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Learning Controllers for Industrial Robots (1996)
| Citations: | 31 - 14 self |
BibTeX
@MISC{Baroglio96learningcontrollers,
author = {C. Baroglio and M. Kaiser},
title = {Learning Controllers for Industrial Robots},
year = {1996}
}
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Abstract
. One of the most significant cost factors in robotics applications is the design and development of real-time robot control software. Control theory helps when linear controllers have to be developed, but it doesn't sufficiently support the generation of non-linear controllers, although in many cases (such as in compliance control), nonlinear control is essential for achieving high performance. This paper discusses how Machine Learning has been applied to the design of (non-)linear controllers. Several alternative function approximators, including Multilayer Perceptrons (MLP), Radial Basis Function Networks (RBFNs), and Fuzzy Controllers are analyzed and compared, leading to the definition of two major families: Open Field Function Function Approximators and Locally Receptive Field Function Approximators. It is shown that RBFNs and Fuzzy Controllers bear strong similarities, and that both have a symbolic interpretation. This characteristics allows for applying both symbolic and statis...
Keyphrases
industrial robot linear controller fuzzy controller several alternative function approximators major family many case compliance control high performance locally receptive field function approximators real-time robot control software robotics application multilayer perceptrons control theory symbolic interpretation non-linear controller strong similarity nonlinear control radial basis function network open field function function approximators machine learning significant cost factor
