Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines
Li, Ming (2014-12-08)
Väitöskirja
Li, Ming
08.12.2014
Lappeenranta University of Technology
Acta Universitatis Lappeenrantaensis
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-265-703-9
https://urn.fi/URN:ISBN:978-952-265-703-9
Tiivistelmä
The dissertation proposes two control strategies, which include the trajectory planning
and vibration suppression, for a kinematic redundant serial-parallel robot machine, with
the aim of attaining the satisfactory machining performance.
For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set
of trajectories in the robot's joint space are generated based on the best stiffness
performance of the robot along the prescribed trajectory.
To construct the required system-wide analytical stiffness model for the serial-parallel
robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation.
The modified method is an evolution of Gosselin's lumped model that can account for the
deformations of a flexible link in more directions. The effectiveness of this VJM variant
is validated by comparing the computed stiffness results of a flexible link with the those
of a matrix structural analysis (MSA) method. The comparison shows that the numerical
results from both methods on an individual flexible beam are almost identical, which, in
some sense, provides mutual validation. The most prominent advantage of the presented
VJM variant compared with the MSA method is that it can be applied in a flexible
structure system with complicated kinematics formed in terms of flexible serial links and
joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide
analytical stiffness model can be easily obtained for mechanisms with both serial
kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a
kinematic redundant serial-parallel robot machine is constructed based on integration of
the VJM variant and the virtual work principle. Numerical results of its stiffness
performance are reported.
For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a
prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed
location of the end-effector, the robot permits an infinite number of inverse solutions,
which consequently yields infinite kinds of stiffness performance. Therefore, a
differential evolution (DE) algorithm in which the positions of redundant joints in the
kinematics are taken as input variables was employed to search for the best stiffness
performance of the robot. Numerical results of the generated joint trajectories are given
for a kinematic redundant serial-parallel robot machine, IWR (Intersector
Welding/Cutting Robot), when a particular trajectory of its end-effector has been
prescribed. The numerical results show that the joint trajectories generated based on the
stiffness optimization are feasible for realization in the control system since they are
acceptably smooth. The results imply that the stiffness performance of the robot machine
deviates smoothly with respect to the kinematic configuration in the adjacent domain of
its best stiffness performance.
To suppress the vibration of the robot machine due to varying cutting force during the
machining process, this dissertation proposed a feedforward control strategy, which is
constructed based on the derived inverse dynamics model of target system. The
effectiveness of applying such a feedforward control in the vibration suppression has
been validated in a parallel manipulator in the software environment. The experimental
study of such a feedforward control has also been included in the dissertation. The
difficulties of modelling the actual system due to the unknown components in its
dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed
for identification of the unknown components of the dynamics model of the target system.
To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can
utilize an experimental input-output data set of the entire dynamic system is introduced in
the dissertation. Validation of the BP neural network and the modified Levenberg-
Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a
second order system parameters estimation, and a friction model estimation of a parallel
manipulator, which represent three different application aspects of this method.
and vibration suppression, for a kinematic redundant serial-parallel robot machine, with
the aim of attaining the satisfactory machining performance.
For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set
of trajectories in the robot's joint space are generated based on the best stiffness
performance of the robot along the prescribed trajectory.
To construct the required system-wide analytical stiffness model for the serial-parallel
robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation.
The modified method is an evolution of Gosselin's lumped model that can account for the
deformations of a flexible link in more directions. The effectiveness of this VJM variant
is validated by comparing the computed stiffness results of a flexible link with the those
of a matrix structural analysis (MSA) method. The comparison shows that the numerical
results from both methods on an individual flexible beam are almost identical, which, in
some sense, provides mutual validation. The most prominent advantage of the presented
VJM variant compared with the MSA method is that it can be applied in a flexible
structure system with complicated kinematics formed in terms of flexible serial links and
joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide
analytical stiffness model can be easily obtained for mechanisms with both serial
kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a
kinematic redundant serial-parallel robot machine is constructed based on integration of
the VJM variant and the virtual work principle. Numerical results of its stiffness
performance are reported.
For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a
prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed
location of the end-effector, the robot permits an infinite number of inverse solutions,
which consequently yields infinite kinds of stiffness performance. Therefore, a
differential evolution (DE) algorithm in which the positions of redundant joints in the
kinematics are taken as input variables was employed to search for the best stiffness
performance of the robot. Numerical results of the generated joint trajectories are given
for a kinematic redundant serial-parallel robot machine, IWR (Intersector
Welding/Cutting Robot), when a particular trajectory of its end-effector has been
prescribed. The numerical results show that the joint trajectories generated based on the
stiffness optimization are feasible for realization in the control system since they are
acceptably smooth. The results imply that the stiffness performance of the robot machine
deviates smoothly with respect to the kinematic configuration in the adjacent domain of
its best stiffness performance.
To suppress the vibration of the robot machine due to varying cutting force during the
machining process, this dissertation proposed a feedforward control strategy, which is
constructed based on the derived inverse dynamics model of target system. The
effectiveness of applying such a feedforward control in the vibration suppression has
been validated in a parallel manipulator in the software environment. The experimental
study of such a feedforward control has also been included in the dissertation. The
difficulties of modelling the actual system due to the unknown components in its
dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed
for identification of the unknown components of the dynamics model of the target system.
To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can
utilize an experimental input-output data set of the entire dynamic system is introduced in
the dissertation. Validation of the BP neural network and the modified Levenberg-
Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a
second order system parameters estimation, and a friction model estimation of a parallel
manipulator, which represent three different application aspects of this method.
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