CN110347146A - A kind of adaptive cruise control system top level control algorithm - Google Patents
A kind of adaptive cruise control system top level control algorithm Download PDFInfo
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- CN110347146A CN110347146A CN201910161127.4A CN201910161127A CN110347146A CN 110347146 A CN110347146 A CN 110347146A CN 201910161127 A CN201910161127 A CN 201910161127A CN 110347146 A CN110347146 A CN 110347146A
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- 230000003044 adaptive effect Effects 0.000 title claims abstract description 21
- 230000002068 genetic effect Effects 0.000 claims abstract description 18
- 238000005457 optimization Methods 0.000 claims abstract description 8
- 238000005094 computer simulation Methods 0.000 claims description 3
- 230000000903 blocking effect Effects 0.000 claims description 2
- 230000010429 evolutionary process Effects 0.000 abstract description 3
- 230000004083 survival effect Effects 0.000 abstract description 2
- 230000001133 acceleration Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Vehicle Engines Or Engines For Specific Uses (AREA)
- Controls For Constant Speed Travelling (AREA)
- Feedback Control In General (AREA)
Abstract
The present invention relates to a kind of adaptive cruise control system top level control algorithms, it is characterised in that: specific steps algorithm is as follows: S1: the selection of adjuster；S2: the selection of basic algorithm；S3: the adjusting of parameter；S4: the realization of function；Linear quadratic regulator and Kalman filter are combined using linear quadratic Gauss adjuster in the present invention, the noise encountered under actual conditions is reduced to Gaussian noise, eliminate the influence that Gaussian noise generates, its performance is better than conventional linear secondary regulator, and it is lower than model predictive controller to calculate cost；Genetic algorithm is incorporated on the basis of linearquadratic regulator in the present invention, solves the adjusting optimization problem of Linear Quadratic Control device by simulating the evolutionary process of nature, is evolved according to the principle of natural selection and " survival of the fittest "；The numerical value of automatically adjusting parameter Q and R are to reach the optimum combination of parameter Q and parameter R, so that cost function minimizes.
Description
Technical field
The present invention relates to intelligent cruise control technology field more particularly to a kind of adaptive cruise control system top level controls
Algorithm.
Background technique
Advanced driving assistance system (ADAS) is always a popular topic since the 1990s.ADAS system is not
Only when vehicle deviates current lane or has the danger of collision, non-driver provides caution signal, can also be in chassis line traffic control system
Under the support of system, the manipulation of vehicle is subjectively intervened by control steering, braking and throttle.
Adaptive cruise control system (ACC) is one of the subfunction in the major function of ADAS system.ACC is that one kind is based on
Sensor identification technology and the intelligent cruise control technology being born, it is therefore an objective to by automatically controlling vehicle longitudinal movement speed, with
Mitigate the long-time bring feeling of fatigue of driver, guarantee driving safety, and provides auxiliary by simple mode for driver
It drives and supports.When too small or excessive with the distance between front truck, ACC control unit can by with anti-blocking brake system,
Body stabilization system, engine control system coordination adjust the output power and vehicle braking force of engine, so that vehicle
Safe distance is remained with front vehicles.
Conventional linear secondary regulator is used in adaptive cruise control system upper controller, the advantage is that: going
In addition to we are considered cumbersome evolutionary process in current MPC method；Compared to MPC, calculates cost and significantly reduce.But certainly
It adapts to control in cruise control system using LQR, also there is certain limitation, be difficult to solve parameter tuning, the integrated circuit of LQR
The problems such as measurement noise under uncertain and actual conditions.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of driving safeties for guaranteeing vehicle, improve traffic efficiency and drive
The adaptive cruise control system top level control algorithm of comfort.
In order to solve the above technical problems, the technical solution of the present invention is as follows: a kind of adaptive cruise control system top level control
Algorithm, innovative point are: specific steps algorithm is as follows:
S1: the selection of adjuster: the noise occurred under reality scene is reduced to Gaussian noise, upper controller uses linear two
Secondary Gaussian adjuster；
S2: it the selection of basic algorithm: in order to obtain the best parameter group of parameter Q and parameter R and minimize cost function, adopts
With the numerical value of genetic algorithm automatically adjusting parameter Q and R；Linear quadratic Gauss adjuster based on genetic algorithm is denoted as
GALQG control algolithm；
S3: it the adjusting of parameter: is influenced by finely tuning the parameter of Q and R to compensate；
S4: the realization of function: by Linear Quadratic Control device, Linear quadratic Gaussian control and genetic algorithm are combined, and are obtained certain
Under constraint condition, make the input combination of previous preset cost function minimization, and system is made to reach preferable performance indicator.
