Some Issues on Dynamic Control of Parallel Kinematic Machines

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Oct 30, 2013 (3 years and 7 months ago)

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Some Issues on Dynamic Control of Parallel Kinematic Machines
1 1 1,2 1,3
Flavien Paccot Omar Ait-Aider Nicolas Andreff Philippe Martinet
Abstract— In this paper, a discussion on parallel kinematic dynamic model of the machine, are widespread for industrial
machine dynamic control shows that such a control has to be
serial manipulators. However, the latter has never been
thought over again. Hence, the well-known computed torque
employed for industrial parallel kinematic machines whereas
control approach is revisited and is shown, when it is performed
a great improvement in dynamic accuracy and workspace
in the Cartesian space and including a Cartesian space dynamic
use could be expected. Indeed, the transposition from serial
model, to be definitely relevant for parallel kinematic machines.
Moreover, it is shown that greater improvements can be robotics to parallel one is not always straight forward. The
expected with an exteroceptive measure of the end-effector pose.
small amount of experimental results in the literature proves
Finally, experimental results on a complex prototype give a
the troubles in setting up a dynamic control for a parallel
comparison between a linear single-axis control and a computed
kinematic machine and obtaining good performances [9],
torque control to prove our assertions.
[10].
I. INTRODUCTION
Actually, the modeling errors are the main limitation in the
Parallel kinematic machines are spreading in the industry accuracy and stability of a computed torque control [8]. How-
because of their advantages over serial kinematic machines, ever, the dynamic modeling of parallel kinematic machines
such as stiffness, high load and high speed capacities [1]. is quite complex [11]. Therefore, the amount of computation
Nevertheless, these good dynamic performances are not often imposes simplifications [10], [12]. This leads to non
always achieved [2]. Indeed, improvements are still needed in neglectable modeling errors with regards to accuracy and
design, modeling, identification and control to take advantage stability. These errors can be decreased with a kinematic
of parallel kinematic machine performances [3]. In our mind, and dynamic identification. The kinematic identification es-
the development of adapted control strategies is probably the sentially reduces the influence of assembly errors [13] while
field where remains the largest potential for improving the the dynamic identification reduces the influence of frictions
tracking performances at high speed. and internal torques due to assembly errors [14]. In many
As far as we know, industrial parallel kinematic machines cases, the identification is nevertheless not sufficient for
have in most cases a linear single-axis control. This control performing a stable and accurate computed torque control.
strategy seems to be efficient with regards to its large In these cases, robust techniques are generally employed to
presence in machining. However, the dynamic behaviour of cope with the error influence [10]. Therefore, an industrial
a parallel kinematic machine is strongly nonlinear due to a implementation of such control strategy is not relevant since
dynamic coupling between the kinematic chains linking the the understanding and the mastery of robust techniques
end-effector to the fixed basis, also known as legs. Therefore, require heavy skills and means.
a linear control strategy ensures a good accuracy only at low However, in many cases, the inverse dynamic model of
speed and in a small part of the workspace [4]. Moreover, the a parallel kinematic machine is written in the joint space
efficiency is not homogeneous over the workspace since the as a function of the joint variables, like a serial kinematic
dynamic behaviour depends on the end-effector pose [5]. To
machine [11]. Nevertheless, since the kinematics are defined
take into account this heterogeneity, a restricted workspace
by the end-effector configuration, the dynamics should also
can be defined as a space where stiffness, kinematic and
be computed in the Cartesian space (SE ), written as a
3
dynamic properties allow for a good accuracy [1], [6]. In
function of the end-effector pose and its time derivatives and
addition, an optimal path can be computed with regards to the
mapped into the active joints space [9], [15]. In this case,
dynamic behaviour [7]. Therefore, these solutions deal with
the dynamic modeling requires less computation and thus
the weakness of the linear single-axis control by proposing presents less modeling errors than a joint space modeling.
a path with restricted speed in a restricted working space, Nevertheless, the use of a Cartesian space dynamic model is
leading to a suboptimal use of parallel kinematics machine. only relevant with a Cartesian space control as developed in
However, improving the dynamic performance of a ma- further words.
chine by employing a nonlinear dynamic control, such as The motivation of this paper is to develop this discussion
the so-called computed torque control, is a well-known solu- on the dynamic control of parallel kinematic machines. It is
tion [8]–[10]. These control strategies, including the inverse thus originally shown that a Cartesian space control is the
most relevant solution to ensure correct tracking. Experimen-
1
LASMEA - UMR CNRS 6602, Universite ´ Blaise Pascal Cler-
tal results are proposed to emphasize this discussion. Notice
mont Ferrand II, Aubie `re, France
2 that the reader is expected to be familiar with kinematic and
LAMI, Insitut Franc ¸ais de Me ´canique Avance ´e (IFMA),
dynamic modeling as well as with standard control schemes.
Aubie `re, France
3
ISRC, Sungkyunkwan University, Suwon, South Korea Thus, we can focus only on the analysis of the existingb b
˙ ˙
D(Xb,X)
Feedforward
2
d d
Db(Xb)
X dt dt
d b˙
e + Γ q
X
X
d +
e + ω Γ q
Path IKM(X ) PID Machine
d
b˙b ¨
b
Path IKM(X) PID IDM(X,X,X) Machine
+ + d Db(Xb)
+ +

