Active control method suitable for traction maneuvering of multi-axle trailer

Abstract

The invention relates to the technical field of multi-axle trailer traction. The active control method comprises the following steps: mounting an inertia unit and an angle sensor, acquiring data, analyzing inertia data and included angle information, performing path following and deviation calculation, performing active steering control, performing active braking control, and performing feedback and control optimization. On the premise that a tractor is not transformed on a large scale, through the active control capacity of the trailer, the stability and safety of traction maneuvering are improved, and meanwhile the requirements of the multi-axle trailer in complex terrains and long-distance transportation are met. According to the method, the motion state of the tractor needs to be sensed in real time, accurate steering and braking control is achieved by combining the multi-axle characteristic of the trailer, and therefore the stability problem easily occurring in the traction process of the multi-axle trailer is solved.

Description

Technical Field

[0001] The invention relates to the technical field of multi-axle trailer traction, and in particular to an active control method suitable for multi-axle trailer traction maneuvers. Background Art

[0002] In long-distance mobile transport and other tasks, multi-axle trailers, as an important part of transportation equipment, are usually used to carry a large amount of materials and equipment. Multi-axle trailers are widely used in various tasks such as complex terrain and long-distance transportation due to their strong carrying capacity and good stability. However, compared with traditional single-axle or double-axle trailers, multi-axle trailers have more degrees of freedom, which makes them more complicated during the traction process. For example, in sharp turns, up and down ramps or soft terrain, the steering response of multi-axle trailers often lags behind the tractor and is prone to a large yaw angle, resulting in a decrease in the stability of the trailer. In addition, when multi-axle trailers are driving at high speeds, they may have a strong swing effect ("snaking" phenomenon) due to their large inertia, which increases the difficulty of control. Especially in complex road conditions, if the trailer deviates from the track or loses control, it may pose a major safety threat to the tractor and the surrounding environment.

[0003] At present, in order to solve the stability problem of multi-axle trailers in towing maneuvers, some control methods have been proposed. These methods mainly rely on full vehicle communication or direct control signals between the tractor and the trailer. However, this method requires a large hardware modification of the tractor, which not only increases the complexity of the system, but also lacks universality among multiple vehicles. In addition, in the existing methods, the coordinated motion analysis of the trailer and the tractor is insufficient, especially in the steering control and active braking control of the multi-axle trailer. There is a lack of targeted design, which makes it difficult for the trailer's followability and stability to meet the efficiency requirements of modern tasks. Summary of the invention

[0004] The purpose of the present invention is to provide an active control method suitable for multi-axle trailer towing maneuvers, so as to solve the problem that the existing technologies for solving the stability of multi-axle trailers in towing maneuvers are insufficient in the coordinated motion analysis of the trailer and the towing vehicle, lack of targeted design, resulting in the followability and stability of the trailer being difficult to meet the high efficiency requirements of modern tasks.

[0005] To achieve the above object, the present invention provides an active control method applicable to multi-axle trailer towing maneuvers, comprising the following steps:

[0006] S1: The tractor's motion data is collected in real time through a quick-release inertial unit installed on the tractor;

[0007] S2: An angle sensor is installed at the connection between the trailer and the tractor to measure the relative angle between the two vehicles in real time;

[0008] S3: The trailer collects the current speed, direction angle and trailer gravity center offset through wheel speed sensor, attitude sensor and laser rangefinder;

[0009] S4: Fusing the collected data from multiple sources to form a set of dynamic motion parameters of the two vehicles;

[0010] S5: Based on the motion parameter set and kinematic model of the two vehicles, the extended Kalman filter algorithm is used to predict the future motion trend of the tractor and the ideal motion trajectory of the trailer;

[0011] S6: Dynamically adjust the steering angle of the trailer through the steering system according to the predicted ideal trajectory and current status;

[0012] S7: dynamically adjusting the braking force through the trailer braking system according to the deceleration information of the tractor and the relative motion state of the two vehicles;

[0013] S8: Introduce fuzzy control algorithm to dynamically adjust control gain parameters and braking force according to real-time road conditions and trailer deviation status to optimize trailer following performance.

