CN111399385A - Method and system for establishing automatic steering model of unmanned vehicle - Google Patents

Method and system for establishing automatic steering model of unmanned vehicle Download PDF

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CN111399385A
CN111399385A CN202010348969.3A CN202010348969A CN111399385A CN 111399385 A CN111399385 A CN 111399385A CN 202010348969 A CN202010348969 A CN 202010348969A CN 111399385 A CN111399385 A CN 111399385A
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transfer function
obtaining
frequency characteristic
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function
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彭育辉
范贤波
张垚
钟聪
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Fuzhou University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • 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

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Abstract

The application provides a method and a system for establishing an automatic steering model of an unmanned vehicle. The method comprises the following steps: determining an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle; respectively obtaining error square sum criterion functions of a real part and an imaginary part of the frequency characteristic function; obtaining parameters in the transfer function through calculation; and judging whether the parameters meet the precision, and if so, substituting the parameters into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle. According to the method and the device, each parameter in the transfer function of the steering system of the unmanned vehicle can be accurately and quickly acquired under fewer iteration times.

Description

Method and system for establishing automatic steering model of unmanned vehicle
Technical Field
The application belongs to the field of unmanned vehicles, and particularly relates to a method and a system for establishing an automatic steering model of an unmanned vehicle.
Background
Unmanned automobiles have become a hotspot for world research today. The control of the automatic steering system of the unmanned vehicle is one of the key points of the research of the unmanned vehicle, and an accurate transverse dynamic model needs to be established in order to design a high-performance automatic steering controller. The prior art has proposed various methods for establishing a mathematical model of an automatic steering system of an unmanned vehicle, and the main problems of the prior methods are some physical parameters such as: the moment of inertia, damping coefficient, torsional stiffness, etc. are difficult to identify accurately.
The invention patent with the Chinese patent application number of CN201710395937.7 discloses an unmanned vehicle steering control method, which takes steering data as training data to train a sensor network until the training data is smaller than a preset threshold value, completes the training of an automatic steering model, and obtains related parameters through repeated training. This method of repetitive training consumes a lot of training and cannot quickly acquire parameters in the automatic steering model.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a method and a system for establishing an automatic steering model of an unmanned vehicle.
In a first aspect, a method for establishing an automatic steering model of an unmanned vehicle is provided, which includes:
determining an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle;
respectively obtaining error square sum criterion functions of a real part and an imaginary part of the frequency characteristic function;
obtaining parameters in the transfer function through calculation;
and judging whether the parameters meet the precision, and if so, substituting the parameters into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle.
In one possible implementation, the determining an expression of a transfer function of a steering system by simplifying an unmanned vehicle automatic steering model includes:
simplifying the model of the automatic steering system of the unmanned vehicle into a linear steady system and obtaining the transfer function of the steering system
Figure 100002_DEST_PATH_IMAGE001
A is the above a1、a2、a3、a4、b0、b1、b2、b3Is a parameter to be identified;
will be provided with
Figure 826962DEST_PATH_IMAGE002
Taking in the transfer function to obtain the frequency characteristic function of the steering system
Figure 100002_DEST_PATH_IMAGE003
Wherein the parameter vector and the information vector of the denominator are
Figure 313438DEST_PATH_IMAGE004
、
Figure 100002_DEST_PATH_IMAGE005
、
Figure 808004DEST_PATH_IMAGE006
、
Figure 100002_DEST_PATH_IMAGE007
The parameter vector and the information vector of the molecule are
Figure 871775DEST_PATH_IMAGE008
、
Figure 100002_DEST_PATH_IMAGE009
、
Figure 468411DEST_PATH_IMAGE010
、
Figure 100002_DEST_PATH_IMAGE011
；
Transforming the frequency characteristic function by transformation
Figure 809394DEST_PATH_IMAGE012
；
Multiplying on both sides of the converted frequency characteristic function
Figure 100002_DEST_PATH_IMAGE013
And after the comparison, the data are compared,obtaining the real part expression of the frequency characteristic function as
Figure 333916DEST_PATH_IMAGE014
The imaginary part is expressed as
Figure 100002_DEST_PATH_IMAGE015
。
In another possible implementation, obtaining the square of error and the criterion function of the real part and the imaginary part of the frequency characteristic function respectively comprises:
defining a vector containing unknown parameters according to the real part expression
Figure 760349DEST_PATH_IMAGE016
The real part error sum of squares criterion function of (1) is:
Figure 100002_DEST_PATH_IMAGE017
wherein the error of the real part is
Figure 169465DEST_PATH_IMAGE018
，
Figure 100002_DEST_PATH_IMAGE019
，
Figure 224009DEST_PATH_IMAGE020
As the real part data of the frequency characteristic,
Figure 100002_DEST_PATH_IMAGE021
is the imaginary data of the frequency characteristic;
defining a vector containing unknown parameters according to the imaginary expression
Figure 60377DEST_PATH_IMAGE022
The imaginary error sum of squares criterion function of (d) is:
Figure 100002_DEST_PATH_IMAGE023
wherein the imaginary error is:
Figure 833161DEST_PATH_IMAGE024
，
Figure 100002_DEST_PATH_IMAGE025
。
estimating parameters in the transfer function according to the real part error and imaginary part error simultaneous recursive least square algorithm, wherein the parameters comprise:
Figure 514810DEST_PATH_IMAGE026
therein, it is made
Figure 100002_DEST_PATH_IMAGE027
And
Figure 689439DEST_PATH_IMAGE028
respectively representing system parameters
Figure 100002_DEST_PATH_IMAGE029
And
Figure 431130DEST_PATH_IMAGE030
at the k frequency wkThe calculated estimate is entered.
