US 2023/0213948 A1 · STABILITY CONTROL METHOD AND DEVICE BASED ON PARTICLE ACTIVE DISTURBANCE REJECTION

Abstract

A stability control method and device based on particle active disturbance rejection are provided. The method includes: establishing an active disturbance rejection controller model based on a dynamic model and a speed loop control model of a tethered balloon system, where the speed loop control model is established through theoretical modeling of executive components of a control system of the tethered balloon system; and optimizing to-be-optimized parameters of the active disturbance rejection controller model using a particle swarm optimization algorithm, determining an optimal active disturbance rejection controller model, and using the optimal active disturbance rejection controller model to implement stability control of a photoelectric pod. An active disturbance rejection controller is optimized by using a particle swarm optimization algorithm, which can effectively isolate the internal and external disturbances of the photoelectric pod and improve the imaging stability of the photoelectric pod.

A stability control method based on particle active disturbance rejection, comprising:

establishing an active disturbance rejection controller model according to a dynamic model and a speed loop control model of a tethered balloon system, wherein the speed loop control model is established through theoretical modeling of executive components of a control system of the tethered balloon system;
and
optimizing to-be-optimized parameters of the active disturbance rejection controller model using a particle swarm optimization algorithm, determining an optimal active disturbance rejection controller model, and using the optimal active disturbance rejection controller model to implement stability control of a photoelectric pod;
wherein the active disturbance rejection controller model is expressed as:
y
¨
=
f
y
,
y
˙
,
w
k
,
k
+
b
u
;
TD
:
f
h
=
f
h
a
n
x
1
k
−
v
k
,
x
2
k
,
r
,
h
0
x
1
k
+
1
=
x
1
k
+
h
x
2
k
x
2
k
+
1
=
x
2
k
+
h
f
h
;
ESO :
e
k
=
z
1
k
−
y
k
z
1
k
+
1
z
1
k
+
h
z
2
k
−
β
01
e
k
z
2
k
+
1
=
z
2
k
+
h
z
3
k
−
β
02
f
a
l
e
k
,
0.5
δ
+
b
u
k
z
3
k
+
1
=
z
3
k
−
h
β
03
f
a
l
e
k
,
0.25
,
δ
;
NLSEF:
e
1
k
=
x
1
k
- z
1
k
e
2
k
=
x
2
k
- z
2
k
u
0
k
=
f
h
a
n
e
1
,
c
e
2
,
r
1
,
h
1
;
u
k
=
u
0
k
−
z
3
k
/
b
0
;
f
h
a
n
x
1
,
x
2
,
r
,
h
=
d
=
r
h
2
a
0
=
h
x
2
y
=
x
1
+
a
0
a
1
=
d
d
+
8
y
a
2
=
a
0
+
s
i
g
n
y
a
1
-
d
/
2
s
y
=
s
i
g
n
y
+
d
-
s
i
g
n
y
-
d
/
2
a
=
a
0
+
y
-
a
2
s
y
+
a
2
s
a
=
s
i
g
n
y
+
d
-
s
i
g
n
y
-
d
/
2
f
h
a
n
=
-
r
a
d
-
s
i
g
n
a
s
a
-
r
s
i
g
n
a
;
f
a
l
x
,
a
,
d
=
x
d
1
-
a
,
x
≤
d
s
i
g
n
x
x
a
,
x
>
d
;
where ÿ represents a differential equation of a control system of the photoelectric pod;
TD represents a tracking differentiator;
ESO represents an extended state observer;
NLSEF represents a nonlinear state error feedback control law;
f(y,ẏ,w(k),k) represents a total disturbance;
v(k) represents a system input signal;
y is represents an output signal;
w(k) represents a total external disturbance;
differential signals v1(k)and v2(k) are generated by the TD through converting the system input signal V(k);
z1(k), z2(k), and z3(k) respectively represent estimators of the differential signals v1(k) and v2(k), and the total disturbance f(y,ẏ,w(t),t);
r,h,h0 are parameters of the TD;
r represents a fast factor;
h represents a system sampling step size;
h0 represents a dimensionless parameter, which is an integer multiple of the system sampling step size h ;
h,β01,β02,β03,δ,b are parameters of the ESO;
β01,β02,β03 are coefficient parameters related to the system sampling step size h ;
δ represents a meaningless coefficient, which is taken as an integer multiple of the system sampling step size h ;
b represents a control quantity coefficient and is a time-varying function, which is a constant b0 approximately;
c,r1,h1 are parameters of the NLSEF, where c represents a damping factor, h1 represents a fast factor, and r1 represents a control quantity factor;
fhan and fal are expressions of nonlinear functions, respectively;
u represents a controlled object;
u0(t) represents an error feedback control quantity;
a, a0, a1 and a2 are dimensionless intermediate parameters;
u0(t) represents an error feedback control quantity;
wherein the to-be-optimized parameters of the active disturbance rejection controller model comprises β01,β02,β03,c,h1;
wherein the optimizing to-be-optimized parameters of the active disturbance rejection controller model using the particle swarm optimization algorithm, comprises:
step 1, initializing a particle swarm;
step 2, calculating a fitness value of each particle in the particle swarm according to a fitness function, wherein each particle corresponds to a combination of the to-be-optimized parameters;
step 3, updating, according to the fitness value of each particle, an individual extreme value of the particle and a global extreme value;
step 4, updating a position and a speed of each particle;
step 5, determining that whether a stop condition is satisfied, outputting an optimal parameter combination, if it is determined that the stop condition is satisfied;
returning to and performing the step 2, if it is determined that the stop condition is not satisfied;
wherein the fitness value of each particle is determined according to the following formula:
I
f
Δ
e
t
<
0
Q
=
∫
0
t
c
1
t
e
t
+
c
2
u
t
2
+
100
Δ
e
t
d
t
,
where e(t) represents a deviation between a given value and an output;
Δe(t) represents a difference between two adjacent step outputs;
c1 represents a weight of the deviation in the fitness function, and C2 represents a weight of a square term of a control quantity in the fitness function;
u(t) represents the control quantity;
and Q represents an objective function value;
wherein during the optimizing, a weight factor W is dynamically adjusted according the following formula:
w
=
w
m
a
x
−
w
m
a
x
−
w
m
i
n
⋅
t
T
m
a
x
,
where Tmax represents a maximum iteration number, wmax and wmin represent a maximum inertia weight and a minimum inertia weight respectively, and t represents a current iteration number.

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Stability control method and device based on particle active disturbance rejection

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Stability control method and device based on particle active disturbance rejection

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Abstract

Claims7