[0014] Among them, in step S1, the real-time collection of the motion data of the tractor is specifically as follows:

[0015] Acceleration a = [a x ,a y ,a z ], used to describe the linear acceleration of the tractor in each axis;

[0016] Angular velocity ω=[ω x ,ω y ,ω z ], used to reflect the rotational motion of the tractor;

[0017] The attitude angle is used to indicate the spatial attitude of the tractor.

[0018] Among them, in step S2, the relative angle between the two vehicles is α t , in step S3, the current speed is v h (t), the direction angle is β h (t), the trailer center of gravity offset is e h (t).

[0019] In step S4, the dynamic motion parameter set of the two vehicles is X(t), which is calculated by the following formula:

[0020]

[0021] Among them, in step S5, the specific formula for predicting the future motion trend of the tractor and the ideal motion trajectory of the trailer using the extended Kalman filter algorithm is:

[0022] X(t+1)=F(X(t),U(t))

[0023] in:

[0024] F is the system state update function;

[0025] U(t) represents the input control quantity of the tractor;

[0026] X(t+1) is the predicted state of the trailer at the next moment.

[0027] Among them, in step S6, the specific formula for dynamically adjusting the steering angle of the trailer through the steering system is:

[0028]

[0029] Among them, δ h (t) is the steering angle of the trailer;

[0030] e h (t) is the current trajectory deviation of the trailer;

[0031] is the deviation change rate;

[0032] k p and k d are the proportional and derivative control gains respectively.

[0033] Among them, in step S7, the formula for dynamically adjusting the braking force is specifically:

[0034] F b =m h ·(a des -a h (t))

[0035] Among them, F b Provide braking force for trailers;

[0036] m h is the mass of the trailer;

[0037] a des and a h (t) are the ideal deceleration of the tractor and the current acceleration of the trailer, respectively.

[0038] The present invention is an active control method for multi-axle trailer traction maneuvers. Through the installation and data collection of inertial units and angle sensors, the analysis of inertial data and angle information, path following and deviation calculation, active steering control, active braking control, and feedback and control optimization, the active control capability of the trailer itself is used to improve the stability and safety of traction maneuvers without large-scale modification of the tractor, and at the same time meet the needs of multi-axle trailers in complex terrain and long-distance transportation. This method needs to be able to perceive the motion state of the tractor in real time, and combine the multi-axle characteristics of the trailer to achieve precise steering and braking control, thereby solving the stability problem that is easy to occur in the traction process of the multi-axle trailer. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a flow chart of the steps of the active control method applicable to the towing maneuver of a multi-axle trailer provided by the present invention.

[0041] Figure 2 It is a schematic diagram of the key kinematic parameter definitions and traction relationships provided by the present invention. DETAILED DESCRIPTION

[0042] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0043] See also Figure 1 The present invention provides an active control method for a multi-axle trailer towing maneuver, comprising the following steps:

[0044] S1: Tractor motion data collection:

[0045] A quick-detachable inertial unit (IMU) is installed on the tractor to collect the acceleration of the tractor in real time. x ,a y ,a z ], angular velocity ω=[ω x ,ω y ,ω z ] and attitude angle

[0046] The data is processed by the IMU internal filtering algorithm and then transmitted to the trailer control unit at a fixed period (such as 100Hz).

[0047] Among them, the IMU can be adapted to different models of tractors through a quick-clip or magnetic mounting structure, without the need for recalibration, which can improve the versatility of the equipment.

[0048] S2: Relative angle measurement between trailer and tractor:

[0049] The angle sensor installed at the towing pin measures the relative angle α between the trailer and the tractor in real time. t .

[0050] The angle measurement range is -180 to +180, with an accuracy of 0.1°.

[0051] Among them, this data is used as one of the input parameters of trailer predictive control to determine the movement state and deviation of the trailer.

