CN111459159A - Path following control system and control method - Google Patents
Path following control system and control method Download PDFInfo
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- CN111459159A CN111459159A CN202010182336.XA CN202010182336A CN111459159A CN 111459159 A CN111459159 A CN 111459159A CN 202010182336 A CN202010182336 A CN 202010182336A CN 111459159 A CN111459159 A CN 111459159A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
Abstract
The invention provides a path following control system and a control method, wherein the control system comprises a vehicle-mounted sensor, a communication network, a controller and a state estimator, and the control method comprises the following steps of acquiring the transverse displacement, the longitudinal displacement, the transverse acceleration, the longitudinal acceleration, the yaw angle and the yaw velocity of an unmanned vehicle by using the vehicle-mounted sensor, taking the transverse displacement, the longitudinal acceleration, the yaw angle and the yaw velocity as the state quantities of the unmanned vehicle, and taking the front wheel steering angle of the unmanned vehicle as the control quantity; setting the controller; the communication network acquires external environment information and plans an exercisable path; the instantaneous state information of the unmanned vehicle is detected through the vehicle-mounted sensor, the detected state information is input into the controller, the controller controls the unmanned vehicle to move straight or turn through controlling the front wheel rotating angle of the unmanned vehicle, and the planned path is controlled to be followed.
Description
Technical Field
The invention relates to the technical field of unmanned automobiles, in particular to a path following control system and a control method.
Background
Traffic is an important aspect of human activities, the traffic activity range of human is greatly expanded due to the appearance of automobiles, and the life rhythm and the production efficiency of human are accelerated. The automobile makes great contribution to the development of modern society and the improvement of human life, but with the increasing preservation amount of automobiles, the automobile brings difficulties such as traffic safety, environmental pollution, energy consumption and the like to human society, and how to overcome the problems becomes a hot topic in the modern society.
Among many problems, the traffic safety problem is particularly serious. The incidence rate of modern road traffic accidents is high, the number of deaths increases year by year, and the economic loss caused by the growth of the deaths is immeasurable. In the face of severe road traffic safety problems, governments and organizations of various countries have urgently required to improve automobile technology to improve automobile safety performance and reduce the occurrence of road traffic accidents. This problem is also a hotspot and focus of concern in the automotive industry. The condition of improving the frequent traffic accidents can be started from two aspects of people and vehicles. The driver has a relatively long response delay in operation, the field of vision is limited, the night vision capability is limited, and fatigue and the like can be caused after the driver drives for a long time. These problems can be solved by improving the automobile technology, so more and more researchers are trying to develop unmanned technology to improve the safety of road traffic. The unmanned technology enables the road traffic system to be changed from a 'people-vehicle-road' system to a 'vehicle-road' system, so that the safety of road traffic is greatly improved, the life loss and property loss caused by road traffic accidents are effectively reduced, and even people can be replaced to complete some special tasks. Therefore, the unmanned technology has immeasurable value and significance for human beings.
Although our unmanned vehicles and their related technologies have been developed more rapidly in recent years, they are deficient in environmental modeling, intelligent decision making, vehicle control, etc., and have a certain gap from developed countries, and it takes a long time to achieve true unmanned driving. For the unmanned vehicle, the realization of the path following control is an important embodiment of the safety and the intelligence of the unmanned vehicle, so that the research on the path following control of the unmanned vehicle is very important.
The method for tracking the target path of the unmanned vehicle, which is proposed by segment constructors of the university of Beijing industry and the like, can reduce the sideslip degree under the condition of low road surface adhesion coefficient and reduce the tire sideslip angle under the condition of high road surface adhesion coefficient, effectively reduce the probability of out-of-control condition of the unmanned vehicle when the target is subjected to path tracking due to overlarge tracking deviation, but only verify the condition under low speed and do not research the following effect under high speed.
