Vehicle brake control system based on model predictive control algorithm and control method thereof

Abstract

The invention discloses a vehicle brake control system based on a model predictive control algorithm and a control method thereof. The vehicle brake control system comprises a sensor module, a controller module and an actuator module. The sensor module is used for collecting vehicle surrounding environment information and vehicle driving state information in real time; the controller module establishes a prediction model for an MPC algorithm based on a vehicle dynamics model, predicts a vehicle state according to real-time information collected by the sensor module, and performs optimization control on a prediction result to obtain braking force distribution information; and the actuator module is used for responding to the control signal output by the controller module. By predicting the motion state of the vehicle in real time, braking force distribution is dynamically adjusted, and more efficient and safer braking control is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle brake control systems, and in particular relates to a vehicle brake control system based on a model predictive control algorithm and a control method thereof. Background Art

[0002] With the development of modern vehicle technology, the performance and safety of vehicle braking systems are particularly important. At present, traditional vehicle braking systems mostly use rule-based control methods, which have problems such as slow response speed and low control accuracy, and are difficult to meet complex road environments and different driving needs.

[0003] The shortcomings of the existing technology mainly include:

[0004] 1. Lack of real-time response capabilities to complex dynamic environments.

[0005] 2. The control algorithm is simple and it is difficult to cope with complex changes in working conditions.

[0006] 3. Uneven distribution of braking force affects the stability and safety of the vehicle. Summary of the invention

[0007] In order to solve the problems of low control accuracy and slow response speed in the prior art, the present invention provides a vehicle braking control system and a control method based on a model predictive control algorithm (MPC), which can realize more efficient and safe braking control by predicting the vehicle's motion state in real time and dynamically adjusting the braking force distribution.

[0008] The objective of the present invention is achieved through the following technical solutions:

[0009] As a first aspect of the present invention, a vehicle braking control system based on a model predictive control algorithm is provided, comprising a sensor module, a controller module and an actuator module;

[0010] The sensor module is used to collect vehicle surrounding environment information and vehicle driving status information in real time;

[0011] The controller module establishes a prediction model for the MPC algorithm based on the vehicle dynamics model, predicts the vehicle state according to the real-time information collected by the sensor module, and optimizes and controls the prediction results to obtain braking force distribution information;

[0012] The actuator module is used to respond to the control signal output by the controller module.

[0013] Furthermore, the vehicle surrounding environment information includes road information and obstacle information; the vehicle driving state information includes the vehicle's current speed, the vehicle's lateral and longitudinal acceleration, the vehicle's driving direction and steering angle, and the rotation speed of each wheel.

[0014] Furthermore, the sensor module comprises:

[0015] Vehicle speed sensor, installed on the wheel, used to detect vehicle speed in real time;

[0016] An accelerometer, mounted at the center of the vehicle, measures longitudinal and lateral acceleration;

[0017] Steering sensor, used to measure the vehicle's steering angle and steering speed;

[0018] Wheel speed sensor, measuring the rotation speed of each wheel;

[0019] Environmental sensors, including radar and cameras, are used to obtain information about the surrounding environment.

[0020] Furthermore, the controller module comprises:

[0021] A data preprocessing unit, used for filtering and noise reduction of the real-time data collected by the sensor module;

[0022] A vehicle dynamics model building unit, used to build a vehicle longitudinal dynamics model, a lateral dynamics model and a braking dynamics model;

[0023] A model prediction unit, which uses a vehicle dynamics model to establish a prediction model for the MPC algorithm, and inputs the pre-processed data into the prediction model to perform vehicle future state prediction and optimization calculation to obtain optimal braking force distribution information;

[0024] The signal output unit is used to output a control signal to the actuator module according to the optimal braking force distribution information obtained by the model prediction unit.

[0025] Furthermore, the controller module adopts an ARM Cortex series embedded processor or DSP.

[0026] Furthermore, the actuator module comprises:

[0027] Electric or hydraulic brakes;

[0028] The electronic brake force distribution system adjusts the braking force of each wheel according to the control signal.