Further, the genetic algorithm, Linear quadratic Gaussian control and my Linear Quadratic Control device, with model prediction control
Device processed is lower than model predictive controller compared to the computing resource occupied.
Further, the genetic algorithm utilizes computer simulation Darwinian evolution, selects qualified target, no
It is qualified to eliminate；Genetic algorithm is multiobjective optimization algorithm, can effectively overcome the problems, such as local solution, reaches the overall situation most
It is excellent, be suitable for multiple-objection optimization the problem of.
Further, the Linear quadratic Gaussian control is Linear Quadratic Control device, linear quadratic regulator and Kalman
Filter combines.
Further, the linear quadratic regulator is the Optimal Regulator of the feedback system of multiple-input and multiple-output, is passed through
Block multi-party journey in solution, so that original system reaches higher performance.
Further, the adaptive cruise control system includes upper controller, lower layer's controller, ACC mode and determines
Logic switch, brake between fast cruise control system mode and the logic switch between throttle.
The present invention has the advantages that
1) linear quadratic regulator and Kalman filter are combined using linear quadratic Gauss adjuster in the present invention, it will be real
The noise encountered in the case of border is reduced to Gaussian noise, and the input state of upper controller is estimated and Kalman filter
Wave eliminates the influence that Gaussian noise generates, and performance is better than conventional linear secondary regulator, calculates cost than model prediction control
Device processed wants low.
2) genetic algorithm is incorporated on the basis of linearquadratic regulator in the present invention, the advantage is that: passing through simulation
The evolutionary process of nature solves the adjusting optimization problem of Linear Quadratic Control device, according to natural selection and " survival of the fittest "
Principle is evolved；The numerical value of automatically adjusting parameter Q and R are to reach the optimum combination of parameter Q and parameter R, so that cost function is minimum
Change.
Detailed description of the invention
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is that a kind of upper and lower layer controller interaction of adaptive cruise control system top level control algorithm of the invention is patrolled
Collect schematic diagram.
Fig. 2 is that a kind of linear quadratic Gauss algorithm of adaptive cruise control system top level control algorithm of the invention is patrolled
Collect schematic diagram.
Fig. 3 is a kind of adaptive cruise control system control of adaptive cruise control system top level control algorithm of the invention
Logic schematic diagram processed.
Specific embodiment
The following examples can make professional and technical personnel that the present invention be more fully understood, but therefore not send out this
It is bright to be limited among the embodiment described range.
A kind of adaptive cruise control system top level control algorithm as shown in Figure 1: specific steps algorithm is as follows:
S1: the selection of adjuster: the noise occurred under reality scene is reduced to Gaussian noise, upper controller uses linear two
Secondary Gaussian adjuster；
S2: it the selection of basic algorithm: in order to obtain the best parameter group of parameter Q and parameter R and minimize cost function, adopts
With the numerical value of genetic algorithm automatically adjusting parameter Q and R；Linear quadratic Gauss adjuster based on genetic algorithm is denoted as
GALQG control algolithm；
S3: it the adjusting of parameter: is influenced by finely tuning the parameter of Q and R to compensate；
S4: the realization of function: by Linear Quadratic Control device, Linear quadratic Gaussian control and genetic algorithm are combined, and are obtained certain
Under constraint condition, make the input combination of previous preset cost function minimization, and system is made to reach preferable performance indicator.
Genetic algorithm, Linear quadratic Gaussian control and my Linear Quadratic Control device occupy compared with model predictive controller
Computing resource it is lower than model predictive controller.
Genetic algorithm utilizes computer simulation Darwinian evolution, selects qualified target, and ineligible washes in a pan
It eliminates；Genetic algorithm is multiobjective optimization algorithm, can effectively overcome the problems, such as local solution, reach global optimum, is suitable for more
The problem of objective optimization.
Linear quadratic Gaussian control is that Linear Quadratic Control device, linear quadratic regulator are combined with Kalman filter.
Linear quadratic regulator is the Optimal Regulator of the feedback system of multiple-input and multiple-output, multi-party by blocking in solving
Journey, so that original system reaches higher performance.
Adaptive cruise control system includes upper controller, lower layer's controller, ACC mode and constant-speed-cruise control system
Logic switch, brake between mode and the logic switch between throttle.
