An active control method for multi-axle trailer tractor maneuvering

Abstract

The present application relates to the technical field of multi-axle trailer traction, in particular to a kind of active control method suitable for multi-axle trailer traction mobile: through inertia unit and angle sensor installation and data acquisition, inertia data and included angle information analysis, path following and deviation calculation, active steering control, active braking control and feedback and control optimization cooperate, under the premise that not to the traction vehicle is large-scale modification, through the active control ability of trailer itself, improve the stability and safety of traction mobile, while meeting the needs of multi-axle trailer in complex terrain and long-distance transportation.This method needs to be able to perceive the motion state of the traction vehicle in real time, combined with the multi-axle characteristics of the trailer, realize accurate steering and braking control, so as to solve the stability problem that multi-axle trailer is prone to occur in the process of traction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-axle trailer traction, and particularly relates to an active control method suitable for multi-axle trailer traction maneuver. BACKGROUND

[0002] In long-distance transportation tasks and the like, multi-axle trailers, as an important part of transportation equipment, are usually used to carry a large amount of supplies and equipment and the like. Due to the characteristics of strong carrying capacity and good stability, multi-axle trailers are widely used in various tasks such as complex terrain and long-distance transportation. However, compared with traditional single-axle or double-axle trailers, multi-axle trailers have more degrees of freedom, resulting in more complexity in the traction process. For example, in sharp turns, uphill or downhill, or soft terrain, the steering response of the multi-axle trailer often lags behind the towing vehicle, and a large yaw angle is easily generated, resulting in a decrease in the stability of the trailer. In addition, when the multi-axle trailer is running at high speed, it may have a strong swing effect (the "snake" phenomenon) due to large inertia, increasing the difficulty of control. Especially in complex road conditions, if the trailer deviates from the track or loses control, it may pose a significant safety threat to the towing vehicle and the surrounding environment.

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

[0004] The present application aims to provide an active control method suitable for multi-axle trailer traction maneuver, which solves the problem of insufficient coordinated motion analysis of the trailer and the towing vehicle in the existing technology for solving the stability of multi-axle trailers in traction maneuver, and the lack of targeted design results in the following ability and stability of the trailer being difficult to meet the efficiency requirements of modern tasks.

[0005] To achieve the above-mentioned purpose, the present application provides an active control method suitable for multi-axle trailer traction maneuver, comprising the following steps:

[0006] S1: collecting the motion data of the towing vehicle in real time through the quick-release inertia unit installed on the towing vehicle;

[0007] S2: installing an angle sensor at the connection between the trailer and the towing vehicle 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 the wheel speed sensor, attitude sensor and laser range finder;

[0009] S4: The collected various data are fused to form a two-vehicle dynamic motion parameter set;

[0010] S5: Based on the two-vehicle motion parameter set and the kinematic model, the future motion trend of the towing vehicle and the ideal motion trajectory of the trailer are predicted by using the extended Kalman filter algorithm;

[0011] S6: According to the predicted ideal trajectory and the current state, the steering angle of the trailer is dynamically adjusted through the steering system;

[0012] S7: According to the deceleration information of the towing vehicle and the relative motion state of the two vehicles, the braking force is dynamically adjusted through the trailer braking system;

[0013] S8: The fuzzy control algorithm is introduced, and the control gain parameters and braking force are dynamically adjusted according to the real-time road conditions and trailer deviation state to optimize the trailer following performance.

[0014] In step S1, the real-time collection of the motion data of the towing vehicle is as follows:

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

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

[0017] Attitude angle is used to represent the spatial attitude of the towing vehicle.

[0018] 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), and the trailer gravity center offset is e h (t).

[0019] In step S4, the two-vehicle dynamic motion parameter set is X(t), and is calculated by the following formula:

[0020]

[0021] In step S5, the specific formula for predicting the future motion trend of the towing vehicle and the ideal motion trajectory of the trailer by using the extended Kalman filter algorithm is as follows:

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

[0023] wherein:

[0024] F is a system state update function;

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

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

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

[0028]

[0029] wherein, 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] wherein, in step S7, the formula for dynamically adjusting the braking force is specifically:

[0034] F b = m h ·(a des -a h (t))

[0035] wherein, F b is the braking force of the trailer;

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

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

[0038] The application provides a kind of active control method suitable for multi-axle trailer traction maneuver, through inertia unit and angle sensor installation and data acquisition, inertia data and included angle information analysis, path following and deviation calculation, active steering control, active braking control and feedback and control optimization cooperation, under the premise of not large-scale modification to towing vehicle, through the active control ability of trailer itself, improve the stability and safety of traction maneuver, meet the demand of multi-axle trailer in complex terrain and long distance transportation.This method needs to be able to perceive the motion state of towing vehicle in real time, combined with the multi-axle characteristics of trailer, realize accurate steering and braking control, so as to solve the stability problem prone to occur in the traction process of multi-axle trailer. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0040] Figure 1 is the step flow chart of the active control method suitable for multi-axle trailer traction maneuver provided by the present application.

