implementation, the mathematical expression of the objective function after introducing the barrier function is as follows: , in, It is the prediction of all control sequences to be optimized in the time domain. , It is the prediction of the first in the time domain The reference state at each sampling time. It is the prediction of the first in the time domain The rollover index at each sampling time. It is the prediction of the first in the time domain The control input vector at each sampling time. It is the prediction of the first in the time domain The state vector at each sampling time. These are state tracking weight coefficients; These are the dynamic weighting coefficients of the barrier function. These are the weighting coefficients that control the input. N It is the number of steps in the prediction range. .

[0031] Based on the objective function described above, the process of dynamically calculating the barrier function in step 4 to determine the degree of vehicle danger and adjusting the weight of the rollover index in the objective function is as follows: First, the barrier function is designed in segments to distinguish between low-risk, medium-risk, and high-risk situations: , in, This is the critical value. To intervene in advance, All are constants.

[0032] Secondly, changes in vehicle parameters during driving (such as increased center of gravity height or decreased suspension stiffness) can lower the rollover threshold, requiring early control intervention. After identifying the current parameters, the system updates them. To reflect the actual rollover risk, the updated formula is as follows: , in, This is the nominal critical value; in this example, it is taken as 0.7. These are coefficients calculated based on nominal parameters; This refers to the current coefficients calculated based on the identification parameters. This embodiment updates the coefficients through parameter identification. This allows the barrier function to dynamically adapt to changes in vehicle parameters, improving the robustness of the controller.

[0033] Example 2 like Figure 2As shown, this embodiment of the invention provides a parameter-adaptive commercial vehicle steering control system based on the rollover index, applicable to the method described in Embodiment 1, comprising: The sensor unit, also known as the sensor module, is used to perceive the status of intelligent commercial vehicles in real time. The parameter estimation unit is used to estimate vehicle parameters in real time based on the current vehicle state variables obtained by the sensor unit. The control unit is used to design corresponding modules to calculate the front wheel steering angle based on the dynamic characteristics of commercial vehicles, and then send the control signal to the execution unit. Specifically, it is used to: re-express the rollover index RI as a linear combination of roll angle and roll rate through the roll dynamics equation and vehicle parameters; establish a three-degree-of-freedom dynamic model of the vehicle and design an objective function based on the model predictive control principle to track and control the desired vehicle trajectory; introduce a barrier function into the objective function, determine the degree of vehicle danger by dynamically calculating the barrier function, and adjust the weight of the rollover index in the objective function; and solve for the optimal solution sequence based on the designed objective function and state constraints, and send it as a control signal to the execution unit. The actuator is a steering motor, which is used to drive the steering motor to control the wheel steering according to the ideal front wheel steering angle in the control signal, and to measure the actual steering angle of the wheel in real time and feed it back to the control unit for closed-loop control.

[0034] For the structure of the sensor unit during implementation, please refer to the appendix. Figure 3 It includes a load detection module, an attitude monitoring module, and a speed detection module, specifically: The load detection module measures the pressure changes experienced by the vehicle through a weighing sensor installed in the suspension system of the intelligent commercial vehicle, and then calculates the actual load.

[0035] The load cell can be piezoelectric, strain gauge, or piezoresistive. Load monitoring is used to update the lateral deviation index in real time, and also helps prevent overloading, ensuring the vehicle operates within a safe load range. Specifically, the following mechanism is employed: Weighing sensors: High-precision weighing sensors are installed at the support points of the suspension system of intelligent commercial vehicles to measure the pressure at each support point.

[0036] Data fusion: By analyzing data from multiple sensors, the load capacity of commercial vehicles can be estimated more accurately.

[0037] Dynamic adjustment: Load data can be used to dynamically adjust the rollover index of intelligent commercial vehicles to ensure stable vehicle operation.

