1 research outputs found
Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks
The values of a given manipulator's dynamics coefficients need to be accurately
identified in order to employ model-based algorithms in the control of its motion. This
thesis details the development of a novel form of adaptive network which is capable of
accurately learning the coefficients of systems, such as manipulator inverse dynamics,
where the algebraic form is known but the coefficients' values are not. Empirical motion
data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear
Combiner (CSLC) network developed, and the coefficients of their inverse dynamics
identified. The resultant precision of control is shown to be superior to that achieved from
employing dynamics coefficients derived from direct measurement.
As part of the development of the CSLC network, the process of network learning is
examined. This analysis reveals that current network architectures for processing analogue
output systems with high input order are highly unlikely to produce solutions that are
good estimates throughout the entire problem space. In contrast, the CSLC network is
shown to generalise intrinsically as a result of its structure, whilst its training is greatly
simplified by the presence of only one minima in the network's error hypersurface.
Furthermore, a fine-tuning algorithm for network training is presented which takes
advantage of the CSLC network's single adaptive layer structure and does not rely upon
gradient descent of the network error hypersurface, which commonly slows the later
stages of network training