Browsing by Author "Shiah, SJ"

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An approach to enlarge learning space coverage for robot learning control
(1997-11-01)
Young, KY
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Shiah, SJ
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交大名義發表
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電控工程研究所
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National Chiao Tung University
In robot learning control, the learning space for executing general motions of multijoint robot manipulators is quite large, Consequently, for most learning schemes, the learning controllers are used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered, although learning controllers are considered to be capable of generalization, In this paper, we propose an approach for larger learning space coverage in robot learning control, In this approach, a new structure for learning control is proposed to organize information storage via effective memory management. The proposed structure is motivated by the concept of human motor program and consists mainly of a fuzzy system and a cerebellar model articulation controller (CMAC)-type neural network. The fuzzy system is used for governing a number of sampled motions in a class of motions. The CMAC-type neural network is used to generalize the parameters of the fuzzy system, which are appropriate for the governing of the sampled motions, to deal with the whole class of motions. Under this design, in some sense the qualitative fuzzy rules in the fuzzy system are generalized by the CMAC-type neural network and then a larger learning space can be covered, Therefore, the learning effort is dramatically reduced in dealing with a wide range of robot motions, while the learning process is performed only once, Simulations emulating ball carrying under various conditions are presented to demonstrate the effectiveness of the proposed approach.
Robot motion classification from the standpoint of learning control
(2004-06-01)
Shiah, SJ
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Young, KY
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電控工程研究所
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Institute of Electrical and Control Engineering
In robot learning control, the learning space for executing the general motions of multi-joint robot manipulators is very complicated. Thus, when the learning controllers are employed as major roles in motion governing, the motion variety requires them to consume excessive amount of memory. Therefore, in spite of their ability to generalize, the learning controllers are usually used as subordinates to conventional controllers or the learning process needs to be repeated each time a new trajectory is encountered. To simplify learning space complexity, we propose, from the standpoint of learning control, that robot motions be classified according to their similarities. The learning controller can then be designed to govern groups of robot motions with high degrees of similarity without consuming excessive memory resources. Motion classification based on using the PUMA 560 robot manipulator demonstrates the effectiveness of the proposed scheme. (C) 2003 Elsevier B.V. All rights reserved.