CN115657645A - Intelligent vehicle chassis and task load integrated control method and system - Google Patents
Intelligent vehicle chassis and task load integrated control method and system Download PDFInfo
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Abstract
The invention provides an intelligent vehicle chassis and task load integrated control method and system, belongs to the technical field of intelligent vehicle integrated control, and is characterized in that road environment information, vehicle attitude information and task target information are collected, and a reconfigurable dynamic model is constructed; under the constraint of the vehicle state observed quantity and the road state observed quantity, resolving a reconfigurable dynamic model based on a force balance optimization algorithm to obtain an acceleration parameter target value of each vehicle action actuator and an acceleration parameter target value of each task action actuator; generating control commands of each vehicle action actuator and each task action actuator; and controlling the corresponding vehicle action executors and the corresponding task action executors to act according to the control instructions of the vehicle action executors and the control instructions of the task action executors, controlling the coordinative action of the executors in the chassis domain and the task load domain, and finally realizing the integrated control of the chassis and the task load.
Description
Technical Field
The invention relates to the technical field of intelligent vehicle integrated control, in particular to an intelligent vehicle chassis and task load integrated control method and system.
Background
The intelligent vehicle has the simple control function requirement of the chassis domain and also has the integrated control requirement of the chassis domain and the task load domain after the upper load task load is carried. The control of the chassis domain needs to realize the cooperative control of a driving system, a steering system, a braking system, a suspension system and the like according to the information obtained by the perception planning of the intelligent driving system; on the basis of chassis domain control, a task load domain of an intelligent vehicle needs to combine a processing result of a task target and a control requirement of the chassis domain according to the task target to realize cooperative control of the chassis domain and the task load domain, and further control a task load actuator to realize functions of task target aiming, accurate striking, fixed-point throwing and the like.
The existing intelligent vehicle control system usually only focuses on realizing single or partial functions, but ignores the interaction relation among subsystems, particularly ignores the cooperative relation among subsystems in a chassis domain and between the chassis domain and a task load domain. Therefore, how to construct the association between the subsystems and realize the integrated modeling and control of the chassis domain and the task load domain based on the perception and planning results of the intelligent domain is a key problem to be solved urgently by the intelligent vehicle domain control system.
Disclosure of Invention
The invention aims to provide an intelligent vehicle chassis and task load integrated control method and system, which are used for controlling the coordinated action of actuators in a chassis domain and a task load domain and finally realizing the integrated control of the chassis and the task load.
To achieve the above object, in one aspect, the present invention provides the following solutions:
an intelligent vehicle chassis and task load integrated control method includes the following steps:
collecting road environment information, vehicle attitude information and task target information;
determining a chassis state parameter target sequence according to the road environment information and the task target information;
determining a task load state parameter target sequence according to the vehicle attitude information and the task target information;
determining road state observation quantity according to the road environment information;
determining vehicle state observation quantity according to the vehicle attitude information;
constructing a reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle;
forming a state parameter matrix by the chassis state parameter target sequence and the task load state parameter target sequence;
under the constraint of the vehicle state observed quantity and the road state observed quantity, resolving the reconfigurable dynamic model according to the state parameter matrix based on a force balance optimization algorithm to obtain an acceleration parameter target value of each vehicle action actuator and an acceleration parameter target value of each task action actuator;
generating a control command corresponding to the vehicle action actuator according to the acceleration parameter target value of each vehicle action actuator and the control state mapping model of each vehicle action actuator;
controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator;
generating a control command corresponding to the task action executer according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor;
and controlling the corresponding task action executors to act according to the control commands of the task action executors.
Optionally, after the corresponding vehicle actuator is controlled to operate according to the control command of each vehicle actuator, the method for controlling the chassis domain load integration of the intelligent vehicle further includes:
acquiring state feedback values of the vehicle action actuators;
and for any vehicle action actuator, performing linear fitting according to the target value of the acceleration parameter of the vehicle action actuator and the state feedback value of the vehicle action actuator, and updating the control state mapping model of the vehicle action actuator.
Optionally, after controlling the corresponding task actuator to operate according to the control instruction of each task actuator, the method for integrally controlling the chassis domain load of the intelligent vehicle further includes:
acquiring state feedback values of task action actuators;
and aiming at any task action executor, performing linear fitting according to the acceleration parameter target value of the task action executor and the state feedback value of the task action executor, and updating a control state mapping model of the task action executor.
Optionally, the constructing a reconfigurable dynamics model based on the incidence relation of each actuator in the intelligent vehicle specifically includes:
taking each vehicle power actuator and each task power actuator as dynamic primitives;
establishing an incidence relation table comprising the relation among the dynamic primitives according to the subordination relation among the dynamic primitives;
and constructing a reconfigurable dynamic model based on the incidence relation table.