[0038] The attitude monitoring module monitors the intelligent commercial vehicle's orientation and roll angle in three-dimensional space. This is primarily accomplished using gyroscopes within the IMU. Gyroscopes measure angular velocities around three axes, accurately obtaining the vehicle's roll angle and roll rate. Attitude monitoring helps the intelligent commercial vehicle maintain stability during sharp turns, specifically through the following mechanisms: IMU sensor: An IMU sensor that integrates an accelerometer and a gyroscope is used to monitor the attitude of intelligent commercial vehicles.

[0039] Inertial navigation: By analyzing IMU data, inertial navigation and attitude stabilization of intelligent commercial vehicles can be achieved.

[0040] Calibration and Compensation: Periodically calibrate the IMU sensor and compensate for system errors to improve the accuracy of attitude monitoring.

[0041] The speed monitoring module monitors both linear and angular velocity, corresponding to the vehicle's movement speed and yaw rate on the ground, respectively. This is typically achieved using a gyroscope, wheel speed sensor, or visual odometer. Speed ​​monitoring helps control the movement of the intelligent commercial vehicle, ensuring it travels at a predetermined speed and avoiding collisions or loss of control. Specifically, the following mechanisms are employed: Wheel speed sensor: A wheel speed sensor located at the drive wheels to measure the vehicle's speed.

[0042] Gyroscope: Based on the conservation of angular momentum, it can directly measure the angular velocity of rotation about the vertical axis.

[0043] Multi-sensor fusion: By combining data from the IMU and other sensors, the speed and direction of intelligent commercial vehicles can be estimated more accurately.

[0044] In implementation, the parameter estimation unit is used to estimate the center of gravity height and suspension parameters, specifically estimating the equivalent roll damping, equivalent roll stiffness, and height from the sprung mass center of gravity to the roll center. The estimation process is implemented using the following steps: Referring to Example 1, based on the vehicle roll dynamics equation: , Rewrite the model in linear parameterized form: , in, For the parameters to be identified, ; The equivalent roll damping of the suspension system. The equivalent roll stiffness of the suspension system. For the sprung mass, The height from the center of mass of the sprung mass to the center of tilt. This is the regression matrix; This represents noise or unmodeled dynamics.

[0045] At each time step The parameter estimates are updated using the following formula: , Among them, Kalman gain Covariance Matrix The updated formula is: , , in, This is a forgetting factor (usually set to 0.95~0.99), used to reduce the influence of old data.

[0046] In this example, the control unit includes a model predictive control (MPC) module, which predicts future system output and state variables based on the current system state variables and future control variables, and implements the optimal control algorithm by rolling the solution of a constrained optimization problem, including a predictive model, a rolling optimization module, and a feedback correction module.

[0047] In this embodiment, the model prediction control module includes: The prediction model is represented as a linear state-space model of the three-degree-of-freedom dynamics model of an intelligent commercial vehicle: , in, The state matrix of the connected system is a continuous matrix. A Discretization yields, for k The vehicle state vector at any given moment includes lateral velocity, yaw angle, roll angle, and roll angular velocity. ; Discrete input matrix, composed of continuous matrix B Discretization yields the result; for k The control input for the moment, in this example, is the front wheel steering angle. ; The discretized constant perturbation vector corresponds to the continuous form. D This represents constant disturbances such as road surface side joints and slope.

[0048] The improved objective function after introducing the barrier function is defined as follows: , in, It is the prediction of all control sequences to be optimized in the time domain. , It is the prediction of the first in the time domain The reference state at each sampling time. It is the prediction of the first in the time domain The rollover index at each sampling time. It is the prediction of the first in the time domain The control input vector at each sampling time. It is the prediction of the first in the time domain The state vector at each sampling time. These are state tracking weight coefficients; These are the dynamic weighting coefficients of the barrier function. These are the weighting coefficients that control the input. N It is the number of steps in the prediction range. .