In another possible implementation manner, obtaining the parameter in the transfer function through calculation includes:
initializing, enabling k =1, and setting an initial value
Figure 100002_DEST_PATH_IMAGE031
Is any real number, and is a real number,
Figure 691210DEST_PATH_IMAGE032
is an arbitrary real vector and is a vector,
Figure 100002_DEST_PATH_IMAGE033
，
Figure 442128DEST_PATH_IMAGE034
，
Figure 100002_DEST_PATH_IMAGE035
，
Figure 471264DEST_PATH_IMAGE036
，
Figure 100002_DEST_PATH_IMAGE037
setting parameter estimation accuracy
Figure 646506DEST_PATH_IMAGE038
；
Inputting sinusoidal excitation signals to the front wheel of the unmanned vehicle to obtain real-frequency characteristic data U (w) under different sinusoidal excitation signalsk) And virtual frequency characteristic data V (w)k) Wherein, the sine excitation signal is:
Figure 100002_DEST_PATH_IMAGE039
，
Figure 128303DEST_PATH_IMAGE040
，
Figure 100002_DEST_PATH_IMAGE041
amplitude M of vibrationiIs: +/-5 deg., 8 deg., 12 deg., 15 deg., frequency wkComprises the following steps: 1,3,5,7,10,15,20,25, vehicle speed: 30 km/h;
according to a preset formula:
Figure 441921DEST_PATH_IMAGE044
、
Figure 100002_DEST_PATH_IMAGE043
、
Figure 441921DEST_PATH_IMAGE044
、
Figure 100002_DEST_PATH_IMAGE045
obtaining information vectors
Figure 915628DEST_PATH_IMAGE046
；
According to a preset formula:
Figure 100002_DEST_PATH_IMAGE047
、
Figure 760087DEST_PATH_IMAGE048
obtaining information vectors
Figure 100002_DEST_PATH_IMAGE049
；
According to a preset formula:
Figure 711862DEST_PATH_IMAGE050
、
Figure 100002_DEST_PATH_IMAGE051
separately acquiring imaginary part information
Figure 590957DEST_PATH_IMAGE052
And real part information
Figure 100002_DEST_PATH_IMAGE053
；
According to a preset formula:
Figure 501144DEST_PATH_IMAGE054
、
Figure 100002_DEST_PATH_IMAGE055
obtaining a gain vector
Figure 832899DEST_PATH_IMAGE056
；
According to a preset formula:
Figure 100002_DEST_PATH_IMAGE057
、
Figure 588366DEST_PATH_IMAGE058
obtaining a covariance matrix
Figure 100002_DEST_PATH_IMAGE059
；
According to a preset formula:
Figure 56387DEST_PATH_IMAGE060
、
Figure 100002_DEST_PATH_IMAGE061
obtaining system parameter estimation vector
Figure 871896DEST_PATH_IMAGE062
。
In a second aspect, a system for establishing an unmanned vehicle automatic steering model is provided, comprising:
the expression acquisition module is used for determining an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle;
the error square sum criterion function acquisition module is used for respectively acquiring error square sum criterion functions of a real part and an imaginary part of the frequency characteristic function;
the parameter acquisition module is used for acquiring parameters in the transfer function through calculation;
and the precision judging module is used for judging whether the parameters meet the precision, and if so, the parameters are brought into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle.
In one possible implementation, the determining an expression of a transfer function of a steering system by simplifying an unmanned vehicle automatic steering model includes:
simplifying the model of the automatic steering system of the unmanned vehicle into a linear steady system and obtaining the transfer function of the steering system
Figure 425369DEST_PATH_IMAGE001
A is the above a1、a2、a3、a4、b0、b1、b2、b3Is a parameter to be identified;
will be provided with
Figure 718947DEST_PATH_IMAGE002
Taking in the transfer function to obtain the frequency characteristic function of the steering system
Figure 166109DEST_PATH_IMAGE003
Wherein the parameter vector and the information vector of the denominator are
Figure 765236DEST_PATH_IMAGE004
、
Figure 930638DEST_PATH_IMAGE005
、
Figure 762328DEST_PATH_IMAGE006
、
Figure 329575DEST_PATH_IMAGE007
The parameter vector and the information vector of the molecule are
Figure 831095DEST_PATH_IMAGE008
、
Figure 483793DEST_PATH_IMAGE009
、
Figure 119174DEST_PATH_IMAGE010
、
Figure 275349DEST_PATH_IMAGE011
；
Transforming the frequency characteristic function by transformation
Figure 213349DEST_PATH_IMAGE012
；
Multiplying on both sides of the converted frequency characteristic function
Figure 87764DEST_PATH_IMAGE013
And after comparison, obtaining a real part expression of the frequency characteristic function as
Figure 792415DEST_PATH_IMAGE014
The imaginary part is expressed as
Figure 944041DEST_PATH_IMAGE015
。
In another possible implementation, obtaining the square of error and the criterion function of the real part and the imaginary part of the frequency characteristic function respectively comprises:
defining a vector containing unknown parameters according to the real part expression
Figure 177577DEST_PATH_IMAGE016
The real part error sum of squares criterion function of (1) is:
Figure 539288DEST_PATH_IMAGE017
wherein the error of the real part is
Figure 516471DEST_PATH_IMAGE018
，
Figure 522604DEST_PATH_IMAGE019
，
Figure 192620DEST_PATH_IMAGE020
As the real part data of the frequency characteristic,
Figure 41627DEST_PATH_IMAGE021
is the imaginary data of the frequency characteristic;
defining a vector containing unknown parameters according to the imaginary expression
Figure 432289DEST_PATH_IMAGE022
The imaginary error sum of squares criterion function of (d) is:
Figure 683141DEST_PATH_IMAGE023
wherein the imaginary error is:
Figure 258479DEST_PATH_IMAGE024
，
Figure 594783DEST_PATH_IMAGE025
。
simultaneously recursing a minimum of two according to the real part error and the imaginary part errorA multiplication algorithm estimates parameters in the transfer function, the parameters including:
Figure 789135DEST_PATH_IMAGE026
therein, it is made
Figure 894494DEST_PATH_IMAGE027
And
Figure 640733DEST_PATH_IMAGE028
respectively representing system parameters
Figure 198753DEST_PATH_IMAGE029
And
Figure 462376DEST_PATH_IMAGE030
at the k frequency wkThe calculated estimate is entered.