+

Xb
Fd KM(q)
Fig. 1. Single-axis control with PID controller and feedforward
Fig. 3. Joint space Computed Torque Control for parallel kinematic
2
d d
machines, explicit form
dt dt
X
d
e + ω Γ
q
2
Path Id KM(X) PID IDM(q,q˙,q¨) Machine d d
d
Db(Xb)
dt dt
+
+

X

X
d
+
e ω Γ q
b˙b ¨
Path PID IDM(Xb,X,X) Machine
Fig. 2. Joint space Computed Torque Control for serial kinematic machines
+ +

Xb
d
FKM(q)
schemes. The core of this paper is organized as follows:
Section II is devoted to control, Section III presents the
Fig. 4. Cartesian space Computed Torque Control for parallel kinematic
machines
test-bed, namely the Isoglide-4 T3R1 [16], and its dynamic
modeling and Section IV contains experimental results.
II. DYNAMIC CONTROL OF A PARALLEL MACHINE
complexity of joint space computed torque control (see
d
Figure 3 where FKM is a numerical solution to the
As stated above, industrial parallel kinematic machines
b
forward kinematic problem and D is the computed for-
use in most cases a linear single-axis control with a linear
ward instantaneous kinematic matrix). Thus, heavy on-line
feedforward in terms of speed and acceleration (see Figure 1,
computation decreases control speed, accuracy and stability.
where IKM is the Inverse Kinematic Model). However, to
Consequently, a joint space computed torque control for a
ensure a good accuracy, the workspace and speeds should
parallel kinematic machine is rarely met alone but with a
be restricted [1], [6], [7].
robust controller [10].
Indeed, the strongly nonlinear dynamics of a parallel
On the opposite, using a Cartesian space dynamic model
kinematic machine have to be compensated for to increase
implies using a Cartesian space computed torque control, as
attainable workspace, speed and accuracy. The so-called
mentioned by Callegari [9]. We propose here a deepest anal-
computed torque control is a well-know solution for serial
ysis of this assertion. The Cartesian space computed torque
manipulators [8]. It encloses an inverse dynamic model
control is well-known for serial kinematic machines [8].
(IDM) depending on joint positions, speeds and accelerations
d
However, it requires, in this case, more computation than
(see Figure 2). Notice that IKM is a numerical solution
a joint space computed torque control, since the numerical
to the inverse kinematic problem, obtained by a numeri-
inverse instantaneous kinematic matrix is used on-line. It
cal inversion of the closed-form forward kinematic model
may lead to a decrease of control speed, accuracy and
and often performed off-line. This control ensures excellent
stability. Consequently, the Cartesian space computed torque
tracking performances. However, its transposition to parallel
control of serial kinematic machine is rarely used. On
kinematic machines is harder than for the linear single-axis
the opposite, by comparing Figure 3 and Figure 4, which
controller. Let us see why.
represents the Cartesian computed torque control for parallel
Computed torque control of a parallel kinematic machine
kinematic machines, it can be noticed that less numerical
met in the literature is generally performed in the joint
transformations are used. Therefore, a more stable and accu-
space [10]. Nevertheless, in most cases, the inverse dynamic
rate control is performed [18]. Hence, only from the control
model of a parallel kinematics machine depends only on
scheme analysis, a Cartesian space computed torque control
the end-effector pose, velocity and acceleration [9], [15].
is relevant for parallel kinematic machines. Nevertheless, we
Therefore, performing a computed torque control in the joint
can point out some additional practical advantages.
space requires transformations from joint space to Cartesian
space. These forward transformations have a closed-form Firstly, trajectories are most often planned in the Cartesian
expression for most serial kinematic machines. However, space. Thus, a Cartesian space control is more natural since
the duality between serial and parallel kinematic machine the control is performed directly in the task space. In addi-
implies that most parallel kinematic machines have algebraic tion, the desired trajectory is not transformed with the inverse
inverse kinematic models and numerical forward kinematic kinematic model, which can present errors. Consequently,
models [17]. the reference trajectory is not biased by the modeling or
Consequently, the presence of on-line computation in- identification errors. Furthermore, the Cartesian space is the
