CN113741549A - Multi-rotor unmanned aerial vehicle control quantity distribution method - Google Patents

Multi-rotor unmanned aerial vehicle control quantity distribution method Download PDF

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Publication number
CN113741549A
CN113741549A CN202111297890.3A CN202111297890A CN113741549A CN 113741549 A CN113741549 A CN 113741549A CN 202111297890 A CN202111297890 A CN 202111297890A CN 113741549 A CN113741549 A CN 113741549A
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control
control variable
unmanned aerial
aerial vehicle
variable vector
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CN113741549B (en
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黄立
张原艺
王龙
林家民
张正飞
薛源
刘华斌
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Puzhou Technology Co ltd
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Puzhou Technology Shenzhen Co ltd
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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
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft

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Abstract

The invention relates to a control quantity distribution method for a multi-rotor unmanned aerial vehicle, which comprises the steps of obtaining the minimum value and the maximum value of the allowable rotating speed of each motor under the normal working state of the unmanned aerial vehicle, and distributing the minimum value and the maximum value to each motoriEstablishing rotational speed and control variables
Figure DEST_PATH_IMAGE001
The relationship of (1); constructing a control matrix based on an included angle between a motor driving direction and an unmanned aerial vehicle head direction; establishing a control quantity vector based on the height control quantity, the roll angle control quantity and the pitch angle control quantity of the unmanned aerial vehicle, and obtaining control variables of n motors by the product of a control matrix and the control quantity vector
Figure 398240DEST_PATH_IMAGE001
Forming a control variable vector; and optimizing the control variable vector through the control variable of the unmanned aerial vehicle, and controlling the flight state of the multi-rotor unmanned aerial vehicle based on the optimized control variable vector. The invention adds the control quantity of each channel and then effectively carries out control quantityThe saturated burden of the motor is reduced due to redistribution, and the capability of the unsaturated motor is fully utilized, so that the performance and safety of multi-rotor flight can be guaranteed under some limit conditions.

Description

Multi-rotor unmanned aerial vehicle control quantity distribution method
Technical Field
The invention relates to the technical field of multi-rotor unmanned aerial vehicles, in particular to a control quantity distribution method of a multi-rotor unmanned aerial vehicle.
Background
At present, most of multi-rotor unmanned aerial vehicles superpose the control quantities of all channels (pitching, rolling, course and height) of multiple rotors through a power distribution matrix, and then input the final control quantities to all motors to realize stable flight. For example, the invention patent application with publication number CN107368091A discloses a stable flight control method of a multi-rotor unmanned aerial vehicle based on finite time neurodynamics, which determines the control quantity of each motor through the real-time orientation and attitude data of the vehicle and based on the differential thought decomposition control process. Although the method can realize the control quantity distribution of the multi-rotor unmanned aerial vehicle, in the actual flight process, the control quantity input to the power system has certain upper limit and lower limit in consideration of the limitations of flight performance and power, so that the control quantity to a certain motor or motors is too large or too small to exceed the amplitude limit under certain conditions, and a better control effect cannot be achieved and even the control is out of control.
Disclosure of Invention
The invention aims to solve the technical problem of providing a control quantity distribution method for limiting the limit value of each motor control quantity for a multi-rotor unmanned aerial vehicle aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for distributing the control amount of multi-rotor unmanned aerial vehicle comprises
Obtaining the minimum value and the maximum value of the allowable rotating speed of each motor under the normal working state of the unmanned aerial vehicle, and setting the minimum value and the maximum value as each motoriEstablishing rotational speed and control variables
Figure 64976DEST_PATH_IMAGE001
In which
Figure 120788DEST_PATH_IMAGE002
nThe total number of the motors is,
Figure 720396DEST_PATH_IMAGE003
in time, the motoriIn order to be the minimum rotational speed of the motor,
Figure 187150DEST_PATH_IMAGE004
in time, the motoriIs the maximum rotation speed;
constructing a control matrix based on an included angle between a motor driving direction and an unmanned aerial vehicle head direction;
establishing a control quantity vector based on the height control quantity, the roll angle control quantity and the pitch angle control quantity of the unmanned aerial vehicle, and obtaining control variables of n motors by the product of a control matrix and the control quantity vector
Figure 239420DEST_PATH_IMAGE001
Forming a control variable vector;
and optimizing the control variable vector through the control variable of the unmanned aerial vehicle, and controlling the flight state of the multi-rotor unmanned aerial vehicle based on the optimized control variable vector.
