Some Issues on Dynamic Control of Parallel Kinematic Machines
1 1 1,2 1,3
Flavien Paccot Omar AitAider 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 wellknown 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 endeffector 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 singleaxis 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 singleaxis 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
endeffector 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 endeffector pose [5]. To
machine [11]. Nevertheless, since the kinematics are deﬁned
take into account this heterogeneity, a restricted workspace
by the endeffector 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 endeffector 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 singleaxis 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 socalled computed torque control, is a wellknown 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. Singleaxis 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
+
+
b˙
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
testbed, namely the Isoglide4 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 singleaxis control with a linear
ward instantaneous kinematic matrix). Thus, heavy online
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 socalled
mentioned by Callegari [9]. We propose here a deepest anal
computed torque control is a wellknow solution for serial
ysis of this assertion. The Cartesian space computed torque
manipulators [8]. It encloses an inverse dynamic model
control is wellknown 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 online. It
cal inversion of the closedform forward kinematic model
may lead to a decrease of control speed, accuracy and
and often performed offline. 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 singleaxis
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 endeffector 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 closedform 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 online 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 endeffector pose [19].X
d
Cartesian space control = correct tracking
Cartesian space
joint space control = tracking is lost
joint space
Disturbance shifts the endeffector pose
q
d
q
Fig. 5. Cartesian space ensures correct endeffector 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 endeffector trajectory tracking is en
sured with a Cartesian space control than a joint space
one. Indeed, one joint variable conﬁguration leads to several
endeffector poses [20]. In the worst cases, a disturbance
on joint trajectory can thus shift the endeffector position
without changing joint conﬁguration. This can happen es
Fig. 6. Global view of the Isoglide4 T3R1
pecially in the neighborhood of singularities (assembling
mode changing trajectory [21]) or cups points (nonsingular
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 endeffector 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 visionbased 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
twoarm robot control, Cartesian space control can minimize,
III. APPLICATION ON THE ISOGLIDE4 T3R1
or cancel in the best cases, internal torques [23]. Indeed,
A. Presentation of the testbed
the regulated errors, which are endeffector pose errors, are
naturally compatible with the endeffector 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 Isoglide4 T3R1. This parallel kinematic machine
presence of the forward kinematics in the feedback loop
is a fullyisotropic 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 Isoglide4 T3R1, as far as
resolution methods [24] or metrological redundancy which
control is concerned, is to have a closedform 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 closedform expression, like in the Isoglide Y = q −Y
e 2 0
(1)
4 T3R1 case [16]. Thus, the estimation of the endeffector 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 endeffector 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 endeffector.
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
NewtonEuler method in a control context. In the Isoglide4 −1
Fv 35.211 N.s.m 6.34
3
−1
T3R1 case, Khalil’s method leads to a closedform inverse Fv 64.793 N.s.m 3.10
4
dynamic model depending only on the endeffector 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 testbed 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 highspeed
sensor of the endeffector 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 endeffector can
(a) Calibration pattern and inter (b) Camera and laser
not be identiﬁed because the endeffector 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 Isoglide4 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 singleaxis control and computed torque control
in joint space.
To achieve this comparison, the endeffector trajectory is
measured with a 512×512 camera as exteroceptive measure
running at 250Hz. This provides us with a measure of the real
endeffector trajectory instead of a model biased estimation.
A comparison between the camera and a laser interferometer
(a) Deviation on Yaxis (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
cutoff 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 singleaxis 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 singleaxis 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
singleaxis 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 Isoglide4 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 endeffector. According
by 7 for Xaxis displacement and 10 for Yaxis 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
singleaxis 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 endeffector 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 endeffector pose. show three major points. First, using computed torque con
Nevertheless, we propose a simulation to compare a joint trol instead of a linear singleaxis 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 Isoglide4 T3R1. Second, the Cartesian space control
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