AU2021104734A4 - A Design Method of a Variable-parameter Neural Dynamic Controller for Drones, and Application Thereof - Google Patents
A Design Method of a Variable-parameter Neural Dynamic Controller for Drones, and Application Thereof Download PDFInfo
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- 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
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- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
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- G—PHYSICS
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- 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
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Abstract
This invention discloses a design method of a variable-parameter neural dynamic controller
for drones, and application thereof. The method comprising the following steps: building a
drone model; based on the drone model, using a variable-parameter recursive neural
dynamic method based on an activation function to design an altitude controller, a yaw
angle controller, a roll angle controller, a pitch angle controller, a X controller and a Y
controller of a drone respectively; inputting control target parameters and drone status
information collected by a drone sensor to a controller of each drone; the controller of each
drone output control components to control a flight of the drone. The present invention
utilizes a non-linear activation function, and the obtained controller may make a drone
converge to a target faster when the error is large, and achieve higher accuracy when it is
close to the mission target, so that the drone may control the drone to track a time-varying
trajectory quickly, accurately and in real time.
z (M) - Tut
0.4
0.2.
0.4 0.4 ~ Initial
0.2 0 5
y (M) 0.25x (M)
o o
Figure 5
3/3
Description
- Tut z (M) 0.4 0.2.
0.4 0.4 ~ Initial 0.2 05 y (M) 0.25x (M) o o Figure 5
3/3
A Design Method of a Variable-parameter Neural Dynamic Controller for Drones, and Application Thereof
Technical field The invention relates to the technical field of drone controllers, in particular to a design method of a variable-parameter neural dynamic controller for drones, and application
thereof.
Technical Background A multi-rotor drone is a flexible and simple drone. In order to reflect its flexible superiority, whether the controller may quickly and efficiently control the multi-rotor drone with high
precision becomes particularly important.
Existing drone controllers mainly use PID controllers, but due to their own time-invariant algorithms, their convergence speed is not sufficient to complete time-varying task
objectives. The advantages of PID are gradually declining. At the same time, due to the
reproducibility of its parameters, it is difficult to complete high-precision control tasks that require constant parameter changes, and the parameter settings of the PID controller are
too dependent on the designer's experience.
Summary of the Invention In order to overcome the shortcomings and deficiencies of the prior art, the present
invention provides a design method of a variable-parameter neural dynamic controller for drones, which inherits the traditional advantages of neural dynamics. At the same time, by
using the nonlinear characteristics of a Power-sigmoid activation function, the obtained controller may make a drone converge to a target faster when the error is large, and achieve
higher accuracy when it is close to the mission target. Compared with prior neural dynamics
controllers, it may converge to a time-varying target faster.
The second objective of the present invention is to provide a design system of a variable parameter neural dynamic controller for drones.
The third objective of the present invention is to provide a storage medium.
The fourth objective of the present invention is to provide a computing device.
In order to achieve the above objectives, the present invention adopts the following technical solutions:
The present invention provides a design method of a variable-parameter neural dynamic controller for drones, characterized in that, comprising the following steps:
building a drone model;
based on the drone model, using a variable-parameter recursive neural dynamic method
based on an activation function to design an altitude controller, a yaw angle controller, a roll angle controller, a pitch angle controller, a X controller and a Y controller of a drone
respectively;
inputting control target parameters and drone state information collected by a drone sensor
to a controller of each drone; the controller of each drone output control components to control a flight of the drone.
As a preferred technical solution, the step of building the drone model specifically comprises:
a dynamic equation of a position state variable of the drone is described as:
= [(CXSoCp + S*SP)uz]
i=-[(S So Cp - C* SP)uz) 1
Z = [COCu] - g m wherein X, Y, Z represent a position of a center of gravity of the drone, ,YZare
corresponding second-order derivatives, m represents a total mass of the drone,
S', C'PS, CO, SP, Cp are respectively expressing
sin#, cos#, sin, cos, sin, cos, uz is a resultant force of the drone on an oz axis
of a body coordinate system, and g is an acceleration of gravity, j means a roll angle, 0
means a pitch angle, 4 means a yaw angle;
using Euler's method to model an attitude angular motion of the drone, a rotation dynamic of the drone is described as:
.. l. .Jy - Jz_ #P = -u(P + 84) Jx Jx .. I . . hz -Ix, 0 = -uO + #PV Jy Jy .. 1 . .J, - Jy IP = -u'P + 0#
wherein j is the roll angle, 0 is the pitch angle, is the yaw angle, ,0, are
corresponding second-order derivatives, J, Jy, Jz are inherent inertias during rotations
around the ox axis, oy axis, and oz axis, respectively, up, ue, up represent resultant
rotation forces in the rotation directions of 0, 0, respectively, and I represents a length
of a motor arm of the drone;
the drone model is expressed as:
$ = axuz:
= ayuz;
azuz - g:
-~u +
wherein
0Csg sC a- C'Sc . -, 1 ay = SgeSoC -CgS, a 1
PzJx Jy
Jz As a preferred technical solution, the activation function adopts a Power-sigmoid function, a specific expression is:
UP ,ul > 1 f(u) + exp(- () 1 - exp(-fu) - exp(-() 1 + exp(-fu) lul<1
wherein p represents an odd number, ( represents a constant, p 1, 1, u represents a position error or a velocity error of a drone tracking.
