Iterative learning control for LTV systems with applications to an industrial robot


Share/Save/Bookmark

Hakvoort, Wouter Bernardus Johannes (2009) Iterative learning control for LTV systems with applications to an industrial robot. thesis.

open access
[img] PDF
4MB
Abstract:Industrial robots are widely used in industry because of their dexterity, the
high manipulation speed and the relatively low price. However, the applicability
of these robots is limited by the mediocre accuracy resulting from the low
bandwidth of standard industrial controllers. Fortunately, the repeatability of
industrial robots is often much better than their tracking accuracy, which can
be exploited to improve the accuracy by the application of Iterative Learning
Control (ILC). ILC is a control technique that reduces the tracking error along a
trajectory that is traced repeatedly by the iterative refinement of a feedforward
signal.
The tracking accuracy of industrial robots can be improved substantially
with ILC by reducing the frequency components of the tracking error beyond
the low bandwidth of the standard industrial controller. Below this bandwidth
the non-linear dynamics of the robot mechanism are linearised by the controller,
but at higher frequencies the closed-loop dynamics depend on the configuration
of the robot mechanism. These configuration dependent dynamics can be approximated
as linear time-varying (LTV) for small deviations from the repetitive
large-scale motion. Therefore, two ILC algorithms for systems with LTV dynamics
are developed in this thesis.
The norm-optimal ILC algorithm iteratively computes the feedforward that
minimises a weighted sum of the norm of the error and the growth of the feedforward.
The error is predicted from an LTV dynamic model. The computation of
the optimal feedforward is formulated as a finite-time optimal control problem
and it is shown that this optimisation problem can be solved using an existing,
computationally efficient algorithm.
The robust ILC algorithm iteratively computes the feedforward that optimises
the reduction of the error for an LTV dynamic model with a given uncertainty.
A sufficient condition is derived under which the feedforward reduces the
error with a specified fraction for the worst case effect of the uncertainty. This
condition takes the finite length of the iteration and the LTV dynamics into
account. The computation of the optimal feedforward is formulated as a finitetime
dynamic game and the check of the convergence condition is formulated
as an anti-causal optimal control problem. It is shown that the dynamic game and the optimal control problem can be solved using existing, computationally
efficient algorithms.
Convergence analysis shows that the proposed ILC algorithms make the
error converge to zero with an adjustable convergence rate if the dynamic model
is sufficiently accurate. Increasing the convergence rate reduces the allowable
model error. A model error that is too large results in divergence of the tracking
error. The allowable model error can be increased by using a robustness filter
that removes the components of the feedforward to which the dynamic response
is not modelled sufficiently accurate. However, the removed components of the
feedforward cannot be used to compensate for the error, which typically results
in a non-zero error after convergence.
The proposed algorithms are suited for systems with LTV dynamics, they are
computationally efficient and they are able to reduce the error monotonically
with an adjustable convergence rate. This unique combination of properties
makes the algorithms suited for improving the tracking accuracy of industrial
robots and other systems with LTV dynamics in practice.
The performance of the ILC algorithms is tested experimentally by the application
to the industrial St¨aubli RX90 robot. The setpoints for the position of
the robot are adjusted with ILC to reduce the tracking error at its end-effector,
which is measured with an optical sensor. The experimental results show that
the proposed ILC algorithms are able to reduce the measured tracking error
substantially, especially if an LTV model of the configuration dependent highfrequency
dynamics of the robot is used. The reduction of the tracking error is
sufficient for the application of the robot to laser welding of complex trajectories
at high speed.
Item Type:Thesis
Research Group:
Link to this item:http://purl.utwente.nl/publications/61388
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page