3 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
Adaptive Neural Trajectory Tracking Control for Flexible-Joint Robots with Online Learning
Collaborative robots and space manipulators contain significant joint
flexibility. It complicates the control design, compromises the control
bandwidth, and limits the tracking accuracy. The imprecise knowledge of the
flexible joint dynamics compounds the challenge. In this paper, we present a
new control architecture for controlling flexible-joint robots. Our approach
uses a multi-layer neural network to approximate unknown dynamics needed for
the feedforward control. The network may be viewed as a linear-in-parameter
representation of the robot dynamics, with the nonlinear basis of the robot
dynamics connected to the linear output layer. The output layer weights are
updated based on the tracking error and the nonlinear basis. The internal
weights of the nonlinear basis are updated by online backpropagation to further
reduce the tracking error. To use time scale separation to reduce the coupling
of the two steps - the update of the internal weights is at a lower rate
compared to the update of the output layer weights. With the update of the
output layer weights, our controller adapts quickly to the unknown dynamics
change and disturbances (such as attaching a load). The update of the internal
weights would continue to improve the converge of the nonlinear basis
functions. We show the stability of the proposed scheme under the "outer loop"
control, where the commanded joint position is considered as the control input.
Simulation and physical experiments are conducted to demonstrate the
performance of the proposed controller on a Baxter robot, which exhibits
significant joint flexibility due to the series-elastic joint actuators.Comment: Accepted by ICRA 202
Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics
Fast and precise motion control is important for industrial robots in
manufacturing applications. However, some collaborative robots sacrifice
precision for safety, particular for high motion speed. The performance
degradation is caused by the inability of the joint servo controller to address
the uncertain nonlinear dynamics of the robot arm, e.g., due to joint
flexibility. We consider two approaches to improve the trajectory tracking
performance through feedforward compensation. The first approach uses iterative
learning control, with the gradient-based iterative update generated from the
robot forward dynamics model. The second approach uses dynamic inversion to
directly compensate for the robot forward dynamics. If the forward dynamics is
strictly proper or is non-minimum-phase (e.g., due to time delays), its stable
inverse would be non-causal. Both approaches require robot dynamical models.
This paper presents results of using recurrent neural networks (RNNs) to
approximate these dynamical models-forward dynamics in the first case, inverse
dynamics (possibly non-causal) in the second case. We use the bi-directional
RNN to capture the noncausality. The RNNs are trained based on a collection of
commanded trajectories and the actual robot responses. We use a Baxter robot to
evaluate the two approaches. The Baxter robot exhibits significant joint
flexibility due to the series-elastic joint actuators. Both approaches achieve
sizable improvement over the uncompensated robot motion, for both random joint
trajectories and Cartesian motion. The inverse dynamics method is particularly
attractive as it may be used to more accurately track a user input as in
teleoperation.Comment: Accepted by IROS 201