In the paper, the problem of controlling the
gimbal camera’s LOS for tracking a moving target is
studied. A dynamic model of a 3-axis gimbal system
is built in consideration with the flying platform’s
motion. A tuning algorithm for the LQR controller to
find shortest tracking time is proposed and the
numerical simulation shows that the designed
controller meets the objective
5 trang |
Chia sẻ: huongthu9 | Lượt xem: 518 | Lượt tải: 0
Bạn đang xem nội dung tài liệu Optimal Control for the Target-Tracking Problem using Three-Axis Camera Gimbals, để tải tài liệu về máy bạn click vào nút DOWNLOAD ở trên
Journal of Science & Technology 127 (2018) 035-039
35
Optimal Control for the Target-Tracking Problem using Three-Axis
Camera Gimbals
Do Dang Khoa*, Le Quang Duong
Hanoi University of Science and Technology, No. 1, Dai Co Viet, Hai Ba Trung, Hanoi, Viet Nam
Received: November 12, 2017; Accepted: June 25, 2018
Abstract
In this paper, the target-tracking problem of a 3-axis camera gimbal mounted on a flying vehicle is
considered. In order to keep the camera’s line of sight continuously pointing to a moving target, an optimal
controller using LQR control techniques is applied. The motion equations of the gimbal system are derived
by the Lagrangian approach considering the vehicle motion. The LQR controller is designed based on the
system’s continuously linearized model. A tuning method for the LQR is also proposed to make the gimbal
system point to a moving target in the shortest time. The feasibility of the proposed controller is shown by
numerical simulations.
Keywords: Optimal Control, LQR, Camera Gimbal, Line of Sight (LOS)
1. Introduction*
Inertial stabilized platforms (ISPs) are
mechanisms to control and stabilize the LOS of
optical equipment. Recently, ISPs have been
popularized in many civil and commercial
applications (e.g. movies shootings, aerial
photography). In such systems, the optical equipment,
which is often mounted on a moving vehicle, must
keep its optical sensor’s LOS pointing to a fixed or
moving target. One of the most common types of
ISPs is based on a gimballed structure [1]. The two
main issues are raised as to build exact physical
models and to develop good control algorithm to
fulfill the target-tracking problems. Basically, there
are two approaches to derive the gimbal mathematical
models: one by Newton-Euler approach [2, 3] and the
other by Lagrangian method [4, 5]. For gimbal
control algorithms, many approaches have been
applied such as robust control in [3], sliding mode
control in [4], and conventional PID control in [5].
Most of the gimbal control challenges in the literature
are related to dealing with two-axis gimballed
configurations.
In this paper, the LOS stabilization and target-
tracking problems of a three-axis camera gimbal
mounted on a flying platform is studied. The aim of
the paper is to design an optimal controller to achieve
good target-tracking performance as quick as possible
under the dynamic disturbances from the flying
platform. To fulfill this task, a nonlinear dynamic
model of the three-axis gimbal is developed based on
* Corresponding author: Tel.: (+84) 982.326.550
Email: khoa.dodang@hust.edu.vn
the Lagrangian approach under the flying platform’s
inertial effects and a linear quadratic regulator (LQR)
is utilized. An offline-tuning procedure for LQR is
proposed to find optimal values of state and control
weight matrices to improve gimbal target-tracking
performance.
2. Problem Formulation
In this paper, a three-axis gimbal system
illustrated in Fig.1 is considered. The gimbal system
is assumed to be mounted on a flying platform at
body 0. The camera fixed on the gimbal’s body 3
must keep its sensor’s LOS pointing to a moving
object on the ground. To keep the object image
stabilized in the camera frame of view, its sensor’s
LOS must also be kept nonrotating in an inertial
space under dynamic disturbances from the platform
motion.
Fig. 1. Model of 3-axis Gimbal
In order to verify the proposed control algorithm, a
mathematical model of the gimbal system needs to be
derived. The gimbal system’s equations of motion are
built based on three generalized coordinates as 1 ,
Journal of Science & Technology 127 (2018) 035-039
36
2 and 3 , which are the rotation angles (yaw, roll
and pitch) of motors at each axis. To determine the
gimbal system’s position, five reference frames are
identified as in Fig. 1. The global frame OXYZ(g) is
fixed to the ground. Local frames
i i i iO x y z are
attached to body i (i from 0 to 3) and
3 3O x is
specified as the camera’s LOS. Those frames are
choosen such that they are parallel to each other when
1 , 2 and 3 are all equal to zero. The camera
LOS is determined by the transformation matrix
method. Let’s define the transformation from frame a
to from b by a 4 by 4 matrix a bT in the form as
1
a a
a b b
b T
=
R r
T
0
(1)
where
a
bR is a 3 by 3 rotation matrix and,
a
br is a 3
by 1 translation vector from frame a to frame b. The
transformation matrix between the ground frame and
the platform frame is specified as follows
0
0
0
0
0 0 0 1
g
c c s c s c s s s c s c
c c c c s s s c s s c s
s c c c
Y
Zs
X
−
=
+
+ −
−
T (2)
where X0, Y0 and Z0 are the flying platform position
of O0 in the ground frame; , , and are roll,
pitch and yaw angles of the flying platform (body 0).