As a preferred technical solution, the variable-parameter recursive neural dynamic method based on the activation function is used to design the altitude controller of the drone, the altitude controller is expressed as:
z (t) =(-dzuz - bz (t) - (A+ tX)f(ez3(t)))/az
az= O'
bz(t) (A+ t)f(ez 2(t)) - Z(t) - g +At-1f(ez1 (t)) +Z(t)(A + t)f (ez(t); ez3 (t) = azuz(t) + bz(t);
ez2 (t) = Z(t) + (A + t)f (ez1(t));
ez1 (t) = Z(t) - ZTt)
wherein A > 1, Z(t) represents an actual altitude value, ZT(t) represents a target altitude value, f(x)represents the activation function, and m represents a total mass of the drone, CO, Cp are respectively expressing cosOand cos#, uzrepresents a control component of the drone in a direction of the oz axis of the body coordinate system, g is an acceleration of gravity, j is a roll angle, 0 is a pitch angle, 4 is a yaw angle, and ez1 (t) represents a first error function, ez 2(t) represents a second error function, and ez 3(t) represents a third error function.
As a preferred technical solution, specific design steps of the altitude controller comprise:
defining the first error function as: ez 1 (t) = Z(t) - ZTZ(t) - Z(t)
wherein Z(t)represents the actual altitude value, and ZT(t)represents the target altitude value;
a variable parameter recursive neural dynamic design formula according to the first error function is defined as: dz 1(t) -(A + t)f(ez1(t))
after substituting ez1 (t), obtaining:
$(t) + (A + t-)f(ez 1 (t)) = 0
defining the second error function as:
ez2 (t) = Z(t) + (A+ tV)f(ez1(t))
a variable parameter recursive neural dynamics design formula according to the second error function is defined as:
ez2 (t) = -(A + t)f(ez 2 (t))
after substituting ez 2 (t), obtaining:
(A + t-)f (ez(t))+ t-i'f(ez1(t)) + Z(t) (A + t)f/(ez1(t))+ Z(t) - Z(T = 0
defining the third error function as:
ez 3 (t)= (A + tX)f (ez(t))+ (t) - Z(t) + At A-1f(ez1 (t)) +Z(t)( + t)f(ez1(t)
a dynamic equation of a drone altitude is:
Z = [CCu] - g m after substituting Z(t), obtaining: ez 3 (t)= azuz(t)+ bz(t)
bz(t) = (A + t)f(ez 2 (t)) - Z(t) - g + 1At-f(ez1(t)) +Z(t) + t)f(ez1(t)
a variable parameter recursive neural dynamic design formula according to the third error function is defined as:
ez3 (t) = -(A + t")f(Ez(t))
after substituting ez3 (t), an expression of the altitude controller is obtained:
itz(t) = (-dzuz - bz(t) - (A + tX)f(ez3(t)))/az.
As a preferred technical solution, a specific step of the controller of each drone output control components to control flights of the drones comprises:
obtaining the drone state information collected by the drone sensor, and inputting the control target parameters into the altitude controller and the yaw angle controller to obtain a drone altitude control component and a yaw angle control component;
inputting the drone state information, the control target parameters, the drone altitude control component and the yaw angle control component to the X controller and the Y controller to obtain X and Y control components;
using an inverse solution method to calculate a roll angle and a pitch angle that satisfy the X and Y control components, as control targets of the roll angle controller and the pitch angle controller;
the roll angle controller and the pitch angle controller calculate and output a roll angle control component and a pitch angle control component; the altitude control component, the yaw angle control component, the roll angle control component and the pitch angle control component are used to control the flight of the drone.
In order to achieve the above-mentioned second objective, the present invention adopts the following technical solutions:
The present invention provides a design system of a variable-parameter neural dynamic controller for drones, characterized in that, comprising a drone model building module, a
drone parameter controller construction module and a controller output control module;
the drone model building module used to build a drone model;
the drone parameter controller construction module used to, based on the drone model, use a variable-parameter recursive neural dynamic method based on an activation function to
design an altitude controller, a yaw angle controller, a roll angle controller, a pitch angle
controller, a X controller and a Y controller of a drone respectively;
the controller output control module used to input control target parameters and drone state information collected by a drone sensor to a controller of each drone; the controller of
each drone output control components to control a flight of the drone.
As a preferred technical solution, the controller output control module comprises a altitude control component output unit, a yaw angle control component output unit, an X control
component output unit, a Y control component output unit, a roll angle control component output unit, and a pitch angle control component output unit;
the altitude control component output unit is used to obtain the drone state information collected by the drone sensor, and input the control target parameters to the altitude
controller to obtain a drone altitude control component; the yaw angle control component output unit is used to obtain the drone state information collected by the drone sensor, and input the control target parameters into the yaw angle controller to obtain a yaw angle control component; the X control component output unit is used to input the drone state information, the control target parameters, the drone altitude control component and the yaw angle control component into the X controller to obtain an X control component; the Y control component output unit is used to input the drone state information, the control target parameters, the drone altitude control component and the yaw angle control component into the Y controller to obtain a Y control component; the roll angle control component output unit is used to calculate the roll angle that satisfies the X and Y control components by using an inverse solution method, as a control target of the roll angle controller, and the roll angle controller calculates and outputs a roll angle control component; the pitch angle control component output unit is used to calculate the pitch angle satisfying the X and Y control components by using an inverse solution method, as a control target of the pitch angle controller, and the pitch angle controller calculates and outputs a pitch angle control component; the altitude control component, the yaw angle control component, the roll angle control component and the pitch angle control component are used to control the flight of the drone.