Optionally, the acquiring the road environment information, the vehicle posture information, and the task target information specifically includes:
jointly collecting road environment information through a vision sensor, a laser radar and a millimeter wave radar;
acquiring vehicle attitude information through a vehicle-mounted integrated navigation system; the vehicle attitude information comprises a yaw angle, a yaw angular speed, a vehicle running speed, a wheel speed and a steering angle;
acquiring task target information through a task target acquisition module; the task target information comprises a task target position, a task target type and a task target measure.
Optionally, the determining a chassis state parameter target sequence according to the road environment information and the task target information specifically includes:
establishing an environment map according to the road environment information;
determining a time sequence track point on the environment map according to the task target information;
calculating a chassis state parameter target sequence in real time according to the time sequence track points; the chassis state parameter target sequence comprises vehicle position coordinates and front wheel turning angles.
Optionally, the road condition observations comprise road surface type, road adhesion coefficient, road slope and road unevenness; the vehicle state observed quantity comprises a tire slip rate and a tire slip angle;
determining a road state observation according to the road environment information specifically comprises:
acquiring a road image according to the road environment information;
and determining the road state observed quantity according to the road image.
Optionally, the state parameter matrix is represented by the following formula:
wherein, the first and the second end of the pipe are connected with each other,qis a state parameter matrix of the intelligent vehicle,
relative motion parameters of a coordinate system of each dynamic element relative to a geodetic coordinate system;
for the angular relative motion quantities of the individual kinetic elements,
for the relative motion quantities of the individual kinetic elements in position,
the angle variation of the father joint of each dynamic element or the position variation of the father joint of each dynamic element.
On the other hand, corresponding to the foregoing method for controlling the integration of the chassis of the intelligent vehicle and the task load, the present invention further provides a system for controlling the integration of the chassis of the intelligent vehicle and the task load, wherein the system for controlling the integration of the chassis of the intelligent vehicle and the task load comprises: an intelligent domain controller, a task load domain controller and a chassis domain controller; the chassis domain controller and the task load domain controller perform data interaction through a CAN FD;
the intelligent domain controller is used for acquiring road environment information, vehicle attitude information and task target information; the system is used for determining a chassis state parameter target sequence according to the road environment information and the task target information; the system comprises a task load state parameter target sequence, a task load state parameter target sequence and a task load state parameter target sequence, wherein the task load state parameter target sequence is used for determining a task load state parameter target sequence according to the vehicle attitude information and the task target information; the system is used for determining road state observation quantity according to the road environment information; determining vehicle state observed quantity according to the vehicle attitude information;
the task load domain controller is used for constructing a reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle; the state parameter matrix is composed of the chassis state parameter target sequence and the task load state parameter target sequence; the reconfigurable dynamic model is calculated according to the state parameter matrix under the constraint of the vehicle state observed quantity and the road state observed quantity and based on a force balance optimization algorithm, and the acceleration parameter target value of each vehicle action actuator and the acceleration parameter target value of each task action actuator are obtained; the control device is used for generating a control command corresponding to the task action executer according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor; the task action executors are used for controlling the corresponding task action executors to act according to the control instructions of the task action executors;
the chassis domain controller is used for generating a control command corresponding to the vehicle action actuator according to the acceleration parameter target value of each vehicle action actuator and the control state mapping model of each vehicle action actuator; and the control device is used for controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator.
