Design and Implementation of A High Power Robot Distributed … · 2016-10-11 · Design and...
Transcript of Design and Implementation of A High Power Robot Distributed … · 2016-10-11 · Design and...
Design and Implementation of A High Power Robot Distributed Control
System on Dependable Responsive Multithreaded Processor (D-RMTP)
Takuma Shirai∗, Kohei Osawa†, Hiroyuki Chishiro‡, Nobuyuki Yamasaki† and Masayuki Inaba∗
∗Department of Mechano-Informatics, University of Tokyo, Japan†Department of Information and Computer Science, Keio University, Japan
‡Graduate School of Industrial Technology, Advanced Institute of Industrial Technology, Japan
Abstract— The robotics field provides with many typicalapplications of Cyber-Physical Systems (CPS). Robots are ex-pected to be deployed in a wide variety of applications includinglife supporting work or disaster response and therefore theyneed to follow strict safety and dependability constraints. Inorder to create robots with high utility, safety and dependability,research has focused both on creating mechanically safe systemsand safe control laws.
Throughout the present work, we use the embedded real-time processor D-RMTP as a motor controller to attain highresponsiveness on a high power robot. D-RMTP has a hardwaremechanism to support hard real-time processing, which enableslower-jitter and lower-overhead processing compared with con-ventional software based real-time execution (Real-Time Task).Responsive Task, a real-time execution mechanism based onD-RMTP, has also been proposed in recent works.
In this paper, we have evaluated the effects of the low-jitter and low-overhead performance of Responsive Task on adistributed robot control system. The result of the experimentshowed that Responsive Task resulted in improving the controlcycle speed and the responsiveness of the controller.
I. INTRODUCTION
The Robotics field provides with many typical examples
of Cyber-Physical Systems (CPS)[1]. In recent years, the
improvement of computer system performance has enlarged
the domain of applications that can be addressed by robots.
Furthermore, since the recent introduction of ISO13482, an
international safety standard for life support robots released
in 2014, robots are expected to work even closer to humans
than before. Therefore, it becomes of prime importance to
develop mechanisms and controllers which fulfill the require-
ments of this safety standard. More notably, the standard
now allows the use of a safety mechanism with a digital
controller which has been thoroughly tested and approved,
and which has been restricted before to mechanically safe
systems only. This opens the door to utilize a life support
robot as a commercial product, which was more difficult due
to these safety problems.
It is imperative to develop high-precision and high-speed
sensing technologies when we address issues of safety and
dependability of robotics control systems. Such improved
sensing also allows the robot to perform increasingly more
complex tasks because of its ability to move their manip-
ulators quickly and with more dexterity. This has been the
case for our human-like robot HRP3L-JSK[2] and JAXON[3]
developed in previous works which can show fast and high
response to external forces, high acceleration when foots are
Fig. 1. High Power Humanoid Leg, HRP3L-JSK[2]
put in contact with ground or objects in the environment (See
Fig.1).
Yamasaki developed an embedded real-time processor,
Responsive Multithreaded Processor (RMTP)[4], and a real-
time communication link, Responsive Link[5], which have
proven strong potential for real-time distributed robot control
systems applicability. Additionally, Watanabe et al. devel-
oped a low-latency real-time execution mechanism for hard
real-time task which works on RMTP, named Responsive
Task, and evaluated its basic performances in a recent
study[6]. This evaluation has only considered the case of
simple mathematical operations and hence performances on
an actual task including data transmission with an outer
device and an actuator controller have still to be verified.
In this paper, we first introduce our distributed control
system which is composed of multiple networked controllers.
This distributed system can be flexibly configured according
to the physical body structure of robots. We then apply the
Responsive Task and verify its performance in the case of
a motor control task. Finally we evaluate the system as a
whole and ensure that real-time execution and responsiveness
are correctly implemented on each of our control nodes and
enforced in the distributed control system.
II. REQUIREMENTS
A. Duty Cycle
Execution cycles of a robot controller are commonly set
to 1[msec] cycle (1000Hz). This value is conventional for
embedded robot control systems which can control a joint
angle with adequate accuracy and do not cause a deadline
miss during a task execution. However, faster cycles become
required in order to improve precision and responsiveness
Frequency[Hz]10
110
210
310
410
510
610
7
Gain
[dB
]
-200
-150
-100
-50
0
DC motor PI control Frequency Responce
AngleTorque
Fig. 2. Bode plot of DC motor PI controller with angle feedback (or torquefeedback).
further as the demand for power, speed and safety of actuator
augments. In general, increasing the speed of a control cycle
and then increasing feedback gain have good effects on
responsiveness. Fig.2 indicates the bode plot of a motor
controller with angle feedback or torque feedback, which
is calculated by using simple DC motor dynamics model
described as below.