Disclosure of Invention
The path following control system and the control method provided by the invention solve the problem that the traditional path following control method cannot ensure the real-time performance and stability of path following, improve the accuracy of path following and improve the real-time performance of path following.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the invention discloses a path following control system, which comprises a vehicle-mounted sensor, a road condition monitoring module and a road condition monitoring module, wherein the vehicle-mounted sensor is used for acquiring running environment information, fusing the sensor information by combining an environment model, and understanding and identifying a running environment;
the communication network is used for providing the congestion condition of the road ahead, the driving trend of surrounding vehicles, and external environment information of the condition and the variation trend of intersection traffic lights sent by the roadbed traffic facility;
the controller is used for carrying out optimization solution by combining a prediction model, an objective function and constraint conditions to obtain an optimal control sequence at the current moment, inputting the optimal control sequence to the controlled platform, controlling the controlled platform according to the current control quantity, and then inputting the current state quantity observation value to the state estimator;
a state estimator for estimating a state quantity which cannot be observed by the vehicle-mounted sensor;
the path planning system is used for planning a collision-free path from a starting point to a target point in an environment with obstacles according to the principle that the path length is shortest or the energy consumption is least.
The invention discloses a path following control method on the other hand, which comprises the following steps:
acquiring the lateral displacement, the longitudinal displacement, the lateral acceleration, the longitudinal acceleration, the yaw angle and the yaw velocity of the unmanned vehicle by using the vehicle-mounted sensor, taking the lateral displacement, the longitudinal acceleration, the yaw angle and the yaw velocity as state quantities of the unmanned vehicle, and taking the front wheel steering angle of the unmanned vehicle as a control quantity of the unmanned vehicle;
setting the controller;
the communication network obtains the congestion condition of the road ahead, the running trend of the vehicles around and the external environment information of the condition and the variation trend of the intersection traffic lights sent by the roadbed traffic facility, and plans an enforceable path;
the instantaneous state information of the unmanned vehicle is detected through the vehicle-mounted sensor, the detected state information is input into the controller, and the controller controls the unmanned vehicle to go straight or turn through controlling the corner of the front wheel of the unmanned vehicle, so that the unmanned vehicle is controlled to follow the planned path.
Preferably, the step of providing a controller comprises:
the state quantity of the unmanned vehicle at any time is related to the state quantity and the control quantity thereof at a certain time, and is expressed as:
ζr＝f(ζr,ur) (1)
therein, ζrIs the state quantity of the system at time r, urThe control quantity of the system at the r moment;
using taylor's formula to obtain:
wherein, ζ is the state quantity of the system at the current moment, and u is the control quantity of the system at the current moment;
subtracting equation (1) from equation (2) yields:
equation (3) is modified to the form x (k +1) ═ ax (k) + bu (k), assuming that
It is possible to obtain:
ξ(k+1|t)＝[A B]ξ(k|t)+BΔu(k|t) (4)
suppose the predicted time domain of the system is NpControl time domain as NcThen, the state quantity and the system output quantity in the prediction time domain can be calculated by using the following formula:
setting an objective function to ensure that the unmanned vehicle quickly and stably follows the expected track, adding the optimization of the deviation of the system state quantity and the control quantity, determining the control action through the optimization of the performance index, and repeatedly optimizing on line:
q is a weight matrix of the state quantity error, R is a weight matrix of the control quantity error, and rho is a weight coefficient and is a relaxation factor;
the optimization objective is adjusted to:
J(ξ(t),u(t-1),Δu(t))＝[Δu(t),]THt[Δu(t)T,]+Gt[Δu(t)T,]+Pt(7)
wherein:
wherein e (t) is a tracking error in the prediction time domain;
converting the constrained optimization solving problem of each step into a quadratic programming problem for solving the following steps:
min[Δu(t)T,]Ht[Δu(t)T,]+Gt[Δu(t)T,](8)
Δumin≤u(k)≤Δumax(10)
Ymin-≤ψtξ(t|t)+ΘtΔu(t)≤Ymax+ (11)
wherein, YminAnd YmaxOutputting a limit value for soft constraints;
solving the quadratic programming problem to obtain a control increment in a control time domain;
after the solution is completed in each control period, a series of control input increments and relaxation factors in the control time domain are obtained
The first element in the control sequence is applied to the system as the actual control input increment, namely:
and after entering the next control period, repeating the process, and circularly realizing the following control of the expected track.
