Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning-space complexity and motion variety require them to consume excessive amount of memory. We propose using a Fuzzy Neural Network (FNN) to learn and analyze robot motions so they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources.
|Number of pages||6|
|Journal||Proceedings of the IEEE Conference on Decision and Control|
|State||Published - 1 Dec 1997|
|Event||Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA|
Duration: 10 Dec 1997 → 12 Dec 1997