In another possible implementation manner, obtaining the parameter in the transfer function through calculation includes:
initializing, enabling k =1, and setting an initial value
Figure 422241DEST_PATH_IMAGE031
Is any real number, and is a real number,
Figure 339382DEST_PATH_IMAGE032
is an arbitrary real vector and is a vector,
Figure 522714DEST_PATH_IMAGE033
，
Figure 183502DEST_PATH_IMAGE034
，
Figure 997875DEST_PATH_IMAGE035
，
Figure 492441DEST_PATH_IMAGE036
，
Figure 25054DEST_PATH_IMAGE037
setting parameter estimation accuracy
Figure 489533DEST_PATH_IMAGE038
；
Inputting sinusoidal excitation signals to the front wheel of the unmanned vehicle to obtain real-frequency characteristic data U (w) under different sinusoidal excitation signalsk) And virtual frequency characteristic data V (w)k) Wherein, the sine excitation signal is:
Figure 158412DEST_PATH_IMAGE039
，
Figure 558300DEST_PATH_IMAGE040
，
Figure 578209DEST_PATH_IMAGE041
amplitude M of vibrationiIs: +/-5 deg., 8 deg., 12 deg., 15 deg., frequency wkComprises the following steps: 1,3,5,7,10,15,20,25, vehicle speed: 30 km/h;
according to a preset formula:
Figure 441921DEST_PATH_IMAGE044
、
Figure 510710DEST_PATH_IMAGE043
、
Figure 206133DEST_PATH_IMAGE044
、
Figure 447759DEST_PATH_IMAGE045
obtaining information vectors
Figure 254041DEST_PATH_IMAGE046
；
According to a preset formula:
Figure 304036DEST_PATH_IMAGE047
、
Figure 904782DEST_PATH_IMAGE048
obtaining information vectors
Figure 899283DEST_PATH_IMAGE049
；
According to a preset formula:
Figure 119043DEST_PATH_IMAGE050
、
Figure 882599DEST_PATH_IMAGE051
separately acquiring imaginary part information
Figure 654246DEST_PATH_IMAGE052
And real part information
Figure 136043DEST_PATH_IMAGE053
；
According to a preset formula:
Figure 159494DEST_PATH_IMAGE054
、
Figure 777557DEST_PATH_IMAGE055
obtaining a gain vector
Figure 985685DEST_PATH_IMAGE056
；
According to a preset formula:
Figure 689198DEST_PATH_IMAGE057
、
Figure 516340DEST_PATH_IMAGE058
obtaining a covariance matrix
Figure 254489DEST_PATH_IMAGE059
；
According to a preset formula:
Figure 367938DEST_PATH_IMAGE060
、
Figure 448763DEST_PATH_IMAGE061
obtaining system parameter estimation vector
Figure 938650DEST_PATH_IMAGE062
。
The beneficial effect that technical scheme that this application provided brought is: under the condition of less iteration times, all parameters in the transfer function of the steering system of the unmanned vehicle are accurately and quickly acquired.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a flowchart of a method for establishing an automatic steering model of an unmanned vehicle according to an embodiment of the present invention;
fig. 2 is a structural diagram of a system for creating an automatic steering model of an unmanned vehicle according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, modules, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, modules, components, and/or groups thereof. It will be understood that when a module is referred to as being "connected" or "coupled" to another module, it can be directly connected or coupled to the other module or intervening modules may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The technical solutions of the present application and the technical solutions of the present application, for example, to solve the above technical problems, will be described in detail with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Example one
Fig. 1 is a flowchart illustrating a method for creating an automatic steering model of an unmanned vehicle according to an embodiment of the present invention, where the method includes:
and S101, determining an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle.
In the embodiment of the invention, the automatic steering model of the unmanned vehicle is the existing mathematical model, and the expression of the transfer function of the steering system can be determined by simplifying the steps.
The method for determining the expression of the transfer function of the steering system by simplifying the automatic steering model of the unmanned vehicle comprises the following steps:
simplifying the model of the automatic steering system of the unmanned vehicle into a linear steady system and obtaining the transfer function of the steering system
Figure 531305DEST_PATH_IMAGE001
A is the above a1、a2、a3、a4、b0、b1、b2、b3Is a parameter to be identified;
will be provided with
Figure 81236DEST_PATH_IMAGE002
Taking in the transfer function to obtain the frequency characteristic function of the steering system
Figure 634708DEST_PATH_IMAGE003
Wherein the parameter vector and the information vector of the denominator are
Figure 928286DEST_PATH_IMAGE004
、
Figure 375448DEST_PATH_IMAGE005
、
Figure 971645DEST_PATH_IMAGE006
、
Figure 137047DEST_PATH_IMAGE007
The parameter vector and the information vector of the molecule are
Figure 968737DEST_PATH_IMAGE008
、
Figure 270405DEST_PATH_IMAGE009
、
Figure 37504DEST_PATH_IMAGE010
、
Figure 690203DEST_PATH_IMAGE011
；
Transforming the frequency characteristic function by transformation
Figure 60004DEST_PATH_IMAGE012
；
Multiplying on both sides of the converted frequency characteristic function
Figure 481758DEST_PATH_IMAGE013
And after comparison, obtaining a real part expression of the frequency characteristic function as
Figure 685337DEST_PATH_IMAGE014
The imaginary part is expressed as
Figure 559753DEST_PATH_IMAGE015
。
Step S102, error square and criterion functions of a real part and an imaginary part of the frequency characteristic function are respectively obtained.
In the embodiment of the present invention, the square error sum criterion function of the real part and the imaginary part can be obtained according to the real part expression and the imaginary part expression of the frequency characteristic function obtained in the foregoing steps.