The beneficial technical effects are as follows:
the invention discloses a path following control system and a control method, wherein the control method comprises the following steps of acquiring the transverse displacement, the longitudinal displacement, the transverse acceleration, the longitudinal acceleration, the yaw angle and the yaw velocity of an unmanned vehicle by utilizing a vehicle-mounted sensor, taking the transverse displacement, the longitudinal displacement, the lateral acceleration, the longitudinal acceleration, the yaw angle and the yaw velocity as state quantities of the unmanned vehicle, and taking the front wheel steering angle of the unmanned vehicle as a control quantity of the unmanned vehicle; setting the controller; the communication network obtains the congestion condition of the road ahead, the running trend of the vehicles around and the external environment information of the condition and the variation trend of the intersection traffic lights sent by the roadbed traffic facility, and plans an enforceable path; the instantaneous state information of the unmanned vehicle is detected through the vehicle-mounted sensor, the detected state information is input into the controller, the controller controls the unmanned vehicle to go straight or turn through controlling the front wheel rotating angle of the unmanned vehicle, and the planned path is controlled to be followed, so that the problem that the traditional path following control method cannot guarantee the real-time performance and stability of path following is solved, the accuracy of path following is improved, and the real-time performance of path following is improved;
2. according to the path following control method, the calculation time of the controller can be greatly reduced by carrying out linearization processing on the system, and the unmanned vehicle can track the track in real time when carrying out path following;
3. according to the path following control method, the quadratic programming problem is completely expressed by the control quantity, and the control quantity is assumed to be unchanged in a certain control time domain, so that the variables in the optimization process are greatly reduced, the calculation speed is improved, the relaxation factor is added, the control increment can be directly limited, the path tracking precision of the unmanned vehicle can be greatly improved, and the deviation between the unmanned vehicle and the following track is reduced;
4. the path following control method enables the unmanned vehicle to travel according to the planned path strictly, parameters needing to be adjusted are few, and the model is simple and convenient to calculate.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiments will be briefly described below.
FIG. 1 is a schematic diagram of a path-following control system according to the present invention;
FIG. 2 is a flow chart of the controller principle of a path following control system according to the present invention;
fig. 3 is a flowchart illustrating a path following control method according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention discloses a path following control system, which comprises a vehicle-mounted sensor, a road condition monitoring module and a road condition monitoring module, wherein the vehicle-mounted sensor is used for acquiring running environment information, fusing the sensor information by combining an environment model, and understanding and identifying a running environment;
the communication network is used for providing the congestion condition of the road ahead, the driving trend of surrounding vehicles, and external environment information of the condition and the variation trend of intersection traffic lights sent by the roadbed traffic facility;
the controller is used for carrying out optimization solution by combining a prediction model, an objective function and constraint conditions to obtain an optimal control sequence at the current moment, inputting the optimal control sequence to the controlled platform, controlling the controlled platform according to the current control quantity, and then inputting the current state quantity observation value to the state estimator;
a state estimator for estimating a state quantity which cannot be observed by the vehicle-mounted sensor;
the system comprises a path planning system and a vehicle control system, wherein the path planning system is used for planning a collision-free path from a starting point to a target point in an environment with an obstacle according to the principle that the path length is shortest or the energy consumption is minimum, particularly, under the condition that a map is known, a feasible and optimal path is determined by using known local information such as the position of the obstacle and a road boundary, and when the environment changes, such as an unknown obstacle appears, a local driving path or track of the unmanned vehicle is generated through local path planning.
