https://scholars.lib.ntu.edu.tw/handle/123456789/332235
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | LI-CHEN FU | en_US |
dc.contributor.author | Chang, W.-D. | en_US |
dc.contributor.author | Yang, J.-H. | en_US |
dc.contributor.author | Kuo, T.-S. | en_US |
dc.creator | Fu, L.-C.;Chang, W.-D.;Yang, J.-H.;Kuo, T.-S. | - |
dc.date.accessioned | 2018-09-10T06:30:53Z | - |
dc.date.available | 2018-09-10T06:30:53Z | - |
dc.date.issued | 1997 | - |
dc.identifier.issn | 07315090 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031103518&doi=10.2514%2f2.4044&partnerID=40&md5=ebb17c204976486a6dec747e18025c5a | - |
dc.identifier.uri | http://scholars.lib.ntu.edu.tw/handle/123456789/332235 | - |
dc.description.abstract | An adaptive robust neural-network-based control approach is proposed for bank-to-turn missile autopilot design. Feedforward neural networks with sigmoid hidden units are analyzed in detail for controller design. Without prior knowledge of the so-called optimal neural networks, we design a controller that exploits the advantages of both neural networks and robust adaptive control theory. For this scheme, a stable adaptive law is determined by using the Lyapunov theory, and the boundedness of all signals in the closed-loop system is guaranteed. No prior offline training phase is necessary, and only a single neural network is employed. It is shown that the tracking errors converge to a neighborhood of zero. Performance of the controller is demonstrated by means of simulations. | - |
dc.language | en | en |
dc.relation.ispartof | Journal of Guidance, Control, and Dynamics | - |
dc.source | AH-Scopus to ORCID | - |
dc.subject.other | Closed loop control systems; Computer simulation; Control theory; Convergence of numerical methods; Errors; Feedforward neural networks; Lyapunov methods; Missiles; Performance; Robustness (control systems); Closed loop system; Optimal neural networks; Robust adaptive control theory; Adaptive control systems | - |
dc.title | Adaptive robust bank-to-turn missile autopilot design using neural networks | - |
dc.type | journal article | en |
dc.identifier.doi | 10.2514/2.4044 | - |
dc.identifier.scopus | 2-s2.0-0031103518 | - |
dc.relation.pages | 346-354 | - |
dc.relation.journalvolume | 20 | - |
dc.relation.journalissue | 2 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0002-6947-7646 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 資訊工程學系 |
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