The terms , cs stand for ( ) ( )sin , cos and so
on for , c , s and , cs . Other transformation
matrices among the gimbal bodies are described as
1 1 1 1
1 1 1 10
1
1
0
0
0 0 1
0 0 0 1
c s l c
s c l s
h
− −
−
=
T (3)
1
2 2 2 21
2
2 2 2 2
1 0 0
0
0 s
0 0 0 1
l
c s b c
s c b
− −
=
−
T (4)
3 3 3 3 3 3
32
3
3 3 3 3 3 3
0 s
0 1 0
0 c
0 0 0 1
c s l c h
b
s l s h c
+
=
− − +
T (5)
The terms 1 1, cs stand for ( ) ( )1 1sin , cos and so
on for 2 2, c , s and 3 3, cs .
The direction of the camera LOS is calculated by the
transformation matrix 3
g
T as follows
0 1 2 3 33 0 1 2 3. . .
1
g g
g g
T
= =
R r
T T T T T
0
(6)
The LOS direction is specified by making the unit
vector 3 3i of axis O3x3 same direction with vector of
3
3
O P . To keep the axis O3y3 in parallel to the
ground, the term 3 (3, 2)
g
R , which is at the third row
and second column of matrix 3
g
R must be zero. Let’s
assume the moving target’s position P in the ground
frame is identified by the vector g Pr . As a result, the
gimbal configuration ( )1 2 3, , to keep its LOS
point to the moving target P while maintaining the
stabilized image of P in the camera view of frame is
determined by the following system of equations
( )
( )
3
3 3 3
3
3
3 3 3
(3,2) 0
.
g T g g
P
g
g T g g
P
− =
=
−
T r r i 0
R
T r r i 0
(7)
The equations of gimbal motion in the frame 0, which
are derived by the Lagrangian approach using the
matrix method [8] has the form as
*( ) ( , ) ( )+ + + =M q q C q q q Dq G q Q (8)
where 1 2 3
T
=q , ( )M q is the 3 by 3 mass
matrix, ( , )C q q is the 3 by 3 Coriolis and centrifugal
matrix determined from the mass matrix, ( )G q is
generalized forces due to the potential energy , D
is a damping matrix and Q* is the generalized forces
due to motor torques and inertial forces and moments
caused by the flying platform. The mass matrix
( )M q is calculated as follows
( ) ( )
3
0
1
T T
Ti i Ti Ri Ci Ri
i
m
=
= +M q J J J I J (9)
where im is mass of body i and
0
CiI is inertia tensor
around the centroid of body i in the frame 0. TiJ and
RiJ are translational and rotational Jacobian matrices
respectively.
0 0
, Ci iTi Ri
= =
r ω
J J
q q
(10)
where
0
Cir is a position vector of the centroid Ci of
body i in frame 0,
0
iω is the angular velocity vector
of body i in frame 0. The matrix ( , )C q q is derived as
Journal of Science & Technology 127 (2018) 035-039
37
( ) ( )
( ) 1 ( )
( , )
2
T
= −
M q M q
C q q E q q E
q q
(11)
where E is the 3 by 3 identity matrix and is the
Kronecker product [8]. The damping matrix D is
determined from the dissipative function ( ) q as
( )
=
q
Dq
q
(12)
where 2 2 21 1 2 2 3 3
1 1 1
( )
2 2 2
b b b = + +q and
1b , 2b ,
and
3b are damping coefficients of the gimbal
motors. The vector ( )G q has the form as
( )
3
0 0
1
, ( )
T
i g Ci
i
m
=
= = −
G R g r
q
(13)
where g is the vector of the form 0 0
T
g=g , and g
is the gravitational acceleration. The vector of
generalized forces Q* is calculated as
* * * *
Fie Mie= + +Q Q Q Q (14)
where
*
Q ,
*
FieQ , and
*
MieQ are generalized forces
corresponding to the gimbal motor torques, the
resultants of inertial forces and inertial couples,
respectively. The Coriolis effect is ignored due to
assumptions of the platform’s small angular velocity.