The invention discloses a path following control method on the other hand, which comprises the following steps:
acquiring the lateral displacement, the longitudinal displacement, the lateral acceleration, the longitudinal acceleration, the yaw angle and the yaw velocity of the unmanned vehicle by using the vehicle-mounted sensor, taking the lateral displacement, the longitudinal acceleration, the yaw angle and the yaw velocity as state quantities of the unmanned vehicle, and taking the front wheel steering angle of the unmanned vehicle as a control quantity of the unmanned vehicle;
the controller is arranged, specifically, the state quantity of the unmanned vehicle at any time is related to the state quantity and the control quantity thereof at a certain time, and the state quantity and the control quantity are expressed as follows:
ζr＝f(ζr,ur) (1)
therein, ζrIs the state quantity of the system at time r, urThe control quantity of the system at the r moment;
using taylor's formula to obtain:
wherein, ζ is the state quantity of the system at the current moment, and u is the control quantity of the system at the current moment;
subtracting equation (1) from equation (2) yields:
equation (3) is modified to the form x (k +1) ═ ax (k) + bu (k), assuming that
It is possible to obtain:
ξ(k+1|t)＝[A B]ξ(k|t)+BΔu(k|t) (4)
suppose the predicted time domain of the system is NpControl time domain as NcThen, the state quantity and the system output quantity in the prediction time domain can be calculated by using the following formula:
setting an objective function to ensure that the unmanned vehicle quickly and stably follows the expected track, adding the optimization of the deviation of the system state quantity and the control quantity, determining the control action through the optimization of the performance index, and repeatedly optimizing on line:
wherein Q is a weight matrix of the state quantity error, R is a weight matrix of the control quantity error, and rho is a weight coefficient and is a relaxation factor; the first item reflects the following capability of the system to the reference straight line, and the second item reflects the requirement for stable change of the control quantity;
the optimization objective is adjusted to:
J(ξ(t),u(t-1),Δu(t))＝[Δu(t),]THt[Δu(t)T,]+Gt[Δu(t)T,]+Pt(7)
wherein:
wherein e (t) is a tracking error in the prediction time domain;
converting the constrained optimization solving problem of each step into a quadratic programming problem for solving the following steps:
min[Δu(t)T,]Ht[Δu(t)T,]+Gt[Δu(t)T,](8)
Δumin≤u(k)≤Δumax(10)
Ymin-≤ψtξ(t|t)+ΘtΔu(t)≤Ymax+ (11)
wherein, YminAnd YmaxOutputting a limit value for soft constraints;
solving the quadratic programming problem to obtain a control increment in a control time domain;
after the solution is completed in each control period, a series of control input increments and relaxation factors in the control time domain are obtained
In order to prevent the deviation of the control system from the ideal state caused by model mismatch or environmental interference, at a new sampling moment, the actual output of an object is firstly detected, the real-time information is utilized to correct the prediction result based on the model, then new optimization is carried out, and the first element in the control sequence is used as the actual control input increment to act on the system, namely:
and after entering the next control period, repeating the process, and circularly realizing the following control of the expected track.
The communication network obtains the congestion condition of the road ahead, the running trend of the vehicles around and the external environment information of the condition and the variation trend of the intersection traffic lights sent by the roadbed traffic facility, and plans an enforceable path;
the instantaneous state information of the unmanned vehicle is detected through the vehicle-mounted sensor, the detected state information is input into the controller, and the controller controls the unmanned vehicle to go straight or turn through controlling the corner of the front wheel of the unmanned vehicle, so that the unmanned vehicle is controlled to follow the planned path.
The above examples are only for describing the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (3)
1. A path-following control system, comprising:
the vehicle-mounted sensor is used for acquiring running environment information, fusing the sensor information by combining an environment model, and understanding and identifying the running environment;
the communication network is used for providing the congestion condition of the road ahead, the driving trend of surrounding vehicles, and external environment information of the condition and the variation trend of intersection traffic lights sent by the roadbed traffic facility;
the controller is used for carrying out optimization solution by combining a prediction model, an objective function and constraint conditions to obtain an optimal control sequence at the current moment, inputting the optimal control sequence to the controlled platform, controlling the controlled platform according to the current control quantity, and then inputting the current state quantity observation value to the state estimator;
a state estimator for estimating a state quantity which cannot be observed by the vehicle-mounted sensor;
the path planning system is used for planning a collision-free path from a starting point to a target point in an environment with obstacles according to the principle that the path length is shortest or the energy consumption is least.