The obtaining of the square error sum criterion function of the real part and the imaginary part of the frequency characteristic function respectively comprises:
defining a vector containing unknown parameters according to the real part expression
Figure 998824DEST_PATH_IMAGE016
The real part error sum of squares criterion function of (1) is:
Figure 150451DEST_PATH_IMAGE017
wherein the error of the real part is
Figure 383986DEST_PATH_IMAGE018
，
Figure 745697DEST_PATH_IMAGE019
，
Figure 457301DEST_PATH_IMAGE020
As the real part data of the frequency characteristic,
Figure 994593DEST_PATH_IMAGE021
is the imaginary data of the frequency characteristic;
defining a vector containing unknown parameters according to the imaginary expression
Figure 399030DEST_PATH_IMAGE022
The imaginary error sum of squares criterion function of (d) is:
Figure 248037DEST_PATH_IMAGE023
wherein the imaginary error is:
Figure 638698DEST_PATH_IMAGE024
，
Figure 889551DEST_PATH_IMAGE025
。
estimating parameters in the transfer function according to the real part error and imaginary part error simultaneous recursive least square algorithm, wherein the parameters comprise:
Figure 464889DEST_PATH_IMAGE026
therein, it is made
Figure 801192DEST_PATH_IMAGE027
And
Figure 992615DEST_PATH_IMAGE028
respectively representing system parameters
Figure 363553DEST_PATH_IMAGE029
And
Figure 109792DEST_PATH_IMAGE030
at the k frequency wkThe calculated estimate is entered.
And step S103, acquiring parameters in the transfer function through calculation.
In the embodiment of the invention, the parameters in the transfer function are the basis for establishing the automatic steering model of the unmanned vehicle, so that the parameters in the transfer function need to be obtained through specific calculation steps.
The obtaining of the parameter in the transfer function through calculation includes:
initializing, enabling k =1, and setting an initial value
Figure 543179DEST_PATH_IMAGE031
Is any real number, and is a real number,
Figure 665855DEST_PATH_IMAGE032
is an arbitrary real vector and is a vector,
Figure 625721DEST_PATH_IMAGE033
，
Figure 542862DEST_PATH_IMAGE034
，
Figure 729123DEST_PATH_IMAGE035
，
Figure 389912DEST_PATH_IMAGE036
，
Figure 204284DEST_PATH_IMAGE037
setting parameter estimation accuracy
Figure 698851DEST_PATH_IMAGE038
；
Inputting sinusoidal excitation signals to the front wheel of the unmanned vehicle to obtain real-frequency characteristic data U (w) under different sinusoidal excitation signalsk) And virtual frequency characteristic data V (w)k) Wherein, the sine excitation signal is:
Figure 231463DEST_PATH_IMAGE039
，
Figure 430363DEST_PATH_IMAGE040
，
Figure 364821DEST_PATH_IMAGE041
amplitude M of vibrationiIs: +/-5 deg., 8 deg., 12 deg., 15 deg., frequency wkComprises the following steps: 1,3,5,7,10,15,20,25, vehicle speed: 30 km/h;
according to a preset formula:
Figure 441921DEST_PATH_IMAGE044
、
Figure 519039DEST_PATH_IMAGE043
、
Figure 787209DEST_PATH_IMAGE044
、
Figure 576174DEST_PATH_IMAGE045
obtaining information vectors
Figure 881384DEST_PATH_IMAGE046
；
According to a preset formula:
Figure 654168DEST_PATH_IMAGE047
、
Figure 460450DEST_PATH_IMAGE048
obtaining information vectors
Figure 979287DEST_PATH_IMAGE049
；
According to a preset formula:
Figure 845612DEST_PATH_IMAGE050
、
Figure 574534DEST_PATH_IMAGE051
separately acquiring imaginary part information
Figure 591031DEST_PATH_IMAGE052
And real part information
Figure 354588DEST_PATH_IMAGE053
；
According to a preset formula:
Figure 126235DEST_PATH_IMAGE054
、
Figure 486328DEST_PATH_IMAGE055
obtaining a gain vector
Figure 634412DEST_PATH_IMAGE056
；
According to a preset formula:
Figure 252475DEST_PATH_IMAGE057
、
Figure 335969DEST_PATH_IMAGE058
obtaining covariance matrixMatrix of
Figure 39483DEST_PATH_IMAGE059
；
According to a preset formula:
Figure 991258DEST_PATH_IMAGE060
、
Figure 463828DEST_PATH_IMAGE061
obtaining system parameter estimation vector
Figure 718223DEST_PATH_IMAGE062
。
And step S104, judging whether the parameters meet the precision, and if so, substituting the parameters into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle.
In the embodiment of the invention, whether the system estimation parameters meet the precision or not is judged
Figure 909033DEST_PATH_IMAGE038
If, if
Figure DEST_PATH_IMAGE063
Increasing k by 1, calculating and acquiring parameters according to the new k value, and if the parameter estimation precision meets the precision requirement, calculating the k value according to the new k value, and acquiring the parameters according to the new k value
Figure 805445DEST_PATH_IMAGE064
And reading out an estimated parameter value from the estimated parameter vector, and ending the recursion calculation process. And (4) substituting the parameters meeting the precision requirement into the transfer function, and obtaining the transfer function of the automatic steering model of the unmanned vehicle.
According to the embodiment of the invention, the unmanned vehicle automatic steering model is simplified, the expression of the transfer function of the steering system is determined, the error square sum criterion function of the real part and the imaginary part of the frequency characteristic function is respectively obtained, the parameter in the transfer function is obtained through calculation, whether the parameter meets the precision or not is judged, if the parameter meets the precision, the parameter is substituted into the transfer function, and the transfer function of the unmanned vehicle automatic steering model is obtained, so that the parameters in the transfer function of the unmanned vehicle steering system can be accurately and quickly obtained under fewer iteration times.