Preferably, the motoriRotational speed of
Figure 597457DEST_PATH_IMAGE005
And a control variable
Figure 51573DEST_PATH_IMAGE001
In a linear relationship, the expression is,
Figure 954806DEST_PATH_IMAGE006
according to
Figure 228793DEST_PATH_IMAGE003
In time, the motoriAt a minimum rotation speed
Figure 626407DEST_PATH_IMAGE007
，
Figure 935029DEST_PATH_IMAGE004
In time, the motoriAt the maximum rotation speed
Figure 274743DEST_PATH_IMAGE008
；
To obtain
Figure 301605DEST_PATH_IMAGE009
To obtain
Figure 470287DEST_PATH_IMAGE010
。
Preferably, the control matrix constructed based on the included angle between the motor driving direction and the unmanned aerial vehicle head direction is as follows,
Figure 164574DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 409610DEST_PATH_IMAGE012
representing the included angle between the output direction of the motor i and the direction of the machine head;
Figure 658189DEST_PATH_IMAGE013
，
Figure 397606DEST_PATH_IMAGE014
、
Figure 946399DEST_PATH_IMAGE015
、
Figure 96758DEST_PATH_IMAGE016
、
Figure 98212DEST_PATH_IMAGE017
and respectively allocating vectors for the control quantities of the height, the roll, the pitch and the heading.
Preferably, the calculation formula of the control variable vector is as follows:
Figure 765954DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 11996DEST_PATH_IMAGE019
in order to be a high degree of control,
Figure 739781DEST_PATH_IMAGE020
is a control quantity of the roll angle,
Figure 822006DEST_PATH_IMAGE021
is the control quantity of the pitch angle, in the above formula, the control quantity of the course angle
Figure 559018DEST_PATH_IMAGE022
Not participating in the calculation, and therefore removing the vector of heading control quantity allocation in the control matrix
Figure 426611DEST_PATH_IMAGE023
。
Preferably, the method for optimizing the control variable vector by the control variable of the drone includes:
for control variable vector
Figure 59718DEST_PATH_IMAGE024
Has a median value of
Figure 894818DEST_PATH_IMAGE025
The control variable outside the range is optimized by using the height control quantity, and the height control quantity correction coefficient is obtained by calculation
Figure 904363DEST_PATH_IMAGE026
Correction of coefficient by height control amount
Figure 119138DEST_PATH_IMAGE027
Updating a control variable vector
Figure 188725DEST_PATH_IMAGE028
Obtaining a highly optimized control variable vector
Figure 245543DEST_PATH_IMAGE029
；
Control variable vector for altitude optimization
Figure 58778DEST_PATH_IMAGE030
Has a median value of
Figure 900963DEST_PATH_IMAGE025
The control variable outside the range is optimized through the roll angle control quantity, and the roll angle control quantity correction coefficient is obtained through calculation
Figure 141452DEST_PATH_IMAGE031
Correction coefficient by roll angle control amount
Figure 685566DEST_PATH_IMAGE031
Updating highly optimized control variable vectors
Figure 36912DEST_PATH_IMAGE032
Obtaining a roll optimization control variable vector
Figure 232139DEST_PATH_IMAGE033
；
Control variable vector for roll optimization
Figure 643529DEST_PATH_IMAGE034
Has a median value of
Figure 674939DEST_PATH_IMAGE025
The control variable outside the range is optimized through the pitch angle control variable, and the correction coefficient of the pitch angle control variable is obtained through calculation
Figure 829977DEST_PATH_IMAGE035
Correction coefficient by pitch angle control amount
Figure 381175DEST_PATH_IMAGE035
Updating roll optimization control variable vector
Figure 963466DEST_PATH_IMAGE036
Obtaining a pitch optimization control variable vector
Figure 623118DEST_PATH_IMAGE037
；
Controlling the course
Figure 440901DEST_PATH_IMAGE038
Adding to modified pitch-optimized control variable vector
Figure 236818DEST_PATH_IMAGE039
In the method, a course control variable vector is obtained
Figure 98333DEST_PATH_IMAGE040
；
For course control variable vector
Figure 979701DEST_PATH_IMAGE040
Has a median value of
Figure 866755DEST_PATH_IMAGE025
The control variable outside the range is optimized through the course angle control quantity, and the course angle control quantity correction coefficient is obtained through calculation
Figure 517179DEST_PATH_IMAGE041
By correction factor of course angle control quantity
Figure 316639DEST_PATH_IMAGE041
Updating course control variable vector
Figure 419724DEST_PATH_IMAGE042
Obtaining a course optimization control variable vector
Figure 579310DEST_PATH_IMAGE043
；
Controlling variable vectors by course optimization
Figure 349820DEST_PATH_IMAGE043
And the rotating speed of each motor is calculated, and the control of the multi-rotor unmanned aerial vehicle is realized.