[0049] The constraints on the control inputs are represented as follows: , , in, For the i-th front wheel steering angle control value, This is the minimum steering angle for the front-wheel mechanical components. This is the maximum steering angle of the front-wheel machinery. At the current initial time k =0 front wheel steering angle; This represents the angle increment for the next moment; For the current moment k The rotation increment; The discrete sampling period of the model; This represents the maximum angular velocity of the front wheels during steering.

[0050] In this embodiment, the model prediction control module includes: The rolling optimization module needs to continuously optimize and solve during the control process. That is, at each moment, the controller will solve the quadratic programming problem based on the current state quantity error to obtain the optimal solution of the control quantity that minimizes the error between the system output and the reference quantity. The optimization process is carried out repeatedly online. The feedback correction module addresses the issue that deviations between model predictions and the ideal state can occur during model predictive control due to factors such as model mismatch and environmental disturbances. To prevent this, the controller corrects the system at each sampling time using feedback, adjusting the model predictions based on the system's actual output before proceeding to the next optimization cycle.

[0051] In this embodiment, the principle of model predictive control is as follows: When the system is at the current time (time k), the model predictive controller can obtain a segment of the system output in the future prediction time domain based on the current state quantity measurement value and control quantity measurement value, combined with the prediction model. Then, by optimizing and solving the constrained objective function, a control sequence in the control time domain is obtained. The controller uses the first element of the control sequence as the actual control quantity of the system and inputs it into the controlled object, thereby controlling the controlled object to reach the next time (time k+1). The above process is repeated to continuously complete the optimization problem of the constrained objective function and realize the control of the research object.

[0052] In this embodiment, the execution unit is a linear steering system, typically composed of a steering motor, a reduction gear, and wheel angle sensors. The steering motor drives the wheels to steer according to commands issued by the ECU, the reduction gear increases the output torque of the steering motor to meet the vehicle's steering needs, and the wheel angle sensors monitor the actual steering angle of the wheels in real time and feed the signal back to the ECU.

[0053] Steering motors are typically brushless DC motors or permanent magnet synchronous motors, characterized by fast response, high control precision, and large output torque. Upon receiving commands from the ECU, the motor controller energizes the motor windings according to the command signal, generating electromagnetic force to make the motor rotor rotate in the specified direction and speed.

[0054] Because the steering motor outputs a high speed but relatively low torque, it cannot directly meet the high torque requirements of vehicle steering. Therefore, a reduction mechanism is needed to reduce the speed and increase the torque. The reduction mechanism typically uses gear transmission, worm gear transmission, or planetary gear transmission to convert the motor's high-speed, low-torque output into a low-speed, high-torque output.

[0055] After the control signal generated by the MPC control algorithm is sent to the steering execution module, the steering motor control motor windings are energized to make the motor rotor rotate in the specified direction and speed, and a speed reduction mechanism is used to reduce the speed and increase the torque. This process requires a high degree of precision and response speed to ensure the stability and accuracy of the bionic transport robot when performing complex actions.

[0056] For linear steering systems, the MPC control algorithm can be applied as follows: First, establish a vehicle dynamics model and a steering system model. Second, use the MPC algorithm to predict the future behavior of the system. Based on the system's dynamic model and current state, the system's behavior over a future period can be predicted. Then, use an optimization algorithm to solve for the optimal control strategy. Based on the prediction results and the optimization objective, the optimal control strategy can be determined. Finally, the optimal control strategy is converted into actual control signals and sent to the steering actuator. By adjusting the steering motor, the ideal steering angle output is achieved.

[0057] The specific implementation method is as follows: First, establish a mathematical model of the system, including a system dynamics model and a steering system model; Then, the MPC algorithm is used to predict the system behavior over a future period and to design the optimal control input; Finally, the control signal generated by the MPC controller is sent to the steering actuator, and the steering motor is adjusted to achieve the ideal steering angle output.

[0058] Through reasonable controller design, parameter adjustment and system configuration, the combination of MPC control algorithm and linear steering system can realize the stable operation and precise control of intelligent commercial vehicles under various complex working conditions.