In order to achieve the above-mentioned third objective, the present invention adopts the
following technical solutions:
A storage medium storing a program, when the program is executed by a processor, the above design method of a variable-parameter neural dynamic controller for drones is
realized.
In order to achieve the above-mentioned fourth objective, the present invention adopts the
following technical solutions:
A computing device comprising a processor and a memory for storing an executable program for the processor, when the processor executes the program stored in the memory,
the above design method of a variable-parameter neural dynamic controller for drones is
realized.
Compared with the prior art, the present invention has the following advantages and beneficial effects:
(1) The present invention adopts a design scheme of a variable-parameter neural dynamic
controller for drones based on an activation function, solving the problem of consistent convergence speed under different errors in drone control and low drone convergence
accuracy, achieving the technical effect that the drone converges to a target faster when the
error is large, and achieving higher accuracy when it is close to a mission target.
(2) The controller of the present invention adopts a neural dynamic method as a control framework, which may calculate in parallel and process task targets with high efficiency.
Compared with prior traditional controller, the obtained drone controller may converge to the time-varying target faster and meet the control requirements of high precision and high
speed.
(3) The present invention adopts a technical solution that the design formula itself satisfies system stability requirements, solving a technical problem that the drone system may be
unstable due to the parameter selections of the drone controller, achieving a design process
specification and relying less on experience, the a designed controller may make the drone achieve an overall stable technical effect.
Description of the Figures Figure 1 is an illustrative diagram of a coordinate system of a drone model in this
embodiment;
Figure 2 is an illustrative diagram of an activation function of this embodiment;
Figure 3 is an illustrative diagram of an overall flow of the design method of a variable
parameter neural dynamic controller for drones in this embodiment;
Figure 4 is a three-dimensional illustrative diagram of a drone tracking target in this embodiment;
Figure 5 is a three-dimensional illustrative diagram of a tracking result of the drone in this
embodiment.
Descriptions
In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the
accompanying figures and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to
limit the present invention.
Embodiments
This embodiment provides a design method of a variable-parameter neural dynamic controller for drones, which comprises the following steps:
Si: establishing a drone model, the specific steps comprise:
In order to accurately define and describe the attitude of a drone, six variables need to be determined, namely the three-dimensional coordinates of the drone and its three attitude
angles. The drone of this embodiment uses a quadrotor drone;
As shown in Figure 1, establishing a Cartesian coordinate system: defining the flying
direction of the drone as the positive direction of the x-axis, defining the upward direction perpendicular to the drone plane as the positive direction of the z-axis, defining the y-axis
direction as the direction perpendicular to the x-axis and z-axis, establishing a coordinate system describing the position and flight attitude of the drone based on the above
mentioned axial direction, called the body coordinate system, and define the X-Y-Z
coordinate system as the ground coordinate system. The definition of the attitude angle is as follows:
<b is the roll angle (that is, rotating around the ox axis);
is the pitch angle (that is, rotating around the oy axis);
4 is the yaw angle (that is, rotating around the oz axis);
First, model the position of the drone. According to Newton's motion theorem, the dynamic equation of the drone's position state variable is described as:
1= CXSOCP +S*SP)uz]
i -[SO eC-P - C* S.)uz) 1
Z = [COCPu] - g m
wherein X, Y, Z represent a position of a center of gravity of the drone, ,YZare
corresponding second-order derivatives, m represents a total mass of the drone,
S(, C(PS, CO, SP, Cp are respectively expressing
sin#, cos#, sin, cos, sin, cos, uz is a resultant force of the drone on an oz axis
of a body coordinate system, and g is an acceleration of gravity; then, using Euler's method to model an attitude angular motion of the drone, a rotation dynamic of the drone is described as:
.. l. .Jy - Jz_ fx fx 0 = -u(P + #4)
Jy Jy .. 1 . .J, - Jy 1P = -u'P + 0#
wherein j is the roll angle, 0 is the pitch angle, is the yaw angle,,0, are
corresponding second-order derivatives, J, Jy, Jz are inherent inertias during rotations
around the ox axis, oy axis, and oz axis, respectively, up, ue, up represent resultant
rotation forces in the rotation directions of 0, 0, 4 respectively, and I represents a length
of a motor arm of the drone;
In this embodiment, the following equations are defined:
aa ay=SP - sc s';
J-Jz
Jyj
AP-= JX-JY; Jz
After the above formula is defined, the control variables may be highlighted, and the
relationship between the state variables and the control variables may be better understood;
Through the above formula definition, this embodiment simplifies the modelling formula as:
X = axuz;
Y ayuz :
azuz - g:
-pu + fP
-eu +fe
It may be seen from the above formula that the control variables have a direct relationship with the second derivative of the state variables, so as long as the relevant information of
the second derivative of the state variables can be obtained, the state variables may be
designed and controlled through the control variable;
S2: based on the drone model in step S1, the method of variable-parameter neural dynamics of drones based on Power-sigmoid activation function is used to design height Z controller,
yaw angle p controller, roll angle < controller, pitch angle 0 controller, X controller, Y controller;
In this embodiment, the specific steps for designing the height Z controller comprise:
First, defining the first error function error, that is, the error function between the target value of the height and the actual value of the height as:
ez1(t) = Z(t) - ZT(t: -- 2(t) (1)
Assuming that the above formula has a unique theoretical solution x* (t), the purpose of
the neural dynamics controller design method is to find x (t) = x* (t). If the error
function ez1(t) converges to zero, that is, the actual value of the height may converge to the
target height, then the only theoretical solution that may be obtained is x* (t) . In order to
ensure the convergence of the error function ez1(t), the time derivative should be negative
definite. Therefore, the design formula of the Power-sigmoid variable-parameter recursive neural dynamic method is defined as:
6z 1 (t) -(A + t)f(ez1(t)) (2) wherein A > 1, f(x) is the Power-sigmoid activation function, as shown in Figure 2, which is defined as follows: f(x) + exp(- () 1 - exp(- u) (3) - exp(-() 1 + exp(-fu)
' In f(x), p 1, and it is an odd number, and ( 2 1 is a constant. When the absolute value
range of the independent variable is greater than 1, it has an exponential nature, and when the absolute value range of the independent variable is less than 1, it has a power function
nature. In the design process of the variable-parameter recursive neural dynamic controller for drones based on an activation function, the independent variable of the function is the
position error or the velocity error of the drone tracking. Therefore, the use of a Power
sigmoid function may make the drone converge to the target faster when the error is large,
and achieve higher accuracy when it is close to a mission target; in ez(t), (+ tx) is a time
varying control parameter function used to control the convergence rate of the solution
process, where the value of A depends on the limitations of the hardware system or the requirements of specific control objectives. In general, the large the value of 1, the faster the
convergence speed of the controller. In practical applications, (A+ t-) is often not allowed
to increase to infinity, because this will greatly increase the hardware requirements of the
controller. It is often limited to an appropriate value. When (A+ t-) exceeds this value, let it
not to increase further, so as to ensure that the controller may successfully converge the
state variables to the set values, and also reduce the impact on the requirements of hardware system.
In the activation function f(x), p is the parameter used by the error function when the error
is large. To ensure that the error function may converge to zero, the control parameter p may only take an odd number. The value of p also depends on the limitations of the
hardware system or the requirements of specific control objectives. In general, the large the value of p, the faster the convergence speed of the state variables. The control parameter
is a parameter used by the error function when the error is small. Its purpose is to ensure
that the error function is less than one while still ensuring high accuracy and quickly converging to the target value. The value of ( will not be restricted by the hardware system
under normal circumstances, but still needs to consider the needs of specific control objectives. In general, the large the value of (, the better the control accuracy of the state variables. At the same time, it should be noted that, in order to ensure that the value of the control variable does not change suddenly when the state variable is at a special value
(x = 1, - 1) , it is necessary to set the parameter p and parameter ( to ensure that the
activation function curve between 1 and -1 can be smoothly converted. In other words, at
x = 1, - 1, the derivatives of the two cases of the activation function should be as equal
as possible.
In actual application, the above design formula is realized by using circuits to form a
recurring neural network. Therefore, this design method is called a neural dynamic design method.
The meaning of this design formula is to ensure that the error function is convergent. By
deriving ez1(t) and substituting it into formula (2), one may obtain:
ez 1(t) = -(A + tX)f(ez(t)) = Z(t) (4)
The above formula may be rewritten as
Z(t) + (A+ t)f(ez1(t)) = 0 (5)
The above equation is the control target to be achieved, that is, if the error function ez1(t) is
to converge to zero, that is, the actual height may track the mission target, the above equation must be established; however, in actual situations, the current derivation of
formula (1) is not equal to formula (2). At the same time, in order to meet the requirements of the controller design, not only the actual height must be converged to the target height,
but also the actual speed must be converged to the target speed. In addition, the control
variable uz is not explicitly included in equation (5). In order to understand the relevant control variables and state variables, it is necessary to continue to use the variable
parameter neural dynamic method for drones based on a Power-sigmoid activation function.
The second error function ez 2(t), that is, the error between the actual rising speed and the target speed, is set as ez2 (t) = Z(t) + (A+tI)f(ez1(t)) (6)
According to the design principle of the variable-parameter neural dynamics for drones based on a Power-sigmoid activation function, the derivative of the error function ez 2 (t) is set as:
eZz -(A + tI)f(ez 2 (t)) (7)
Similarly, substituting the derivation of formula (6) into formula (7), one may obtain:
6Zz(t)= -(A + t)ff(ez 2 (t)) =(t)-
( +t'-1f(ez 1 (t)) + Z(t)(A + t)f/(ez1(t)) (8)
Then, rewrite it as: (A + tx)f(ez 2 (t))+ At-'f(ez 1 (t))
+Z(t)(1A+ t)f(ez1(t)) + Z(t) - Z(t) 0 (9)
Again, this is just a design goal. This means that if the derivative of the state variable Z can track the target smoothly, the above equation needs to be established. According to the dynamic equation formula for drones dynamic modelling (9), the control variable uz is determined by Z, and the control variable uz is explicitly comprised in equation (9). Therefore, in order to establish equation (9), that is, the control variable uz may converge to the set value, it is still necessary to use the variable-parameter neural dynamics design method for drones based on a Power-sigmoid activation function.