Jmθ̈ = Kti+ τin (1)
Lmi̇+K−1
vθ̇ +Ri = Vin (2)
where θ is motor angle, i is current, Vin is input voltage,
τin is input torque, and [Jm, Kt, Lm, Kv , R] are physical
parameters of motor (parameters are set to be the same value
of the motor used for HRP3L-JSK[2]). This bode plot figures
out that the commonly used duty cycle 1000Hz is enough
speed to control the joint angle, but not for torque control
with large room for improvement. Therefore, improving the
duty cycle of the main controller from 1000Hz dramatically
is one of our targets. However in the case of resource
restricted embedded systems, the increase of duty cycle may
lead to cause a deadline miss and timing jitter which will
conversely affect and ultimately hinder control performance.
B. Network Speed And Latency
Distributed control system is often applied for internal
architecture of human-like robots. Controllers in the system
are connected with each other by internal communication
links such as Ethernet, CAN, I2C and UART. However,
using these ordinary links is getting more difficult because
the increase of control duty cycle consequently raises the
number of packets per unit time. Therefore, high-speed and
low-latency communication links must be implemented in
the control system when considering to increase the duty
cycle of controllers.
C. Jitter
The effect of jitter on the control systems appears as
sensor noise due to sampling timing jitter and performance
deterioration due to control timing jitter. The effect on a
cyclic task is depicted in Fig.3. The real-time cyclic task is
fundamentally scheduled to complete between a release time
and a deadline. By sampling the input signal at shifted timing
from an assumed ideal sampling timing as presented in Fig.3,
the measured input signal trajectory shows differences from
the true trajectory. The same logic applies to the output
Input Signal
Ideal Timing
Actual Timing
T
Sampled Data IdealActual
Time
Time
Release Time Deadline
Sampling Cycle
with Jitter
Fig. 3. Effect of jitter on a cyclic real-time task.
trajectory from the controller. Such output includes noise
which further adds to the ideal values.
Many research works have analyzed the effect of sampling
timing jitter which adds as noise on the top of measured
sensor values. Da[7] computed the effect of the input power
and the timing jitter power on Signal-to-Noise Ratio (SNR).
This work has indicated that if we would like to increase
the SNR of the measured sensor value, the variation of jitter
must be suppressed.
In addition, Skaf[8] evaluated the effect of the control tim-
ing jitter on a feedback controller based on Linear Quadratic
Regulator (LQR). The LQR is a basic controller usually
applied for robot control systems. The evaluation of Skaf
revealed that the percentage of jitter to the control period ∆
T
restricts the upper bound of the evaluated value of LQR. This
evaluation also reported that the evaluated value of LQR on
the system where maximally 20% timing jitter exists gets
worse by about 5% compared to a nominal system, and
also over 25% on the system where a maximum 50% of
timing jitter exists. According to this work, it can be said
that the timing jitter on a control system should be ideally
0 or restricted to less than 20% at the worst case scenario.
The control period of a conventional robot control system
is usually 1–2[msec], therefore, it can be said that there is
enough margin in the system where 100–200[µsec] jitter is
existing. However, when creating a cyclic task with 100–
200[µsec] cycle, the desirable jitter value can be less than
10–20[µsec]. This point becomes more important in the case
of a distributed control system where network latency also
exists due to the overhead in transmitting and receiving data.
This latency generates further timing jitter which also has
adverse effects on a feedback controller[9].
III. ABOUT D-RMTP
A. Dependable Responsive Multithreaded Processor
Dependable Responsive Multithreaded Processor (D-
RMTP) is an embedded processor developed by Yamasaki
and Suito[4][10] and designed to be applied to distributed
real-time systems. The processor has a mechanism to execute
parallel real-time multithreaded processing in hardware. It
is also composed of Responsive Link[5] designed for real-
time communication between distributed control nodes. Re-
Time
Task 0
Task 1
Task 2
Task 3
Low Priority
High Priority
Task 4
Task 5
Task 6
Task 7
System
Release Time
Deadline
Fig. 4. Real-time execution on RMT PU (from [4]). Eight tasks withpriority value are executed in parallel. A higher priority task can accesshardware resources on the processor in advance to meet its scheduleddeadline.
