Automatic driving system of two-wheeled vehicle

Abstract

The invention provides an automatic balance adjustment method and device for automatic driving of a two-wheeled vehicle, and solves the problems of single sensor data acquisition and insufficient control precision in the prior art. The IMU sensor, the Hall sensor and the TMR sensor are integrated, and vehicle information is collected in an omnibearing mode. The IMU sensor measures and corrects dynamic data such as acceleration, angular velocity and attitude angle; the TMR sensor measures the rotating angle of the handlebar; the Hall sensor detects the rotating speed of wheels and the position of a motor rotor. The dynamic state of the vehicle is estimated by fusing data through Kalman filtering and particle filtering, and the precision is improved. A multivariable PID controller is adopted, control output is calculated based on parameters such as a vehicle body inclination angle, a vehicle speed, a handlebar rotation angle and a motor rotation speed, and accurate control is achieved. Sensor data, a PID controller and an execution mechanism are integrated to form a complete control system, the system has the advantages of multi-sensor cooperation, multivariable PID control, accurate data fusion and the like, and an efficient and reliable scheme is provided for automatic driving of the two-wheeled vehicle.

Description

Technical Field

[0001] The present invention relates to the field of automatic driving of two-wheeled vehicles, and in particular to an automatic balancing adjustment method and device. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, in the field of two-wheeled vehicle autonomous driving technology, many systems rely on only a single or a few sensors, data collection is not comprehensive, and is easily affected by environmental interference or sensor failure. Traditional PID control is usually based on the feedback of a single variable, which is difficult to deal with the multi-variable coupling problem of complex systems, resulting in limited control accuracy and response speed. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned background technology, the present invention provides an automatic balancing adjustment method and device, and the present invention realizes the automatic driving balancing of a two-wheeled vehicle.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] The invention provides a control method for an automatic balancing device.

[0007] A two-wheeled vehicle automatic balance adjustment control method, comprising:

[0008] Get vehicle body information;

[0009] First, the posture of the two-wheeled vehicle is measured using an IMU sensor, which includes an accelerometer, gyroscope, and magnetometer.

[0010] First, the acceleration is measured by the accelerometer, and then converted from the vehicle body coordinate system to the navigation coordinate system, using the attitude angle, pitch angle, and roll angle for coordinate conversion;

[0011] Further, the gravitational acceleration g is subtracted from the converted acceleration a to obtain the pure motion acceleration;

[0012] Furthermore, the acceleration is integrated over time t to obtain the change in velocity v:

[0013] v=∫(ag)dt

[0014] Integrating the velocity v over time t gives the change in displacement x:

[0015] x=∫vdt

[0016] Furthermore, the gyroscope is used to measure the angular velocity;

[0017] Furthermore, by integrating the velocity of the yaw angle, the change of the yaw angle is obtained:

[0018]

[0019] Furthermore, after obtaining the attitude angle, the displacement, velocity and direction change can be calculated in combination with the acceleration data;

[0020] Furthermore, a magnetometer is used to measure the magnetic field strength and direction of the surrounding environment;

[0021] Furthermore, after the IMU provides the dynamic motion data of the vehicle in real time, the accelerometer is used to measure the linear acceleration of the vehicle on three axes (X, Y, Z), and the gyroscope is used to measure the angular velocity of the vehicle on three axes;

[0022] Furthermore, the dynamic motion data of the vehicle is provided in real time. These data are used to calculate the displacement, speed and direction change of the vehicle, thereby inferring the vehicle's attitude (pitch angle, roll angle and yaw angle);

[0023] Since IMU has cumulative errors, it needs to be calibrated regularly:

[0024] When the vehicle is stationary, the accelerometer data is used to correct the velocity error.