Preferably, for control variable vector
Figure 818716DEST_PATH_IMAGE044
Has a median value of
Figure 409097DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 372374DEST_PATH_IMAGE045
Correction of height thereof
Figure 997391DEST_PATH_IMAGE046
Is composed of
Figure 138653DEST_PATH_IMAGE047
Wherein the content of the first and second substances,
Figure 950751DEST_PATH_IMAGE048
representing allocation vectors
Figure 452140DEST_PATH_IMAGE049
To (1) aiThe number of the data is one,
Figure 45844DEST_PATH_IMAGE050
；
height control amount correction coefficient
Figure 217063DEST_PATH_IMAGE051
The calculation formula of (2) is as follows:
Figure 375511DEST_PATH_IMAGE052
highly optimized control variable vector
Figure 165744DEST_PATH_IMAGE053
Comprises the following steps:
Figure 358828DEST_PATH_IMAGE054
。
preferably, the control variable vector is optimized for height
Figure 74849DEST_PATH_IMAGE055
Has a median value of
Figure 861539DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 704731DEST_PATH_IMAGE056
Coefficient of roll correction thereof
Figure 503053DEST_PATH_IMAGE057
Is composed of
Figure 281654DEST_PATH_IMAGE058
Wherein the content of the first and second substances,
Figure 680274DEST_PATH_IMAGE059
representing allocation vectors
Figure 310844DEST_PATH_IMAGE060
To (1) aiData, i.e.
Figure 619466DEST_PATH_IMAGE061
；
Roll angle control amount correction coefficient
Figure 428022DEST_PATH_IMAGE062
The calculation formula of (2) is as follows:
Figure 64671DEST_PATH_IMAGE063
roll optimization control variable vector
Figure 125031DEST_PATH_IMAGE064
Comprises the following steps:
Figure 147213DEST_PATH_IMAGE065
。
preferably, the control variable vector is optimized for roll
Figure 641517DEST_PATH_IMAGE066
Has a median value of
Figure 217992DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 957409DEST_PATH_IMAGE067
Its pitch correction coefficient
Figure 975044DEST_PATH_IMAGE068
Is composed of
Figure 125402DEST_PATH_IMAGE069
Wherein the content of the first and second substances,
Figure 799677DEST_PATH_IMAGE070
representing allocation vectors
Figure 326474DEST_PATH_IMAGE071
To (1) aiData, i.e.
Figure 464194DEST_PATH_IMAGE072
；
Correction coefficient of pitch angle control amount
Figure 801766DEST_PATH_IMAGE073
The calculation formula of (2) is as follows:
Figure 24936DEST_PATH_IMAGE074
pitch optimized control variable vector
Figure 355424DEST_PATH_IMAGE075
Comprises the following steps:
Figure 347650DEST_PATH_IMAGE076
。
preferably, the heading control amount
Figure 620238DEST_PATH_IMAGE077
Adding to modified pitch-optimized control variable vector
Figure 330705DEST_PATH_IMAGE078
In the method, a course control variable vector is obtained
Figure 199304DEST_PATH_IMAGE079
The method comprises the following steps:
Figure 311616DEST_PATH_IMAGE080
。
preferably, the vector of the heading control variable
Figure 990990DEST_PATH_IMAGE081
Has a median value of
Figure 454333DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 1989DEST_PATH_IMAGE082
Its course correction coefficient
Figure 93441DEST_PATH_IMAGE083
Is composed of
Figure 333930DEST_PATH_IMAGE084
Wherein the content of the first and second substances,
Figure 127311DEST_PATH_IMAGE085
representing allocation vectors
Figure 478658DEST_PATH_IMAGE086
To (1) aiData, i.e.
Figure 424617DEST_PATH_IMAGE087
；
Course angle control quantity correction coefficient
Figure 101586DEST_PATH_IMAGE088
The calculation formula of (2) is as follows:
Figure 883729DEST_PATH_IMAGE089
course optimization control variable vector
Figure 773187DEST_PATH_IMAGE090
Comprises the following steps:
Figure 839232DEST_PATH_IMAGE091
。
the invention has the beneficial effects that: through the rotational speed relation of establishing controlled variable and motor, convert unmanned aerial vehicle controlled variable distribution problem into controlled variable's optimization problem, then obtain controlled variable optimization controlled variable through unmanned aerial vehicle, ensure that controlled variable's numerical value is in between 0~1, thereby make the motor can be at the rotational speed within range internal rotation of safety, realize the effective distribution of many rotor unmanned aerial vehicle controlled variable, improve controlled variable distribution calculated speed, ensure many rotor unmanned aerial vehicle's safe flight. After the control quantities of all the channels are superposed, the control quantities are effectively redistributed, the saturation burden of the motor is reduced, and the capacity of the unsaturated motor is fully utilized, so that the performance and safety of multi-rotor flight can be ensured under some limit conditions.
Drawings
The invention will be further explained with reference to the drawings and examples.