Optionally, the chassis domain controller is further configured to obtain a state feedback value of each vehicle action actuator; and the control state mapping model of the vehicle action actuator is updated by performing linear fitting on any vehicle action actuator according to the target value of the acceleration parameter of the vehicle action actuator and the state feedback value of the vehicle action actuator.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides an intelligent vehicle chassis and task load integrated control method and system, wherein the method comprises the following steps: collecting road environment information, vehicle attitude information and task target information; determining a chassis state parameter target sequence according to the road environment information and the task target information; determining a task load state parameter target sequence according to the vehicle attitude information and the task target information; determining road state observed quantity and vehicle state observed quantity according to road environment information and vehicle attitude information respectively; constructing a reconfigurable dynamic model based on the state parameter matrix; under the constraint of the vehicle state observed quantity and the road state observed quantity, resolving a reconfigurable dynamic model according to a state parameter matrix based on a force balance optimization algorithm to obtain an acceleration parameter target value of each vehicle action actuator and an acceleration parameter target value of each task action actuator; generating a control command corresponding to the vehicle action actuator according to the acceleration parameter target value of each vehicle action actuator and the control state mapping model of each vehicle action actuator; controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator; generating a control command corresponding to the task action executer according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor; and controlling the corresponding task action executer to act according to the control command of each task action executor. According to the method, road environment information and vehicle attitude information are collected, planning processing is carried out according to task target information to generate control parameter target values, an integrated reconfigurable dynamic model is established, acceleration parameter target values of a task action actuator generating a task load domain and a vehicle action actuator generating a chassis domain are solved, a specific control command is generated by combining control state mapping models of the actuators, the actuators are controlled to act in a coordinated mode, and finally integrated control of the chassis and the task load is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an intelligent vehicle chassis and task load integrated control method provided in embodiment 1 of the present invention;
fig. 2 is a detailed flowchart of step S1 in the method provided in embodiment 1 of the present invention;
fig. 3 is a detailed flowchart of step S2 in the method provided in embodiment 1 of the present invention;
fig. 4 is a specific flowchart of step S4 in the method provided in embodiment 1 of the present invention;
fig. 5 is a specific flowchart of step S5 in the method provided in embodiment 1 of the present invention;
fig. 6 is a schematic structural diagram of an intelligent vehicle chassis and task load integrated control system according to embodiment 2 of the present invention;
fig. 7 is a schematic structural diagram of a system provided in embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention aims to provide an intelligent vehicle chassis and task load integrated control method and system, which are used for controlling the coordinated action of actuators in a chassis domain and a task load domain and finally realizing the integrated control of the chassis and the task load.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1:
the embodiment provides an intelligent vehicle chassis and task load integrated control method, which is described by combining a specific example of an urban unmanned fire truck for operation, in order to facilitate understanding, the chassis of the urban unmanned fire truck is an intelligent vehicle chassis, and a task action actuator is a high-pressure water gun mechanical arm with three rotation angles. As shown in a flow chart of FIG. 1, the integrated control method for the chassis and the mission load of the intelligent vehicle comprises the following steps:
s1, collecting road environment information, vehicle attitude information and task target information; in this embodiment, as shown in fig. 2, step S1 specifically includes:
and S11, jointly acquiring road environment information through a vision sensor, a laser radar and a millimeter wave radar.
S12, acquiring vehicle attitude information through a vehicle-mounted integrated navigation system; the vehicle attitude information includes a yaw angle, a yaw rate, a vehicle running speed, a wheel speed, and a steering angle.
S13, acquiring task target information through a task target acquisition module; the task target information comprises a task target position, a task target type and a task target measure.
Corresponding to the step S1, when the unmanned fire fighting truck drives into a working area to execute a fire extinguishing task, the intelligent domain of the unmanned fire fighting truck firstly collects information and acquires road environment information acquired by a vision sensor and a laser radar; acquiring vehicle position and attitude information in the integrated navigation system, and acquiring vehicle state information of a wheel speed sensor and a steering angle sensor in a chassis area; acquiring task target information through a task target acquisition module; the task target information comprises a task target position, a task target type and a task target measure; in the present embodiment, the mission target acquisition module gives the unmanned fire fighting vehicle mission target information about fire, including fire point position information, fire point type, and water or sand blast amount.
S2, determining a chassis state parameter target sequence according to the road environment information and the task target information; in this embodiment, as shown in fig. 3, step S2 specifically includes:
and S21, establishing an environment map according to the road environment information.
And S22, determining a time sequence track point on the environment map according to the task target information.
S23, calculating a chassis state parameter target sequence in real time according to the time sequence track points; the chassis state parameter target sequence comprises vehicle position coordinates and front wheel turning angles.
Corresponding to the step S2, the intelligent domain of the unmanned fire fighting truck processes the road environment information and the information of the ignition point position to obtain an environment map required by motion planning, and completes the motion planning task of the unmanned fire fighting truck on the map according to the environment information and a path planning algorithm to obtain a time sequence track point of track tracking; and according to the time sequence track points, calculating a motion control target of the whole vehicle in real time, such as vehicle position coordinates and front wheel steering angle information, and taking the motion control target as a chassis state parameter target sequence.
And S3, determining a task load state parameter target sequence according to the vehicle attitude information and the task target information. Corresponding to the step S3, the intelligent domain of the unmanned fire fighting truck processes and obtains each degree-of-freedom corner of the mechanical arm capable of aiming at the ignition point as a task load state parameter target sequence according to the vehicle posture information and the task target information.
S4, determining road state observed quantity and vehicle state observed quantity according to the road environment information and the vehicle attitude information respectively; determining road state observation quantity according to the road environment information; determining vehicle state observation quantity according to the vehicle attitude information; in this embodiment, as shown in fig. 4, step S4 specifically includes:
and S41, acquiring a road image according to the road environment information.
