Intelligent fault-tolerant control system for electric wheel automobile driver and working method thereof
阅读说明：本技术 一种电动轮汽车驾驶员智能容错控制系统及其工作方法 (Intelligent fault-tolerant control system for electric wheel automobile driver and working method thereof ) 是由 刘畅 赵万忠 张寒 王春燕 章波 张自宇 王一松 刘津强 于 2019-10-31 设计创作，主要内容包括：一种电动轮汽车驾驶员智能容错控制系统及其工作方法。涉及电动轮汽车主动安全系统。提出了一种根据特定驾驶员的日常操作,汽车的实时行驶状态以及路面信息等进行分析、判断和处理后发出控制信号给四个驱动模块,以控制轮毂电机转矩,轮毂电机作用于车辆,从而控制车辆按照驾驶员意图安全稳定行驶,完成故障容错的电动轮汽车驾驶员智能容错控制系统。本发明的技术方案为：所述电动轮汽车包括车身、转向机构、前桥、后桥、四个电动轮集成；所述智能容错控制系统包括方向盘转角传感器、侧向加速度传感器、陀螺仪、GPS系统、摄像头、电子测控单元、四个车速传感器和四个驱动模块。具有个性化、智能化辅助驾驶员安全驾驶的优点。(An intelligent fault-tolerant control system for an electric wheel automobile driver and a working method thereof. Relates to an active safety system of an electric wheel automobile. The intelligent fault-tolerant control system for the electric wheel automobile driver is used for analyzing, judging and processing the real-time driving state of the automobile, road surface information and the like according to the daily operation of a specific driver and then sending control signals to four driving modules so as to control the torque of the hub motor, wherein the hub motor acts on the automobile, so that the automobile is controlled to safely and stably drive according to the intention of the driver, and fault tolerance is completed. The technical scheme of the invention is as follows: the electric wheel automobile comprises an automobile body, a steering mechanism, a front axle, a rear axle and four electric wheel assemblies; the intelligent fault-tolerant control system comprises a steering wheel corner sensor, a lateral acceleration sensor, a gyroscope, a GPS system, a camera, an electronic measurement and control unit, four vehicle speed sensors and four driving modules. The intelligent auxiliary driving system has the advantages of individualization and intellectualization and assisting a driver to drive safely.)
1. An intelligent fault-tolerant control system for a driver of an electric wheel automobile comprises an automobile body, a steering mechanism, a front axle, a rear axle and four electric wheel assemblies, wherein the front axle and the rear axle are connected in the automobile body; the steering mechanism comprises a steering wheel, a steering rod and a steering gear, wherein one end of the steering rod is connected with the steering wheel, and the other end of the steering rod is connected with the front axle through the steering gear; it is characterized in that the preparation method is characterized in that,
the intelligent fault-tolerant control system comprises a steering wheel corner sensor, a lateral acceleration sensor, a gyroscope, a GPS system, a camera, an electronic measurement and control unit, four vehicle speed sensors and four driving modules;
the steering wheel corner sensor is fixedly connected to the steering rod and used for collecting the corner of the steering wheel;
the lateral acceleration sensor is fixedly connected in the automobile body and used for collecting the lateral acceleration of the automobile;
the gyroscope is fixedly connected in the vehicle body and used for acquiring the course angle and the yaw angular speed of the vehicle;
the GPS system is fixedly connected in the automobile body and used for collecting the displacement of the automobile;
the camera is fixedly connected to the front part of the automobile body and used for collecting road surface information of the front part of the automobile;
the four speed sensors are respectively arranged in the four electric wheel assemblies and are used for acquiring the speed of the automobile;
the four driving modules are respectively arranged in the four electric wheel assemblies, and when one or more hub motors are in failure, the remaining normal hub motors are controlled;
the electronic measurement and control unit comprises a driver parameter and path prediction module, an image processing and path recognition module and a fault-tolerant control module, wherein the steering wheel corner sensor, the lateral acceleration sensor, the gyroscope, the GPS system and four vehicle speed sensors are all connected with the driver parameter and path prediction module, the camera is connected with the image processing and path recognition module, the driver parameter and path prediction module, the image processing and path recognition module are all connected with the fault-tolerant control module, and the fault-tolerant control module is also connected with four driving modules;
identifying and storing the driver parameters through a driver parameter and path prediction module, and predicting a path according to the turning angle of a steering wheel and the driving state of the automobile;
identifying a current path according to information collected by a camera through an image processing and path identification module;
and analyzing and judging according to the driver information, the automobile running state and the current path through the fault-tolerant control module, and sending a control signal to the driving module after processing.