[0029] As a second aspect of the present invention, a control method for a vehicle braking control system based on a model predictive control algorithm is also provided, comprising the following steps:

[0030] S1. Obtain vehicle surrounding environment information and vehicle driving status information in real time through the sensor module and send it to the controller module;

[0031] S2. The controller module uses the MPC algorithm to predict the vehicle state and perform optimization calculations based on the real-time information collected by the sensor module to obtain the optimal braking force distribution information;

[0032] S3. The actuator module responds to the control signal of the controller module and adjusts the braking force of each wheel according to the control signal.

[0033] Furthermore, in step S1, the vehicle surrounding environment information includes road information and obstacle information; the vehicle driving state information includes the vehicle's current speed, the vehicle's lateral and longitudinal accelerations, the vehicle's driving direction and steering angle, and the rotation speed of each wheel.

[0034] Furthermore, the step S2 comprises:

[0035] S21. Preprocessing the vehicle surrounding environment information and vehicle driving status information collected by the sensor module;

[0036] S22. Establishing a vehicle dynamics model, including a vehicle longitudinal dynamics model, a lateral dynamics model, and a braking dynamics model;

[0037] S23. Using the vehicle dynamics model, a prediction model for the MPC algorithm is established, and the vehicle state is predicted through the prediction model according to the preprocessed data to obtain a model prediction result;

[0038] S24. Add optimization objectives and constraints, optimize the model prediction results, and obtain optimal braking force distribution information;

[0039] S25. According to the optimal braking force distribution information, a control signal is output to the actuator module for response.

[0040] Furthermore, in step S24, the optimization objectives include:

[0041] Minimize braking distance to ensure the vehicle can be stopped safely in the shortest distance;

[0042] Ensuring vehicle stability means maintaining the stability of the vehicle during braking to prevent skidding or rollover;

[0043] Comfort refers to reducing unnecessary emergency braking and improving riding comfort;

[0044] The constraints include:

[0045] Vehicle dynamics constraints, which ensure that control inputs and states conform to the physical characteristics of the vehicle;

[0046] Braking force limitation, which limits the maximum and minimum values ​​of braking force to prevent over- or under-braking;

[0047] Security constraints: Set corresponding security constraints considering environmental factors.

[0048] The present invention has the following advantages:

[0049] The present invention provides a vehicle braking control system and a control method thereof based on a model predictive control algorithm. The MPC algorithm is used to optimize and control the vehicle braking process, a vehicle dynamics model is established, the motion state of the vehicle under different working conditions is predicted, and the braking force of each wheel is adjusted in real time, thereby improving the response speed and control accuracy of the braking system, realizing dynamic distribution of the braking force, improving the stability and safety of the vehicle, adapting to complex road environments, and enhancing the driving experience.

[0050] Improved braking performance: By precisely controlling and optimally distributing the braking force, the system can significantly shorten braking distances and improve braking effectiveness.

[0051] Enhance vehicle stability: Ensure vehicle stability in various complex working conditions (such as slippery roads, emergency obstacle avoidance, etc.) to prevent skidding, tail swinging, etc.

[0052] Improved safety: By comprehensively considering the vehicle status and surrounding environment, the system can better respond to emergencies and improve overall driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art will be briefly introduced below.

[0054] Figure 1 This is a block diagram of the components of a vehicle braking control system based on a model predictive control algorithm as described in Example 1 of the present invention;

[0055] Figure 2 This is a flowchart of the controller module described in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Example 1

[0058] like Figure 1 As shown, this embodiment is a vehicle braking control system based on a model predictive control algorithm, including a sensor module, a controller module and an actuator module;

[0059] The sensor module is used to collect vehicle surrounding environment information and vehicle driving status information in real time;

[0060] Specifically, the vehicle surrounding environment information includes road information and obstacle information. In this embodiment, the vehicle surrounding environment information includes the road conditions around the vehicle and the distance of obstacles in front of the vehicle; the vehicle driving status information includes the vehicle's current speed, the vehicle's lateral and longitudinal acceleration, the vehicle's driving direction and steering angle, and the speed of each wheel.