Example two
Fig. 2 is a structural diagram of a system for building an automatic steering model of an unmanned vehicle according to an embodiment of the present invention, where the system includes:
the expression obtaining module 201 is configured to determine an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle.
In the embodiment of the invention, the automatic steering model of the unmanned vehicle is the existing mathematical model, and the expression of the transfer function of the steering system can be determined by simplifying the steps.
The method for determining the expression of the transfer function of the steering system by simplifying the automatic steering model of the unmanned vehicle comprises the following steps:
simplifying the model of the automatic steering system of the unmanned vehicle into a linear steady system and obtaining the transfer function of the steering system
Figure 398100DEST_PATH_IMAGE001
A is the above a1、a2、a3、a4、b0、b1、b2、b3Is a parameter to be identified;
will be provided with
Figure 948030DEST_PATH_IMAGE002
Taking in the transfer function to obtain the frequency characteristic function of the steering system
Figure 360557DEST_PATH_IMAGE003
Wherein the parameter vector and the information vector of the denominator are
Figure 529501DEST_PATH_IMAGE004
、
Figure 976663DEST_PATH_IMAGE005
、
Figure 697494DEST_PATH_IMAGE006
、
Figure 862897DEST_PATH_IMAGE007
The parameter vector and the information vector of the molecule are
Figure 569952DEST_PATH_IMAGE008
、
Figure 871621DEST_PATH_IMAGE009
、
Figure 763353DEST_PATH_IMAGE010
、
Figure 150472DEST_PATH_IMAGE011
；
Transforming the frequency characteristic function by transformation
Figure 661219DEST_PATH_IMAGE012
；
Multiplying on both sides of the converted frequency characteristic function
Figure 82973DEST_PATH_IMAGE013
And after comparison, obtaining a real part expression of the frequency characteristic function as
Figure 411187DEST_PATH_IMAGE014
The imaginary part is expressed as
Figure 160968DEST_PATH_IMAGE015
。
A square error sum criterion function obtaining module 202, configured to obtain square error sum criterion functions of the real part and the imaginary part of the frequency characteristic function, respectively.
In the embodiment of the present invention, the square error sum criterion function of the real part and the imaginary part can be obtained according to the real part expression and the imaginary part expression of the frequency characteristic function obtained in the foregoing steps.
The obtaining of the square error sum criterion function of the real part and the imaginary part of the frequency characteristic function respectively comprises:
defining a vector containing unknown parameters according to the real part expression
Figure 334460DEST_PATH_IMAGE016
The real part error sum of squares criterion function of (1) is:
Figure 610721DEST_PATH_IMAGE017
wherein the error of the real part is
Figure 844256DEST_PATH_IMAGE018
，
Figure 78404DEST_PATH_IMAGE019
，
Figure 55587DEST_PATH_IMAGE020
As the real part data of the frequency characteristic,
Figure 717513DEST_PATH_IMAGE021
is the imaginary data of the frequency characteristic;
defining a vector containing unknown parameters according to the imaginary expression
Figure 997315DEST_PATH_IMAGE022
The imaginary error sum of squares criterion function of (d) is:
Figure 580743DEST_PATH_IMAGE023
wherein the imaginary error is:
Figure 361618DEST_PATH_IMAGE024
，
Figure 612470DEST_PATH_IMAGE025
。
estimating parameters in the transfer function according to the real part error and imaginary part error simultaneous recursive least square algorithm, wherein the parameters comprise:
Figure 63174DEST_PATH_IMAGE026
which isIn the middle, let
Figure 133899DEST_PATH_IMAGE027
And
Figure 452884DEST_PATH_IMAGE028
respectively representing system parameters
Figure 699189DEST_PATH_IMAGE029
And
Figure 445428DEST_PATH_IMAGE030
at the k frequency wkThe calculated estimate is entered.
A parameter obtaining module 203, configured to obtain a parameter in the transfer function through calculation.
In the embodiment of the invention, the parameters in the transfer function are the basis for establishing the automatic steering model of the unmanned vehicle, so that the parameters in the transfer function need to be obtained through specific calculation steps.
The obtaining of the parameter in the transfer function through calculation includes:
initializing, enabling k =1, and setting an initial value
Figure 269028DEST_PATH_IMAGE031
Is any real number, and is a real number,
Figure 126125DEST_PATH_IMAGE032
is an arbitrary real vector and is a vector,
Figure 226937DEST_PATH_IMAGE033
，
Figure 144077DEST_PATH_IMAGE034
，
Figure 189393DEST_PATH_IMAGE035
，
Figure 850182DEST_PATH_IMAGE036
，
Figure 805499DEST_PATH_IMAGE037
setting parameter estimation accuracy
Figure 159120DEST_PATH_IMAGE038
；
Inputting sinusoidal excitation signals to the front wheel of the unmanned vehicle to obtain real-frequency characteristic data U (w) under different sinusoidal excitation signalsk) And virtual frequency characteristic data V (w)k) Wherein, the sine excitation signal is:
Figure 691733DEST_PATH_IMAGE039
，
Figure 31579DEST_PATH_IMAGE040
，
Figure 966036DEST_PATH_IMAGE041
amplitude M of vibrationiIs: +/-5 deg., 8 deg., 12 deg., 15 deg., frequency wkComprises the following steps: 1,3,5,7,10,15,20,25, vehicle speed: 30 km/h;
according to a preset formula:
Figure 441921DEST_PATH_IMAGE044
、
Figure 244888DEST_PATH_IMAGE043
、
Figure 388425DEST_PATH_IMAGE044
、
Figure 177389DEST_PATH_IMAGE045
obtaining information vectors
Figure 607233DEST_PATH_IMAGE046
；
According to a preset formula:
Figure 246594DEST_PATH_IMAGE047
、
Figure 52876DEST_PATH_IMAGE048
acquisition messageInformation vector
Figure 696347DEST_PATH_IMAGE049
；
According to a preset formula:
Figure 562672DEST_PATH_IMAGE050
、
Figure 166960DEST_PATH_IMAGE051
separately acquiring imaginary part information
Figure 42512DEST_PATH_IMAGE052
And real part information
Figure 540490DEST_PATH_IMAGE053
；
According to a preset formula:
Figure 453082DEST_PATH_IMAGE054
、
Figure 669300DEST_PATH_IMAGE055
obtaining a gain vector
Figure 817384DEST_PATH_IMAGE056
；
According to a preset formula:
Figure 435447DEST_PATH_IMAGE057
、
Figure 518941DEST_PATH_IMAGE058
obtaining a covariance matrix
Figure 222455DEST_PATH_IMAGE059
；
According to a preset formula:
Figure 439810DEST_PATH_IMAGE060
、
Figure 787745DEST_PATH_IMAGE061
obtaining system parameter estimation vector
Figure 166774DEST_PATH_IMAGE062
。
And the precision judging module 204 is used for judging whether the parameters meet the precision, and if so, the parameters are brought into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle.