[0025] Then, the TMR sensor can measure the rotation angle of the handlebars to provide precise directional control information;

[0026] Further, a magnet is mounted on the rotating shaft of the handlebar, and a TMR sensor is mounted at a fixed position;

[0027] When the handlebars rotate, the direction of the magnetic field of the magnet changes, and the TMR sensor can detect this change and output a corresponding electrical signal;

[0028] Furthermore, the Hall sensor is used to measure the wheel speed:

[0029] Install a magnet on the rotating part of the wheel and install a Hall sensor at a fixed position. When the wheel rotates, the magnet passes by the Hall sensor, causing the magnetic field to change;

[0030] Speed ​​= number of pulses / time / number of magnets × wheel circumference

[0031] Then, further, the Hall sensor is used to detect the rotor position of the motor, thereby helping to control the rotation speed and direction of the motor. The rotor position is determined by detecting the change in the magnetic field, and the rotation speed and direction of the motor are further controlled;

[0032] Furthermore, the vehicle body information obtained includes: acceleration, angular velocity, pitch angle, roll angle, yaw angle of the vehicle when it is running, the rotation angle of the handlebars during the operation of the vehicle, and the rotation speed of the wheels;

[0033] Furthermore, these signals need to be converted into digital signals by signal processing circuits so that they can be processed by the microcontroller;

[0034] Furthermore, based on the vehicle body inclination angle, vehicle speed, handlebar angle, and motor speed, a PID controller is used to obtain the momentum wheel motor torque and change the vehicle posture (body inclination angle, vehicle speed, etc.), thereby achieving control of the vehicle body.

[0035] Model the system and clarify the relationship between various variables;

[0036] Furthermore, Kalman filtering and particle filtering methods are used to estimate the state of the dynamic system:

[0037] Furthermore, the Kalman filter can effectively estimate the attitude angle of the dynamic system by combining the state space model and observation data through two steps of prediction and update.

[0038] Furthermore, the particle filter determines the degree of match with the measured value through the steps of particle initialization, prediction update, resampling and state estimation;

[0039] Furthermore, the speed information provided by the Hall sensor can be used as a feedback signal to achieve closed-loop speed control through the PID controller algorithm;

[0040] Furthermore, the PID controller is used to calculate the control output (motor torque) based on the error signal to achieve precise control of the vehicle's attitude;

[0041] Furthermore, according to different control objectives, the corresponding control logic is designed:

[0042] For example, by adjusting the torque of the momentum wheel motor, the inclination angle of the vehicle body can be changed, by adjusting the motor speed, the forward speed of the vehicle can be controlled, and by adjusting the handlebar angle, the steering of the vehicle can be controlled.

[0043] Furthermore, a multivariable PID controller is adopted to integrate multiple control objectives into one controller.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The collaborative working mode of multiple sensors not only improves the diversity and redundancy of data, but also enhances the adaptability and robustness of the system in complex environments. Integrating the feedback of multiple sensors into a multivariable PID controller improves the control accuracy, and can better cope with multivariable coupling problems and enhance the dynamic response capability of the system. Using Kalman filtering method and other methods for data fusion processing can achieve more accurate data fusion.

[0046] The present invention integrates sensor data, a PID controller and an actuator into a complete control system to realize an automatic balancing driving system for a two-wheeled vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute improper limitations of the present invention.

[0048] Figure 1 is a flow chart showing a method for controlling an automatic driving system according to the present invention;

[0049] Figure 2 It is a structural block diagram showing an automatic driving system device of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose and technical solution of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0051] The present application embodiment provides an electronic device, such as Figure 2 As shown in the figure, the device contains a PID controller, MCU, IMU sensor, Hall sensor, and TMR sensor.

[0052] The IMU sensor integrates an accelerometer, a gyroscope, and a magnetometer to measure motion state and direction; a Hall sensor (such as a linear or three-axis Hall sensor) is used to detect magnetic field strength; and a TMR sensor (such as a magnetoresistive sensor or a closed-loop TMR current sensor) is used to accurately measure magnetic field changes.