S42, determining a road state observation quantity according to the road image; the road state observation includes a road surface type, a road surface adhesion coefficient, a road slope, and a road surface unevenness.
Corresponding to the steps S41-S42, the intelligent domain of the unmanned fire fighting truck receives the image information obtained by the vision sensor, identifies the road surface types of the operation road surface, such as a paved road surface, a rough road surface and the like, and determines the road surface adhesion coefficient by contrasting different types of road surface adhesion coefficient tables recorded in the system; and receiving point cloud information fed back by the laser radar, obtaining observation information of the road slope and the road surface unevenness on the basis of feeding back the height difference of the point clouds of the wire harnesses, and taking the observation information of the road slope and the road surface unevenness, the road surface type, the road surface adhesion coefficient and the road slope as the road state observation quantity.
S43, determining vehicle state observed quantity according to the vehicle attitude information; the vehicle state observed quantity includes a tire slip ratio and a tire slip angle.
Corresponding to the step S43, the intelligent domain of the unmanned fire truck receives vehicle attitude information monitored by the vehicle-mounted integrated navigation system, wherein the vehicle attitude information comprises a yaw angle, a yaw angular velocity, a vehicle running speed and the like of the whole vehicle; and receiving vehicle state observed quantities monitored by a sensor in the chassis area, including vehicle position coordinates, wheel rotating speed and wheel rotating angle, further processing to obtain a tire slip rate and a tire slip angle, and using the tire slip rate and the tire slip angle as constraint conditions for subsequent model calculation. And identifying the whole vehicle state parameters required by vehicle modeling for subsequent state updating of the reconfigurable dynamic model.
S5, constructing a reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle; the reconfigurable dynamic model building module introduces a reconfigurable concept in the field of robots, the dynamic model is built in a unit combination mode, and the model building and resolving processes are more flexible and changeable due to a modularized processing mode. The reconfigurable dynamic model gives definition to each dynamic element which the intelligent vehicle belongs to and the combination of the dynamic elements. In this embodiment, step S5 specifically includes:
and S51, taking each vehicle power actuator and each task power actuator as dynamic primitives.
S52, establishing an incidence relation table comprising the relation among the dynamic elements according to the subordination relation among the dynamic elements; the combination relation among all dynamic elements in the whole vehicle dynamic model can complete the coordinate system association with each other in a motion tree form. The tree root of the motion tree is a vehicle body coordinate system b, and the vehicle body coordinate system has 3-direction translation freedom degrees and 3-axis rotation freedom degrees relative to a terrestrial world coordinate system. The definitions of the remaining coordinate systems and their associations with each other are shown in table 1:
in table 1, the remaining child nodes except the root node have only one degree of freedom with respect to the respective parent nodes; the first column in table 1 is the number value of the coordinate system to which each dynamic primitive belongs, and according to the relevant definition in table 1, the coordinate system number of each child node is greater than the coordinate system number of the parent node; the second column of sub-nodes represents basic dynamic elements including a vehicle body, a task load, a left front wheel suspension, a right front wheel suspension, a left front wheel steering, a right front wheel steering, a left front wheel, a right front wheel, a left rear wheel suspension, a right rear wheel suspension, a left rear wheel and a right rear wheel; the third column of parent nodes represents kinematic primitives to which child nodes are directly connected; the first letter in the fourth column of joint type codes represents the concrete form of the joint, wherein P represents a linear motion joint, R represents a rotation joint, and the second letter represents an axis corresponding to relative motion, namely an x axis, a y axis and a z axis; the active relation in the fifth row of active and passive relations means that the joint has the capability of generating an active moment, and the passive relation means that the joint has a driven attribute; x, y and z in the sixth to eighth columns represent the relative position relation of a child node coordinate system relative to a parent node coordinate system when the child node coordinate system is at the initial position, the positive directions of the x axis, the y axis and the z axis are defined to be in accordance with a vehicle body coordinate system, namely, the origin of the vehicle body coordinate is the intersection point of the longitudinal vertical plane of the front driving shaft and the vehicle body, x is the length direction of the vehicle and points to the tail of the vehicle and is positive, y is the width direction and points to the right side and is positive, z is the height direction and points to the roof of the vehicle and is positive, L is the wheelbase of the vehicle, and W is half of the wheelbase.
And S53, constructing a reconfigurable dynamic model based on the incidence relation table.
And S6, forming a state parameter matrix by the chassis state parameter target sequence and the task load state parameter target sequence. The state parameter matrix of the intelligent vehicle is composed of the state parameter target sequences of all dynamic elements together and can be represented by the following formulaqAnd expressing that the state parameter matrix is an integrally controlled target value, namely a task load state parameter target sequence of each degree of freedom of a task load input by the intelligent domain trajectory tracking module and a task load transmitted by the task target identification module comprises a front wheel corner at the next moment, a rotation angle position of a kinematic element of the task load at the next moment and the like.