[0059] In this embodiment, the intelligent stability control system further includes an adaptive control module, which specifically includes: Adaptive load control: When an intelligent commercial vehicle carries cargo of varying weights, its dynamic characteristics change significantly, directly impacting its stability and motion control performance. Adaptive control strategies can dynamically adjust the state tracking weights (i.e., the barrier function) by detecting and estimating parameters such as sprung mass and center of gravity height online. When a load change is detected, the weights of the RI (Restrained Interest Rate) are dynamically adjusted, allowing the MPC (Multi-Purpose Control) controller to prioritize safety when the risk of rollover is high, while preserving vehicle handling during normal driving.

[0060] The specific implementation method is as follows: Using the sprung mass data obtained from the load sensor and parameters such as the center of gravity height, suspension stiffness, and damping estimated by the least squares method, the current rollover index and rollover threshold of the intelligent commercial vehicle are adjusted; a model predictive control algorithm is used to calculate the barrier function Q based on the current rollover index and rollover threshold. RI To determine the level of vehicle danger, the weight of the rollover index in model predictive control is adjusted so that control intervention is increased when the RI approaches the critical value in order to obtain the optimal control input; Multi-sensor data fusion: To improve the adaptive capability of intelligent stability systems, multi-sensor data fusion technology can be employed. By fusing data from multiple sensors such as gyroscopes and accelerometers, the state of intelligent commercial vehicles can be estimated more accurately, and the robustness of control strategies can be improved.

[0061] Sensor fusion: Utilizing multiple sensors (such as IMU, GPS, etc.) to acquire information such as the attitude, speed, and position of intelligent commercial vehicles, as well as environmental parameters such as load weight and ground slope.

[0062] Real-time data processing: Through a high-speed processor, sensor data is analyzed in real time to quickly identify environmental changes and provide decision-making basis for adaptive control.

[0063] Dynamic parameter adjustment: Design an adaptive control algorithm to adjust control parameters in real time according to environmental changes, such as rollover index RI, rollover threshold, barrier function, etc., to optimize control performance.

[0064] Model prediction and learning: By combining model predictive control with machine learning technology, the predictability and adaptability of control strategies are improved, enabling intelligent commercial vehicles to make more accurate responses in unknown or changing environments.

[0065] Dynamic adjustment of control system parameters in real time: To achieve real-time control, intelligent and stable systems require high-speed data processing and transmission capabilities. High-performance microprocessors, real-time operating systems, and high-speed communication interfaces can be employed to ensure the real-time performance and effectiveness of the control strategy.

[0066] In summary, an intelligent stability system should possess adaptive capabilities, enabling it to adjust control strategies in real time according to environmental changes to maintain the stability of intelligent commercial vehicles. By employing adaptive control strategies, multi-sensor data fusion technology, and real-time control systems, precise control of intelligent commercial vehicles can be achieved, improving their stability and reliability in complex environments.

[0067] In the adaptive control module, for changes in parameters such as sprung mass, the controller adjusts the barrier function in real time based on the current rollover index to determine the degree of vehicle danger, thereby adjusting the weight of the rollover index in the model predictive control so as to increase control intervention when the RI approaches the critical value. The following are the formulas for the rollover index and the barrier function: Rollover Index: , Among them, coefficient and Based on vehicle parameters (such as suspension stiffness) Damping Center of mass height )Decide. Responsive suspension stiffness The influence of center of gravity position on rollover Reflects suspension damping The effects on rollover are expressed as follows: , , Barrier function RI Dynamic weighting coefficients The row segmentation design distinguishes between low-risk, medium-risk, and high-risk situations: , in, This is the critical value. To intervene in advance, All are constants. The proportional coefficient of the polynomial barrier in the medium-risk segment controls the barrier function. RI Enter[ [RI]. The magnitude of the penalty increase after the interval; m The power exponent for the medium-risk segment determines the penalty. RI one The rate of growth and the shape of the curve; The proportional coefficient of the high-risk segment index barrier, i.e. RI = RI c The penalty baseline value at that location; k For the high-risk segment, the exponential growth rate is controlled by RI exceeding the critical value RI, and then a penalty is imposed for the rate of sharp increase.