[0053] The MCU is directly connected to the analog sensor to ensure a common ground; it is connected to the data sensor (such as IMU) using the I2C or SPI interface and the corresponding communication protocol is configured.

[0054] The core of the device is the PID controller. First, determine the control target value, connect the PID controller to the controlled object, adjust the three parameters of proportional coefficient (KP), integral time (TI) and differential time (TD) according to the actual effect, and then start the PID controller. The PID controller continuously adjusts the control output according to the sensor feedback data to achieve precise control.

[0055] like Figure 2 As shown in the figure, the electronic device has a flexible structure and can add or remove components according to needs. It is a closed-loop system that collects information, makes adjustments and provides feedback in real time. Data is collected through IMU, Hall and TMR sensors, pre-processed by MCU and transmitted to PID controller, which calculates the error and adjusts the control output to achieve real-time control of the target object.

[0056] One application of the embodiment of the present application is based on autonomous driving of two-wheeled vehicles. The electronic device collects motion and magnetic field data through sensors, and the MCU transmits the data to the PID controller, which adjusts the motor output to achieve speed and direction control. According to the control effect, the PID parameters can be further optimized to improve the performance of autonomous driving. This design improves the control accuracy and provides an efficient and reliable solution for autonomous driving.

[0057] Next, we will Figure 1 The process shown is described in detail.

[0058] The following are examples:

[0059] Get vehicle body information;

[0060] First, the posture of the two-wheeled vehicle is measured using an IMU sensor, which includes an accelerometer, gyroscope, and magnetometer.

[0061] An IMU (Inertial Measurement Unit) sensor is a device used to measure and report the speed and direction of an object's three-dimensional motion;

[0062] First, the acceleration is measured by the accelerometer, and then converted from the vehicle body coordinate system to the navigation coordinate system, using the attitude angle, pitch angle, and roll angle for coordinate conversion;

[0063] Further, the gravitational acceleration g is subtracted from the converted acceleration a to obtain the pure motion acceleration;

[0064] Furthermore, the acceleration is integrated over time t to obtain the change in velocity v:

[0065] v=∫(ag)dt

[0066] Integrating the velocity v over time t gives the change in displacement x:

[0067] x=∫vdt

[0068] Furthermore, the gyroscope is used to measure the angular velocity;

[0069] Furthermore, by integrating the velocity of the yaw angle, the change of the yaw angle is obtained:

[0070]

[0071] Furthermore, after obtaining the attitude angle, the displacement, velocity and direction change can be calculated in combination with the acceleration data;

[0072] Furthermore, complementary filtering is used to combine the data from the accelerometer and gyroscope to calculate the attitude;

[0073] Furthermore, a magnetometer is used to measure the magnetic field strength and direction of the surrounding environment;

[0074] Furthermore, after the IMU provides the dynamic motion data of the vehicle in real time, the accelerometer is used to measure the linear acceleration of the vehicle on three axes (X, Y, Z), and the gyroscope is used to measure the angular velocity of the vehicle on three axes;

[0075] Furthermore, the dynamic motion data of the vehicle is provided in real time. These data are used to calculate the displacement, speed and direction change of the vehicle, thereby inferring the vehicle's attitude (pitch angle, roll angle and yaw angle);

[0076] Since IMU has cumulative errors, it needs to be calibrated regularly:

[0077] When the vehicle is stationary, the accelerometer data is used to correct the velocity error.

[0078] Then, the TMR sensor can measure the rotation angle of the handlebars to provide precise directional control information;

[0079] The TMR sensor is based on the tunnel magnetoresistance effect. Its core structure consists of two strong magnetic layers (free layer and fixed layer) sandwiched by a very thin insulating layer (barrier layer). When the magnetization direction of the free layer changes with the change of the external magnetic field, the resistance of the sensor will also change;

[0080] Further, a magnet is mounted on the rotating shaft of the handlebar, and a TMR sensor is mounted at a fixed position;

[0081] When the handlebars rotate, the direction of the magnetic field of the magnet changes, and the TMR sensor can detect this change and output a corresponding electrical signal;

[0082] Furthermore, by calculating the ratio of the sine and cosine signals, the rotation angle of the handlebar can be obtained.