creases the complexity of the control scheme. We propose state space of most parallel kinematic machine since the
an explicit form of this control to emphasize the inherent latter are completely defined by their end-effector pose [19].X
d
Cartesian space control = correct tracking
Cartesian space
joint space control = tracking is lost
joint space
Disturbance shifts the end-effector pose
q
d
q
Fig. 5. Cartesian space ensures correct end-effector reference tracking
contrary to joint space control
Therefore, a Cartesian space control is a state feedback
control, which is known to ensure a better accuracy and
robustness than a control without a state feedback.
Secondly, a better end-effector trajectory tracking is en-
sured with a Cartesian space control than a joint space
one. Indeed, one joint variable configuration leads to several
end-effector poses [20]. In the worst cases, a disturbance
on joint trajectory can thus shift the end-effector position
without changing joint configuration. This can happen es-
Fig. 6. Global view of the Isoglide-4 T3R1
pecially in the neighborhood of singularities (assembling
mode changing trajectory [21]) or cups points (non-singular
be used.
posture changing trajectory [22]). This change of the end-
However, a kinematic model is always biased by the
effector pose is not observed by a joint space control whereas
unavoidable geometrical and assembly errors contrary to a
a Cartesian space one is able to do so (see Figure 5).
Consequently, the Cartesian space control tries to bring direct measure using simpler physics, such as optics. As far
back the end-effector pose to its reference or fails when as we know, the means to measure an object pose (Cartesian
the singularity can not be crossed again. On the contrary, position and orientation) are rare. For example, a laser tracker
a converging joint space control can not tell whether the is an accurate sensor (about 20μm for recent sensors) but
−2
Cartesian reference tracking fails or not. not fast enough (20m.s maximal object acceleration) [26],
[27]. To our knowledge, this sensor has not been integrated in
Last but not least, even on planned path dealing with
a control scheme but it is only used for calibration [27]. On
kinematic and dynamic constraints, the joint position errors
the opposite, the computer vision is a well known solution
are independent from each other when using a joint space
for robot control [28]. However, the accuracy and speed are
control. Therefore, the constraint can not be ensured and
generally quite low. Nevertheless, the technological advances
two types of defects may appear: uncontrolled parasite end-
allows for a fast and accurate vision-based control in a near
effector moves or internal torques on the contrary if these
future [29], [30].
moves are impossible, thus degrading passive joints. Like
two-arm robot control, Cartesian space control can minimize,
III. APPLICATION ON THE ISOGLIDE-4 T3R1
or cancel in the best cases, internal torques [23]. Indeed,
A. Presentation of the test-bed
the regulated errors, which are end-effector pose errors, are
naturally compatible with the end-effector moves.
To validate the above discussion, we propose to apply the
Consequently, Cartesian space control is particularly rel-
Cartesian space dynamic modeling and computed torque con-
evant for parallel kinematic machines. Nevertheless, the
trol to the Isoglide-4 T3R1. This parallel kinematic machine
presence of the forward kinematics in the feedback loop
is a fully-isotropic one with decoupled motion (see Figure 6
can reduce the improvement of a Cartesian space control
and [16]). It is a four degrees of freedom machine with three
over a joint space one. In the general case, this numerical
translations and one rotation. This machine is designed for
transformation can disturb the feedback loop thus leading
high speed machining. Hence, stiffness requirements impose
to stability, accuracy and speed losses and thus imposing
an important weight: 31kg per leg and 14kg for the end-
a robust control [12]. In the author opinion, this issue
effector.
could be improved by using performant forward kinematics
The main advantage of the Isoglide-4 T3R1, as far as
resolution methods [24] or metrological redundancy which
control is concerned, is to have a closed-form expression of
simplifies the forward kinematics [25]. Of course, the ability
the forward kinematic and instantaneous kinematic models:
of employing these methods at high rate should be tested.