[0068] Secondly, changes in vehicle parameters during driving (such as increased center of gravity height or decreased suspension stiffness) can lower the rollover threshold, requiring early control intervention. After identifying the current parameters, the system updates them. To reflect the actual rollover risk, the updated formula is as follows: , in: This is the nominal critical value, which is taken as 0.7 here; These are coefficients calculated based on nominal parameters; This refers to the current coefficients calculated based on the identification parameters. This embodiment updates the coefficients through parameter identification. This allows the barrier function to dynamically adapt to changes in vehicle parameters, improving the robustness of the controller.

[0069] Example 3 like Figure 4 As shown, an embodiment of the present invention provides a computer device, including: One or more processors; A memory configured to store one or more programs that, when executed by one or more processors, cause the one or more processors to perform the method as described in Embodiment 1.

[0070] In some feasible implementations, the processor can be a central processing unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0071] The memory may include read-only memory and random access memory, and provides instructions and data to the processor and input / output interfaces. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information about the device type.

[0072] In practice, the computer device can execute the implementation methods provided by the steps in the above embodiments through its built-in functional modules. For details, please refer to the implementation methods provided by the steps in the above embodiments, which will not be repeated here.

[0073] This disclosure provides a computer device including a processor, an input / output interface, and a memory. The processor retrieves a computer program from the memory and executes the steps of the method shown in the above embodiments to perform a transmission operation.

[0074] This invention also provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the methods provided in the steps of the above embodiments. Specific implementations of the steps in the above embodiments can be found therein and will not be repeated here. Furthermore, the beneficial effects of using the same method will not be repeated here either. For technical details not disclosed in the computer-readable storage medium embodiments described herein, please refer to the description of the method embodiments of this disclosure. As an example, the computer program may be deployed to execute on a single computer device, or on multiple computer devices located in one location, or on multiple computer devices distributed across multiple locations and interconnected via a communication network.

[0075] The computer-readable storage medium can be the apparatus provided in any of the foregoing embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of the computer device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0076] This disclosure also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative embodiments described above.

[0077] The terms "first," "second," etc., used in the specification, claims, and drawings of this disclosure are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other step units inherent to these processes, methods, apparatuses, products, or devices.

[0078] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0079] The methods and related apparatuses provided in this disclosure are described with reference to the method flowcharts and / or structural diagrams provided in this disclosure. Specifically, each block of the method flowchart and / or structural diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions are provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable transmission device to create a machine, such that the instructions, which execute via the processor of the computer or other programmable transmission device, generate instructions for implementing the process. Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable transmission device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable transmission device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and / or structures illustrate the steps of the functions specified in one or more boxes.

[0080] The technical solution provided by this invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make several improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A parametric adaptive steering control method for commercial vehicles based on rollover index, characterized in that, Includes the following steps: Step 1: Acquire vehicle sensor data to obtain the vehicle's current state and estimate vehicle parameters in real time; Step 2: Using the roll dynamics equation and vehicle parameters, the rollover index is re-expressed as a linear combination of roll angle and roll rate; Step 3: Establish a three-degree-of-freedom dynamic model of the vehicle and design an objective function based on the model predictive control principle to track and control the desired vehicle trajectory; Step 4: Introduce a barrier function into the objective function, determine the degree of vehicle danger by dynamically calculating the barrier function, and adjust the weight of the rollover index in the objective function; Step 5: Based on the improved objective function and state constraints from Step 4, the optimal solution sequence is obtained through rolling solution, and it is sent as a control signal to the actuator to output the ideal front wheel steering angle.