Wherein, the first and the second end of the pipe are connected with each other,qis a state parameter matrix of the intelligent vehicle,
for each kinetic radicalRelative motion parameters of the coordinate system of the element itself with respect to the geodetic coordinate system;
for the angular relative motion quantities of the individual kinetic elements,
for the positional relative motion quantities of the individual kinetic elements,
is the angle variation of the parent joint to which each dynamic element belongs or the position variation of the parent joint to which each dynamic element belongs.
Corresponding to the step S5-the step S6, the dynamics elements of the unmanned fire truck are approximately the same as the tables, wherein the task load is a three-degree-of-freedom mechanical arm, so that three task load dynamics elements are provided; building the mutual correlation of all dynamic elements, constructing a reconfigurable dynamic model, and combining the acquired chassis state parameter target sequence and the task load state parameter target sequence to obtain a state parameter matrixq。
S7, resolving the reconfigurable dynamic model to obtain the acceleration parameter target value of each actuator; in this embodiment, under the constraint of the vehicle state observed quantity and the road state observed quantity, the reconfigurable dynamical model is solved according to the state parameter matrix based on a force balance optimization algorithm, so as to obtain an acceleration parameter target value of each vehicle action actuator and an acceleration parameter target value of each task action actuator.
And (3) solving the reconfigurable dynamic model by adopting a force balance optimization algorithm, namely converting the problem of solving the reconfigurable dynamic model into the problem of optimizing the Newton method. Optimizing variables to individual kinetic elementary accelerations
The target values of the acceleration parameters of each actuator in the chassis domain and the task load domain; the optimization objective is to make the constraint force λ satisfying the kinetic equation and to determine via each constraint force modelRestraint force offDifference value therebetweene(q) Minimum, as shown by the following formula:
wherein, the first and the second end of the pipe are connected with each other,Ais a matrix of coefficients.
The constraint conditions considered are respectively the contact force constraint of the tire and the ground, the active moment constraint of the actuator and the spring damping force constraint of the suspension joint, which are shown as the following formula:
in which the contact force of the tyre with the ground is constrainedf c In (1)RωIs the product of the radius of the tire and the angular velocity of rotation, and can be related to the vehicle speedv c The solution of the slip rate and the slip angle in the tire model is realized together,μthen the friction coefficient of the ground is represented; actuator active torque restraintf a When the current speed of the actuator is equal to the current speed of the actuatorv a And corresponding control inputuThe representation form of the constraint force can be described according to a torque-speed characteristic curve of the motor, and the characteristics of each actuator part are obtained by updating a control state mapping model of each actuator part in real time through online learning; restraining force of suspensionf s The system is considered to be a typical spring-damped system, with the position of the suspension translational jointθAnd the velocity of the suspension linear motion jointv s And realizing the characterization of the suspension restraint force.
Corresponding to the step S7, in the task load domain of the unmanned fire fighting truck, a force balance optimization algorithm is adopted to solve the reconfigurable dynamic model established in the step S5, and acceleration parameter target values of the chassis actuators and the mechanical arm actuators with three degrees of freedom are obtained.
S8, generating control commands of the vehicle action actuators, and performing corresponding actions; in the embodiment, a control command corresponding to each vehicle action actuator is generated according to the target value of the acceleration parameter of each vehicle action actuator and the control state mapping model of each vehicle action actuator; and controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator.
After step S8, the intelligent vehicle chassis domain load integrated control method further includes:
and acquiring state feedback values of the vehicle action actuators.
And for any vehicle action actuator, performing linear fitting according to the target value of the acceleration parameter of the vehicle action actuator and the state feedback value of the vehicle action actuator, and updating the control state mapping model of the vehicle action actuator.
Corresponding to the step S8, the chassis domain of the unmanned fire fighting vehicle receives the target value of the acceleration parameter of the chassis actuator, including an acceleration value, a vehicle front wheel deflection angle acceleration value and the like, and generates control commands of each actuator, such as steering motor current and the like, according to the actuator control state mapping model. And the actuator executes the relevant control command. And meanwhile, updating a control state mapping model of the actuator according to the newly obtained actuator state parameter feedback value and the acceleration parameter target value, such as updating the mapping relation between the front wheel corner and the steering motor current.
S9, generating control instructions of the task action executors and carrying out corresponding actions; in the embodiment, a control command corresponding to each task action executor is generated according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor; and controlling the corresponding task action executors to act according to the control commands of the task action executors.