TABLE I
COMPARISON OF RESPONSIVE TASK AND REAL-TIME TASK
Responsive Task Real-Time Task
Method Hardware (using D-RMTPutilities)
Software (using softwarescheduler)
Overhead less than 1[µsec] (not de-pending on # of tasks)
more than 13[µsec] (de-pending on # of tasks)
Jitter quite low depend on # of tasks, CPUusage, scheduling policy
Usability limited # of executabletasks
not restricted
sponsive Multithreaded Processing Unit (RMT PU), a core
processor inside the D-RMTP chip, has eight logical cores,
which enables to run eight tasks in parallel. Priority values
can be set for each task, then RMT PU can assign arithmetic
units according to real-time requirements of each task. RMT
PU has also a dedicated context cache mechanism to store the
information of registers during a context switch. A context
switch occurs when switching between tasks run in parallel
by a user program.
All these functions significantly reduce the overhead of a
conventional real-time multithread processing through soft-
ware, therefore, a good real-time performance such as a
short real-time execution cycle and low-jitter characteristics
can be attained by this mechanism. An example of par-
allel eight tasks processing with priority on RMT PU is
depicted in Fig.4. In addition to these functions, D-RMTP
is implemented as a System-on-a-Chip (SoC) to be a small
single chip device which is equipped with other peripheral
interfaces such as DDR SDRAM I/Fs, DMAC, UART and
pulse counter.
B. Responsive Task
Responsive Task[6] is a low-latency real-time execution
mechanism developed by Watanabe et al. Responsive Task is
designed to be waked up by interruptions including processor
timer which each logical core is equipped with. When the
processor timer triggers an interruption, the corresponding
active task starts immediately with only one clock latency,
which is a dedicated mechanism of RMT PU (Fig.5). Be-
cause the active task associated with the logical core can
be only one task for each core, Responsive Task must
occupy one of the eight logical cores by one task only. This
mechanism eliminates the overheads such as releasing a task,
scheduler processing and the context switch to the next task,
Release Time Deadline
Responsive
Task 0
Scheduler
Kernel
Real-Time
Task 0
Real-Time
Task 1
return from interrupt
- return from interrupt
- release real-time tasks
- schedule real-time tasks
- perform context switch
Fig. 5. Example of real-time execution of Responsive Task and Real-TimeTask. Responsive Task occupies one core, and immediately wakes up on aninterrupt. Real-Time Task is managed by the scheduler and is dispatchedafter making a scheduler decision.
and which are caused by using a usual software based real-
time scheduler. Owing to this mechanism, Responsive Task
can execute a process with low-jitter in even shorter periods
than that of a real-time multi-task execution through a
software based real-time scheduler. In this paper, we call the
real-time multi-task execution mechanism with the software
based real-time scheduler Real-Time Task as opposed to
Responsive Task we use throughout this paper.
According to the research by Watanabe et al., the overhead
of Responsive Task is about 1[µsec] regardless of the number
of tasks, whereas Real-Time Task shows 13[µsec] overhead
depending on the number of tasks. Moreover, Watanabe
et al. also reported that the release jitter of Responsive
Task is 0.04% in average, and 1.8% at maximum when
processing simple calculation tasks and 0.4% in average
and 3.2% at maximum when processing a task including
multiple memory access. 10µsec cycle execution is available
through Responsive Task. As described in II-C, the desirable
jitter value is less than 10–20[µsec] when the task cycle
is 5k–10k[Hz]. This means that Responsive Task can meet
the jitter value requirements even when its control duty
cycle is increased from current 1kHz. Note that because
Responsive Task occupies the logical cores, the maximum
number of parallel executable tasks is limited by the number
of these logical cores. Therefore, a task with a higher real-
time requirement has to be configured to have higher priority
in order to be assigned the hardware resources in priority and
avoid conflicts with other tasks. The summary of comparison
of Responsive Task and Real-Time Task is shown in TableI.