Fig. 1 is a flow chart of a multi-rotor drone control distribution method provided by an embodiment of the present invention;
fig. 2 is a flowchart of a control variable optimization method of a multi-rotor drone control assignment method provided by an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail below 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.
Example 1:
as shown in fig. 1, the present embodiment provides a multi-rotor unmanned aerial vehicle control amount distribution method, including the steps of:
obtaining the minimum value and the maximum value of the allowable rotating speed of each motor under the normal working state of the unmanned aerial vehicle, and setting the minimum value and the maximum value as each motoriEstablishing rotational speed and control variables
Figure 421523DEST_PATH_IMAGE092
In which
Figure 923918DEST_PATH_IMAGE093
nThe total number of the motors is,
Figure 882647DEST_PATH_IMAGE094
in time, the motoriIn order to be the minimum rotational speed of the motor,
Figure 537619DEST_PATH_IMAGE095
in time, the motoriIs the maximum rotation speed;
constructing a control matrix based on an included angle between a motor driving direction and an unmanned aerial vehicle head direction;
establishing a control quantity vector based on the height control quantity, the roll angle control quantity and the pitch angle control quantity of the unmanned aerial vehicle, and obtaining control variables of n motors by the product of a control matrix and the control quantity vector
Figure 290811DEST_PATH_IMAGE092
Forming a control variable vector;
and optimizing the control variable vector through the control variable of the unmanned aerial vehicle, and controlling the flight state of the multi-rotor unmanned aerial vehicle based on the optimized control variable vector.
This embodiment is through the rotational speed relation of establishing controlled variable and motor, convert unmanned aerial vehicle controlled variable distribution problem into controlled variable's optimization problem, then obtain controlled variable optimization controlled variable through unmanned aerial vehicle, ensure that controlled variable's numerical value is between 0~1, thereby make the motor can be at the rotational speed within range internal rotation of safety, realize the effective distribution of many rotor unmanned aerial vehicle controlled variable, improve controlled variable distribution calculated speed, ensure many rotor unmanned aerial vehicle's safe flight.
The method for distributing the control quantity of the multi-rotor unmanned aerial vehicle provided by the embodiment specifically comprises the following steps:
obtaining the minimum value and the maximum value of the allowable rotating speed of each motor under the normal working state of the unmanned aerial vehicle, and setting the minimum value and the maximum value as each motoriEstablishing rotational speed and control variables
Figure 47546DEST_PATH_IMAGE092
In which
Figure 544386DEST_PATH_IMAGE093
nThe total number of the motors is,
Figure 319444DEST_PATH_IMAGE094
in time, the motoriIn order to be the minimum rotational speed of the motor,
Figure 977959DEST_PATH_IMAGE095
in time, the motoriIs the maximum rotation speed;
in particular establishing the speed and the controlled variable
Figure 612202DEST_PATH_IMAGE092
The relationship (c) can be obtained by fitting the relationship type through prior experience, and the common relationship is linear relationship, quadratic function relationship and the like. The present embodiment is described by taking a linear relationship as an example, and assuming that the motor is a motoriRotational speed of
Figure 26915DEST_PATH_IMAGE005
And a control variable
Figure 797425DEST_PATH_IMAGE092
In a linear relationship, the expression is,
Figure 751474DEST_PATH_IMAGE096
according to
Figure 607435DEST_PATH_IMAGE094
In time, the motoriAt a minimum rotation speed
Figure 321444DEST_PATH_IMAGE097
，
Figure 946461DEST_PATH_IMAGE095
In time, the motoriAt the maximum rotation speed
Figure 336991DEST_PATH_IMAGE098
；
To obtain
Figure 414668DEST_PATH_IMAGE099
To obtain
Figure 165324DEST_PATH_IMAGE100
Constructing a control matrix based on an included angle between a motor driving direction and an unmanned aerial vehicle head direction;
the control matrix constructed in this embodiment is as follows:
Figure 910426DEST_PATH_IMAGE101
wherein the content of the first and second substances,
Figure 471858DEST_PATH_IMAGE102
representing the included angle between the output direction of the motor i and the direction of the machine head;
Figure 36831DEST_PATH_IMAGE103
，
Figure 92643DEST_PATH_IMAGE104
、
Figure 426672DEST_PATH_IMAGE105
、
Figure 299950DEST_PATH_IMAGE106
、
Figure 476854DEST_PATH_IMAGE107
and respectively allocating vectors for the control quantities of the height, the roll, the pitch and the heading.