After step S9, the intelligent vehicle chassis domain load integrated control method further includes:
and acquiring the state feedback value of each task action actuator.
And for any task action actuator, performing linear fitting according to the acceleration parameter target value of the task action actuator and the state feedback value of the task action actuator, and updating the control state mapping model of the task action actuator.
Corresponding to the step S9, the task load domain of the unmanned fire fighting truck receives the target value of the acceleration parameter of the three degrees of freedom of the mechanical arm, and generates the current target value and the corresponding control command of each motor according to the control state mapping model of the mechanical arm control motor. And the mechanical arm motor executes a control command to control the mechanical arm to finish actions, aim at a fire point and finish a fire extinguishing task. And meanwhile, updating a control state mapping model of the motor according to the latest obtained mechanical arm state parameter feedback value and the motor current target value.
Example 2:
as shown in fig. 6, a schematic structural diagram corresponds to a method for controlling a vehicle chassis and a load integrally provided in embodiment 1, in this embodiment, a system for controlling a vehicle chassis and a load integrally is provided, and the system for controlling a vehicle chassis and a task load integrally includes: an intelligent domain controller, a task load domain controller and a chassis domain controller; the intelligent domain controller, the vision sensor, the laser radar, the millimeter wave radar, the integrated navigation system, the cloud data support and the V2X component perform data interaction through the Ethernet; the intelligent domain controller and the chassis domain controller perform data interaction through a vehicle-mounted Ethernet; the intelligent domain controller and the task load domain controller carry out data interaction through a vehicle-mounted Ethernet; interaction is carried out inside the chassis area controller through a CAN FD network; and the task load domain controller is internally interacted through a CAN FD network. And the chassis domain controller and the task load domain controller perform data interaction through a shared CAN FD between the chassis domain controller and the task load domain controller.
The intelligent domain controller is used for acquiring road environment information, vehicle attitude information and task target information; determining a chassis state parameter target sequence according to the road environment information and the task target information; the system is used for determining a task load state parameter target sequence according to the vehicle attitude information and the task target information; the system is used for determining road state observation quantity according to the road environment information; determining vehicle state observed quantity according to the vehicle attitude information;
the task load domain controller is used for constructing a reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle; the state parameter matrix is composed of the chassis state parameter target sequence and the task load state parameter target sequence; the reconfigurable dynamic model is calculated according to the state parameter matrix under the constraint of the vehicle state observed quantity and the road state observed quantity and based on a force balance optimization algorithm, and the acceleration parameter target value of each vehicle action actuator and the acceleration parameter target value of each task action actuator are obtained; the control device is used for generating a control command corresponding to the task action executer according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor; the task action executors are used for controlling the corresponding task action executors to act according to the control instructions of the task action executors;
the chassis domain controller is used for generating a control command corresponding to the vehicle action actuator according to the acceleration parameter target value of each vehicle action actuator and the control state mapping model of each vehicle action actuator; and the control device is also used for controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator.
In this embodiment, the chassis domain controller is further configured to: acquiring state feedback values of the vehicle action actuators; and for any vehicle action actuator, performing linear fitting according to the target value of the acceleration parameter of the vehicle action actuator and the state feedback value of the vehicle action actuator, and updating the control state mapping model of the vehicle action actuator.
In the following, a specific example is combined to provide an integrated control system for a vehicle chassis and a load according to the present invention, as shown in fig. 7, the integrated control system for a vehicle chassis and a load specifically includes: an intelligent domain controller, a chassis domain controller and a task load domain controller;
the information transmission of the intelligent domain controller, the chassis domain controller and the task load domain controller is respectively transmitted through a corresponding first vehicle-mounted Ethernet gateway and a corresponding second vehicle-mounted Ethernet gateway; the first vehicle-mounted Ethernet gateway is connected with a vision sensor, a laser radar and a millimeter wave radar and is used for transmitting road environment information data collected by the vision sensor, the laser radar and the millimeter wave radar to the intelligent domain controller; the second vehicle-mounted Ethernet gateway is connected with the integrated navigation system, the cloud data system and the V2X component and is used for transmitting vehicle attitude data input by the integrated navigation system, remote control data input by the cloud and task target information input by the V2X component to the intelligent domain controller; the cloud remote control data comprises starting key distribution data for realizing remote starting, online diagnosis/calibration data for presetting an initial value of an actuator characteristic curve, and remote manual remote control data for realizing remote control.
The information of the chassis domain controller and the task load domain controller is transmitted through the chassis domain and the task load domain interaction CAN FD network; in addition, the chassis domain controller is provided with two independent CAN FD networks, and the task load domain controller is provided with one independent CAN FD network so as to complete information transmission in each domain.
