7,322 research outputs found
Learning for Advanced Motion Control
Iterative Learning Control (ILC) can achieve perfect tracking performance for
mechatronic systems. The aim of this paper is to present an ILC design tutorial
for industrial mechatronic systems. First, a preliminary analysis reveals the
potential performance improvement of ILC prior to its actual implementation.
Second, a frequency domain approach is presented, where fast learning is
achieved through noncausal model inversion, and safe and robust learning is
achieved by employing a contraction mapping theorem in conjunction with
nonparametric frequency response functions. The approach is demonstrated on a
desktop printer. Finally, a detailed analysis of industrial motion systems
leads to several shortcomings that obstruct the widespread implementation of
ILC algorithms. An overview of recently developed algorithms, including
extensions using machine learning algorithms, is outlined that are aimed to
facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced
Motion Control, 202
CAD enabled trajectory optimization and accurate motion control for repetitive tasks
As machine users generally only define the start
and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mecha- nism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical set-up. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profil
Crew interface with a telerobotic control station
A method for apportioning crew-telerobot tasks has been derived to facilitate the design of a crew-friendly telerobot control station. To identify the most appropriate state-of-the-art hardware for the control station, task apportionment must first be conducted to identify if an astronaut or a telerobot is best to execute the task and which displays and controls are required for monitoring and performance. Basic steps that comprise the task analysis process are: (1) identify space station tasks; (2) define tasks; (3) define task performance criteria and perform task apportionment; (4) verify task apportionment; (5) generate control station requirements; (6) develop design concepts to meet requirements; and (7) test and verify design concepts
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