In the embodiment of the invention, whether the system estimation parameters meet the precision or not is judged
Figure 357584DEST_PATH_IMAGE038
If, if
Figure 847471DEST_PATH_IMAGE063
Increasing k by 1, calculating and acquiring parameters according to the new k value, and if the parameter estimation precision meets the precision requirement, calculating the k value according to the new k value, and acquiring the parameters according to the new k value
Figure 315493DEST_PATH_IMAGE064
And reading out an estimated parameter value from the estimated parameter vector, and ending the recursion calculation process. And (4) substituting the parameters meeting the precision requirement into the transfer function, and obtaining the transfer function of the automatic steering model of the unmanned vehicle.
According to the embodiment of the invention, the unmanned vehicle automatic steering model is simplified, the expression of the transfer function of the steering system is determined, the error square sum criterion function of the real part and the imaginary part of the frequency characteristic function is respectively obtained, the parameter in the transfer function is obtained through calculation, whether the parameter meets the precision or not is judged, if the parameter meets the precision, the parameter is substituted into the transfer function, and the transfer function of the unmanned vehicle automatic steering model is obtained, so that the parameters in the transfer function of the unmanned vehicle steering system can be accurately and quickly obtained under fewer iteration times.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (8)

1. A method for establishing an automatic steering model of an unmanned vehicle is characterized by comprising the following steps:
determining an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle;
respectively obtaining error square sum criterion functions of a real part and an imaginary part of the frequency characteristic function;
obtaining parameters in the transfer function through calculation;
and judging whether the parameters meet the precision, if so, substituting the parameters into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle.
2. The method of claim 1, wherein determining the expression for the steering system transfer function by simplifying the unmanned vehicle auto-steering model comprises:
simplifying the model of the automatic steering system of the unmanned vehicle into a linear steady system and obtaining the transfer function of the steering system
Figure DEST_PATH_IMAGE001
A is the above a1、a2、a3、a4、b0、b1、b2、b3Is a parameter to be identified;
will be provided with
Figure 317985DEST_PATH_IMAGE002
Taking in the transfer function to obtain the frequency characteristic function of the steering system
Figure DEST_PATH_IMAGE003
Wherein the parameter vector and the information vector of the denominator are
Figure 566564DEST_PATH_IMAGE004
、
Figure DEST_PATH_IMAGE005
、
Figure 758511DEST_PATH_IMAGE006
、
Figure DEST_PATH_IMAGE007
The parameter vector and the information vector of the molecule are
Figure 182670DEST_PATH_IMAGE008
、
Figure DEST_PATH_IMAGE009
、
Figure 473974DEST_PATH_IMAGE010
、
Figure DEST_PATH_IMAGE011
；
Transforming the frequency characteristic function by transformation
Figure 334483DEST_PATH_IMAGE012
；
Multiplying on both sides of the converted frequency characteristic function
Figure DEST_PATH_IMAGE013
And after comparison, obtaining a real part expression of the frequency characteristic function as
Figure 95501DEST_PATH_IMAGE014
The imaginary part is expressed as
Figure DEST_PATH_IMAGE015
。
3. The method of claim 1, wherein the obtaining of the sum of square error criteria functions for the real and imaginary parts of the frequency characteristic function, respectively, comprises:
defining a vector containing unknown parameters according to the real part expression
Figure 233222DEST_PATH_IMAGE016
The real part error sum of squares criterion function of (1) is:
Figure DEST_PATH_IMAGE017
wherein the error of the real part is
Figure 820061DEST_PATH_IMAGE018
，
Figure DEST_PATH_IMAGE019
，
Figure 308811DEST_PATH_IMAGE020
As the real part data of the frequency characteristic,
Figure DEST_PATH_IMAGE021
is the imaginary data of the frequency characteristic;
defining a vector containing unknown parameters according to the imaginary expression
Figure 390031DEST_PATH_IMAGE022
Imaginary error ofThe square and criteria function is:
Figure DEST_PATH_IMAGE023
wherein the imaginary error is:
Figure 382257DEST_PATH_IMAGE024
，
Figure DEST_PATH_IMAGE025
，
estimating parameters in the transfer function according to the real part error and imaginary part error simultaneous recursive least square algorithm, wherein the parameters comprise:
Figure 405577DEST_PATH_IMAGE026
therein, it is made
Figure DEST_PATH_IMAGE027
And
Figure 194673DEST_PATH_IMAGE028
respectively representing system parameters
Figure DEST_PATH_IMAGE029
And
Figure 938638DEST_PATH_IMAGE030
at the k frequency wkThe calculated estimate is entered.