Anyhow, the forward kinematics of some parallel kinematic X = q −X
 e 1 0


machines have a closed-form expression, like in the Isoglide- Y = q −Y
e 2 0
(1)
4 T3R1 case [16]. Thus, the estimation of the end-effector Z = q −Z
 e 3 0


q −q +δZ
4 3
pose is reliable and stable and a Cartesian space control could sinθ =
Land
Parameter CAD values Identified values Units σ(%)
 
1 0 0 0
MXR 3.235 5.054 kg.m 0.42
3
 
2
0 1 0 0
ZZR 1.787 2.443 kg.m 1.29
3
 
D(X) = (2)
2
  ZZR 6.429 8.420 kg.m 0.54
0 0 1 0 2
M 45.011 39.513 kg 0.62
1 1 t
0 0 −
Lcosθ Lcosθ
M 31.4380 39.999 kg 0.40
R1
M X 2.059 0 kg.m
T
P P
where X = [X Y Z θ] is the end-effector pose,
e e e
YY 0.411 0 kg.m
P
X , Y , Z and δZ are constant parameters depending on
0 0 0 Mcomp 45.011 49.180 kg 0.50
3
Mcomp 31.4380 41.005 kg 0.39
4
the actuators position in the reference frame and L is one
Fs 10.907 N 2.76
1
dimension of the end-effector.
Fs 25.558 N 1.25
2
Khalil’s method [31] is preferred over the classical Carte-
Fs 21.044 N 1.71
3
Fs 28.980 N 1.07
4
sian space dynamic modeling [9], [15] since this approach
−1
Fv 36.108 N.s.m 3.81
1
is easy to implement and ensures the known advantages of a
−1
Fv 89.419 N.s.m 2.45
2
Newton-Euler method in a control context. In the Isoglide-4 −1
Fv 35.211 N.s.m 6.34
3
−1
T3R1 case, Khalil’s method leads to a closed-form inverse Fv 64.793 N.s.m 3.10
4
dynamic model depending only on the end-effector pose and
Observation matrix condition number: 355.56
time derivatives. For conciseness concern, the expression of
Number of samples: 65404
the obtained model is not given here.
This test-bed is well suited to the validation of the ap-
TABLE I
proach, since its weight prevents us from neglecting the
Dynamic identification results
dynamics. Moreover, its straightforward kinematic models
allow for using a Cartesian control easily and compensate
for the technological lack of reliable and accurate high-speed
sensor of the end-effector pose.
IV. EXPERIMENTAL RESULTS
A. Dynamic identification
In order to fit the inverse dynamic model to the real dy-
namics of the machine and ensure the best performances for
computed torque control, dynamic identification was realized
(see Table I). The method and notations used here were
proposed by Gue ´gan [32]. Results lead to an observation
matrix condition number of 355.56 which is relatively good.
Inertia parameters (MXR , ZZR , ZZR , M , M ) are
3 3 2 t R1
identified with a standard deviation from 0.40% to 1.29%,
friction terms (Fs and Fv ) from 1.07% to 6.34%. Let us
i i
remark that some parameters describing the end-effector can
(a) Calibration pattern and inter- (b) Camera and laser
not be identified because the end-effector is lighter than the
ferometer mounted on the end-
effector
legs, thus having a little influence on dynamics. Anyhow, the
good results of the identification process allows for ensuring
Fig. 7. Straightness measure with an high speed camera and a laser
a stable and accurate computed torque control.
interferometer
B. Dynamic control
The Isoglide-4 T3R1 is designed to be controlled either
in joint space or in Cartesian space thanks to the kinematic
decoupling. Consequently, we first propose a comparison be-
tween linear single-axis control and computed torque control
in joint space.
To achieve this comparison, the end-effector trajectory is
measured with a 512×512 camera as exteroceptive measure
running at 250Hz. This provides us with a measure of the real
end-effector trajectory instead of a model biased estimation.
A comparison between the camera and a laser interferometer
(a) Deviation on Y-axis (b) Error between laser interferom-
is performed (see Figure 8) showing that the camera has an eter and camera measures
average accuracy of 26μm and validating further results.
Fig. 8. Comparison between laser interferometer and camera
Both control schemes have the same gain tuning with same
cut-off frequency (ω ) of 5Hz. Nevertheless, derivative gain
cFig. 10. Tracking error on X axis measured with an high speed camera
on a 100mm square
Fig. 9. Comparison between single-axis and CTC controller measured with
an high speed camera on a 100mm XY square
PID CTC
Left edge 0.733mm 0.154mm
Right edge 2.255mm 0.330mm
Bottom edge 3.318mm 0.443mm
Top edge 3.143mm 0.293mm
TABLE II
Measured straightness error on square segment with an high speed camera
in the single-axis controller can not be set at the theoretical
Fig. 11. Orientation tracking error with a computed torque control in the
joint and the Cartesian space, with Forward Kinematics and direct measure
value because the linear actuators we use do not cope with
noise, even filtered. Figure 9 shows a comparison between
single-axis and computed torque controls. The reference
trajectory is a simple 100 mm square in the XY frame.
is applied. The joint sensors have a 1μm accuracy. The
−2
A fifth degree path generation with a 3m.s maximal
direct measure has a 50μm and 0.001rad accuracy. The
acceleration is used. The trajectory is executed segment by
geometrical errors are fixed to 50μm and the dynamic
segment.
parameters errors to 10%. In the Isoglide-4 T3R1 case, the
According to Figure 9, computed torque control in the geometrical errors have a great influence on the orientation
joint space achieves an accurate tracking while the single- estimation (see Eq 1). Consequently, the comparison is