2. The method of claim 1, wherein the driver parameter and path prediction module comprises the following tasks:
task one, driver parameter identification: establishing a steering characteristic model of a driver, and identifying parameters of the model according to operation data of the driver under various driving conditions received by an electronic measurement and control unit;
the steering characteristic model of the driver is:
wherein, thetasw(s) is the driver steering wheel angle, GhIs the steering gain, τLIs the differential time constant, τd1Is the driver's neural response lag time, τd2Is the driver steering response lag time, Δ y(s) is the lateral displacement difference between the predicted position and the driver's predicted aim point;
according to the driver visual preview mechanism, Δ y(s) can be described as:
wherein the content of the first and second substances,
the driver's pre-aim distance L can be described approximately as:
L＝vx(s)τP；
wherein v isx(s) is the vehicle longitudinal speed;
task two, path prediction: when the vehicle runs, predicting a vehicle path by using a steering characteristic model of a driver and a running state of the automobile;
the algorithm of the fault tolerance module is designed based on a sliding mode control algorithm, and a sliding mode surface is defined as;
θswe＝θswd-θsw
wherein, c1、c2Are all gain and c1>0、c2>0，θswdIs a reference steering wheel angle calculated based on a steering characteristic model of the driver.
3. A method for operating the intelligent fault-tolerant control system for the electric-wheel vehicle driver as claimed in claim 2, wherein the method for identifying the parameters of the steering characteristic model of the driver in task one comprises the following steps: determining an approximate value of a parameter to be identified by utilizing an MATLAB global optimization tool box;
defining the upper limit and the lower limit of the parameter to be identified as follows:
0.5≤Gh≤1.2
0.02≤τL≤0.3
0.1≤τd1≤0.34
0.03≤τd2≤0.3
0.4≤τP≤2.5
defining an objective function:
θris the steering wheel angle of the driver during actual driving;
the MATLAB global optimization toolkit is used to find the global minimum of this non-smooth objective function.
4. A working method of the intelligent fault-tolerant control system for the electric-wheel automobile driver as claimed in claim 2, wherein the path prediction function in task two is specifically realized by the following formula:
wherein (X, Y) is the position of the center of mass of the vehicle relative to a ground coordinate system, X0、Y0And phi0And t is the position of the automobile at the moment of 0, β is the centroid slip angle of the automobile body, phi is the vehicle yaw angle, and r is the automobile yaw rate.
Technical Field
The invention relates to an active safety system of an electric wheel automobile, in particular to an intelligent coordination auxiliary fault-tolerant control system for a driver of the electric wheel automobile.