4. The method of claim 1, wherein said obtaining parameters in said transfer function by calculation comprises:
initializing, enabling k =1, and setting an initial value
Figure DEST_PATH_IMAGE031
Is any real number, and is a real number,
Figure 175584DEST_PATH_IMAGE032
is an arbitrary real vector and is a vector,
Figure DEST_PATH_IMAGE033
，
Figure 245171DEST_PATH_IMAGE034
，
Figure DEST_PATH_IMAGE035
，
Figure 318301DEST_PATH_IMAGE036
，
Figure DEST_PATH_IMAGE037
setting parameter estimation accuracy
Figure 865957DEST_PATH_IMAGE038
；
Inputting sinusoidal excitation signals to the front wheel of the unmanned vehicle to obtain real-frequency characteristic data U (w) under different sinusoidal excitation signalsk) And virtual frequency characteristic data V (w)k) Wherein, the sine excitation signal is:
Figure DEST_PATH_IMAGE039
，
Figure 160672DEST_PATH_IMAGE040
，
Figure DEST_PATH_IMAGE041
amplitude M of vibrationiIs: +/-5 deg., 8 deg., 12 deg., 15 deg., frequency wkComprises the following steps: 1,3,5,7,10,15,20,25, vehicle speed: 30 km/h;
according to a preset formula:
Figure 207890DEST_PATH_IMAGE044
、
Figure DEST_PATH_IMAGE043
、
Figure 693077DEST_PATH_IMAGE044
、
Figure DEST_PATH_IMAGE045
obtaining information vectors
Figure 169057DEST_PATH_IMAGE046
；
According to a preset formula:
Figure DEST_PATH_IMAGE047
、
Figure 990383DEST_PATH_IMAGE048
obtaining information vectors
Figure DEST_PATH_IMAGE049
；
According to a preset formula:
Figure 542718DEST_PATH_IMAGE050
、
Figure DEST_PATH_IMAGE051
separately acquiring imaginary part information
Figure 449494DEST_PATH_IMAGE052
And real part information
Figure DEST_PATH_IMAGE053
；
According to a preset formula:
Figure 463587DEST_PATH_IMAGE054
、
Figure DEST_PATH_IMAGE055
obtaining a gain vector
Figure 404998DEST_PATH_IMAGE056
；
According to a preset formula:
Figure DEST_PATH_IMAGE057
、
Figure 65917DEST_PATH_IMAGE058
obtaining a covariance matrix
Figure DEST_PATH_IMAGE059
；
According to a preset formula:
Figure 584623DEST_PATH_IMAGE060
、
Figure DEST_PATH_IMAGE061
obtaining system parameter estimation vector
Figure 277773DEST_PATH_IMAGE062
。
5. A system for establishing an unmanned vehicle automatic steering model is characterized by comprising:
the expression acquisition module is used for determining an expression of a transfer function of a steering system by simplifying an automatic steering model of the unmanned vehicle;
the error square sum criterion function acquisition module is used for respectively acquiring error square sum criterion functions of a real part and an imaginary part of the frequency characteristic function;
the parameter acquisition module is used for acquiring parameters in the transfer function through calculation;
and the precision judging module is used for judging whether the parameters meet the precision, and if so, the parameters are brought into the transfer function to obtain the transfer function of the automatic steering model of the unmanned vehicle.
6. The system of claim 5, wherein determining the expression for the steering system transfer function by simplifying the unmanned vehicle auto-steering model comprises:
simplifying the model of the automatic steering system of the unmanned vehicle into a linear steady system and obtaining the transfer function of the steering system
Figure 683478DEST_PATH_IMAGE001
A is the above a1、a2、a3、a4、b0、b1、b2、b3Is a parameter to be identified;
will be provided with
Figure 233408DEST_PATH_IMAGE002
Taking in the transfer function to obtain the frequency characteristic function of the steering system
Figure 849197DEST_PATH_IMAGE003
Wherein the parameter vector and the information vector of the denominator are
Figure 142775DEST_PATH_IMAGE004
、
Figure 652254DEST_PATH_IMAGE005
、
Figure 310768DEST_PATH_IMAGE006
、
Figure 476170DEST_PATH_IMAGE007
The parameter vector and the information vector of the molecule are
Figure 123839DEST_PATH_IMAGE008
、
Figure 691086DEST_PATH_IMAGE009
、
Figure 254923DEST_PATH_IMAGE010
、
Figure 907621DEST_PATH_IMAGE011
；
Transforming the frequency characteristic function by transformation
Figure 605319DEST_PATH_IMAGE012
；
Multiplying on both sides of the converted frequency characteristic function
Figure 964756DEST_PATH_IMAGE013
And after comparison, obtaining a real part expression of the frequency characteristic function as
Figure 27390DEST_PATH_IMAGE014
The imaginary part is expressed as
Figure 714854DEST_PATH_IMAGE015
。
7. The system of claim 5, wherein said obtaining the sum of square error criteria functions for the real and imaginary parts of the frequency characteristic function, respectively, comprises:
defining a vector containing unknown parameters according to the real part expression
Figure 153926DEST_PATH_IMAGE016
The real part error sum of squares criterion function of (1) is:
Figure 367870DEST_PATH_IMAGE017
wherein the error of the real part is
Figure 601405DEST_PATH_IMAGE018
，
Figure 291012DEST_PATH_IMAGE019
，
Figure 205878DEST_PATH_IMAGE020
As the real part data of the frequency characteristic,
Figure 336646DEST_PATH_IMAGE021
is the imaginary data of the frequency characteristic;
defining a vector containing unknown parameters according to the imaginary expression
Figure 554131DEST_PATH_IMAGE022
The imaginary error sum of squares criterion function of (d) is:
Figure 403139DEST_PATH_IMAGE023
wherein the imaginary error is:
Figure 856117DEST_PATH_IMAGE024
，
Figure 106970DEST_PATH_IMAGE025
，
estimating parameters in the transfer function according to the real part error and imaginary part error simultaneous recursive least square algorithm, wherein the parameters comprise:
Figure 744624DEST_PATH_IMAGE026
therein, it is made
Figure 284190DEST_PATH_IMAGE027
And
Figure 603176DEST_PATH_IMAGE028
respectively representing system parameters
Figure 521584DEST_PATH_IMAGE029
And
Figure 267824DEST_PATH_IMAGE030
at the k frequencywkThe calculated estimate is entered.
