| Summary: | 碩士 === 國立交通大學 === 控制工程系 === 84 === In the application of learning control for robot motion
governing, learningcontrollers are usually used as subordinates
to conventional controllers, although they are considered to be
capable of generalization. One reason is that when a learning
controller alone is applied to govern general motions ofmulti-
joint robot manipulators, the learning space encountered will be
extremely complicated, due to the variations exhibited in
motions corresponding to different task requirements. Hence, in
this thesis, we first discuss the generalization capability in
different levels to find what level command is with the
bestgeneralization effect. In addition, in order to reduce the
complexity of the learning space for robot learning control, we
propose to perform similarity analysis for robot motions by
using an FNN learning algorithm, such that robot motions can be
classified according to their similarity. In the analysis,the
FNN is first used to learn to govern various robot motions, and
the similarity between motions is then evaluated according to
the number of linguistic labels and the shape of the membership
functions of the FNN under successful motion governing. Thus,
groups of robot motions with high similarity can be governed by
using learning controllers with reasonable sizes, because these
motions correspond to similar fuzzy parameters in the FNN,
implicating a simplified learning space.
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