1 research outputs found
Industrial Robot Trajectory Tracking Using Multi-Layer Neural Networks Trained by Iterative Learning Control
Fast and precise robot motion is needed in certain applications such as
electronic manufacturing, additive manufacturing and assembly. Most industrial
robot motion controllers allow externally commanded motion profile, but the
trajectory tracking performance is affected by the robot dynamics and joint
servo controllers which users have no direct access and 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 of combining neural networks and iterative learning control 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 can
generalize well to trajectories beyond the training set. In applying the
trained neural networks to the physical robot, the tracking performance still
improves but not as much as in the simulator. We show that transfer learning
can effectively bridge the gap between simulation and the physical robot. In
the end, 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 is based on the ABB IRB6640-180
industrial robot and ABB RobotStudio software packages