C. D-RMTP 20mm SiP Board
D-RMTP 20mm SiP (Fig.6) is a System-in-a-Package
(SiP) in which a D-RMTP SoC and necessary devices for
an embedded controller such as a Power module, SDRAM
and FLASH ROM are packed. D-RMTP 20mm SiP board has
also been developed and is equipped with some connectors
for I/Os between D-RMTP 20mm SiP and FPGA (Xilinx,
XC6SLX150) to enable further system extension. In this
paper, we use this D-RMTP 20mm SiP board as a main
actuator controller to develop a real-time control system for
high power robots. The main memories of this board are
39mm
26mm
SDRAMD-RMTP SoC
FLASH ROM
D-RMTP 20mm SiP Board
Fig. 6. D-RMTP 20mm SiP board (photograph from [10]).
FET Board
Water Block
Active Optical Fiber
Port
RMTP-02D Control Board D-RMTP 20mm SiP Board
(on back side)
Fig. 7. RMTP-02D control board assembled with a FET board, a waterblock module and a D-RMTP 20mm SiP board.
64kB SRAM and 64MB SDRAM. The CPU clock frequency
is 45MHz, which can be configured through a user program.
IV. SYSTEM DESIGN
A. High Power Motor Driver
RMTP-02D control board is designed based on a high
power motor driver for a humanoid robot developed by Urata
et al.[11][12] by adding new functions such as an optical
communication interface to enable fast data transmission
under electrically noisy environment in the robot body and
extendable connectors to attach a D-RMTP 20mm SiP Board.
RMTP-02D control board utilizes a vector controller for a
brushless DC motor (BLDC motor) implemented in FPGA
logic also developed by Urata et al.[12]. The assembled
motor driver module of RMTP-02D control board and a
high power FET thick copper board can perform large torque
outputs and fast motion that exceeds that of a human joint.
The assembled module is depicted in Fig.7. This motor driver
module can supply 50[A] (continuous) and 200[A] (peak)
with 80[V] input voltage. The motor driver module can drive
one motor at a time.
B. High Reactive Robot Control System
The overall structure of our control system has three
control layers as depicted in Fig.8.
The top layer control system is based on a commonly
used x86 computer. We designed this control system to
have a compatibility with the existing system used for
JAXON[3]. The main control PC has two types of real-time
communication interfaces that communicate with actuator
boards and sensor modules at a lower control layer. One is
our custom serial interface and the other is EtherCAT. These
two interfaces are selectable and both interfaces can transmit
and receive data at 1kHz cycle.
In addition, we developed a middle layer controller that
bridges data from the bottom layer controller to the top
layer controller. This board also has functions such as a
Main Control
Computer
・Joint Angle Sequencer
・Real-Time Interface
Top Layer
500Hz-1000Hz
Bottom Layer
5000Hz
Interface Bridge (+ Data Logger)
・D-RMTP(Servo Control)
- Low-Jitter, Low-Latency Feedback
・RMTP-02D
- High Speed Link
- Three Phase Inverter
- Sensor Interfaces
...
・Traffic Control
・Data Buffer
・Packet Capture
Whole Body Controller
(HRPSYS[3])
Distributed Motor Controllers
Middle Layer
1000Hz-5000Hz
- EtherCAT
- Custom Serial Interface
(100Mbps, 1kHz)
(20Mbps, 1kHz)
Fig. 8. Distributed robot control system by using RMTP-02D control boardand D-RMTP.
D-RMTP FPGA
Thread Status Monitor Task(1Hz)
Vector ControllerIq target
Id target
Temperature Estimation Task(100Hz)
Servo Control Task(1k-5kHz)
Scheduler (only for Real-Time Task)
PD Controller
Data Receiver
Error Checker
Limiter
Data Transmitter
Actu
ato
r S
tatu
s
✁current,✁target
Iqtarget,Idtarget
Distributed
Shared
Memory
Functional
Protocol
Block
Hi-S
peed
Tra
nsce
ive
r
High-Speed Link Core
Sensor Data Register
32bit D
ata
Bu
s
32bit D
ata
Bu
s
✁target
mode
✁current
Iq,Id
✁current
✁target
P gain
D gain
Fig. 9. Motor control system and data flows inside the D-RMTP and theFPGA on RMTP-02D control board.
network hub to connect multiple channels and a data buffer.
Furthermore, an embedded Linux system is running on the
board, where data logger and error detecting applications
are implemented. A high-speed communication mechanism
with RMTP-02D control board on the bottom layer is im-
plemented through the Active Optical Fiber which supports
a high-speed transceiver.
