標題: | Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings |
作者: | Lin, CM Hsu, CF 電機資訊學士班 Undergraduate Honors Program of Electrical Engineering and Computer Science |
關鍵字: | recurrent fuzzy neural network (RFNN);supervisory control;wing rock system |
公開日期: | 1-十月-2004 |
摘要: | Wing rock is a highly nonlinear phenomenon in which an aircraft undergoes limit cycle roll oscillations at high angles of attack. In this paper, a supervisory recurrent fuzzy neural network control (SRFNNC) system is developed to control the wing rock system. This SRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. The RFNN is inherently a recurrent multilayered neural network for realizing fuzzy inference using dynamic fuzzy rules. Moreover, an on-line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. Finally, a comparison between the sliding-mode control, the fuzzy sliding control and the proposed SRFNNC of a wing rock system is presented to illustrate the effectiveness of the SRFNNC system. Simulation results demonstrate that the proposed design method can achieve favorable control performance for the wing rock system without the knowledge of system dynamic functions. |
URI: | http://dx.doi.org/10.1109/TFUZZ.2004.834803 http://hdl.handle.net/11536/26345 |
ISSN: | 1063-6706 |
DOI: | 10.1109/TFUZZ.2004.834803 |
期刊: | IEEE TRANSACTIONS ON FUZZY SYSTEMS |
Volume: | 12 |
Issue: | 5 |
起始頁: | 733 |
結束頁: | 742 |
顯示於類別: | 期刊論文 |