[0061] Preferably, the sensor module comprises:

[0062] Vehicle speed sensors, such as Hall effect sensors, are mounted on wheels to detect vehicle speed in real time;

[0063] Accelerometer, using a high-precision MEMS accelerometer, installed at the center of the vehicle to measure longitudinal and lateral acceleration;

[0064] Steering sensors, such as gyroscopes, measure the vehicle's steering angle and steering speed;

[0065] Wheel speed sensor, each wheel is equipped with a wheel speed sensor to accurately measure the rotation speed of each wheel;

[0066] Environmental sensors, equipped with radar and cameras, obtain information about the surrounding environment to enhance the decision-making ability of the braking system.

[0067] The controller module is the core of the system. It establishes a prediction model for the MPC algorithm based on the vehicle dynamics model, predicts the vehicle state according to the real-time information collected by the sensor module, optimizes the prediction results, and obtains braking force distribution information.

[0068] The controller module usually adopts a high-performance embedded processor, such as ARM Cortex series or DSP (digital signal processor).

[0069] Specifically, the controller module includes:

[0070] A data preprocessing unit, used to perform data processing such as filtering and noise reduction on the real-time data collected by the sensor module to ensure the accuracy of the sensor data;

[0071] A vehicle dynamics model building unit, used to build a vehicle longitudinal dynamics model, a lateral dynamics model and a braking dynamics model;

[0072] A model prediction unit, which uses a vehicle dynamics model to establish a prediction model for the MPC algorithm, and inputs the pre-processed data into the prediction model to perform vehicle future state prediction and optimization calculation to obtain optimal braking force distribution information;

[0073] The signal output unit is used to output a control signal to the actuator module according to the optimal braking force distribution information obtained by the model prediction unit to execute the optimal braking force distribution.

[0074] Preferably, the prediction model for the MPC algorithm is used to predict the future state of the vehicle, and usually multiple time steps are considered to form a prediction window.

[0075] The actuator module is used to respond to the control signal output by the controller module.

[0076] Specifically, the actuator module includes:

[0077] Brake Actuator: An electric or hydraulic brake that responds quickly to control signals and precisely adjusts the braking force.

[0078] Brake force distribution device: Electronic brake force distribution (EBD) system adjusts the braking force of each wheel according to the control signal to ensure the stability of the vehicle.

[0079] Example 2

[0080] This embodiment is a control method of a vehicle braking control system based on a model predictive control algorithm, which is implemented based on the control system described in Embodiment 1 and includes the following steps:

[0081] S1. The vehicle surrounding environment information and vehicle driving state information are acquired in real time through the sensor module and sent to the controller module; the vehicle surrounding environment information includes road information and obstacle information, and in this embodiment, includes the road conditions around the vehicle and the distance of obstacles in front of the vehicle; the vehicle driving state information includes the current speed of the vehicle, the lateral and longitudinal acceleration of the vehicle, the driving direction and steering angle of the vehicle, and the speed of each wheel;

[0082] S2. The controller module uses the MPC algorithm to predict the vehicle state and perform optimization calculations based on the real-time information collected by the sensor module to obtain the optimal braking force distribution information, such as Figure 2 As shown:

[0083] S21. Preprocess the vehicle surrounding environment information and vehicle driving status information collected by the sensor module; including data preprocessing such as filtering and noise reduction to ensure the accuracy of the sensor data.

[0084] S22. Establish a vehicle dynamics model, including a vehicle longitudinal dynamics model, a lateral dynamics model, and a braking dynamics model.