The bottom layer controller consists of some RMTP-02D
control boards. Each board has two Active Optical Fiber
ports, and by daisy chain connection, the required number
of controller boards for a target robot can be connected in
single network. The block diagram of the motor servo task
we developed in RMTP-02D control board with Responsive
Task and Real-Time Task is depicted in Fig.9. The inputs of
the controller, such as the current joint angle (an encoder
counter value) and the target joint angle and controller
parameters, are obtained from FPGA on RMTP-02D control
board through the High-Speed Link Core. This High-Speed
Link has an ability to transmit and receive at 2.5Gbps. For
the time being, a motor core temperature estimation task and
a thread status monitoring task which computes statistics on
each thread are also executed.
TABLE II
CONFIGURATIONS OF THE EXPERIMENT
Label Execution Mechanism Period P Gain D Gain
A Responsive Task 1000Hz 1200 1000
B Responsive Task 5000Hz 2000 1000
C Real-Time Task 1000Hz 960 1000
D Real-Time Task 2000Hz 880 1000
V. EVALUATIONS
In order to evaluate the effect of Responsive Task on
the performance of the motor servo task, we conducted an
experiment that measures the trajectories of step response
by using a single joint test bed. The single joint test bed
is depicted in Fig.10. This test bed is actuated by a 200W
BLDC motor manufactured by MAXON, which is the same
product used for HRP3L-JSK and JAXON. The structure of
this test bed is simply composed of a 3.75:1 low gear ratio
reduction pulley and a single bar link, therefore, losses due
to reduction pulley and vibration caused by the stiffness of
the joint can be ignored.
In this experiment we gave a circular trajectory which
spans 360◦ during 0.100[sec] as a target angle. This target
trajectory is generated by a joint angle sequencer running on
the main control PC at 500Hz cycle using the minimum jerk
interpolation method. The target angle is then transmitted
to D-RMTP through the real-time interface at every period
of the control cycle. In addition, this rotational angle is
equivalent to about 5.6◦ in an actual robot angle with 240:1
reduction ratio, and the maximum joint angular velocity on
the trajectory is about 1/10 of the maximum angular speed
capability of the motor driver. Therefore, the target trajectory
has enough margin from the output power perspective.
The motor servo task is implemented through a PD con-
troller, and this task needs about 31.2µsec processing time
in average on the D-RMTP running with a 45MHz clock.
We logged and compared the response trajectory on four
configurations presented in TableII. The execution cycle of
motor servo task and the real-time execution mechanism
(Responsive Task or Real-Time Task) was combined in these
configurations. Since the scheduler must be called more
frequently than the shortest period of the tasks processed
on Real-Time Task, an increase in the cycle of the tasks
results in an even bigger increase in the scheduler overhead.
For this reason, the practical maximum cycle of the motor
servo task reaches 2000Hz with Real-Time Task under this
experiment. In addition, the PD controller parameters, the
P gain and the D gain, were tuned to minimize time until
stabilization and oscillations (TableII). When higher gain
value than the value on TableII was applied, the joint angle
controller got difficulty in stabilizing after joint movement.
Also, the time constant Ts of a low-pass filter which is
used during calculation of the D term has been adjusted to
Ts = 0.001 for each control cycle.
A. Results
The joint angle trajectories of the step response gathered
from experiments are shown in Fig.11. We adjusted the time
offset of each trajectory so as to align the leading edges of
Fig. 10. Test instrument witha single bar link.
time[sec]0 0.1 0.2 0.3 0.4
angl
e[de
g]
0
100
200
300
400Joint Angle
Target1000Hz-resp-01-105000Hz-resp-01-101000Hz-rt-01-102000Hz-rt-01-10
Fig. 11. Trajectories of a joint angle.
time[sec]0.1 0.105 0.11 0.115 0.12
angl
e[de
g]
-10
0
10
20
30
40
50Joint Angle
Target1000Hz-resp-01-105000Hz-resp-01-101000Hz-rt-01-102000Hz-rt-01-10
time[sec]0.18 0.19 0.2 0.21 0.22
angl
e[de
g]
350
352
354
356
358
360
362
364
366
368
370Joint Angle
Target1000Hz-resp-01-105000Hz-resp-01-101000Hz-rt-01-102000Hz-rt-01-10
Fig. 12. Trajectories of a joint angle (Showing leading edge area on theleft and the settling area on the right).