Set the third error function ez3 to:
ez 3 (t) = (A + t)ff(ez(t)) + 2(t) - 21 (t) +t'-1f(ez 1 (t)) + Z(t) (A+ tA)f(ez1(t)) (10)
Substituting the definition of Z(t) in the dynamic modelling equation into equation (10), the above equation can be rewritten as:
ez3(1 = azuz(t) + bz(t (11 wherein bz(t) = (A + t-X)f(ez 2(t)) - Z(t) - g + A-t'f(ez1 (t)) +Z(t)(A
+ t)f/(ez1(t)). According to the design method of variable-parameter neural dynamics for drones based on a Power-sigmoid activation function, the derivative of the error function
ez3 is defined as:
eZ3 (t) = -(A + t)f (Ez (t)) (12)
By deriving formula (11) and substituting into formula (12), one may get:
nz (t) az + dzuz + bz (t) -(A + tI)f(ez3 (t)) (13)
The above formula may also be rewritten as:
itz(t) = (-dzuz - bz(t) - (A + tX)f(ez3(t)))/az (14)
The above formula is called the implicit dynamics equation for altitude in drone dynamic
modelling. Through this formula, the control variable uz may be obtained through iteration. When the control variable tz(t) satisfies the above equation, ez 3 (t) will converge to zero,
which means that equation (9) will be established, and equation (5) will also be established,
which shows that the height state variables Z(t), I(t) will converge to the target set values
ZT (t), ZT (t).
For three attitude angles, namely roll angle <(t), pitch angle (t), yaw angle p(t), the controller design is similar to the height controller design process, and the final controller is:
n (t) = (-douo - bh (t) - (A + t")f(egs (t)))/az
ne (t) = (-deue - be (t) - (A + t-)f(es (t)))/az
ngP(t) = (-duv - bp(t) - (A + t")f(eIs (t)))/az
wherein
b4 (t) = (A + ta)f (etP2(t)) + P- T(t) + At'f (e4 1t(t) + (t)(A + t)f(e1(t))
be(t) (A + t-)f(ee(t))+ A9- T(t) + t'-'f(eO1 (t)) + (t)(A + t)f(ee1 (t))
b 4 ,(t) (A + ta)f (eP 2 (t)) + P- T(t) + t-i'f(ep1 (t)) + P(t)(A + t)f(e, 1 (t))
The third error function of the attitude angle controller is: egp (t) (+ t)f (eP2 (t))+ (t) - T( )+1AtA'f(ep 1 (t)
+4Kt)(A+t^)/(eg t))
e0 3 (t) =(A+t^)f(e 02 (t))+ s (t)- 1 t)+-At'f(e1i(t)) +(t)(A +t)f(e1(t))
e t)=(t) 2 (t))+ ())(A+t")f(eiP - 1 (t)+^At-f(e 1 (t)) + (t)(A +t )f(ep1 (t))
In bg(t), be(t), bp(t), ep3 (t), e 0 3 (t), eP (t), the first error function of the attitude angle
controller is expressed as: ep (t) =4 t)(-4 (Tt) -4 t
ee1(t)= 0(t) -8Ot)-6(t)
ep (t) = Vt)- V)- 4
The second error function of the attitude angle controller is expressed as:
e 2 (t) = (t) + (A + t)f (ep(t))
e9 2 (t) =0 (t) + (A+ t)f(eo1(t)) e2 =t) 1Pt)+ (A+t^)f (ep (t))
The design process of X, Y controller is as follows. The drone dynamic modelling equation of state variable X and state variable Y is:
k =[(C4 ,SOCP + S*SP)uz], (15)
?=[(SO C- -C*S.P)uz], (16)
It is not difficult to find that both the state variable X and the state variable Y of the drone dynamics modelling equation contain the control variable uz. This means that if the controllers of the state variable X and the state variable Y are designed in a similar way to the height controller, the value of the different control variable uz will be solved. This is obviously unreasonable, that is to say, the position controllers designed by this method are coupled. In order to be able to correctly design and control the value of the state variable X and the state variable Y, it is necessary to design the relevant controller through an inverse method. By observing the above formula, it is found that the values of the state variable X and the state variable Y may be controlled by changing the roll angle 0(t) and the pitch angle 0(t). In practical terms, by changing the values of the roll angle <(t) and the pitch angle 0(t), the drone may obtain a thrust of lateral movement in different directions.