[0085] Specifically, the vehicle longitudinal dynamics model is used to describe the motion characteristics of the vehicle along the driving direction: Assuming the vehicle mass m, vehicle speed v, and braking force F_b, the longitudinal dynamics equation is:

[0086] m\frac{dv}{dt}=-F_b

[0087] After discretization, the state equation is:

[0088] v_{k+1}=v_k-\frac{F_{b_k}\Delta t}{m}

[0089] The vehicle lateral dynamics model is used to describe the motion characteristics of the vehicle when turning or moving laterally: Assuming the lateral velocity u, steering angle \Delta, and lateral dynamics equation of the vehicle are:

[0090] m\frac{du}{dt}=F_y

[0091] Where, F_y is the lateral force;

[0092] After discretization, the state equation is:

[0093] u_{k+1}=u_k+\frac{F_{y_k}\Delta t}{m}

[0094] The braking dynamics model is used to describe the influence of braking force on vehicle motion: Assuming the braking torque T_b, the braking dynamics equation is:

[0095] I\frac{d\omega}{dt}=-T_b

[0096] Where I is the moment of inertia and \omega is the wheel speed. After discretization, the state equation is:

[0097] \omega_{k+1}=\omega_k-\frac{T_{b_k}\Delta t}{I}

[0098] S23. Using the vehicle dynamics model, a prediction model for the MPC algorithm is established, and the vehicle state is predicted through the prediction model according to the preprocessed data to obtain a model prediction result;

[0099] Preferably, the prediction model used in the MPC algorithm is used to predict the future state of the vehicle, usually taking into account multiple time steps to form a prediction window.

[0100] The prediction model is:

[0101] x_{k+1}=Ax_k+Bu_k

[0102] y_k=Cx_k

[0103] Among them, x_k is the vehicle state vector, u_k is the control input vector, y_k is the output vector, and A, B, and C are system matrices.

[0104] S24. Add optimization objectives and constraints, optimize the model prediction results, and obtain optimal braking force distribution information.

[0105] Specifically, the optimization objectives include:

[0106] Minimize braking distance: ensure the vehicle is stopped safely in the shortest distance;

[0107] Ensure vehicle stability: During braking, maintain vehicle stability to prevent skidding or rollover;

[0108] Comfort: Reduce unnecessary emergency braking and improve riding comfort;

[0109] The constraints include:

[0110] Vehicle dynamics constraints: Ensure that control inputs and states comply with the physics of the vehicle.

[0111] Braking force limitation: Limits the maximum and minimum braking force to prevent over- or under-braking.

[0112] Safety constraints: Consider environmental factors, such as road conditions and surrounding obstacles, and set corresponding safety constraints.

[0113] S25. According to the optimal braking force distribution information, a control signal is output to the actuator module for response.

[0114] S3. The actuator module responds to the control signal of the controller module and adjusts the braking force of each wheel according to the control signal to ensure the stability of the vehicle.

[0115] This embodiment performs system performance tests in urban road scenarios and highway scenarios respectively:

[0116] In urban road scenarios, vehicles need to start and brake frequently, and the system needs to respond quickly to ensure safety. The specific steps are as follows:

[0117] Sensor layout: A radar and a camera are arranged at the front and rear of the vehicle to monitor the front and rear vehicles and obstacles.

[0118] Algorithm optimization goal: minimize braking distance to ensure that the vehicle can stop quickly in an emergency.

[0119] Test verification: Through actual road testing, emergency braking tests are carried out under urban road conditions to verify system performance.

[0120] In highway scenarios, vehicles need to remain stable and safe at high speeds. The specific steps are as follows:

[0121] Sensor placement: High-precision radars are placed around the vehicle to monitor the surrounding environment when driving at high speeds.

[0122] Algorithm optimization goal: Ensure vehicle stability at high speeds while keeping the braking distance as short as possible.

[0123] Test verification: Conduct long-distance driving tests on highways to verify the stability and safety of the system at high speeds.

[0124] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0125] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0126] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A vehicle braking control system based on a model predictive control algorithm, characterized in that: It includes a sensor module, a controller module and an actuator module; The sensor module is used to collect vehicle surrounding environment information and vehicle driving status information in real time; The controller module establishes a prediction model for the MPC algorithm based on the vehicle dynamics model, predicts the vehicle state according to the real-time information collected by the sensor module, and optimizes and controls the prediction results to obtain braking force distribution information; The actuator module is used to respond to the control signal output by the controller module.

