Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44351
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorHsu, C.H.en_US
dc.date2009zh_TW
dc.date.accessioned2014-06-06T08:12:11Z-
dc.date.available2014-06-06T08:12:11Z-
dc.identifier.issn0278-0046zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44351-
dc.description.abstractThis paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC.en_US
dc.language.isoen_USzh_TW
dc.relationIeee Transactions on Industrial Electronicsen_US
dc.relation.ispartofseriesIeee Transactions on Industrial Electronics, Volume 56, Issue 10, Page(s) 3931-3940.en_US
dc.relation.urihttp://dx.doi.org/10.1109/tie.2009.2017557en_US
dc.subjectAnt colony optimization (ACO)en_US
dc.subjectfuzzy Q-learningen_US
dc.subjectreinforcement learningen_US
dc.subjectrobot motion controlen_US
dc.subjecttype-2 fuzzy systemsen_US
dc.subjectfpga implementationen_US
dc.subjectinference systemsen_US
dc.subjectnetworken_US
dc.subjectdesignen_US
dc.subjectnavigationen_US
dc.titleReinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Controlen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1109/tie.2009.2017557zh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
Appears in Collections:電機工程學系所
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