Background
In recent years, driven by the double pressure of energy and environment, various forms of electric vehicles are becoming the focus of research and development in the global automobile industry, and electric wheel vehicles are considered by the industry as one of the most promising. The driving force of the traditional automobile is generated by an internal combustion engine and transmitted to wheels through a series of mechanical transmission devices such as a gearbox, a transmission shaft, a differential and the like to drive the automobile to run. The driving torque of the electric wheel automobile is directly provided by the hub motor, and the electronic measurement and control unit generates a corresponding control signal according to an accelerator pedal signal of a driver, transmits the control signal to the driving unit of the motor and generates driving force. Compared with the prior art, the electric wheel automobile directly drives the wheels by power, so that the intermediate link is omitted, the efficiency is obviously improved, each motor can be independently controlled, and various advanced chassis control technologies are easily realized. However, because the number of actuators is increased and the driving control mode of the actuators strongly depends on an electric control system, the reliability of the mechanical driving system is greatly reduced compared with that of the traditional mechanical driving system, and when a motor fails, fault-tolerant control is performed on the whole automobile, so that it is very important to keep the automobile running safely and stably.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent fault-tolerant control system for the electric wheel automobile driver, which analyzes, judges and processes the real-time running state of the automobile, the road surface information and the like according to the daily operation of a specific driver, and then sends control signals to four driving modules so as to control the torque of a hub motor, wherein the hub motor acts on the automobile, so that the automobile is controlled to run safely and stably according to the intention of the driver, and the fault tolerance is completed.
The technical scheme of the invention is as follows: the electric wheel automobile comprises an automobile body, a steering mechanism, a front axle, a rear axle and four electric wheel assemblies, wherein the front axle and the rear axle are connected in the automobile body; the steering mechanism comprises a steering wheel, a steering rod and a steering gear, wherein one end of the steering rod is connected with the steering wheel, and the other end of the steering rod is connected with the front axle through the steering gear;
the intelligent fault-tolerant control system comprises a steering wheel corner sensor, a lateral acceleration sensor, a gyroscope, a GPS system, a camera, an electronic measurement and control unit, four vehicle speed sensors and four driving modules;
the steering wheel corner sensor is fixedly connected to the steering rod and used for collecting the corner of the steering wheel;
the lateral acceleration sensor is fixedly connected in the automobile body and used for collecting the lateral acceleration of the automobile;
the gyroscope is fixedly connected in the vehicle body and used for acquiring the course angle and the yaw angular speed of the vehicle;
the GPS system is fixedly connected in the automobile body and used for collecting the displacement of the automobile;
the camera is fixedly connected to the front part of the automobile body and used for collecting road surface information of the front part of the automobile;
the four speed sensors are respectively arranged in the four electric wheel assemblies and are used for acquiring the speed of the automobile;
the four driving modules are respectively arranged in the four electric wheel assemblies, and when one or more hub motors are in failure, the remaining normal hub motors are controlled;
the electronic measurement and control unit comprises a driver parameter and path prediction module, an image processing and path recognition module and a fault-tolerant control module, wherein the steering wheel corner sensor, the lateral acceleration sensor, the gyroscope, the GPS system and four vehicle speed sensors are all connected with the driver parameter and path prediction module, the camera is connected with the image processing and path recognition module, the driver parameter and path prediction module, the image processing and path recognition module are all connected with the fault-tolerant control module, and the fault-tolerant control module is also connected with four driving modules;
identifying and storing the driver parameters through a driver parameter and path prediction module, and predicting a path according to the turning angle of a steering wheel and the driving state of the automobile;
identifying a current path according to information collected by a camera through an image processing and path identification module;
and analyzing and judging according to the driver information, the automobile running state and the current path through the fault-tolerant control module, and sending a control signal to the driving module after processing.
The driver parameter and path prediction module includes the following tasks:
task one, driver parameter identification: establishing a steering characteristic model of a driver, and identifying parameters of the model according to operation data of the driver under various driving conditions received by an electronic measurement and control unit;
the steering characteristic model of the driver is:
wherein, thetasw(s) is the driver steering wheel angle, GhIs the steering gain, τLIs a differential time parameter, τd1Is the driver's neural response lag time, τd2Is the driver steering response lag time, Δ y(s) is the lateral displacement difference between the predicted position and the driver's predicted aim point;
according to the driver visual preview mechanism, Δ y(s) can be described as:
wherein the content of the first and second substances,
the driver's pre-aim distance L can be described approximately as:
L＝vx(s)τP；
wherein v isx(s) is the vehicle longitudinal speed;
task two, path prediction: when the vehicle runs, predicting a vehicle path by using a steering characteristic model of a driver and a running state of the automobile;
the algorithm of the fault tolerance module is designed based on a sliding mode control algorithm, and a sliding mode surface is defined as;
θswe＝θswd-θsw
wherein, c1、c2Are all gain and c1>0、c2>0，θswdIs a reference steering wheel angle calculated based on a steering characteristic model of the driver.