[0052] S3: Trailer status perception:

[0053] Sensors installed inside the trailer collect its key motion parameters:

[0054] 1. Current speed v h (t): obtained in real time through wheel speed sensor;

[0055] 2. Yaw angular velocity ω h (t): collected by the IMU on the trailer;

[0056] 3. Deviation e h (t): The vertical distance from the center of the trailer to the path of the tractor is calculated and the distance measuring sensor is used to achieve accurate measurement;

[0057] 4.Trailer tilt angle γ h (t): Used to determine the trailer posture in complex terrain.

[0058] S4: Trailer Movement Prediction:

[0059] The trailer control unit is based on the extended Kalman filter (EKF) algorithm, combining the state data X(t) of the two vehicles:

[0060]

[0061] Build a kinematic model of the tractor and trailer to predict the ideal trajectory of the trailer.

[0062] The prediction model formula is as follows:

[0063] X(t+1)=F(X(t),U(t))

[0064] in:

[0065] F is the state update function;

[0066] U(t) represents the control input of the tractor.

[0067] The prediction model is implemented through the following key formulas:

[0068] α(t+1)=α(t)+Δ t ·(ω h (t)-ω c (t))

[0069] e h (t+1)=e h (t)+v h (t)·Δ t sin(α(t))

[0070] Furthermore, the prediction results will dynamically adjust the control gain parameters so that the trailer can follow the ideal trajectory of the tractor more accurately.

[0071] S5: Active Steering Control:

[0072] The trailer's steering system is based on the deviation e h (t) and the predicted path to adjust the steering angle δ h (t):

[0073]

[0074] in:

[0075] k p : Proportional gain, control deviation response speed;

[0076] k d : Differential gain, suppressing the oscillation caused by deviation changes;

[0077] The rate of change of the deviation is calculated by the time derivative.

[0078] The steering motor actuator adjusts the steering angle of the trailer wheels after receiving instructions to ensure the stability and followability of the trailer in complex road conditions.

[0079] S6: Active Brake Control:

[0080] The trailer braking system adjusts the braking force F according to the tractor deceleration command and relative motion state. b :

[0081] F b =m h ·(a des -ah (t))

[0082] in:

[0083] m h : Trailer mass;

[0084] a des : Target deceleration of the tractor;

[0085] a h (t): current acceleration of the trailer.

[0086] Furthermore, in an emergency, the trailer braking system gives priority to the automatic emergency braking function to prevent rear-end collisions caused by coasting.

[0087] S7: Dynamic optimization and adaptive adjustment:

[0088] Introduce fuzzy control algorithm to adjust parameters in real time according to the trailer operating environment and deviation status:

[0089] Proportional gain k p and the differential gain k d Optimization;

[0090] Braking force F b Dynamic allocation of

[0091] Update the prediction model parameters to adapt to different road conditions or trailer load changes.

[0092] The optimization formula is as follows:

[0093] k p =k p0 ·(1+λ1·|e h (t)|)

[0094]

[0095] in:

[0096] k p0 , k d0 is the initial control parameter;

[0097] λ1 and λ2 are environmental adaptability coefficients, which are adjusted in real time according to the trailer operating environment.

[0098] In this embodiment, by installing and collecting data of the inertial unit and angle sensor, analyzing inertial data and angle information, path following and deviation calculation, active steering control, active braking control, and feedback and control optimization, without large-scale modification of the tractor, the active control capability of the trailer itself is used to improve the stability and safety of the traction maneuver, and at the same time meet the needs of multi-axle trailers in complex terrain and long-distance transportation. This method requires the ability to perceive the motion state of the tractor in real time, and combines the multi-axle characteristics of the trailer to achieve precise steering and braking control, thereby solving the stability problem that is prone to occur in the traction process of the multi-axle trailer.

[0099] Example:

[0100] See also Figure 2 This embodiment is an application of a 6×6 unmanned all-terrain vehicle for long-distance maneuvering under towing conditions, aiming to solve the stability and efficient following problems of the trailer during high-speed towing. The trailer includes an inertial unit, an angle sensor, a steering control system, a braking system, and an embedded controller. The towing vehicle is a 4×4 light high-mobility off-road vehicle, in which the attached Figure 2 In the figure, the tractor is marked as 1, the multi-axle unmanned trailer is marked as 2, the towing rod is marked as 3, the towing ball head is marked as 4, the trailer ball head is marked as 5, the inertial measurement unit is marked as 6, and the angle measurement unit is marked as 7.

