Development of the EAST articulated maintenance arm and an algorithm study of deflection prediction and error compensation
Shi, Shanshuang (2017-06-30)
Väitöskirja
Shi, Shanshuang
30.06.2017
Lappeenranta University of Technology
Acta Universitatis Lappeenrantaensis
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-335-100-4
https://urn.fi/URN:ISBN:978-952-335-100-4
Tiivistelmä
Experimental Advanced Superconducting Tokamak (EAST) is the world's first fully
superconducting tokamak with a non-circular cross-section. In recent years, with
increased device performance and experimental parameters, EAST has achieved a series
of important research results and scientific discoveries. However, EAST inner
components of the first wall will also be facing an increasingly tough operating
environment with higher heat loads. Although plasma facing components (PFCs) have
been updated and upgraded several times, the high heat flux during experiments will
cause damage or even failure to local small parts in the EAST vacuum vessel (VV). Any
failure of the internal components might influence the obtaining of high-quality plasma
or even lead to a plasma discharge and cause safety problems of the operation device.
Therefore, it is essential to have timely maintenance based on the condition of damaged
internal components in the experimental period. In conventional manual maintenance,
even if small sign of damage is seen, the device has to be shut down, which seriously
affects the efficiency of physical experiments, and increases the time and economic costs.
Therefore, to ensure adequate running time, the demand for remote handling maintenance
of the EAST device during physical experiments is urgent.
The EAST articulated maintenance arm (EAMA) system is developed for real-time
detection and rapid repair operations to damaged internal components during plasma
discharges without breaking the EAST ultra-high vacuum (UHV) condition. To achieve
the desired objectives, the EAMA system design should guarantee that the robot can
stably run in the harsh environments of high temperature (80-120 oC) and high vacuum
(~ 10-5Pa). Meanwhile, the errors caused by the deformation of long flexible robot arms
should also be predicted and compensated in real-time to obtain high accurate
maintenance operations. The main contributions of this dissertation include: design,
development and analysis of the whole EAMA hardware system; study of two different
methods of matrix structural analysis (MSA) and a back-propagation neural network (BPNN) to predict flexible deformations of a manipulator system; the eventual
developing of an EAMA dynamic error compensation algorithm to precisely control the
EAMA system operation.
Firstly, during the EAST experiments, environment conditions inside the vacuum vessel
were very harsh with complex geometry; from the design view, the requirements for an
EAMA robot were more critical than those for a conventional industrial robot arm.
Beyond the conventional techniques in robot design, many unique techniques were
considered in the EAMA manipulator arm design: (1) Redundant articulated
configuration with modular techniques were utilized in robot arm design; (2) All joint
actuators and high-speed transmission components were set up in a sealed box, using a
planetary roller screw to change rotation into linear motion, which was transferred outside
by the bellows; (3) Solid lubrication using MoS2-Ti-C composite coatings were
developed for the low-speed transmission parts exposed to the vacuum environment; (4)
A parallelogram link mechanism was developed to provide more than 50,000 times of
reduction ratio and more than 1000Nm of drive torque. Secondly, as the total length of the EAMA system reaches more than 10 m, it will produce
significant flexible deformation under the effects of torques and gravity. The deformation
values will always be constantly changing as the arm movements and postures change. If
this flexibility could not be accurately predicted and reasonably compensated, it would
seriously affect the position accuracy of the manipulator system. Based on the rigid-body
dynamic model, the prediction models of flexible arm deflections have been established
using two different methods: a stiffness matrix method based on matrix structural analysis
(MSA) theory and a forecasting by BP neural network method based on real experimental
data. The prediction accuracy, pros and cons of the two methods were compared. Both
methods have good real-time, which can provide the basis for achieving precise control.