Establishing a control quantity vector based on the height control quantity, the roll angle control quantity and the pitch angle control quantity of the unmanned aerial vehicle, and obtaining control variables of n motors by the product of a control matrix and the control quantity vector
Figure 195411DEST_PATH_IMAGE092
Forming a control variable vector;
the calculation formula of the control variable vector is as follows:
Figure 23428DEST_PATH_IMAGE108
wherein the content of the first and second substances,
Figure 802028DEST_PATH_IMAGE109
in order to be a high degree of control,
Figure 466227DEST_PATH_IMAGE110
is a control quantity of the roll angle,
Figure 254055DEST_PATH_IMAGE111
is the control quantity of the pitch angle, in the above formula, the control quantity of the course angle
Figure 438043DEST_PATH_IMAGE112
Not participating in the calculation, and therefore removing the vector of heading control quantity allocation in the control matrix
Figure 387544DEST_PATH_IMAGE113
。
Optimizing a control variable vector through the control quantity of the unmanned aerial vehicle, and controlling the flight state of the multi-rotor unmanned aerial vehicle based on the optimized control variable vector;
referring to fig. 2, after the controlled variable matrix is obtained by superimposing the controlled variable vector and the control matrix, the method for optimizing the controlled variable vector by the controlled variable of the unmanned aerial vehicle includes:
for control variable vector
Figure 273460DEST_PATH_IMAGE114
Has a median value of
Figure 599400DEST_PATH_IMAGE025
The control variable outside the range is optimized by using the height control quantity, and the height control quantity correction coefficient is obtained by calculation
Figure 136429DEST_PATH_IMAGE115
Correction of coefficient by height control amount
Figure 522411DEST_PATH_IMAGE115
Updating a control variable vector
Figure 36569DEST_PATH_IMAGE116
Obtaining a highly optimized control variable vector
Figure 25254DEST_PATH_IMAGE117
；
For control variable vector
Figure 42888DEST_PATH_IMAGE118
Has a median value of
Figure 740717DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 476592DEST_PATH_IMAGE119
Height correction factor of
Figure 268967DEST_PATH_IMAGE120
Is composed of
Figure 141108DEST_PATH_IMAGE121
Wherein the content of the first and second substances,
Figure 236935DEST_PATH_IMAGE122
representing allocation vectors
Figure 460106DEST_PATH_IMAGE123
To (1) aiData, i.e.
Figure 931538DEST_PATH_IMAGE124
；
Height control amount correction
Figure 48399DEST_PATH_IMAGE125
The calculation formula of (2) is as follows:
Figure 556872DEST_PATH_IMAGE126
highly optimized control variable vector
Figure 267339DEST_PATH_IMAGE127
Comprises the following steps:
Figure 276883DEST_PATH_IMAGE128
control variable vector for altitude optimization
Figure 513829DEST_PATH_IMAGE129
Has a median value of
Figure 317837DEST_PATH_IMAGE025
The control variable outside the range is optimized through the roll angle control quantity, and the roll angle control quantity correction coefficient is obtained through calculation
Figure 889502DEST_PATH_IMAGE130
Correction coefficient by roll angle control amount
Figure 702737DEST_PATH_IMAGE130
Updating highly optimized control variable vectors
Figure 794190DEST_PATH_IMAGE131
Obtaining a roll optimization control variable vector
Figure 769099DEST_PATH_IMAGE132
；
Control variable vector for altitude optimization
Figure 329525DEST_PATH_IMAGE133
Has a median value of
Figure 680871DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 767776DEST_PATH_IMAGE134
Coefficient of roll correction thereof
Figure 303800DEST_PATH_IMAGE135
Is composed of
Figure 210576DEST_PATH_IMAGE136
Wherein the content of the first and second substances,
Figure 473936DEST_PATH_IMAGE137
representing allocation vectors
Figure 415347DEST_PATH_IMAGE138
To (1) aiData, i.e.
Figure 122272DEST_PATH_IMAGE139
；
Roll angle control amount correction coefficient
Figure 516344DEST_PATH_IMAGE140
The calculation formula of (2) is as follows:
Figure 84860DEST_PATH_IMAGE141
roll optimization control variable vector
Figure 880778DEST_PATH_IMAGE142
Comprises the following steps:
Figure 758604DEST_PATH_IMAGE143
control variable vector for roll optimization
Figure 905551DEST_PATH_IMAGE144
Has a median value of
Figure 136812DEST_PATH_IMAGE025
And (4) calculating out the control variable outside the range to obtain the pitch angle control quantity correction through the pitch angle control quantity optimization
Figure 161138DEST_PATH_IMAGE145
Correction coefficient by pitch angle control amount
Figure 85232DEST_PATH_IMAGE145
Updating roll optimization control variable vector
Figure 578530DEST_PATH_IMAGE146
Obtaining a pitch optimization control variable vector
Figure 879061DEST_PATH_IMAGE147
；
Control variable vector for roll optimization
Figure 259358DEST_PATH_IMAGE148
Has a median value of
Figure 354353DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 334947DEST_PATH_IMAGE149
Its pitch correction coefficient
Figure 908011DEST_PATH_IMAGE150
Is composed of
Figure 912788DEST_PATH_IMAGE151
Wherein the content of the first and second substances,
Figure 178685DEST_PATH_IMAGE152
representing allocation vectors
Figure 380996DEST_PATH_IMAGE153
To (1) aiData, i.e.
