https://scholars.lib.ntu.edu.tw/handle/123456789/332235
標題: | Adaptive robust bank-to-turn missile autopilot design using neural networks | 作者: | LI-CHEN FU Chang, W.-D. Yang, J.-H. Kuo, T.-S. |
公開日期: | 1997 | 卷: | 20 | 期: | 2 | 起(迄)頁: | 346-354 | 來源出版物: | Journal of Guidance, Control, and Dynamics | 摘要: | 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. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031103518&doi=10.2514%2f2.4044&partnerID=40&md5=ebb17c204976486a6dec747e18025c5a http://scholars.lib.ntu.edu.tw/handle/123456789/332235 |
ISSN: | 07315090 | DOI: | 10.2514/2.4044 | SDG/關鍵字: | 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 |
顯示於: | 資訊工程學系 |
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