Therefore, defining the input control variables ux and uy as follows:
uX = (C4 SCP + SS ) (17) ur = (S 4 ,SOCP -C 1 SP) (18)
According to formula (17) and formula (18), formula (15) and formula (16) may be simplified
to:
Then first using the design method of variable-parameter neural dynamic for drones based
on a Power-sigmoid activation function to solve the implicit dynamic equations about the control variables ux and uy.
itx(t) = -dXUX - bx(t) - (A + tX)f(exs(t))/ax (19)
ty(t) = -dyuy - by(t) - (A + tX)f(eys(t))/ay (20)
wherein
bx(t) (A+tx)f(ex 2 (t)) - k 1 (t) + At-'f(ex1(t) + X(t)(A+ t)f(ex1(t)) by(t) = (A + t-)f (ey 2(t)) - 1 (t) - g + At-f(ey1 (t)) +Y(t)(A + t )f(ey(t)
The third error function of the X and Y controllers are expressed as:
ex 3 (t) (A+t^)f(ex 2 (t))+ At)- k 1 (t)+-At'f(ex1(t)) + t)(A+t)f (ex1(t))
e 3 (t) (A+t^)f(eY 2 (t))+ (t)- if1 (t)+At-f (e 1 (t))+Y(t) (A+t)f(ey1 (t)
In bx(t), by(t), ex3 (t) and eY3 (t), the first error function of the X and Y controllers are:
ex, (t)=X (t)-X (t) -(t)
ey1(t) = Y(t) - YT(t) - ?(t)
The second error function of the X and Y controllers are:
ex 2 (t) = X(t) + (A + t-)f(ex,(t))
eY 2 (t) = Y(t) + (A + t^)f(eyj(t))
Through the above formula, the control variables ux and uy may be obtained iteratively. According to formula (19) and formula (20), given the setting value of yaw angle 4(t), two
attitude angles of roll angle 0(t)and pitch angle 0(t) are used to form two control variables ux and uy. Therefore, if the control variables uxand uy and the setting values of the yaw
angle 4(t)are given in advance when controlling the drone, the two attitude angles of the roll angle 0(t)and the pitch angle 0(t) is used to control the state variable Xand the state
variable Y, then the controller is successfully decoupled at this time, and the design of the
controller is completed. In this disclosure, an inverse method is used to solve the two attitude angles. The solution process is as follows: First, the control variables ux and uy and
the set value of the yaw angle PT(t) are given. Then the set value of roll angle OT(t) and the set value of pitch angle OT(t) may be solved by the following formula:
T arcsin(uxSp - u arcsin(uxC + uySp) CP
Since this embodiment uses the control variables ux and uy to obtain the set values of the roll angle and the pitch angle, when designing the controller, the set values of the roll angle
and the pitch angle are unknown. At the same time, when solving the state variable X and
the state variable Y, the set values of the roll angle and the pitch angular velocity need to be used. So consider using a differential tracker to achieve the derivation of the roll and pitch
angle settings. The derivative tracker is a program that can obtain the approximate derivative of a function by tracking the input signal. According to the actual situation of the
controller, set the parameters of the differential tracker to r = 500000 and h = 0.001. The
meanings of these two parameters are to determine the speed of the differential tracker and to determine the differential tracker after receiving interference. Effects should be achieved
in terms of filtering.
S3: inputting the control target and the actual system state information obtained by the
sensors carried by the drone into the controller, and the controller calculates the control
component through iteration to control the movement of the drone;
In this embodiment, the height Z, the yaw angleqy, the roll angle# , the pitch angleO, the
output of the X and Y controller control components obtained by the Power-sigmoid variable-parameter recursive neural dynamic method are realized as follows:
As shown in Figure 3, firstly obtaining the real-time flight state information of the aircraft
itself through on-board sensors, and inputting the control target into the altitude Z and yaw
angle V controller to obtain the Z and V control components; secondly, inputting sensor
information, control target, height and yaw angle control components into X and Y controllers to obtain X and Y control components; then, an inverse solution method is used
to solve the roll angle # and pitch angle 0 values that satisfy the X and Y control components,
as the control target of the # and 0 controller; Finally, the # and0 controllers calculate
# and 0 control components, and transfers the # and 0 control components and Z and V
control components to the controller to control the movement of the aircraft.
As shown in Figure 4 and Figure 5, the drone may track a three-dimensional time-varying trajectory well. When the drone reaches a target trajectory, the tracking trajectory almost
completely overlaps the target trajectory, which shows the accuracy of tracking. At the same time, it may be seen that there is no overshoot in the tracking trajectory, and the tracking
stability is better, wherein the unit of each coordinate value in the figure is: meter (m).
In this embodiment, the variable-parameter neural dynamic controller for drones based on a
Power-sigmoid activation function may quickly, accurately, and in real-time approach the correct solution of the problem, and the obtained controller may well control the drone to
track a time-varying trajectory.
This embodiment also provides a design system of a variable-parameter neural dynamic controller for drones, comprising a drone model building module, a drone parameter
controller construction module and a controller output control module;
In this embodiment, the drone model building module used to build a drone model;
In this embodiment, the drone parameter controller construction module used to, based on
the drone model, use a variable-parameter recursive neural dynamic method based on an activation function to design an altitude controller, a yaw angle controller, a roll angle
controller, a pitch angle controller, a X controller and a Y controller of a drone respectively;
In this embodiment, the controller output control module used to input control target
parameters and drone status information collected by a drone sensor to a controller of each drone; the controller of each drone output control components to control a flight of the
drone.