[0101] Preparation before driving:

[0102] 1. Install an inertial unit (IMU) on the tractor through a quick-release structure to record its initial attitude angle and initial value of acceleration (a0), no additional calibration is required;

[0103] 2. Install an angle sensor at the trailer traction pin to initialize the relative angle between trailer and tractor to α0 = 0;

[0104] 3. Connect the trailer control system to the tractor and synchronously transmit the tractor's motion data via wired CAN protocol or wireless communication protocol.

[0105] Specific steps:

[0106] Step 1: Tractor data collection and real-time transmission

[0107] The tractor collects motion data in real time through the IMU, including:

[0108] Acceleration a(t) = [a x (t),a y (t),a z (t)];

[0109] Angular velocity ω(t)=[ωx (t),ω y (t),ω z (t)];

[0110] Heading Angle Pitch Angle Roll angle θ(t).

[0111] The IMU data is filtered through a preset filtering algorithm to eliminate noise and then transmitted to the trailer control unit at a frequency of 100 Hz.

[0112] Step 2: Trailer status data collection

[0113] The trailer collects the following status parameters:

[0114] 1. Trailer speed v h (t): collected by the Hall sensor installed on the wheel;

[0115] 2. Trailer yaw angular velocity ω h (t): collected by the trailer’s own IMU;

[0116] 3. The relative angle α(t) between the trailer and the tractor: collected by the angle sensor;

[0117] 4. Deviation e h (t): The distance measuring sensor measures the vertical distance from the center of the trailer to the path of the tractor in real time;

[0118] 5.Trailer tilt angle γ h (t): Real-time measurement of trailer attitude through gyroscope.

[0119] Step 3: Trailer Movement Prediction

[0120] The extended Kalman filter (EKF) is used to predict the motion state of the trailer.

[0121] Tractor motion status X c (t):

[0122]

[0123] Trailer status:

[0124] X h (t) = [v h (t),α(t),e h (t),ω h (t),γ h (t)]

[0125] Predict the ideal state of the trailer at the next moment:

[0126] X h (t+1)=F(Xh (t),U(t))

[0127] Where F is the kinematic model function and U(t) is the control input of the tractor.

[0128] The core formula includes:

[0129] α(t+1)=α(t)+Δ t ·(ω h (t)-ω c (t))

[0130] e h (t+1)=e h (t)+v h (t)·Δ t sin(α(t))

[0131] Step 4: Active Steering Control

[0132] The trailer control unit is based on the deviation e h (t) and the angle a(t) to calculate the steering angle δ h (t):

[0133]

[0134] in:

[0135] k p =0.8: proportional gain;

[0136] k d =0.2: differential gain;

[0137] The rate of change of the deviation, calculated by numerical differentiation.

[0138] Steering angle δ h The variation range of (t) is limited to [-30°, +30°], and the steering actuator adjusts the trailer wheel angle in real time.

[0139] Step 5: Active Brake Control

[0140] When the tractor is detected to be slowing down, the trailer calculates the required braking force F b :

[0141] F b =m h ·(a des -a h (t))

[0142] in:

[0143] m h =2000kg, the mass of the trailer;

[0144] a des =-3m / s 2 , target deceleration of the tractor;

[0145] a h (t) is the current acceleration of the trailer.

[0146] The trailer applies braking force through the hydraulic braking system to ensure that rear-end collisions do not occur.

[0147] Step 6: Dynamic Optimization and Adaptive Adjustment

[0148] The trailer control unit adjusts the control parameters in real time according to the environment and deviation status:

[0149] k p =0.8·(1+0.05·|e h (t)|)

[0150]

[0151] In addition, if a slope or sharp turn (γ h (t)>10° or |α(t)|>15°), the system will increase the braking force and steering angle limit.