In task one, the method for identifying the parameters of the steering characteristic model of the driver comprises the following steps: determining an approximate value of a parameter to be identified by utilizing an MATLAB global optimization tool box;
defining the upper limit and the lower limit of the parameter to be identified as follows:
0.5≤Gh≤1.2
0.02≤τL≤0.3
0.1≤τd1≤0.34
0.03≤τd2≤0.3
0.4≤τP≤2.5
defining an objective function:
θris the steering wheel angle of the driver during actual driving;
the MATLAB global optimization toolkit is used to find the global minimum of this non-smooth objective function.
The path prediction function in task two is specifically realized by the following formula:
wherein (X, Y) is the position of the center of mass of the vehicle relative to a ground coordinate system, X0、Y0And phi0And t is the position of the automobile at the moment of 0, β is the centroid slip angle of the automobile body, phi is the vehicle yaw angle, and r is the automobile yaw rate.
Compared with the prior art, the invention has the beneficial effects that: through the collection of daily driving operation data of a driver, the proper driving torque difference between the normal electric wheels is calculated, the yaw moment generated due to the failure of the motor is compensated, and the requirement of fault-tolerant control is met. Compared with the traditional fault-tolerant control, the invention can fully consider the characteristics of the driver and provide proper control quantity on the premise of not interfering the normal operation of the driver, thereby finishing the fault post-processing and meeting the requirement of keeping the safe and stable running of the vehicle. Meanwhile, the system is provided with a friendly human-computer interaction interface, collects information of a specific driver in real time, and has the advantages of individuation and intellectualization to assist the driver in safe driving.
Drawings
Fig. 1 is a schematic structural view of the present invention.
In the figure, 1 is a steering wheel angle sensor, 2 is a vehicle body, 3 is a lateral acceleration sensor, 4 is a gyroscope, 5 is a GPS system, 6 is a vehicle speed sensor, 7 is electric wheel integration, 8 is a driving module, 9 is a camera, 10 is a front axle, 11 is a rear axle, 12 is an electronic measurement and control unit, 13 is a driver parameter and path prediction module, 14 is an image processing and path identification module, 15 is a fault-tolerant control module, 16 is a steering wheel, 17 is a steering rod, and 18 is a steering gear.
Detailed Description
As shown in fig. 1, the electric wheel automobile comprises an automobile body 2, a steering mechanism, a front axle 10, a rear axle 11 and four electric wheel assemblies 7, wherein the front axle 10 and the rear axle 11 are connected in the automobile body 2, the four electric wheel assemblies 7 are respectively arranged at two ends of the front axle 10 and two ends of the rear axle 11, and the steering mechanism is connected with the front axle 10; the steering mechanism comprises a steering wheel 16, a steering rod 17 and a steering gear 18, wherein one end of the steering rod 17 is connected with the steering wheel 16, and the other end of the steering rod 17 is connected with the front axle 10 through the steering gear 18;
the intelligent fault-tolerant control system comprises a steering wheel corner sensor 1, a lateral acceleration sensor 3, a gyroscope 4, a GPS system 5, a camera 9, an electronic measurement and control unit 12, four vehicle speed sensors 6 and four driving modules 8;
the steering wheel corner sensor 1 is fixedly connected to the steering rod 17 and used for collecting the corner of the steering wheel;
the lateral acceleration sensor 3 is fixedly connected in the automobile body 2 and used for collecting the lateral acceleration of the automobile; the driver model in the scheme is a driver steering model and mainly considers the lateral component of the acceleration of the automobile;
the gyroscope 4 is fixedly connected in the vehicle body 2 and is used for acquiring the course angle and the yaw angular velocity of the vehicle;
the GPS system 5 is fixedly connected in the vehicle body 2 and used for collecting the displacement of the vehicle;
the camera 9 is fixedly connected to the front part of the automobile body 2 and used for collecting road surface information of the front part of the automobile;