[0041] Figure 2 is the key kinematic parameter definition and traction relationship schematic diagram provided by the present application. DETAILED DESCRIPTION

[0042] The embodiments of the present application will be described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0043] Please refer to Figure 1 , the present application provides an active control method for multi-axle trailer traction maneuver, comprising the following steps:

[0044] S1: towing vehicle motion data acquisition:

[0045] A quick-release inertia unit (IMU) is installed on the towing vehicle to collect the acceleration a = [a x , a y , a z ], angular velocity ω = [ω x , ω y , ω z ] and attitude angle

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

[0047] Where, the IMU can be adapted to different models of tractor through fast buckle or magnetic mounting structure, without the need for recalibration, which can improve the universality of equipment.

[0048] S2: Measurement of the relative angle between the trailer and the tractor:

[0049] By installing an angle sensor at the drawbar, the relative angle α between the trailer and the tractor is measured in real time t .

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

[0051] Where, this data is one of the input parameters for trailer predictive control, used to judge the motion state and deviation of the trailer.

[0052] S3: Trailer state perception:

[0053] The sensors installed inside the trailer collect key motion parameters:

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

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

[0056] 3. Deviation e h (t): calculated using the perpendicular distance from the trailer center to the path of the tractor, and measured accurately using a distance measuring sensor;

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

[0058] S4: Trailer motion prediction:

[0059] The trailer control unit is based on the Extended Kalman Filter (EKF) algorithm, combined with the state data X(t) of the two vehicles:

[0060]

[0061] The kinematic model of the tractor and trailer is established 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] where:

[0065] F is the state update function;

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

[0067] The prediction model is implemented by the following key equations:

[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] Further, the prediction result dynamically adjusts the control gain parameters, enabling the trailer to more accurately follow the ideal trajectory of the towing vehicle.

[0071] S5: Active steering control:

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

[0073]

[0074] where:

[0075] k p : proportional gain, controls the response speed of the deviation;

[0076] k d : derivative gain, suppresses oscillation caused by deviation changes;

[0077] Deviation rate, calculated by the time derivative.

[0078] The steering motor executor adjusts the steering angle of the trailer wheels after receiving the instructions, ensuring the stability and followability of the trailer in complex road conditions.

[0079] S6: Active braking control:

[0080] The trailer braking system adjusts the braking force F b based on the towing vehicle's deceleration instructions and relative motion state:

[0081] F b = m h ·(a des -ah (t))

[0082] wherein:

[0083] m h : trailer mass;

[0084] a des : target deceleration of the towing vehicle;

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

[0086] Further, in emergency situations, the trailer braking system prioritizes the use of the automatic emergency braking function to prevent rear-end collisions caused by inertia sliding.

[0087] S7: Dynamic optimization and adaptive adjustment:

[0088] Introduce fuzzy control algorithm, real-time adjust parameters according to trailer running environment and deviation state:

[0089] Optimization of proportional gain k p and differential gain k d ;

[0090] Dynamic distribution of braking force F b ;

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

[0092] The optimization formula is as follows:

[0093] k p = k p0 ·(1+λ1·|e h (t)|)

[0094]

[0095] wherein:

[0096] k p0 , k d0 are initial control parameters;

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

[0098] In this embodiment, through the installation and data collection of the inertial unit and the angle sensor, the analysis of inertial data and the included angle information, the path following and deviation calculation, the active steering control, the active braking control, and the feedback and control optimization, the stability and safety of the traction machine are improved under the premise of not making large-scale modifications to the tractor, and the needs of multi-axle trailers in complex terrain and long-distance transportation are met. This method needs to be able to sense the motion state of the tractor in real time, combine the multi-axle characteristics of the trailer, and realize precise steering and braking control, so as to solve the stability problem of multi-axle trailers in the traction process.