Primary structure is related to two parts: upper controller and lower layer's control in terms of automobile dynamics in this patent
Device, as shown in Figure 1；Wherein upper controller inputs major parameter are as follows: this vehicle speed, this vehicle acceleration, with front truck it is practical away from
From, expectation spacing, desired speed and front truck velocity and acceleration；Upper controller is exported to the major parameter of lower layer's controller
Are as follows: expectation acceleration, and it is converted into brake braking force and output of the engine torque as lower layer's controller.The control
It is expected the advantage of method is from the aspect of automobile dynamics to the time extension and gain between acceleration and actual acceleration,
Driver is solved because of comfortableness problem caused by generating pause and transition in rhythm or melody sense during vehicle speed variation.
It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, above-described embodiment and explanation
It is merely illustrated the principles of the invention described in book, without departing from the spirit and scope of the present invention, the present invention also has
Various changes and modifications, these changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention
It is defined by the appending claims and its equivalent thereof.
Claims (6)
1. a kind of adaptive cruise control system top level control algorithm, it is characterised in that: specific steps algorithm is as follows:
S1: the selection of adjuster: the noise occurred under reality scene is reduced to Gaussian noise, upper controller uses linear two
Secondary Gaussian adjuster；
S2: it the selection of basic algorithm: in order to obtain the best parameter group of parameter Q and parameter R and minimize cost function, adopts
With the numerical value of genetic algorithm automatically adjusting parameter Q and R；Linear quadratic Gauss adjuster based on genetic algorithm is denoted as
GALQG control algolithm；
S3: it the adjusting of parameter: is influenced by finely tuning the parameter of Q and R to compensate；
S4: the realization of function: by Linear Quadratic Control device, Linear quadratic Gaussian control and genetic algorithm are combined, and are obtained certain
Under constraint condition, make the input combination of previous preset cost function minimization, and system is made to reach preferable performance indicator.
2. a kind of adaptive cruise control system top level control algorithm according to claim 1, it is characterised in that: the something lost
Propagation algorithm, Linear quadratic Gaussian control and my Linear Quadratic Control device, the computing resource occupied compared with model predictive controller
It is lower than model predictive controller.
3. a kind of adaptive cruise control system top level control algorithm according to claim 1, it is characterised in that: the something lost
Propagation algorithm utilizes computer simulation Darwinian evolution, selects qualified target, and ineligible eliminates；Genetic algorithm
It is multiobjective optimization algorithm, can effectively overcomes the problems, such as local solution, reach global optimum, suitable for asks for multiple-objection optimization
Topic.
4. a kind of adaptive cruise control system top level control algorithm according to claim 1, it is characterised in that: the line
Property secondary Gauss control be that Linear Quadratic Control device, linear quadratic regulator are combined with Kalman filter.
5. a kind of adaptive cruise control system top level control algorithm according to claim 4, it is characterised in that: the line
Property secondary regulator be multiple-input and multiple-output feedback system Optimal Regulator, by blocking multi-party journey in solving, so that former system
System reaches higher performance.
6. a kind of adaptive cruise control system top level control algorithm according to claim 4, it is characterised in that: it is described from
Adapting to cruise control system includes between upper controller, lower layer's controller, ACC mode and constant-speed-cruise control system model
Logic switch between logic switch, brake and throttle.
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| CN201910161127.4A CN110347146A (en) | 2019-03-04 | 2019-03-04 | A kind of adaptive cruise control system top level control algorithm |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101417655A (en) * | 2008-10-14 | 2009-04-29 | 清华大学 | Vehicle multi-objective coordinated self-adapting cruise control method |
| WO2014149042A1 (en) * | 2013-03-20 | 2014-09-25 | International Truck Intellectual Property Company, Llc | Smart cruise control system |
-
2019
- 2019-03-04 CN CN201910161127.4A patent/CN110347146A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101417655A (en) * | 2008-10-14 | 2009-04-29 | 清华大学 | Vehicle multi-objective coordinated self-adapting cruise control method |
| WO2014149042A1 (en) * | 2013-03-20 | 2014-09-25 | International Truck Intellectual Property Company, Llc | Smart cruise control system |
Non-Patent Citations (3)
| Title |
|---|
| 党宏社: "《高等学校电子与电气工程及自动化专业"十一五"规划教材 控制***仿真》", 31 March 2008, 西安：西安电子科技大学出版社 * |
| 刘丁: "汽车ACC***控制算法仿真研究", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》 * |
| 曹洁等: "基于遗传算法的LQ控制器的权值优化", 《工业仪表与自动化装置》 * |
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