Figure 288909DEST_PATH_IMAGE154
；
Correction coefficient of pitch angle control amount
Figure 768432DEST_PATH_IMAGE155
The calculation formula of (2) is as follows:
Figure 80596DEST_PATH_IMAGE156
pitch optimized control variable vector
Figure 645569DEST_PATH_IMAGE157
Comprises the following steps:
Figure 685069DEST_PATH_IMAGE158
controlling the course
Figure 550257DEST_PATH_IMAGE159
Adding to modified pitch-optimized control variables
Figure 531857DEST_PATH_IMAGE160
In the method, a course control variable vector is obtained
Figure 584127DEST_PATH_IMAGE161
The formula is as follows:
Figure 427318DEST_PATH_IMAGE162
for course control variable vector
Figure 881433DEST_PATH_IMAGE163
Has a median value of
Figure 925613DEST_PATH_IMAGE025
The control variable outside the range is optimized through the course angle control quantity, and the course angle control quantity correction coefficient is obtained through calculation
Figure 340545DEST_PATH_IMAGE164
By correction factor of course angle control quantity
Figure 862793DEST_PATH_IMAGE164
Updating course control variable vector
Figure 296048DEST_PATH_IMAGE165
Obtaining a course optimization control variable vector
Figure 511129DEST_PATH_IMAGE166
；
To the course control variable
Figure 646313DEST_PATH_IMAGE167
Has a median value of
Figure 972252DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 260014DEST_PATH_IMAGE168
Its course correction coefficient
Figure 380417DEST_PATH_IMAGE169
Is composed of
Figure 769941DEST_PATH_IMAGE170
Wherein the content of the first and second substances,
Figure 899571DEST_PATH_IMAGE171
representing allocation vectors
Figure 917205DEST_PATH_IMAGE172
To (1) aiThe number of the data is one,
Figure 598722DEST_PATH_IMAGE173
；
course angle control quantity correction coefficient
Figure 334597DEST_PATH_IMAGE174
The calculation formula of (2) is as follows:
Figure 110661DEST_PATH_IMAGE175
course optimization control variable vector
Figure 513961DEST_PATH_IMAGE176
Comprises the following steps:
Figure 366379DEST_PATH_IMAGE177
calculating to obtain the final course optimization control variable vector
Figure 323971DEST_PATH_IMAGE178
And then, based on the relation between the control variable and the motor rotating speed, the rotating speed of each motor can be calculated, and the control of the multi-rotor unmanned aerial vehicle is realized.
In conclusion, the unmanned aerial vehicle control quantity distribution problem is converted into the control variable optimization problem by establishing the rotating speed relation between the control variable and the motor, and then the control quantity optimization control variable is obtained through the unmanned aerial vehicle, so that the motor can rotate in a safe rotating speed range, the control quantity of the multi-rotor unmanned aerial vehicle is effectively distributed, the control quantity distribution calculation speed is improved, and the safe flight of the multi-rotor unmanned aerial vehicle is ensured. Meanwhile, the control quantity of each channel is redistributed after being superposed, so that the saturation burden of the motor is reduced, and the capacity of the unsaturated motor is fully utilized.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus (system) or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.

Claims (10)

1. A control quantity distribution method for a multi-rotor unmanned aerial vehicle is characterized by comprising the following steps: comprises that
Obtaining the minimum value and the maximum value of the allowable rotating speed of each motor under the normal working state of the unmanned aerial vehicle, and setting the minimum value and the maximum value as each motoriEstablishing rotational speed and control variables
Figure 778194DEST_PATH_IMAGE001
In which
Figure 247353DEST_PATH_IMAGE002
nThe total number of the motors is,
Figure 557986DEST_PATH_IMAGE003
in time, the motoriIn order to be the minimum rotational speed of the motor,
Figure 810107DEST_PATH_IMAGE004
in time, the motoriIs the maximum rotation speed;
constructing a control matrix based on an included angle between a motor driving direction and an unmanned aerial vehicle head direction;
establishing a control quantity vector based on the height control quantity, the roll angle control quantity and the pitch angle control quantity of the unmanned aerial vehicle, and obtaining control variables of n motors by the product of a control matrix and the control quantity vector
Figure 351947DEST_PATH_IMAGE001
Forming a control variable vector;
and optimizing the control variable vector through the control variable of the unmanned aerial vehicle, and controlling the flight state of the multi-rotor unmanned aerial vehicle based on the optimized control variable vector.