In this embodiment, the controller output control module comprises a altitude control component output unit, a yaw angle control component output unit, an X control
component output unit, a Y control component output unit, a roll angle control component
output unit, and a pitch angle control component output unit;
In this embodiment, the altitude control component output unit is used to obtain the drone status information collected by the drone sensor, and input the control target parameters to
the altitude controller to obtain a drone altitude control component;
In this embodiment, the yaw angle control component output unit is used to obtain the drone status information collected by the drone sensor, and input the control target
parameters into the yaw angle controller to obtain a yaw angle control component;
In this embodiment, the X control component output unit is used to input the drone status
information, the control target parameters, the drone altitude control component and the yaw angle control component into the X controller to obtain an X control component;
In this embodiment, the Y control component output unit is used to input the drone status
information, the control target parameters, the drone altitude control component and the
yaw angle control component into the Y controller to obtain a Y control component;
In this embodiment, the roll angle control component output unit is used to calculate the roll angle that satisfies the X and Y control components by using an inverse solution method, as a
control target of the roll angle controller, and the roll angle controller calculates and outputs
a roll angle control component;
In this embodiment, the pitch angle control component output unit is used to calculate the pitch angle satisfying the X and Y control components by using an inverse solution method,
as a control target of the pitch angle controller, and the pitch angle controller calculates and outputs a pitch angle control component;
In this embodiment, the altitude control component, the yaw angle control component, the
roll angle control component and the pitch angle control component are used to control the
flight of the drone.
This embodiment also provides a storage medium. The storage medium may be a storage medium such as ROM, RAM, magnetic disk, and optical disc etc. The storage medium stores
one or more programs, and when the programs are executed by the processor, the design method of the aforementioned variable-parameter neural dynamic controller for drones is
realized.
This embodiment also provides a computing device. The computing device may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet
computer, or other terminal device with a display function. The computing device comprises
a processor and a memory. The memory stores one or more programs, and when the processor executes the programs stored in the memory, the design method of the
aforementioned variable-parameter neural dynamic controller for drones is realized.
The above-mentioned embodiments are preferred embodiments of the present invention,
but the embodiments of the present invention are not limited by the above-mentioned
embodiments, and any other changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principle of the present invention,
all should be equivalent replacement methods, and they are all included in the protection scope of the present invention.
Claims (10)
1. A design method of a variable-parameter neural dynamic controller for drones, characterized in that, comprising the following steps: building a drone model; based on the drone model, using a variable-parameter recursive neural dynamic method based on an activation function to design an altitude controller, a yaw angle controller, a roll angle controller, a pitch angle controller, a X controller and a Y controller of a drone respectively; inputting control target parameters and drone state information collected by a drone sensor to a controller of each drone; the controller of each drone output control components to control a flight of the drone.
2. The design method of a variable-parameter neural dynamic controller for drones according to claim 1, characterized in that, the step of building the drone model specifically comprises: a dynamic equation of a position state variable of the drone is described as:
1 i=- m
[(C So Cp + S* SP)uz)
i=-[(S So Cp - C* SP)uz)
Z = [COCPu] - g m wherein X, Y, Z represent a position of a center of gravity of the drone,XYZ are corresponding second-order derivatives, m represents a total mass of the drone,
S', C'PS, Ce, SP, Cp are respectively expressing
sin#, cos#, sin, cos, sin, cos, uz is a resultant force of the drone on an oz axis of a body coordinate system, and g is an acceleration of gravity, j means a roll angle, 0 means a pitch angle, 4 means a yaw angle;
using Euler's method to model an attitude angular motion of the drone, a rotation dynamic of the drone is described as:
.. I . . Jy - Jz_ Ix Ix
P= -UO + #4) Jy Jy .. 1 . .J, - Jy
wherein j is the roll angle, 0 is the pitch angle, is the yaw angle, ,0,4are corresponding second-order derivatives, J, Jy, Jz are inherent inertias during rotations
around the ox axis, oy axis, and oz axis, respectively, up, ue, up represent resultant
rotation forces in the rotation directions of 0, 0, respectively, and I represents a length
of a motor arm of the drone; the drone model is expressed as:
$ = axuz,;
= ayuz;
z azuz
aeue+ ; 4'= au* +
wherein
a - cIoe~g* aP -X
ay ' ' -c-c s'P;a
az =a
26 Jy Jx
Jz
3. The design method of a variable-parameter neural dynamic controller for drones
according to claim 1, characterized in that, the activation function adopts a Power-sigmoid
function, a specific expression is:
UP ,ul > 1 f(u) + exp(- () 1 - exp(-fu) - exp(-() 1 + exp(-fu) lul<1
wherein p represents an odd number, ( represents a constant, p 1, 1, u represents a position error or a velocity error of a drone tracking.
4. The design method of a variable-parameter neural dynamic controller for drones
according to claim 1, characterized in that, the variable-parameter recursive neural dynamic method based on the activation function is used to design the altitude controller of the
drone, the altitude controller is expressed as:
z (t) =(dzuz -- bfz(t) - (A+ tV)f(ez3(t)))/az
az= O'
bz(t) (A + tV)f(ezz(t)) - ZT0-g+AA-f(z~) + ZSz11 + Cf(z1(0) i
ez3(t)= azuz(t) + bz(t);
ez2 (t = Z (t) + (A + t -)f (ez1 (t)) ;
ez1(t) = Z(t) - ZT (t);
wherein /I > 1, Z(t) represents an actual altitude value, ZT(t) represents a target altitude value, f (x)represents the activation function, and m represents a total mass of the
drone, CO, C4, are respectively expressing cos8 and cos#, uzrepresents a control
component of the drone in a direction of the oz axis of the body coordinate system, g is an acceleration of gravity, # is a roll angle, 0 is a pitch angle, ip is a yaw angle, and ez1(t)
represents a first error function, ezz (t) represents a second error function, and ez3(t) represents a third error function.