The purpose of the chassis domain and task load domain interaction CAN FD network is to establish a high real-time transmission link for control and feedback signals for cooperative control of the chassis domain and the task load domain, and specifically comprises the following steps for an intelligent vehicle with n task action executors: the task action executor comprises a task action executor cooperative control unit and a human-computer interaction domain unit. The task action executor cooperative control unit comprises: control signals of driving motors of the 1 st to n task actuators and feedback signals of position/corner feedback sensors of the 1 st to n task actuators. The man-machine interaction unit realizes the information interaction with the outside, and mainly comprises: and the task intention identification sensor feedback signal, the voice interaction information processing feedback signal and the display information of each instrument.
The information that the first CAN FD network of the chassis domain controller needs to transmit includes: information of transverse, longitudinal and vertical cooperative units. Wherein the information of the horizontal cooperative unit comprises: the control command of the steering motor and the signal of the first steering angle sensor, and the information of the longitudinal coordination unit comprises: the control command of the driving motor, the control command of the service brake, a first wheel speed sensor signal and a first master cylinder pressure sensor signal. The information of the vertical cooperative unit comprises: a control command of the damping regulator, a first damping sensor signal.
The information that needs to be transmitted by the second CAN FD network of the chassis domain controller includes: safety redundant units and power management units. The purpose of the safety redundant unit is to configure an additional set of independent feedback system for each actuator in the transverse direction, the longitudinal direction and the vertical direction so as to monitor the running state of the system, and in addition, the system can also realize the parking brake function. The information that the second CAN FD network of the chassis domain controller specifically needs to transmit includes: a parking brake control signal, a parking sensor signal, a second steering angle sensor signal, a second wheel speed sensor signal, a second master cylinder pressure sensor signal, a second damping sensor signal. The power management unit includes: the power battery and BMS information, a first low-voltage power supply line control signal and a second low-voltage power supply line control signal.
The CAN FD of the task load domain is mainly used for information transmission of a vehicle body indicating module and a safety redundant unit. Wherein the automobile body indicating module contains: the control signal of the steering indicator light, the control signal of the braking indicator light, the control signal of the emergency condition indicator light and the control signal of the task trigger indicator light. The safety redundant unit includes: position or rotation angle sensor signals of 1-nth task action actuators, execution feedback signals of 1-nth task action actuators and electric power safety switching module control signals.
Although specific examples are employed herein, the foregoing description is only illustrative of the principles and implementations of the present invention, and the following examples are provided only to facilitate the understanding of the method and its core concepts; it will be understood by those skilled in the art that the above-described modules or steps of the present invention may be implemented by a general-purpose computer device, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by the computing device, or separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the foregoing, the description is not to be taken in a limiting sense.
Claims (10)
1. An intelligent vehicle chassis and task load integrated control method is characterized in that an intelligent vehicle comprises a chassis domain and a task load domain, the chassis domain and the task load domain carry out data interaction through CAN FD, the chassis domain comprises a plurality of vehicle action actuators, and the task load domain comprises a plurality of task action actuators; the intelligent vehicle chassis and task load integrated control method comprises the following steps:
collecting road environment information, vehicle attitude information and task target information;
determining a chassis state parameter target sequence according to the road environment information and the task target information;
determining a task load state parameter target sequence according to the vehicle attitude information and the task target information;
determining road state observed quantity according to the road environment information;
determining vehicle state observation quantity according to the vehicle attitude information;
constructing a reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle;
forming a state parameter matrix by the chassis state parameter target sequence and the task load state parameter target sequence;
under the constraint of the vehicle state observed quantity and the road state observed quantity, resolving the reconfigurable dynamic model according to the state parameter matrix based on a force balance optimization algorithm to obtain an acceleration parameter target value of each vehicle action actuator and an acceleration parameter target value of each task action actuator;
generating a control command corresponding to the vehicle action actuator according to the acceleration parameter target value of each vehicle action actuator and the control state mapping model of each vehicle action actuator;
controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator;
generating a control command corresponding to the task action executer according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor;
and controlling the corresponding task action executer to act according to the control command of each task action executor.
2. The intelligent vehicle chassis and task load integrated control method according to claim 1, wherein after the corresponding vehicle action actuator is controlled to act according to the control command of each vehicle action actuator, the intelligent vehicle chassis and task load integrated control method further comprises:
acquiring state feedback values of each vehicle action actuator;
and for any vehicle action actuator, performing linear fitting according to the target value of the acceleration parameter of the vehicle action actuator and the state feedback value of the vehicle action actuator, and updating the control state mapping model of the vehicle action actuator.