the four speed sensors 6 are respectively arranged in the four electric wheel assemblies 7 and are used for collecting the speed of the automobile;
the four driving modules 8 are respectively arranged in the four electric wheel assemblies 7, and when one or more wheel hub motors break down, the remaining normal wheel hub motors are controlled, so that the rotating speeds of the remaining normal wheel hub motors are readjusted, and the automobile is steered according to a preset track;
the electronic measurement and control unit 12 comprises a driver parameter and path prediction module 13, an image processing and path recognition module 14 and a fault-tolerant control module 15, the steering wheel angle sensor 1, the lateral acceleration sensor 3, the gyroscope 4, the GPS system 5 and four vehicle speed sensors 6 are all connected with the driver parameter and path prediction module 13, the camera 9 is connected with the image processing and path recognition module 14, the driver parameter and path prediction module 13 and the image processing and path recognition module 14 are all connected with the fault-tolerant control module 15, and the fault-tolerant control module 15 is also connected with four driving modules 8;
the driver parameters are identified and stored through a driver parameter and path prediction module 13, and a path is predicted according to the turning angle of a steering wheel and the driving state of the automobile;
identifying the current path according to the information collected by the camera 9 through an image processing and path identifying module 14;
the fault-tolerant control module 15 analyzes and judges the information of the driver, the driving state of the automobile and the current path, and sends a control signal to the driving module after processing.
Therefore, when a certain hub motor fails, the fault-tolerant control module 15 analyzes and judges according to driver information, the driving state of the automobile and road surface information, and sends control signals to the four motor driving modules after processing so as to control the torque of the hub motor, and the hub motor acts on the vehicle, so that the vehicle is controlled to safely and stably drive according to the intention of the driver, and fault tolerance is completed.
Specifically, the method comprises the following steps: the driver parameter and path prediction module comprises two tasks;
task one, driver parameter identification: according to the input of direction turning angle torque and the like under different working conditions of different paths in the daily driving process of a driver, a steering characteristic model of the driver is established, and the parameters of the model are accurately identified through historical driving data;
task twoAnd path prediction: when the vehicle runs, the vehicle path is predicted by using a steering characteristic model of a driver and the running state of the vehicle, wherein the running state of the vehicle comprises a longitudinal vehicle speed, a transverse vehicle speed, a lateral acceleration, a yaw angular velocity, a course angle and a real-time path, and the longitudinal/transverse vehicle speed can be obtained according to the information of the vehicle speed v (measured by a vehicle speed sensor) and the course angle theta (measured by a gyroscope) and is represented as follows: longitudinal vehicle speed vxTransverse vehicle speed v ═ vcos θy＝vsinθ。
And the image processing and path identification module identifies current road information such as road edges, traffic sign lines and the like according to the pictures acquired by the camera, and identifies the current path of the driver.
The fault-tolerant control module fully considers the characteristics of the driver according to the preprocessing information (predicted path and real-time path) of the first two modules and the steering characteristic model of the driver and the current vehicle running state (longitudinal/transverse vehicle speed, lateral acceleration, yaw angular velocity, course angle and real-time path), and provides proper control quantity on the premise of not interfering the normal operation of the driver, thereby completing the post-processing of the fault and meeting the requirement of keeping the vehicle running safely and stably.
The intelligent fault-tolerant control system further comprises a human-computer interaction interface.
