US 11,378,983 B2 · Stable flight control method for multi-rotor unmanned aerial vehicle based on finite-time neurodynamics

Abstract

Provided is a stable flight control method for a multi-rotor unmanned aerial vehicle based on finite-time neurodynamics, comprising the following implementation process: 1) acquiring real-time flight orientation and attitude data through airborne sensors, and analyzing and processing kinematic problems of the aerial vehicle through an airborne processor to establish a dynamics model of the aerial vehicle; 2) designing a finite-time varying-parameter convergence differential neural network solver according to a finite-time varying-parameter convergence differential neurodynamics design method; 3) solving output control parameters of motors of the aerial vehicle through the finite-time varying-parameter convergence differential neural network solver using the acquired real-time orientation and attitude data; and 4) transmitting results to speed regulators of the motors of the aerial vehicle to control the motion of the unmanned aerial vehicle. Based on the finite-time varying-parameter convergence differential neurodynamics method, the invention can approximate the correct solution of the problem in a quick, accurate and real-time way, and can well solve a variety of time-varying problems such as matrix, vector, algebra and optimization.

A stable flight control method for a multi-rotor unmanned aerial vehicle based on finite-time neurodynamics, the control method comprising the steps of:

S1, acquiring real-time flight orientation and attitude data of the multi-rotor unmanned aerial vehicle through sensors thereof, and analyzing and processing kinematic problems of the aerial vehicle correspondingly through an airborne processor to establish a dynamics model of the aerial vehicle;
S2, designing a finite-time varying-parameter convergence differential neural network solver for the dynamics model of the multi-rotor aerial vehicle according to a finite-time varying-parameter convergence differential neurodynamics design method;
S3, solving output control parameters of motors of the aerial vehicle through the finite-time varying-parameter convergence differential neural network solver using the acquired real-time orientation and attitude data of the aerial vehicle;
and
S4, transmitting the solved output control parameters to speed regulators of the motors of the aerial vehicle to control the motion of the unmanned aerial vehicle;
wherein the analyzing and processing kinematic problems of the aerial vehicle correspondingly through an airborne processor to establish a dynamics model of the aerial vehicle of step S1 specifically comprises:
ignoring the effect of air resistance on the aerial vehicle, such that a physical model can be established for the aerial vehicle system:
[
mI
0
3
×
3
0
3
×
3
J
]
⁡
[
v
.
w
.
]
+
[
w
×
(
mv
)
w
×
(
Jw
)
]
=
[
F
T
]
+
[
G
0
3
×
3
]
,
(
1
)
wherein m is a total mass of the aerial vehicle, I is a 3×3 identity matrix, J is a rotational inertia matrix of the aerial vehicle, v and w are a velocity vector and an angular velocity vector of the aerial vehicle in a ground coordinate system, F and G are an axial component vector of an output resultant force of the motors of the aerial vehicle and an axial component vector of gravity of the aerial vehicle, respectively, and T is a rotational torque vector of the aerial vehicle;
establishing a ground coordinate system XG and an aerial vehicle body coordinate system XU, wherein the ground coordinate system and the body coordinate system have the following relationship:
UG=KXG, in which conversion relationship, K is a rotation conversion matrix between the ground coordinate system and the body coordinate system, which can be expressed as
K
=
[
C
θ
⁢
C
ψ
C
θ
⁢
S
ψ
-
S
θ
S
ϕ
⁢
S
θ
⁢
C
ψ
S
ϕ
⁢
S
θ
⁢
S
ψ
+
C
ϕ
⁢
S
ψ
S
ϕ
⁢
C
θ
S
ϕ
⁢
S
θ
⁢
C
ψ
-
C
ϕ
⁢
S
ψ
C
ϕ
⁢
S
θ
⁢
S
ψ
-
S
θ
⁢
C
ψ
C
θ
⁢
C
ϕ
]
,
wherein Cθ represents cos θ(t), Sθ represents sin θ(t), θ(t) is the pitch angle, ψ(t) is the yaw angle, and ϕ(t) is the roll angle;
according to the coordinate conversion theory, in a translation direction and a rotation direction of the aerial vehicle, basing on the above physical model, such that the following dynamics equation in the aerial vehicle body coordinate system can be obtained
{
x
¨
=
u
1
⁡
(
t
)
⁢
(
cos
⁢
⁢
ϕ
⁡
(
t
)
⁢
⁢
sin
⁢
⁢
θ
⁡
(
t
)
⁢
⁢
cos
⁢
⁢
ψ
⁡
(
t
)
+
sin
⁢
⁢
θ
⁡
(
t
)
⁢
⁢
sin
⁢
⁢
ψ
⁡
(
t
)
)
m
y
¨
=
u
1
⁡
(
t
)
⁢
(
sin
⁢
⁢
ϕ
⁡
(
t
)
⁢
⁢
sin
⁢
⁢
θ
⁡
(
t
)
⁢
⁢
cos
⁢
⁢
ψ
⁡
(
t
)
-
sin
⁢
⁢
ψ
⁡
(
t
)
⁢
⁢
cos
⁢
⁢
ψ
⁡
(
t
)
)
m
z
¨
=
u
1
⁡
(
t
)
⁢
⁢
cos
⁢
⁢
θ
⁡
(
t
)
⁢
⁢
cos
⁢
⁢
ϕ
⁡
(
t
)
m
-
g
⁢
ϕ
¨
=
l
·
u
2
⁡
(
t
)
+
(
J
y
-
J
z
)
⁢
ψ
.
⁡
(
t
)
⁢
θ
.
⁡
(
t
)
J
y
⁢
θ
¨
=
l
·
u
3
⁡
(
t
)
+
(
J
z
-
J
x
)
⁢
ψ
.
⁡
(
t
)
⁢
ϕ
.
⁡
(
t
)
J
y
⁢
ψ
¨
=
u
4
⁡
(
t
)
+
(
J
x
-
J
y
)
⁢
θ
.
⁡
(
t
)
⁢
ϕ
.
⁡
(
t
)
J
z
,
⁢
(
2
)
wherein x, y, z are position coordinates of the aerial vehicle in the world coordinate system, respectively, Jx, Jy and Jz are rotational inertia of the aerial vehicle in x-axis, y-axis and z-axis directions, respectively, l is an arm length, g is gravitational acceleration, synthesized control parameters u1˜u4 consist of output thrust of the motors of the aerial vehicle and a synthesized torque, u1(t) is a resultant force in a vertical ascending direction of the aerial vehicle, u2(t) is a resultant force in a roll angle direction, u3(t) is a resultant force in a pitch angle direction, and u4(t) is the synthesized torque in a yaw angle direction;
wherein the designing a finite-time varying-parameter convergence differential neural network solver for the dynamics model of the multi-rotor aerial vehicle according to a finite-time varying-parameter convergence differential neurodynamics design method of step S2 specifically comprises:
by means of the finite-time varying-parameter convergence differential neurodynamics design method, designing a system parameter deviation function of the finite-time varying-parameter convergence differential neural network about the output control parameters u1˜u4 in respect to the z-axis height z(t), the roll angle ϕ(t), the pitch angle θ(t) and the yaw angle ψ(t), respectively;
and
designing the finite-time varying-parameter convergence differential neural network solver according to the obtained system parameter deviation function of the finite-time varying-parameter convergence differential neural network about the output control parameters u1˜u4, respectively.

405 more elements

Stable flight control method for multi-rotor unmanned aerial vehicle based on finite-time neurodynamics

Life of this patent

Stable flight control method for multi-rotor unmanned aerial vehicle based on finite-time neurodynamics

Life of this patent

Abstract

Claims3