2. A multi-rotor drone control assignment method according to claim 1, characterized by: the motoriRotational speed of
Figure 726428DEST_PATH_IMAGE005
And a control variable
Figure 88139DEST_PATH_IMAGE001
In a linear relationship, the expression is,
Figure 845748DEST_PATH_IMAGE006
according to
Figure 914198DEST_PATH_IMAGE003
In time, the motoriAt a minimum rotation speed
Figure 584214DEST_PATH_IMAGE007
，
Figure 338281DEST_PATH_IMAGE004
In time, the motoriAt the maximum rotation speed
Figure 525680DEST_PATH_IMAGE008
；
To obtain
Figure 917478DEST_PATH_IMAGE009
To obtain
Figure 961658DEST_PATH_IMAGE010
。
3. A multi-rotor drone control assignment method according to claim 1, characterized by: the control matrix constructed based on the included angle between the motor driving direction and the unmanned aerial vehicle head direction is as follows,
Figure 603686DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 532459DEST_PATH_IMAGE012
representing the included angle between the output direction of the motor i and the direction of the machine head;
Figure 637818DEST_PATH_IMAGE013
，
Figure 961221DEST_PATH_IMAGE014
、
Figure 50400DEST_PATH_IMAGE015
、
Figure 845181DEST_PATH_IMAGE016
、
Figure 742729DEST_PATH_IMAGE017
and respectively allocating vectors for the control quantities of the height, the roll, the pitch and the heading.
4. A multi-rotor drone control assignment method according to claim 3, characterized by: the calculation formula of the control variable vector is as follows:
Figure 768192DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 62776DEST_PATH_IMAGE019
in order to be a high degree of control,
Figure 395668DEST_PATH_IMAGE020
is a control quantity of the roll angle,
Figure 147724DEST_PATH_IMAGE021
is the control quantity of the pitch angle, in the above formula, the control quantity of the course angle
Figure 881105DEST_PATH_IMAGE022
Not participating in the calculation, and therefore removing the vector of heading control quantity allocation in the control matrix
Figure 351401DEST_PATH_IMAGE023
。
5. The multi-rotor unmanned aerial vehicle control amount distribution method according to claim 4, wherein: the method for optimizing the control variable vector through the control quantity of the unmanned aerial vehicle comprises the following steps:
for control variable vector
Figure 956826DEST_PATH_IMAGE024
Has a median value of
Figure 891284DEST_PATH_IMAGE025
The control variable outside the range is optimized by using the height control quantity, and the height control quantity correction coefficient is obtained by calculation
Figure 291172DEST_PATH_IMAGE026
Correction of coefficient by height control amount
Figure 747299DEST_PATH_IMAGE027
Updating a control variable vector
Figure 687573DEST_PATH_IMAGE028
Obtaining a highly optimized control variable vector
Figure 86325DEST_PATH_IMAGE029
；
Control variable vector for altitude optimization
Figure 516169DEST_PATH_IMAGE029
Has a median value of
Figure 459592DEST_PATH_IMAGE025
The control variable outside the range is optimized through the roll angle control quantity, and the roll angle control quantity correction coefficient is obtained through calculation
Figure 141240DEST_PATH_IMAGE030
Correction coefficient by roll angle control amount
Figure 50290DEST_PATH_IMAGE031
Updating highly optimized control variable vectors
Figure 854298DEST_PATH_IMAGE032
Obtaining a roll optimization control variable vector
Figure 894804DEST_PATH_IMAGE033
；
Control variable vector for roll optimization
Figure 770356DEST_PATH_IMAGE034
Has a median value of
Figure 206017DEST_PATH_IMAGE025
The control variable outside the range is optimized through the pitch angle control variable, and the correction coefficient of the pitch angle control variable is obtained through calculation
Figure 118609DEST_PATH_IMAGE035
Correction coefficient by pitch angle control amount
Figure 272510DEST_PATH_IMAGE035
Updating roll optimization control variable vector
Figure 608812DEST_PATH_IMAGE036
Obtaining a pitch optimization control variable vector
Figure 226875DEST_PATH_IMAGE037
；
Controlling the course
Figure 575948DEST_PATH_IMAGE038
Adding to modified pitch-optimized control variable vector
Figure 217145DEST_PATH_IMAGE039
In the method, a course control variable vector is obtained
Figure 434500DEST_PATH_IMAGE040
；
For course control variable vector
Figure 484233DEST_PATH_IMAGE040
Has a median value of
Figure 128841DEST_PATH_IMAGE025
The control variable outside the range is optimized through the course angle control quantity, and the course angle control quantity correction coefficient is obtained through calculation
Figure 195017DEST_PATH_IMAGE041
By correction factor of course angle control quantity
Figure 622587DEST_PATH_IMAGE041
Updating course control variable vector
Figure 215243DEST_PATH_IMAGE042
Obtaining a course optimization control variable vector
Figure 640539DEST_PATH_IMAGE043
；
Controlling variable vectors by course optimization
Figure 754863DEST_PATH_IMAGE043
And the rotating speed of each motor is calculated, and the control of the multi-rotor unmanned aerial vehicle is realized.