The human-computer interaction interface is provided by a notebook computer provided with a main control program, and the whole control implementation process is as follows: after the whole system is installed on the electric wheel automobile, each device is electrified, a human-computer interaction interface is accessed, and a user name and a password of a driver are logged in. And after the program is checked to be correct, jumping to a starting interface, starting a button by a motor, and running the system. After the vehicle is started, the electronic measurement and control unit starts to operate, the steering wheel angle sensor, the lateral acceleration sensor, the gyroscope, the GPS system and the camera respectively collect operation data of a driver under various working conditions of daily driving, the operation data are transmitted to the electronic measurement and control unit, a steering characteristic model of the driver is generated through analysis of the operation data, and parameters of the model are subjected to fitting calibration and stored in the electronic measurement and control unit. When the wheel hub motor fails, the electronic measurement and control unit predicts the automobile path through analysis processing of the current automobile driving state and road surface information and a stored steering characteristic model of a driver, performs intelligent coordination auxiliary control pertinently according to the predicted path, the real-time path and the real-time driving state of the automobile, calculates a proper driving torque difference between normal electric wheels, generates a control signal, transmits the control signal to the wheel hub motor driving unit, readjusts the driving torque of each motor, compensates the yaw torque generated by the fault motor, and meets the requirement of keeping the safe and stable driving of the automobile.
The invention has the beneficial effects that: through the collection of daily driving operation data of a driver, the proper driving torque difference between the normal electric wheels is calculated, the yaw moment generated due to the failure of the motor is compensated, and the requirement of fault-tolerant control is met. Compared with the traditional fault-tolerant control, the invention can fully consider the characteristics of the driver and provide proper control quantity on the premise of not interfering the normal operation of the driver, thereby finishing the fault post-processing and meeting the requirement of keeping the safe and stable running of the vehicle. And the system is provided with a friendly human-computer interaction interface, collects the information of a specific driver in real time, and has the advantages of individuation and intellectualization for assisting the driver in safe driving.
The driver parameter and path prediction module includes the following tasks:
task one, driver parameter identification: establishing a steering characteristic model of a driver, and identifying parameters of the model according to operation data of the driver under various driving conditions received by an electronic measurement and control unit;
the steering characteristic model of the driver is:
wherein, thetasw(s) is the driver steering wheel angle, GhIs the steering gain, τLIs a differential time parameter, τd1Is the driver's neural response lag time, τd2Is the driver steering response lag time, Δ y(s) is the lateral displacement difference between the predicted position and the driver's predicted aim point;
according to the driver visual preview mechanism, Δ y(s) can be described as:
wherein the content of the first and second substances,
the driver's pre-aim distance L can be described approximately as:
L＝vx(s)τP；
wherein v isx(s) is the vehicle longitudinal speed;
task two, path prediction: when the vehicle runs, the steering characteristic model of the driver and the running state of the vehicle (longitudinal vehicle speed, transverse vehicle speed, lateral acceleration, yaw angular velocity, course angle and real-time path) are utilized to measure the vehicle path;
the algorithm of the fault tolerance module is designed based on a sliding mode control algorithm, and a sliding mode surface is defined as;
θswe＝θswd-θsw
wherein, c1、c2Are all gain and c1>0、c2>0，θswdIs a reference steering wheel angle calculated based on a steering characteristic model of the driver.
In task one, the method for identifying the parameters of the steering characteristic model of the driver comprises the following steps: determining an approximate value of a parameter to be identified by using a MATLAB (Matrix Laboratory) global optimization tool box;
defining the upper limit and the lower limit of the parameter to be identified as follows:
0.5≤Gh≤1.2
0.02≤τL≤0.3
0.1≤τd1≤0.34
0.03≤τd2≤0.3
0.4≤τP≤2.5
defining an objective function:
θris the steering wheel angle of the driver during actual driving;
the MATLAB global optimization toolkit is used to find the global minimum of this non-smooth objective function.
The path prediction function in task two is specifically realized by the following formula:
wherein (X, Y) is the position of the center of mass of the vehicle relative to a ground coordinate system, X0、Y0And phi0And t is the position of the automobile at the moment of 0, β is the centroid slip angle of the automobile body, phi is the vehicle yaw angle, and r is the automobile yaw rate.