axis can not. Numerically, the straightness error are divided achieved on a 30 rotation of the end-effector. According
by 7 for X-axis displacement and 10 for Y-axis displacement to the Figure 11, the improvement between joint space and
(see Table II). Furthermore, there is no overshoot at the Cartesian space control with the forward kinematics is small
end of travel with the computed torque control (see Figure since the Cartesian and joint reference are in a very close
10). Thus, using computed torque control instead of a linear relation (see Eq 1). On the opposite, the use of a direct
single-axis control improves tracking, even in the joint space. measure greatly improves the tracking even with a less
Indeed, due to heavy inertia, the dynamic coupling between accurate end-effector pose sensor than the joint one (50μm
−2
legs is not neglectable even at 3m.s . Consequently, the against 1μm). Indeed, the orientation measure leads to a
dynamic behaviour of the machine should be compensated better compensation of the dynamics since the measure is
for to improve accuracy. closer to the real orientation than the estimated one.
At the moment, we are not able to propose experiments On the whole, these simulation and experiment results
on a control with a direct measure of the end-effector pose. show three major points. First, using computed torque con-
Nevertheless, we propose a simulation to compare a joint trol instead of a linear single-axis control improves accu-
space computed torque control, a Cartesian space computed racy especially on heavy parallel kinematic machines like
torque control using the forward kinematics and one using the Isoglide-4 T3R1. Second, the Cartesian space control
a direct measure of the end-effector pose. Realistic noise improves the Cartesian reference tracking. Third, a directmeasure of the end-effector pose, instead of an estimation [15] B. Dasgupta and P. Choudhury. A general strategy based on the
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