Finally, considering manipulator kinematics and trajectory planning, an algorithm for
dynamic error compensation of the EAMA manipulator has been developed according to
the existing deformation prediction model and calculation results. The related feedback
control codes were programed to compensate the dynamic errors of end-effector in any
positions or postures of the manipulator system in real-time. Therefore, sufficiently high
control accuracy for successful completion of the relevant remote operation and
maintenance tasks can be achieved for the EAMA robot system.
superconducting tokamak with a non-circular cross-section. In recent years, with
increased device performance and experimental parameters, EAST has achieved a series
of important research results and scientific discoveries. However, EAST inner
components of the first wall will also be facing an increasingly tough operating
environment with higher heat loads. Although plasma facing components (PFCs) have
been updated and upgraded several times, the high heat flux during experiments will
cause damage or even failure to local small parts in the EAST vacuum vessel (VV). Any
failure of the internal components might influence the obtaining of high-quality plasma
or even lead to a plasma discharge and cause safety problems of the operation device.
Therefore, it is essential to have timely maintenance based on the condition of damaged
internal components in the experimental period. In conventional manual maintenance,
even if small sign of damage is seen, the device has to be shut down, which seriously
affects the efficiency of physical experiments, and increases the time and economic costs.
Therefore, to ensure adequate running time, the demand for remote handling maintenance
of the EAST device during physical experiments is urgent.
The EAST articulated maintenance arm (EAMA) system is developed for real-time
detection and rapid repair operations to damaged internal components during plasma
discharges without breaking the EAST ultra-high vacuum (UHV) condition. To achieve
the desired objectives, the EAMA system design should guarantee that the robot can
stably run in the harsh environments of high temperature (80-120 oC) and high vacuum
(~ 10-5Pa). Meanwhile, the errors caused by the deformation of long flexible robot arms
should also be predicted and compensated in real-time to obtain high accurate
maintenance operations. The main contributions of this dissertation include: design,
development and analysis of the whole EAMA hardware system; study of two different
methods of matrix structural analysis (MSA) and a back-propagation neural network (BPNN) to predict flexible deformations of a manipulator system; the eventual
developing of an EAMA dynamic error compensation algorithm to precisely control the
EAMA system operation.
Firstly, during the EAST experiments, environment conditions inside the vacuum vessel
were very harsh with complex geometry; from the design view, the requirements for an
EAMA robot were more critical than those for a conventional industrial robot arm.
Beyond the conventional techniques in robot design, many unique techniques were
considered in the EAMA manipulator arm design: (1) Redundant articulated
configuration with modular techniques were utilized in robot arm design; (2) All joint
actuators and high-speed transmission components were set up in a sealed box, using a
planetary roller screw to change rotation into linear motion, which was transferred outside
by the bellows; (3) Solid lubrication using MoS2-Ti-C composite coatings were
developed for the low-speed transmission parts exposed to the vacuum environment; (4)
A parallelogram link mechanism was developed to provide more than 50,000 times of
reduction ratio and more than 1000Nm of drive torque. Secondly, as the total length of the EAMA system reaches more than 10 m, it will produce
significant flexible deformation under the effects of torques and gravity. The deformation
values will always be constantly changing as the arm movements and postures change. If
this flexibility could not be accurately predicted and reasonably compensated, it would
seriously affect the position accuracy of the manipulator system. Based on the rigid-body
dynamic model, the prediction models of flexible arm deflections have been established
using two different methods: a stiffness matrix method based on matrix structural analysis
(MSA) theory and a forecasting by BP neural network method based on real experimental
data. The prediction accuracy, pros and cons of the two methods were compared. Both
methods have good real-time, which can provide the basis for achieving precise control.
Finally, considering manipulator kinematics and trajectory planning, an algorithm for
dynamic error compensation of the EAMA manipulator has been developed according to
the existing deformation prediction model and calculation results. The related feedback
control codes were programed to compensate the dynamic errors of end-effector in any
positions or postures of the manipulator system in real-time. Therefore, sufficiently high
control accuracy for successful completion of the relevant remote operation and
maintenance tasks can be achieved for the EAMA robot system.
Kokoelmat
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