[0083] Furthermore, the Hall sensor is used to measure the wheel speed:

[0084] Install a magnet on the rotating part of the wheel and install a Hall sensor at a fixed position. When the wheel rotates, the magnet passes by the Hall sensor, causing the magnetic field to change;

[0085] Speed ​​= number of pulses / time / number of magnets × wheel circumference

[0086] Then, further, the Hall sensor is used to detect the rotor position of the motor, thereby helping to control the rotation speed and direction of the motor. The rotor position is determined by detecting the change in the magnetic field, and the rotation speed and direction of the motor are further controlled;

[0087] Furthermore, the vehicle body information obtained includes: acceleration, angular velocity, pitch angle, roll angle, yaw angle of the vehicle when it is running, the rotation angle of the handlebars during the operation of the vehicle, and the rotation speed of the wheels;

[0088] Furthermore, these signals need to be converted into digital signals by signal processing circuits so that they can be processed by the microcontroller;

[0089] Furthermore, based on the vehicle body inclination angle, vehicle speed, handlebar angle, and motor speed, a PID controller is used to obtain the momentum wheel motor torque and change the vehicle posture (body inclination angle, vehicle speed, etc.), thereby achieving control of the vehicle body.

[0090] Model the system and clarify the relationship between various variables;

[0091] Furthermore, Kalman filtering and particle filtering methods are used to estimate the state of the dynamic system:

[0092] Kalman filter method:

[0093] Equation of state: x k =F k x k -h+B k u k +w k

[0094] Among them, x k is the state vector of the system at time k, F k is the state transition matrix, u k is the control input vector, B k is the control input matrix, w k is the process noise, assumed to be Gaussian white noise, with a mean of 0 and a covariance matrix of Q k .

[0095] Observation equation: z k =H k x k +c k

[0096] Among them, z k is the observed value of the system at time k, H k is the observation matrix, v k is the observation noise, assumed to be Gaussian white noise, with a mean of 0 and a covariance matrix of R k

[0097] Furthermore, Kalman filtering can effectively estimate the data of dynamic systems by combining state-space models and observation data through two steps: prediction and update;

[0098] In detail, we first use the prediction equation and the state transition model to predict the next state and covariance:

[0099] State prediction: x^ k|k-1 =F k x^ k-h|k-1 +B k u k

[0100] Among them, x^ k|k-1 is the predicted state estimate, x^ k-1|k-1 is the optimal state estimate at the previous moment.

[0101] Covariance prediction:

[0102] Among them, P k|k-1 is the predicted covariance matrix, P k-1|k-1 is the optimal covariance matrix at the previous moment.

[0103] Furthermore, the following equation is used to correct the predicted state in combination with the observed data:

[0104] Calculate Kalman gain

[0105] Updated state estimate x^ k|k =x^ k|k-1 +K k (z k -H k x^ k|k-1 );

[0106] Updated covariance matrix P k|k =(IK k H k ) k|k-1 ;

[0107] Where I is the identity matrix.

[0108] In IMU data processing, Kalman filtering can be used to estimate the dynamic state of the vehicle, such as attitude angle (pitch angle, roll angle, yaw angle), speed, displacement, etc. The following is a simple application example:

[0109] State vector: in, is the pitch angle, θ is the roll angle, is the yaw angle, ω x ,ω y ,ω z is the angular velocity;

[0110] State transition matrix:

[0111] Observation matrix:

[0112] Through the above model, the Kalman filter can effectively estimate the state of the vehicle and make corrections based on the observation data of the IMU to obtain a more accurate dynamic state estimation.