2. A control method of a path following control system according to claim 1, characterized by comprising the steps of:
acquiring the lateral displacement, the longitudinal displacement, the lateral acceleration, the longitudinal acceleration, the yaw angle and the yaw velocity of the unmanned vehicle by using the vehicle-mounted sensor, taking the lateral displacement, the longitudinal acceleration, the yaw angle and the yaw velocity as state quantities of the unmanned vehicle, and taking the front wheel steering angle of the unmanned vehicle as a control quantity of the unmanned vehicle;
setting the controller;
the communication network obtains the congestion condition of the road ahead, the running trend of the vehicles around and the external environment information of the condition and the variation trend of the intersection traffic lights sent by the roadbed traffic facility, and plans an enforceable path;
the instantaneous state information of the unmanned vehicle is detected through the vehicle-mounted sensor, the detected state information is input into the controller, and the controller controls the unmanned vehicle to go straight or turn through controlling the corner of the front wheel of the unmanned vehicle, so that the unmanned vehicle is controlled to follow the planned path.
3. A path-following control method according to claim 2, wherein the step of providing the controller comprises:
the state quantity of the unmanned vehicle at any time is related to the state quantity and the control quantity thereof at a certain time, and is expressed as:
ζr＝f(ζr,ur) (1)
therein, ζrIs the state quantity of the system at time r, urThe control quantity of the system at the r moment;
using taylor's formula to obtain:
wherein, ζ is the state quantity of the system at the current moment, and u is the control quantity of the system at the current moment;
subtracting equation (1) from equation (2) yields:
equation (3) is modified to the form x (k +1) ═ ax (k) + bu (k), assuming that
It is possible to obtain:
ξ(k+1|t)＝[A B]ξ(k|t)+BΔu(k|t) (4)
suppose the predicted time domain of the system is NpControl time domain as NcThen, the state quantity and the system output quantity in the prediction time domain can be calculated by using the following formula:
setting an objective function to ensure that the unmanned vehicle quickly and stably follows the expected track, adding the optimization of the deviation of the system state quantity and the control quantity, determining the control action through the optimization of the performance index, and repeatedly optimizing on line:
q is a weight matrix of the state quantity error, R is a weight matrix of the control quantity error, and rho is a weight coefficient and is a relaxation factor;
the optimization objective is adjusted to:
J(ξ(t),u(t-1),Δu(t))＝[Δu(t),]THt[Δu(t)T,]+Gt[Δu(t)T,]+Pt(7)
wherein:
wherein e (t) is a tracking error in the prediction time domain;
converting the constrained optimization solving problem of each step into a quadratic programming problem for solving the following steps:
min[Δu(t)T,]Ht[Δu(t)T,]+Gt[Δu(t)T,](8)
Δumin≤u(k)≤Δumax(10)
Ymin-≤ψtξ(t|t)+ΘtΔu(t)≤Ymax+ (11)
wherein, YminAnd YmaxOutputting a limit value for soft constraints;
solving the quadratic programming problem to obtain a control increment in a control time domain;
after the solution is completed in each control period, a series of control input increments and relaxation factors in the control time domain are obtained
The first element in the control sequence is applied to the system as the actual control input increment, namely:
and after entering the next control period, repeating the process, and circularly realizing the following control of the expected track.
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| CN114155711A (en) * | 2021-11-30 | 2022-03-08 | 交通运输部公路科学研究所 | Driving speed prediction method and system based on front driving behaviors |
| CN114179818A (en) * | 2021-12-31 | 2022-03-15 | 江苏理工学院 | Intelligent automobile transverse control method based on adaptive preview time and sliding mode control |
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Application publication date: 20200728 |