3. The method for integrally controlling the chassis and the task load of the intelligent vehicle according to claim 1, wherein after the corresponding task actuator is controlled to operate according to the control command of each task actuator, the method for integrally controlling the chassis and the task load of the intelligent vehicle further comprises:
acquiring state feedback values of task action actuators;
and for any task action actuator, performing linear fitting according to the acceleration parameter target value of the task action actuator and the state feedback value of the task action actuator, and updating the control state mapping model of the task action actuator.
4. The intelligent vehicle chassis and task load integrated control method according to claim 1, wherein the building of the reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle specifically comprises:
taking each vehicle power actuator and each task power actuator as dynamic primitives;
establishing an incidence relation table comprising the relation among the dynamic elements according to the subordination relation among the dynamic elements;
and constructing a reconfigurable dynamic model based on the incidence relation table.
5. The intelligent vehicle chassis and task load integrated control method according to claim 1, wherein the collecting of road environment information, vehicle attitude information and task target information specifically comprises:
jointly collecting road environment information through a vision sensor, a laser radar and a millimeter wave radar;
acquiring vehicle attitude information through a vehicle-mounted integrated navigation system; the vehicle attitude information comprises a yaw angle, a yaw angular velocity, a vehicle running speed, a wheel speed and a steering angle;
acquiring task target information through a task target acquisition module; the task target information comprises a task target position, a task target type and a task target measure.
6. The intelligent vehicle chassis and task load integrated control method according to claim 1, wherein the determining a chassis state parameter target sequence according to the road environment information and the task target information specifically comprises:
establishing an environment map according to the road environment information;
determining a time sequence track point on the environment map according to the task target information;
calculating a chassis state parameter target sequence in real time according to the time sequence track points; the chassis state parameter target sequence comprises vehicle position coordinates and front wheel corners.
7. The intelligent vehicle chassis and mission load integrated control method according to claim 1, wherein the road state observations comprise road surface type, road adhesion coefficient, road slope and road surface irregularity; the vehicle state observed quantity comprises a tire slip rate and a tire slip angle;
determining a road state observation according to the road environment information specifically comprises:
acquiring a road image according to the road environment information;
and determining the road state observed quantity according to the road image.
8. The intelligent vehicle chassis and mission load integrated control method according to claim 1, wherein the state parameter matrix is as follows:
wherein the content of the first and second substances,qis a state parameter matrix of the intelligent vehicle,
relative motion parameters of a coordinate system of each dynamic element relative to a geodetic coordinate system;
for the angular relative motion quantities of the kinetic elements,
for the positional relative motion quantities of the individual kinetic elements,
the angle variation of the father joint of each dynamic element or the position variation of the father joint of each dynamic element.
9. An intelligent vehicle chassis and task load integrated control system is characterized by comprising:
the intelligent domain controller is used for acquiring road environment information, vehicle attitude information and task target information; the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for determining a chassis state parameter target sequence according to the road environment information and the task target information; the system comprises a task load state parameter target sequence, a task load state parameter target sequence and a task load state parameter target sequence, wherein the task load state parameter target sequence is used for determining a task load state parameter target sequence according to the vehicle attitude information and the task target information; the system is used for determining road state observation quantity according to the road environment information; determining vehicle state observed quantity according to the vehicle attitude information;
the task load domain controller is used for constructing a reconfigurable dynamic model based on the incidence relation of each actuator in the intelligent vehicle; the state parameter matrix is composed of the chassis state parameter target sequence and the task load state parameter target sequence; the reconfigurable dynamic model is calculated according to the state parameter matrix under the constraint of the vehicle state observed quantity and the road state observed quantity and based on a force balance optimization algorithm, and the acceleration parameter target value of each vehicle action actuator and the acceleration parameter target value of each task action actuator are obtained; the control device is used for generating a control command corresponding to the task action executer according to the acceleration parameter target value of each task action executor and the control state mapping model of each task action executor; the task action executors are used for controlling the corresponding task action executors to act according to the control instructions of the task action executors;
the chassis domain controller is used for generating a control command corresponding to the vehicle action actuator according to the acceleration parameter target value of each vehicle action actuator and the control state mapping model of each vehicle action actuator; and the control device is used for controlling the corresponding vehicle action actuator to act according to the control command of each vehicle action actuator.
10. The intelligent vehicle chassis and mission load integrated control system of claim 9, wherein the chassis domain controller is further configured to obtain a state feedback value for each vehicle action actuator; and the control state mapping model of the vehicle action actuator is updated by performing linear fitting on any vehicle action actuator according to the target value of the acceleration parameter of the vehicle action actuator and the state feedback value of the vehicle action actuator.
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