[0152] This technical solution can provide a simple way to explain:

[0153] The following is a C++ language implementation based on the embodiment of this patent, including the core logic code of inertial unit data acquisition, trailer status update, active steering control and braking control:

[0154]

[0155]

[0156]

[0157] in,

[0158] 1.IMU data collection: The updateIMUData function simulates the real-time data of the tractor IMU, including acceleration and angular velocity.

[0159] 2. Trailer status update: The updateTrailerState function obtains trailer status data, such as relative angle, speed, and deviation.

[0160] 3. Steering control: calculateSteeringAngle function calculates the steering angle based on the deviation and the rate of change of the deviation.

[0161] 4. Braking control: The calculateBrakeForce function calculates the braking force based on the current acceleration of the trailer to ensure that the trailer does not rear-end or slip.

[0162] 5. Control output: The controlTrailer function integrates the control logic and outputs the steering angle and braking force.

[0163] This program demonstrates the core logic of a simple trailer control method and can be extended to adapt to more complex dynamic environments.

[0164] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. An active control method for multi-axle trailer traction maneuvers, characterized in that: The steps include: S1: The tractor's motion data is collected in real time through a quick-detachable inertial unit installed on the tractor; S2: An angle sensor is installed at the connection between the trailer and the tractor to measure the relative angle between the two vehicles in real time; S3: The trailer collects the current speed, direction angle and trailer gravity center offset through wheel speed sensor, attitude sensor and laser rangefinder; S4: Fusing the collected data from multiple sources to form a set of dynamic motion parameters of the two vehicles; S5: Based on the motion parameter set and kinematic model of the two vehicles, the extended Kalman filter algorithm is used to predict the future motion trend of the tractor and the ideal motion trajectory of the trailer; S6: Dynamically adjust the steering angle of the trailer through the steering system according to the predicted ideal trajectory and current status; S7: dynamically adjusting the braking force through the trailer braking system according to the deceleration information of the tractor and the relative motion state of the two vehicles; S8: Introduce fuzzy control algorithm to dynamically adjust control gain parameters and braking force according to real-time road conditions and trailer deviation status to optimize trailer following performance.

2. The active control method for multi-axle trailer traction maneuvers according to claim 1, characterized in that: In step S1, the real-time collection of the motion data of the tractor is specifically as follows: Acceleration a = [a x , a y , a z ], used to describe the linear acceleration of the tractor in each axis; Angular velocity ω=[ω x ,ω y ,ω z ], used to reflect the rotational motion of the tractor; The attitude angle is used to indicate the spatial attitude of the tractor.

3. The active control method for multi-axle trailer towing maneuvers according to claim 2, characterized in that: In step S2, the relative angle between the two vehicles is α t , in step S3, the current speed is v h (t), the direction deviation angle is β h (t), the trailer center of gravity offset is e h (t).

4. The active control method for multi-axle trailer towing maneuvers according to claim 3, characterized in that: In step S4, the dynamic motion parameter set of the two vehicles is X(t), which is calculated by the following formula:

5. The active control method for multi-axle trailer towing maneuvers according to claim 3, characterized in that: In step S5, the specific formula for predicting the future motion trend of the tractor and the ideal motion trajectory of the trailer using the extended Kalman filter algorithm is: X(t+1)=F(X(t),U(t)) in: F is the system state update function; U(t) represents the input control quantity of the tractor; X(t+1) is the predicted state of the trailer at the next moment.

6. The active control method for multi-axle trailer towing maneuvers as claimed in claim 3, characterized in that: In step S6, the specific formula for dynamically adjusting the steering angle of the trailer through the steering system is: Among them, δ h (t) is the steering angle of the trailer; e h (t) is the current trajectory deviation of the trailer; is the deviation change rate; k p and k d are the proportional and derivative control gains respectively.

7. The active control method for multi-axle trailer towing maneuvers as claimed in claim 3, characterized in that: In step S7, the formula for dynamically adjusting the braking force is specifically: F b =m h ·(a des -a h (t)) Among them, F b Provide braking force for trailers; m h is the mass of the trailer; a des and a h (t) are the ideal deceleration of the tractor and the current acceleration of the trailer, respectively.

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