5. The design method of a variable-parameter neural dynamic controller for drones
according to claim 4, characterized in that, a specific design steps of the altitude controller
comprise: defining the first error function as:
ez1(t) = Z(t) - (t)
wherein Z(t)represents the actual altitude value, and ZT(t)represents the target
altitude value; a variable parameter recursive neural dynamic design formula according to the first
error function is defined as:
6z 1(t) -(A + t)f(ez1(t))
after substituting ez1(t), obtaining:
Z(t) + (A + t-)f(ez1(t)) = 0 defining the second error function as:
ez2 (t) = Z(t) + (A+ tV)f(ez1(t))
a variable parameter recursive neural dynamics design formula according to the
second error function is defined as:
z 2 (t) -(A + t)f (ez 2 (t))
after substituting ez 2 (t), obtaining:
(A + t-)f (ez 2 (t)) + At'-f(ez 1 (t)) + Z(t)(1+ t)f (ez1(t)) + Z(t) - Z(T)= 0 defining the third error function as:
ezs(t) = (A + t)f (ez(t))+ (t) - Z(t) + At-f(ez 1 (t)) +Z(t)( + t)f(ez1(t) a dynamic equation of a drone altitude is:
Z = [CCu] - g m after substituting Z(t), obtaining: ez 3 (t)= azuz(t)+ bz(t)
bz(t) = (A+ tX)f(ez 2(t)) - Z(t) - g + 1At-f(ez1 (t)) +Z(t)( + t)f(ez(t) a variable parameter recursive neural dynamic design formula according to the third error function is defined as:
6z 3 (t) = -(A + t )f (Ez (t)) after substituting ez3 (t), an expression of the altitude controller is obtained: nz (t) = (-dzuz - bfz(t) - (A+ tV)f(ez3(t)))/az
6. The design method of a variable-parameter neural dynamic controller for drones according to claim 4, characterized in that, a specific step of the controller of each drone
output control components to control flights of the drones comprises:
obtaining the drone state information collected by the drone sensor, and inputting the control target parameters into the altitude controller and the yaw angle controller to
obtain a drone altitude control component and a yaw angle control component;
inputting the drone state information, the control target parameters, the drone
altitude control component and the yaw angle control component to the X controller and the Y controller to obtain X and Y control components;
using an inverse solution method to calculate a roll angle and a pitch angle that
satisfy the X and Y control components, as control targets of the roll angle controller and the pitch angle controller;
the roll angle controller and the pitch angle controller calculate and output a roll
angle control component and a pitch angle control component;
the altitude control component, the yaw angle control component, the roll angle
control component and the pitch angle control component are used to control the flight of the drone.
7. A design system of a variable-parameter neural dynamic controller for drones,
characterized in that, comprising a drone model building module, a drone parameter controller construction module and a controller output control module;
the drone model building module used to build a drone model; the drone parameter controller construction module used to, based on the drone
model, use a variable-parameter recursive neural dynamic method based on an activation function to design an altitude controller, a yaw angle controller, a roll angle controller, a pitch angle controller, a X controller and a Y controller of a drone respectively; the controller output control module used to input control target parameters and drone state information collected by a drone sensor to a controller of each drone; the controller of each drone output control components to control a flight of the drone.
8. The design system of a variable-parameter neural dynamic controller for drones according
to claim 7, characterized in that, the controller output control module comprises a altitude control component output unit, a yaw angle control component output unit, an X control
component output unit, a Y control component output unit, a roll angle control component output unit, and a pitch angle control component output unit;
the altitude control component output unit is used to obtain the drone state
information collected by the drone sensor, and input the control target parameters to the altitude controller to obtain a drone altitude control component;
the yaw angle control component output unit is used to obtain the drone state information collected by the drone sensor, and input the control target parameters into the
yaw angle controller to obtain a yaw angle control component;
the X control component output unit is used to input the drone state information, the control target parameters, the drone altitude control component and the yaw angle
control component into the X controller to obtain an X control component;
the Y control component output unit is used to input the drone state information, the control target parameters, the drone altitude control component and the yaw angle
control component into the Y controller to obtain a Y control component;
the roll angle control component output unit is used to calculate the roll angle that
satisfies the X and Y control components by using an inverse solution method, as a control target of the roll angle controller, and the roll angle controller calculates and outputs a roll
angle control component; the pitch angle control component output unit is used to calculate the pitch angle satisfying the X and Y control components by using an inverse solution method, as a control target of the pitch angle controller, and the pitch angle controller calculates and outputs a pitch angle control component; the altitude control component, the yaw angle control component, the roll angle control component and the pitch angle control component are used to control the flight of the drone.
9. A storage medium storing a program, characterized in that, when the program is executed
by a processor, the design method of a variable-parameter neural dynamic controller for drones according to any one of claims 1 to 6 is realized.
10. A computing device comprising a processor and a memory for storing an executable
program for the processor, characterized in that, when the processor executes the program
stored in the memory, the design method of a variable-parameter neural dynamic controller for drones according to any one of claims 1 to 6 is realized.
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