Landing control of carrier-based aircraft based on optimal iterative learning

Purpose As an important national defense force, the research of carrier-based aircraft landing control technology has been widely concerned. In the actual mission, the existing uncertainties pose a challenge to the control performance of carrier-based aircraft. In addition, due to the influence of external environmental factors such as the Airwake during landing, the design of the control scheme is more difficult. To improve the safety of carrier-based aircraft landing, the purpose of this study is to develop a more robust control scheme. Design/methodology/approach Considering the external disturbance during the landing process, a high-order disturbance observer is designed to estimate the external disturbance, and the control quantity is modified based on the observed information. To solve the problem of the slow convergence of conventional iterative learning control (ILC) methods, a feedback controller is designed based on the optimal iterative learning theory, and the gradient descent algorithm is used to iteratively update the ILC actions, to achieve faster error convergence. Findings To achieve accurate trajectory tracking of carrier-based aircraft, a composite control scheme is designed by combining the high-order disturbance observer (HDOB) with the optimal ILC theory. The gradient descent algorithm is used to iteratively update the control motion, which achieves a faster error convergence rate. Compared with other control methods, the simulation results show that the designed control scheme can effectively suppress Airwake and improve tracking accuracy. Originality/value The value of this paper is that an optimal iterative learning binary channel control scheme based on a HDOB is proposed to ensure the precise landing of carrier-based aircraft under the influence of disturbance.