2. A vehicle braking control system based on a model predictive control algorithm as claimed in claim 1, characterized in that: The vehicle surrounding environment information includes road information and obstacle information; the vehicle driving state information includes the vehicle's current speed, the vehicle's lateral and longitudinal accelerations, the vehicle's driving direction and steering angle, and the speed of each wheel.

3. A vehicle braking control system based on a model predictive control algorithm as claimed in claim 1, characterized in that: The sensor module comprises: Vehicle speed sensor, installed on the wheel, used to detect vehicle speed in real time; An accelerometer, mounted at the center of the vehicle, measures longitudinal and lateral acceleration; Steering sensor, used to measure the vehicle's steering angle and steering speed; Wheel speed sensor, measuring the rotation speed of each wheel; Environmental sensors, including radar and cameras, are used to obtain information about the surrounding environment.

4. A vehicle braking control system based on a model predictive control algorithm as claimed in claim 1, characterized in that: The controller module comprises: A data preprocessing unit, used for filtering and noise reduction of the real-time data collected by the sensor module; A vehicle dynamics model building unit, used to build a vehicle longitudinal dynamics model, a lateral dynamics model and a braking dynamics model; A model prediction unit, which uses a vehicle dynamics model to establish a prediction model for the MPC algorithm, and inputs the pre-processed data into the prediction model to perform vehicle future state prediction and optimization calculation to obtain optimal braking force distribution information; The signal output unit is used to output a control signal to the actuator module according to the optimal braking force distribution information obtained by the model prediction unit.

5. A vehicle braking control system based on a model predictive control algorithm as claimed in claim 1, characterized in that: The controller module adopts an ARM Cortex series embedded processor or a DSP.

6. A vehicle braking control system based on a model predictive control algorithm as claimed in claim 1, characterized in that: The actuator module comprises: Electric or hydraulic brakes; The electronic brake force distribution system adjusts the braking force of each wheel according to the control signal.

7. A control method for a vehicle braking control system based on a model predictive control algorithm as claimed in any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Obtain vehicle surrounding environment information and vehicle driving status information in real time through the sensor module and send it to the controller module; S2. The controller module uses the MPC algorithm to predict the vehicle state and perform optimization calculations based on the real-time information collected by the sensor module to obtain the optimal braking force distribution information; S3. The actuator module responds to the control signal of the controller module and adjusts the braking force of each wheel according to the control signal.

8. The control method of a vehicle braking control system based on a model predictive control algorithm as claimed in claim 7, characterized in that: In the step S1, the vehicle surrounding environment information includes road information and obstacle information; the vehicle driving state information includes the vehicle's current speed, the vehicle's lateral and longitudinal accelerations, the vehicle's driving direction and steering angle, and the speed of each wheel.

9. The control method of a vehicle braking control system based on a model predictive control algorithm as claimed in claim 7, characterized in that: The step S2 comprises: S21. Preprocessing the vehicle surrounding environment information and vehicle driving status information collected by the sensor module; S22. Establishing a vehicle dynamics model, including a vehicle longitudinal dynamics model, a lateral dynamics model, and a braking dynamics model; S23. Using the vehicle dynamics model, a prediction model for the MPC algorithm is established, and the vehicle state is predicted through the prediction model according to the preprocessed data to obtain a model prediction result; S24. Add optimization objectives and constraints, optimize the model prediction results, and obtain optimal braking force distribution information; S25. According to the optimal braking force distribution information, a control signal is output to the actuator module for response.

10. The control method of a vehicle braking control system based on a model predictive control algorithm as claimed in claim 9, characterized in that: In step S24, the optimization objectives include: Minimize braking distance to ensure the vehicle can be stopped safely in the shortest distance; Ensuring vehicle stability means maintaining the stability of the vehicle during braking to prevent skidding or rollover; Comfort refers to reducing unnecessary emergency braking and improving riding comfort; The constraints include: Vehicle dynamics constraints, which ensure that control inputs and states conform to the physical characteristics of the vehicle; Braking force limitation, which limits the maximum and minimum values ​​of braking force to prevent over- or under-braking; Security constraints: Set corresponding security constraints considering environmental factors.

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