TABLE III
WORST CASE ERROR OF THE JOINT ANGLE FOR EACH TRIAL
A B C D
overshoot[deg] 9.69 4.94 6.57 13.05
max error[deg] -42.52 -26.92 -53.32 -46.08
TABLE IV
AVERAGE MANHATTAN DISTANCES OF JOINT ANGLE TRAJECTORIES
A:1000Hz(resp) B:5000Hz(resp) C:1000Hz(RT) D:2000Hz(RT)
0.2008 0.0639 0.0873 1.7911
each target joint angle trajectory on 0.100[sec]. The left part
of Fig.12 shows an enlarged view near the leading edge of
Fig.11 and the right part of Fig.12 shows the settling area of
the same figure. Also, Fig.13 shows the error angle trajectory
of the target joint angle and the current joint angle.
TableIII shows the worst case value of the overshoot
and the maximum value of the error angle at the leading
edge during 10 times trial for each experiment configuration.
The configuration B shows 4.94◦ of overshoot and −26.92◦
of maximum error angle. This is the best performance of
overshoot and maximum error among all 4 configurations.
We then evaluated the variation of the joint angle trajec-
tories in 10 experiments and between each configuration.
Throughout this experiment, we used the Manhattan distance
of trajectories as an evaluation index since each trajectory
had a similar curve. The Manhattan distance reflects the
difference of trajectory shape and the phase shift of the
trajectory. TableIV shows the results of calculating the aver-
age Manhattan distance from the average trajectory for each
configuration. The variation of trajectories was less than 1◦
for the configuration A,B and C, and only the configuration
D showed a large variation.
time[sec]0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
erro
r an
gle[
deg]
-60
-40
-20
0
20Joint Angle Error
1000Hz-resp5000Hz-resp1000Hz-rt2000Hz-rt
Fig. 13. Trajectories of joint angle error.
B. Discussion
The experiment results showed that the configuration B
was obviously the best configuration with significant dif-
ference than others in accuracy and the repetitive precision
of the control. The value of P gain can be considered to
be predominant for the error value at leading edge, where
the quick increase of target current value is effective in
order to follow the changing target joint angle. In general,
increasing the speed of control cycle prevents oscillating
when increasing the value of P gain, and then improves the
responsiveness to quick change in input values.
On the other hand, the configuration D showed that the
variation throughout the 10 times experiments was about
1.8◦ in average when converted to joint angle variation.
This variation is particularly large compared with other
configurations. The scheduler is also frequently called, at
more than 2000Hz, when we use Real-Time Task with
2000Hz cycle configuration. In this case, the overhead due
to the scheduler relatively increases the CPU usage, which in
turn causes important timing jitter during the servo task. We
think this is the reason why configuration D did not improve
the performance or even got worse when considering the
repetitive precision of servo control. This was observed de-
spite the increase of the value of P gain thanks to the in turn
increase of the control cycle compared with configuration C.
In the light of the discussion above, Responsive Task,
which held apparent repetitive precision with 5000Hz cycle
execution and even improved the accuracy of control through
the increase of the speed of control cycle, has provided with
superior results in comparison with Real-Time Task.
VI. CONCLUSIONS
In this paper, we designed and developed a real-time
distributed control system for a high power robot using
D-RMTP and Responsive Task. We developed RMTP-02D
control board to drive high power motors using D-RMTP,
and then composed a distributed robot control system which
implements a high speed communication network which can
connect to an existing upper layer robot control system.
The experiment showed that Responsive Task provides with
improved results in terms of responsiveness and precision of
a high speed servo motor control. This was due to its low-
jitter and low-overhead superior performance. The control
law used in experiments was a PD control which has small
calculation amount and does not require important CPU
resources. Still, results have proved that the improvement of
the real-time mechanism for a cyclic task has the potential
to improve the performance of a control system to a great
effect.
In the future, we would like to work on improving the
control law itself in order to achieve even higher control
performance for the system as a whole. A high responsive
force controller with a wide controllable bandwidth, and
which enables the robot manipulator to act as a safe arm with
flexibility comparable to springs and damper based systems,
could be challenging to implement on Responsive Task.
ACKNOWLEDGMENT
This research was supported by Japan Science and Tech-
nology (JST)’s CREST project for “Fundamental Technolo-
gies for Dependable VLSI System”.
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