[0113] Furthermore, after using Kalman filtering to process the linear Gaussian part in the sensor, a particle filter is used to process the nonlinear relationship and non-Gaussian noise in the sensor data;

[0114] Furthermore, the particle filter determines the degree of match with the measured value through the steps of particle initialization, prediction update, resampling and state estimation;

[0115] Furthermore, particle filtering can fuse multiple data sensors;

[0116] Centralized fusion: Process multi-sensor data centrally, update particle distribution in state space through particle filtering, and optimize particle weights and positions.

[0117] Distributed fusion: The data from each sensor is processed by particle filtering separately, and then the results are fused to improve the robustness and real-time performance of the system].

[0118] Furthermore, the speed information provided by the Hall sensor can be used as a feedback signal to achieve closed-loop speed control through the PID controller algorithm;

[0119] Furthermore, the PID controller is used to calculate the control output (motor torque) based on the error signal to achieve precise control of the vehicle's attitude;

[0120] The output of the PID controller consists of three parts: proportional (P), integral (I) and differential (D):

[0121]

[0122] Where T(t) is the motor torque, e(t) is the error signal, i.e. the difference between the expected value and the actual value, and K P is the proportional gain, K iis the integral gain, K d is the differential gain.

[0123] Furthermore, according to different control objectives, the corresponding control logic is designed:

[0124] For example, by adjusting the torque of the momentum wheel motor, the inclination angle of the vehicle body can be changed, by adjusting the motor speed, the forward speed of the vehicle can be controlled, and by adjusting the handlebar angle, the steering of the vehicle can be controlled.

[0125] Furthermore, a multivariable PID controller is used to integrate multiple control objectives into one controller;

[0126]

[0127] Compared with the prior art, the present invention has the following beneficial effects:

[0128] The collaborative working mode of multiple sensors not only improves the diversity and redundancy of data, but also enhances the adaptability and robustness of the system in complex environments. Integrating the feedback of multiple sensors into a multivariable PID controller improves the control accuracy, and can better cope with the multivariable coupling problem and enhance the dynamic response capability of the system. Using Kalman filtering and particle filtering methods for data fusion processing can achieve more accurate data fusion.

[0129] The present invention integrates sensor data, a PID controller and an actuator into a complete control system to realize an automatic balancing driving system for a two-wheeled vehicle.

Claims

1. An automatic driving system for a two-wheeled vehicle, characterized in that: include: Sensor system, the sensor system includes IMU sensor, TMR sensor, Hall sensor The controller system includes a PID controller and an MCU, and the controller system is connected to the sensor.

2. The automatic driving system for a two-wheeled vehicle according to claim 1, characterized in that: The sensor systems have different functions, including: The IMU sensor provides the vehicle's dynamic motion data, acceleration, and angular velocity in real time; The Hall sensor detects the rotor position of the motor; The TMR sensor measures the rotation angle of the handlebar.

3. The automatic driving system for a two-wheeled vehicle according to claim 2, characterized in that: The sensor system is connected to the controller system, and the filtering method is used to estimate and update the posture, including: The IMU sensor is used to calculate the displacement, speed and direction change of the vehicle, thereby inferring the posture of the vehicle; The Hall sensor controls the rotation speed and direction of the motor; The TMR sensor provides precise directional control information; The PID controller obtains the torque of the momentum wheel motor and changes the vehicle posture (body inclination angle, vehicle speed) to achieve control of the vehicle body.

4. The automatic driving system for a two-wheeled vehicle according to claim 3, characterized in that: The controller system calculation output consists of three parts, including: Single variable calculation: Multivariate calculations: The output consists of three parts: proportional (P), integral (I) and differential (D).

5. The automatic driving system for a two-wheeled vehicle according to claim 4, characterized in that: An automatic driving system for a two-wheeled vehicle as described in any one of claims 1 to 4 is applied, wherein the sensor system includes TMU, Hall, and TMR sensors, the controller system includes MCU and PID controller, and the sensor system and the controller system are connected.

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