8. The system of claim 5, wherein said obtaining parameters in said transfer function by calculation comprises:
initializing, enabling k =1, and setting an initial value
Figure 763527DEST_PATH_IMAGE031
Is any real number, and is a real number,
Figure 886204DEST_PATH_IMAGE032
is an arbitrary real vector and is a vector,
Figure 908386DEST_PATH_IMAGE033
，
Figure 28789DEST_PATH_IMAGE034
，
Figure 339685DEST_PATH_IMAGE035
，
Figure 810593DEST_PATH_IMAGE036
，
Figure 624965DEST_PATH_IMAGE037
setting parameter estimation accuracy
Figure 916269DEST_PATH_IMAGE038
；
Inputting sinusoidal excitation signals to the front wheel of the unmanned vehicle to obtain real-frequency characteristic data U (w) under different sinusoidal excitation signalsk) And virtual frequency characteristic data V (w)k) Wherein, the sine excitation signal is:
Figure 448882DEST_PATH_IMAGE039
，
Figure 975678DEST_PATH_IMAGE040
，
Figure 644557DEST_PATH_IMAGE041
amplitude M of vibrationiIs: +/-5 deg., 8 deg., 12 deg., 15 deg., frequency wkComprises the following steps: 1,3,5,7,10,15,20,25, vehicle speed: 30 km/h;
according to a preset formula:
Figure 207890DEST_PATH_IMAGE044
、
Figure 126671DEST_PATH_IMAGE043
、
Figure 207890DEST_PATH_IMAGE044
、
Figure 934538DEST_PATH_IMAGE045
obtaining information vectors
Figure 629961DEST_PATH_IMAGE046
；
According to a preset formula:
Figure 933904DEST_PATH_IMAGE047
、
Figure 740186DEST_PATH_IMAGE048
obtaining information vectors
Figure 586919DEST_PATH_IMAGE049
；
According to a preset formula:
Figure 266293DEST_PATH_IMAGE050
、
Figure 260794DEST_PATH_IMAGE051
separately acquiring imaginary part information
Figure 542871DEST_PATH_IMAGE052
And real part information
Figure 306427DEST_PATH_IMAGE053
；
According to a preset formula:
Figure 140391DEST_PATH_IMAGE054
、
Figure 622188DEST_PATH_IMAGE055
obtaining a gain vector
Figure 707956DEST_PATH_IMAGE056
；
According to a preset formula:
Figure 326019DEST_PATH_IMAGE057
、
Figure 347196DEST_PATH_IMAGE058
obtaining a covariance matrix
Figure 253972DEST_PATH_IMAGE059
；
According to a preset formula:
Figure 940168DEST_PATH_IMAGE060
、
Figure 740634DEST_PATH_IMAGE061
obtaining system parameter estimation vector
Figure 854083DEST_PATH_IMAGE062
。
CN202010348969.3A 2020-04-28 2020-04-28 Method and system for establishing automatic steering model of unmanned vehicle Pending CN111399385A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111985347A (en) * 2020-07-28 2020-11-24 广州汽车集团股份有限公司 Body-in-white modal identification method and device

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008109810A (en) * 2006-10-26 2008-05-08 Chugoku Electric Power Co Inc:The Evaluation index analysis method for control of load frequency
CN104239718A (en) * 2014-09-17 2014-12-24 北京航空航天大学 Recursive least squares-differential evolution algorithm-based magnetic bearing identification method
CN105045092A (en) * 2014-08-15 2015-11-11 上海交通大学 PI^lambdaD^mu controller parameter optimization method for unmanned intelligent automobile
CN105159311A (en) * 2015-09-14 2015-12-16 湖北航天技术研究院总体设计所 Design method of automatic pilot for strapdown seeker
CN107092256A (en) * 2017-05-27 2017-08-25 中国科学院自动化研究所 A kind of unmanned vehicle rotating direction control method
CN110501910A (en) * 2019-09-09 2019-11-26 朱嘉慧 More factor open loops pass letter and system features auxiliary function and closed loop passes letter measuring method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008109810A (en) * 2006-10-26 2008-05-08 Chugoku Electric Power Co Inc:The Evaluation index analysis method for control of load frequency
CN105045092A (en) * 2014-08-15 2015-11-11 上海交通大学 PI^lambdaD^mu controller parameter optimization method for unmanned intelligent automobile
CN104239718A (en) * 2014-09-17 2014-12-24 北京航空航天大学 Recursive least squares-differential evolution algorithm-based magnetic bearing identification method
CN105159311A (en) * 2015-09-14 2015-12-16 湖北航天技术研究院总体设计所 Design method of automatic pilot for strapdown seeker
CN107092256A (en) * 2017-05-27 2017-08-25 中国科学院自动化研究所 A kind of unmanned vehicle rotating direction control method
CN110501910A (en) * 2019-09-09 2019-11-26 朱嘉慧 More factor open loops pass letter and system features auxiliary function and closed loop passes letter measuring method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
丁锋，等: "传递函数辨识（9）:基于频率响应的递推参数估计方法", 《青岛科技大学学报(自然科学版)》 *
李海鹏，等: "预定道路下的电动赛车转矩优化控制策略", 《福州大学学报(自然科学版)》 *
田海波，等: "无人驾驶探测车的转向特性分析与仿真", 《计算机仿真》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111985347A (en) * 2020-07-28 2020-11-24 广州汽车集团股份有限公司 Body-in-white modal identification method and device
CN111985347B (en) * 2020-07-28 2024-02-02 广州汽车集团股份有限公司 Body-in-white modal identification method and device

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