The generalized forces are defined as
* 1 2 3
T T
= =Q u (15)
3
*
0
1
T g T gFie Ti ci
i=
=Q J R F (16)
3
*
0
1
T g T gMie Ri ci
i=
=Q J R M (17)
where
g
ciF and
g
ciM are the resultant of inertial force
and couple at the centroid Ci of body i in the ground
frame, respectively.
( )( )00 0 0 0
0 0
0 0 0 0 0 0 0
g g g g gci i ci
g g g T g g g g T g
ci Ci Ci
m= − + +
= − −
F r α ω ω r
M R I R α ω R I R ω
(18)
where 0
gω and is 0
g α are skew-symmetric tensors of
angular velocity 0
gω and angular acceleration 0
g α
of body 0 in the ground frame, respectively. Both
0
gω and 0
g α are assumingly known by sensor
measurement. In the following section, the control
torques in (15) need to be specified to force the
equations (8) realize the conditions in (7).
3. Optimal Controller Design
Generally, the gimbal nonlinear equations of
motion (8) can also be converted into the form as
( )
21
1 *
2 ie
−
= = − − − − +
xx
x
x M Cq Dq G Q u
(19)
where 1 1 2 3
T
q q q=x , 2 1 2 3
T
q q q=x and,
* * *
ie Fie Mie= +Q Q Q . The measurable and controlled
variables are
1( )t =y x (20)
From (19) and (20), the gimbal system’s
nonlinear model can be expressed as follows
0 0 0
0 0 0
( ) ( ( ), ( ), ( ), ( ), ( ))
( ) ( ( ), ( ), ( ), ( ), ( ))
g g g
g g g
t f t t t t t
t h t t t t t
=
=
x x u r ω α
y x u r ω α (21)
Where 0 0 0( ), ( ), ( )
g g gt t tr ω α are the platform’s
acceleration, angular velocity and acceleration in the
ground frame, respectively. To determine the motor
torques u(t) for making the gimbal system’s LOS
track a moving target, an optimal controller of LQR
will be designed based on the continuously linearized
model of the form
( ) ( ) ( ) ( ) ( )
( ) ( ) ( ) ( ) ( )
t t t t t
t t t t t
= +
= +
x A x B u
y C x D u (22)
Where ( ) ( ) ot t= −x x x , ( ) ( ) ( , )o ot t= −y y h x u ,
and ( ) ( ) ot t= −u u u . The matrices A(t), B(t), and C(t)
D(t) are defined as follows
, ,
( , ) ( , )
,
o o o o
= =
x u x u
f x u f x u
A B
x u
(23)
, ,
( , ) ( , )
,
o o o o
= =
x u x u
h x u h x u
C D
x u (24)
where the operation point ( , )o ox u is
determined from equation (7) at operating time to, its
first derivative and the steady state equations as.
0 0 0( , , ( ), ( ), ( )) 0
g g g
o o o o of t t t =x u r ω α (25)
The system’s controllability and observability are
satisfied. To apply LQR controller, the control signals
u should have the form as [9]
( ) ( )t t= −u Kx (26)
to minimize the cost function of the form as
( )
0
1
2
T TJ dt
= + x Qx u Ru (27)
Journal of Science & Technology 127 (2018) 035-039
38
Where Q and R are symmetric positive semi-definite
and positive definite matrices, respectively. The
optimal solution u is identified from the Hamiltonian
approach as follows
1( ) ( )t t−= −u R BPx (28)
Where P is the solution of the Riccatti equation as
1T T−+ + − =Q A P PA PBR B P 0 (29)
As seen in (28), the LQR provides a negative
feedback gain K with large stability margin [9]. The
controller performance depends on the selection of
the weight matrices Q and R.
In this section, a practical method to select the
weight matrices is introduced. Matrices Q and R are
selected in the form as
,
T = =Q C C R I (30)
where is a tuning parameter to design the LQR
such that the control signal u will drive the gimbal
system point to the moving target in the shortest time.
