Scholarlink

<p>Researchers have always been challenged in deriving an accurate altitude control strategy for the high performance aircrafts subject to uncertain dynamics. Modeling uncertainties caused by the external random parameters and actuator saturation make the control design extremely difficult, and thereby make this system quite similar to the real-time model. The study proposes an adaptive observer-controller approach with the support of self-recurrent wavelet neural network (SRWNN) to mitigate the functional uncertainties of the non-linear dynamics of the aircraft having 6 degree of freedom(6DoF). SRWNN has been used to gain superior learning features as it is a modified version of artificial neural network with the wavelets as activation function. The adaptive observer is utilized to estimate the unknown states of the system. The feedback control strategy has employed the estimated states and the uncertainties for the high performance aircrafts. The novelty of paper lies in the fact that it uses SRWNN, and a corresponding controller-observer technique to estimate these extremely unpredictable nonlinear uncertainties in high performance aircraft model. Performance of the theoretical development has been assessed by performing simulation. The accurate tracking&#xa0;and the corresponding estimation and tracking error are two additional metrics used to demonstrate the efficacy of the proposed work.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SRWNN Based Surface Control Strategy for High-Performance Aircraft with Uncertain Aerodynamics

  • 期刊论文

  • 发表日期：

    2025年5月21日

查看全文
  • Manish Sharma,
  • Somya Dubey,
  • Sandeep S. Sarnobat,
  • Nitin K. Kamble

摘要

Researchers have always been challenged in deriving an accurate altitude control strategy for the high performance aircrafts subject to uncertain dynamics. Modeling uncertainties caused by the external random parameters and actuator saturation make the control design extremely difficult, and thereby make this system quite similar to the real-time model. The study proposes an adaptive observer-controller approach with the support of self-recurrent wavelet neural network (SRWNN) to mitigate the functional uncertainties of the non-linear dynamics of the aircraft having 6 degree of freedom(6DoF). SRWNN has been used to gain superior learning features as it is a modified version of artificial neural network with the wavelets as activation function. The adaptive observer is utilized to estimate the unknown states of the system. The feedback control strategy has employed the estimated states and the uncertainties for the high performance aircrafts. The novelty of paper lies in the fact that it uses SRWNN, and a corresponding controller-observer technique to estimate these extremely unpredictable nonlinear uncertainties in high performance aircraft model. Performance of the theoretical development has been assessed by performing simulation. The accurate tracking and the corresponding estimation and tracking error are two additional metrics used to demonstrate the efficacy of the proposed work.