[0099] Embodiment:

[0100] Please refer to Figure 2 , this embodiment is an application of a 6×6 unmanned all-terrain vehicle in long-distance traction, aiming to solve the stability and efficient following problem of the trailer in high-speed traction. The trailer contains inertial units, angle sensors, steering control systems, braking systems, and embedded controllers, etc. The tractor is a 4×4 light high-mobility off-road vehicle, which includes Figure 2 In this embodiment, the tractor is marked as 1, the multi-axle unmanned trailer is marked as 2, the drawbar is marked as 3, the tractor 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 the inertial unit (IMU) on the tractor through the quick-release structure, record the initial attitude angle and acceleration initial value (a0), without additional calibration;

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

[0104] 3. Connect the trailer control system to the tractor, and transmit the motion data of the tractor through wired CAN protocol or wireless communication protocol.

[0105] Specific steps:

[0106] Step 1: Data collection and real-time transmission of the tractor

[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] IMU data is transmitted to the trailer control unit at a frequency of 100 Hz after noise is removed by a preset filtering algorithm.

[0112] Step 2: Trailer state data acquisition

[0113] The trailer collects the following state parameters:

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

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

[0116] 3. Relative angle α(t) between trailer and towing vehicle: collected by an angle sensor;

[0117] 4. Deviation e h (t): the ranging sensor measures the vertical distance from the trailer center to the towing vehicle path in real time;

[0118] 5. Trailer tilt angle γ h (t): measured by a gyroscope in real time.

[0119] Step 3: Trailer motion prediction

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

[0121] Towing vehicle motion state X c (t):

[0122]

[0123] Trailer state:

[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 time:

[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 formulas include:

[0129] a(t+1) = a(t) + D t · (w h (t) - w c (t))

[0130] e h (t+1) = e h (t) + v h (t) · D t · sin(a(t))

[0131] Step 4: Active steering control

[0132] The trailer control unit calculates the steering angle d h (t) based on the deviation e h (t) and the included angle a(t):

[0133]

[0134] where:

[0135] k p = 0.8: proportional gain;

[0136] k d = 0.2: derivative gain;

[0137] Rate of change of deviation, calculated by numerical differentiation.

[0138] The change range of the steering angle d h (t) is limited to [-30°, +30°], and the trailer wheel angle is adjusted in real time by the steering actuator.

[0139] Step 5: Active braking control

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

[0141] F b = m h · (a des - a h (t))

[0142] where:

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

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

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

[0146] The trailer applies braking force through the hydraulic braking system to ensure no rear-end collision occurs.

[0147] Step 6: Dynamic optimization and adaptive adjustment

[0148] The trailer control unit adjusts the control parameters in real-time based on the environment and deviation state:

[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°) is detected, 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 this patent embodiment, including the core logic code for inertial unit data acquisition, trailer state update, active steering control, and braking control:

[0154]

[0155]

[0156]

[0157] where,

[0158] 1. IMU data acquisition: The updateIMUData function simulates real-time data from the towing vehicle's IMU, including acceleration and angular velocity.

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

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

[0161] 4. Brake Control: The calculateBrakeForce function calculates the brake force based on the current acceleration of the trailer, ensuring it does not rear-end or skid.

[0162] 5. Control Output: The controlTrailer function integrates the control logic, outputting the steering angle and brake force.

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

[0164] The above disclosure is only a preferred embodiment of the present application, of course, cannot be limited by this, those skilled in the art can understand that the implementation of all or part of the above-mentioned embodiments, and the equivalent changes made by the claims of the present application, still belong to the scope covered by the invention.

Claims

1. An active control method for multi-axle trailer towing 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 center of gravity offset through wheel speed sensors, attitude sensors and laser range finders; S4: Perform multi-source information fusion on the collected data to form a set of dynamic motion parameters of the two vehicles; S5: Based on the motion parameter sets and kinematic models 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 trailer's steering angle through the steering system based on the predicted ideal trajectory and current status; S7: Dynamically adjust the braking force through the trailer braking system based on the tractor deceleration information and the relative motion state of the two vehicles; S8: A fuzzy control algorithm is introduced to dynamically adjust the control gain parameters and braking force according to the real-time road conditions and trailer deviation status to optimize the trailer following performance.

2. The active control method for multi-axle trailer towing maneuvers according to claim 1, characterized in that: In step S1, the real-time collection of the tractor's motion data is 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; Attitude angle, 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's 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 parameters of the two vehicles are set as X(t) and are 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 variable 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 according to 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 rate of change of deviation; k p and k d are the proportional and derivative control gains respectively.

7. The active control method for multi-axle trailer towing maneuvers according to 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 the trailer; 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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