Let’s define ( )st is the time period for the maximum
norm of the state perturbations in (22) getting smaller
than the predefined error 0.01 ( )rad =
( )st x (31)
The parameter
* to make the gimbal system catch
the moving target in the optimal time is the solution
of the function
*
*( ) min(max( ( )))s st t
→
= (32)
4. Gimbal System Simulation
The 3D model of gimbal (Fig.1) was built using
based on a real prototype. The gimbal parameters are
measured and shown in Tables 1, 2 and 3 as follows:
Table 1. Dimensions and Mass of the Gimbal
Link l(m) b(m) h(m) ( )im kg
1 0.13 0 0.155 0.32341
2 0.125 0.072 0 0.32325
3 0.0325 0.049 0.01405 0.67008
Table 2. The Centroids of the Gimbal Links
Link i
Cix
i
Ciy
i
Ciz
1 0.01325 0 -0.07642
2 -0.05791 0.05261 0
3 -0.03237 0.02294 0.00578
Table 3. Moment of Inertia about the Centroids
Link
Ci
i (xx)
I
Ci
i (yy)
I
Ci
i (zz)
I
1 0.001396709 0.002011077 0.000675883
2 0.001289047 0.000817588 0.002076275
3 0.001682153 0.000614997 0.001274903
The tuning process from solving equation (32) is
shown in Fig. 2, with * *0.005, ( ) 0.035( )st s = = .
Fig. 2. Weight Parameter Tuning Process
The flying platform’s position of O0 and roll, pitch
and yaw angles are assumingly known as (Fig. 3)
0
0
0
sin(1.4 )
60
sin(1.4 ) ; 0( ) 10( )
6
sin( ) 4
sin(1.4 )
6
t
X
Y t t s t s
Z t
t
=
=
= =
= +
=
The moving target’s position P is defined as
0.2sin(1.2 ) 1 0
Tg
P t t= +r
Fig. 3. Trajectories of the drone and moving target
Fig. 4. Gimbal Torques for Tracking Problem
Journal of Science & Technology 127 (2018) 035-039
39
Fig. 5. Tracking Responses of the Gimbal Angles
The pertubation results of motor torques and
gimbal angles between two cases 1 = and * =
are compared in Fig. 4 and 5. The optimal case tracks
the object in much faster time with the trade off of
higher motor torques.
5. Conclusion
In the paper, the problem of controlling the
gimbal camera’s LOS for tracking a moving target is
studied. A dynamic model of a 3-axis gimbal system
is built in consideration with the flying platform’s
motion. A tuning algorithm for the LQR controller to
find shortest tracking time is proposed and the
numerical simulation shows that the designed
controller meets the objective.
Acknowledgments
This work was supported by Hanoi university of
Science and Technology under the research project
T2016-PC-057.
References
[1] J.M. Hilkert, Inertially stabilized platform technology
Concepts and principles, in IEEE Control Systems
(2008), vol. 28, no. 1, 26-46.
[2] B. Ekstrand, Equations of motion for a two-axes
gimbal system, IEEE Transactions on Aerospace and
Electronic Systems (2001), vol. 37, no. 3, 1083-1091.
[3] S. B. Kim, S. H. Kim and Y. K. Kwak, Robust
control for a two-axis gimbaled sensor system with
multivariable feedback systems, in IET Control
Theory & Applications (2010), vol. 4, no. 4, 539-551.
[4] H. Özgür, E. Aydan, E. İsmet, Proxy-based sliding
mode stabilization of a two-axis gimbaled platform,
Proceedings of the World Congress on Engineering
and Computer Science (2011), Vol 1.
[5] M. Abdo, A. Toloei, A. R. Vali, & M. R. Arvan,
Cascade control system for two axes gimbal system
with mass unbalance. International Journal of
Scientific & Engineering Research (2013), 4(9), 903-
913.
[6] A. Cabarbaye, J. C. Escobar, R. Lozano, M. B.
Estrada, Fast adaptive control of a 3-DOF inertial
stabilised platforms based on quaternions,
International Conference on Unmanned Aircraft
Systems (ICUAS) (2017), 1463-1469.
[7] A. Altan, R. Hacioğlu, Modeling of three-axis gimbal
system on unmanned air vehicle (UAV) under
external disturbances, 25th Signal Processing and
Communications Applications Conference (SIU)
(2017), 1-4.
[8] Nguyen Van Khang, Dynamics of Multibody
Systems (in Vietnamese), Science and Technology
Publishing House, 2nd edition, 2017.
[9] F.L. Lewis, D. Vrabie, V.L. Syrmos, Optimal
Control, John Wiley & Sons, 3rd edition, 2012.
Các file đính kèm theo tài liệu này:
- optimal_control_for_the_target_tracking_problem_using_three.pdf