2. The parametric adaptive commercial vehicle steering control method based on rollover index according to claim 1, characterized in that: Step 1, which involves acquiring vehicle sensor data to obtain the vehicle's current state and estimating vehicle parameters in real time, includes: Acquire data from onboard sensors to obtain the vehicle's roll angle, roll rate, and sprung mass; The vehicle roll dynamics equations are rewritten in linear parameterized form; Vehicle parameters are estimated using recursive least squares.

3. The parametric adaptive commercial vehicle steering control method based on rollover index according to claim 2, characterized in that: The vehicle parameters include the equivalent roll damping of the suspension system, the equivalent roll stiffness of the suspension system, and the height from the sprung mass center to the roll center.

4. The parametric adaptive commercial vehicle steering control method based on rollover index according to claim 1, characterized in that: The rollover index mentioned in step 2, expressed as a linear combination of roll angle and roll rate, is as follows: ； in, RI The rollover index; This refers to the vehicle body roll angle; This refers to the roll angular velocity; and is a coefficient.

5. The parametric adaptive commercial vehicle steering control method based on rollover index according to claim 1, characterized in that: Step 3, which describes establishing a three-degree-of-freedom dynamic model of the vehicle and designing an objective function based on model predictive control principles to track and control the desired vehicle trajectory, includes: Establish a three-degree-of-freedom dynamic model of the vehicle, encompassing lateral, yaw, and roll maneuvers. Discretize the continuous-time vehicle dynamics model to obtain the discrete-time state-space equation; Design the objective function and its constraints based on the principle of model predictive control.

6. The parametric adaptive commercial vehicle steering control method based on rollover index according to claim 1, characterized in that: The mathematical expression of the objective function after introducing the barrier function in step 4 is as follows: ； in, It is the prediction of all control sequences to be optimized in the time domain. , It is the prediction of the first in the time domain The reference state at each sampling time. It is the prediction of the first in the time domain The rollover index at each sampling time. It is the prediction of the first in the time domain The control input vector at each sampling time. It is the prediction of the first in the time domain The state vector at each sampling time. These are state tracking weight coefficients; These are the dynamic weighting coefficients of the barrier function. These are the weighting coefficients that control the input. N It is the number of steps in the prediction range. .

7. The parametric adaptive commercial vehicle steering control method based on rollover index according to claim 1 or 6, characterized in that: Step 4, which involves dynamically calculating the barrier function to determine the vehicle's hazard level and adjusting the weight of the rollover index in the objective function, includes: The barrier function is designed in segments to distinguish between low-risk, medium-risk, and high-risk situations: ； in, RI The rollover index; RI c This is the critical value for the rollover index. To intervene in advance, All are constants; After obtaining the current parameters through parameter identification, update To reflect the actual rollover risk, the updated formula is as follows: ； in, This is the nominal critical value; These are coefficients calculated based on nominal parameters; The current coefficients are calculated based on the identification parameters.

8. A parametric adaptive commercial vehicle steering control system based on rollover index, applicable to the method described in any one of claims 1 to 7, characterized in that, include: The sensor unit is used to perceive the status of the intelligent commercial vehicle in real time; The parameter estimation unit is used to estimate vehicle parameters in real time based on the current vehicle state variables obtained by the sensor unit. The control unit is used to re-express the rollover index RI as a linear combination of roll angle and roll rate using the roll dynamics equation and vehicle parameters; it is also used to establish a three-degree-of-freedom dynamic model of the vehicle and design an objective function based on model predictive control principles to track and control the desired vehicle trajectory; it is also used to introduce a barrier function into the objective function, determine the degree of vehicle danger by dynamically calculating the barrier function, and adjust the weight of the rollover index in the objective function; and it is also used to solve for the optimal solution sequence based on the designed objective function and state constraints, and send it as a control signal to the actuator. The actuator is used to drive the steering motor to control the wheel steering according to the ideal front wheel steering angle in the control signal, and to measure the actual steering angle of the wheel in real time and feed it back to the control unit for closed-loop control.

9. A computer device, characterized in that, include: One or more processors; A memory configured to store one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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