6. The multi-rotor unmanned aerial vehicle control amount distribution method according to claim 5, wherein: for control variable vector
Figure 314020DEST_PATH_IMAGE044
Has a median value of
Figure 636548DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 622959DEST_PATH_IMAGE045
Height correction factor of
Figure 194886DEST_PATH_IMAGE046
Is composed of
Figure 869318DEST_PATH_IMAGE047
Wherein the content of the first and second substances,
Figure 843091DEST_PATH_IMAGE048
representing allocation vectors
Figure 672506DEST_PATH_IMAGE049
To (1) aiData, i.e.
Figure 59625DEST_PATH_IMAGE050
；
Height control amount correction coefficient
Figure 835952DEST_PATH_IMAGE051
The calculation formula of (2) is as follows:
Figure 699783DEST_PATH_IMAGE052
highly optimized control variable vector
Figure 762417DEST_PATH_IMAGE053
Comprises the following steps:
Figure 918723DEST_PATH_IMAGE054
。
7. the multi-rotor unmanned aerial vehicle control amount distribution method according to claim 6, wherein: control variable vector for altitude optimization
Figure 92216DEST_PATH_IMAGE055
Has a median value of
Figure 804694DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 382437DEST_PATH_IMAGE056
Coefficient of roll correction thereof
Figure 416252DEST_PATH_IMAGE057
Is composed of
Figure 564075DEST_PATH_IMAGE058
Wherein the content of the first and second substances,
Figure 632525DEST_PATH_IMAGE059
representing allocation vectors
Figure 551808DEST_PATH_IMAGE060
To (1) aiData, i.e.
Figure 400816DEST_PATH_IMAGE061
；
Roll angle control amount correction coefficient
Figure 119373DEST_PATH_IMAGE062
The calculation formula of (2) is as follows:
Figure 245592DEST_PATH_IMAGE063
roll optimizationVector of control variables
Figure 820930DEST_PATH_IMAGE064
Comprises the following steps:
Figure 994117DEST_PATH_IMAGE065
。
8. the multi-rotor unmanned aerial vehicle control amount distribution method according to claim 7, wherein: control variable vector for roll optimization
Figure 985206DEST_PATH_IMAGE066
Has a median value of
Figure 356145DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 977750DEST_PATH_IMAGE067
Its pitch correction coefficient
Figure 739033DEST_PATH_IMAGE068
Is composed of
Figure 861710DEST_PATH_IMAGE069
Wherein the content of the first and second substances,
Figure 195477DEST_PATH_IMAGE070
to express allocation vectoriData, i.e.
Figure 361196DEST_PATH_IMAGE073
；
Correction coefficient of pitch angle control amount
Figure 162930DEST_PATH_IMAGE074
The calculation formula of (2) is as follows:
Figure 180564DEST_PATH_IMAGE075
pitch optimized control variable vector
Figure 268606DEST_PATH_IMAGE076
Comprises the following steps:
Figure 237437DEST_PATH_IMAGE077
。
9. the multi-rotor drone control assignment method according to claim 8, wherein: the course control quantity
Figure 842862DEST_PATH_IMAGE078
Adding to modified pitch-optimized control variable vector
Figure 777320DEST_PATH_IMAGE079
The method for obtaining the course control variable vector comprises the following steps:
Figure 134800DEST_PATH_IMAGE082
。
10. the multi-rotor drone control assignment method according to claim 9, wherein: for course control variable vector
Figure 308030DEST_PATH_IMAGE083
Has a median value of
Figure 96994DEST_PATH_IMAGE025
Out-of-range controlled variables
Figure 464522DEST_PATH_IMAGE084
Its course correction coefficient
Figure 112672DEST_PATH_IMAGE085
Is composed of
Figure 122216DEST_PATH_IMAGE086
Wherein the content of the first and second substances,
Figure 411027DEST_PATH_IMAGE087
representing allocation vectors
Figure 949456DEST_PATH_IMAGE088
To (1) aiData, i.e.
Figure 209536DEST_PATH_IMAGE089
；
Course angle control quantity correction coefficient
Figure 429296DEST_PATH_IMAGE090
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE091
course optimization control variable vector
Figure 97912DEST_PATH_IMAGE092
Comprises the following steps:
Figure 807242DEST_PATH_IMAGE093
。
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