Journal
ROBOTICS
Volume 10, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/robotics10010050
Keywords
deep learning in robotics and automation; industrial robots; iterative learning control; motion control
Categories
Funding
- Office of the Secretary of Defense [W911NF-17-3-0004]
- New York State Matching Grant program
- Manufacturing Innovation Center (MIC) at Rensselaer Polytechnic Institute under New York State Empire State Development Division of Science, Technology and Innovation (NYSTAR)
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This paper introduces a method that combines neural networks and iterative learning controls to enhance the trajectory tracking performance of multi-axis articulated industrial robots. By training neural networks to emulate the dynamical inverse of the nonlinear inner-loop dynamics, the performance can be improved.
Fast and precise robot motion is needed in many industrial applications. Most industrial robot motion controllers allow externally commanded motion profiles, but the trajectory tracking performance is affected by the robot dynamics and joint servo controllers, to which users have no direct access and about which they have little information. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. This paper presents an approach for combining neural networks and iterative learning controls to improve the trajectory tracking performance for a multi-axis articulated industrial robot. For a given desired trajectory, the external command is iteratively refined using a high-fidelity dynamical simulator to compensate for the robot inner-loop dynamics. These desired trajectories and the corresponding refined input trajectories are then used to train multi-layer neural networks to emulate the dynamical inverse of the nonlinear inner-loop dynamics. We show that with a sufficiently rich training set, the trained neural networks generalize well to trajectories beyond the training set as tested in the simulator. In applying the trained neural networks to a physical robot, the tracking performance still improves but not as much as in the simulator. We show that transfer learning effectively bridges the gap between simulation and the physical robot. Finally, we test the trained neural networks on other robot models in simulation and demonstrate the possibility of a general purpose network. Development and evaluation of this methodology are based on the ABB IRB6640-180 industrial robot and ABB RobotStudio software packages.
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