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Design of Automatic Carrier-Landing Controller Based on Compensating States and Dynamic Inversion

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In this paper, a design scheme of automatic carrier landing system (ACLS) control law based on compensating states and dynamic inversion is proposed, in order to eliminate landing risk and… Click to show full abstract

In this paper, a design scheme of automatic carrier landing system (ACLS) control law based on compensating states and dynamic inversion is proposed, in order to eliminate landing risk and air wake disturbance and improve flight quality during landing. First of all, the mathematical model for an aircraft during carrier landing is established and transformed into a low-order linear perturbed model with the involved state variables, which denotes the reference model. Second, a high-dimension field of the landing risk is addressed, which reflects the current and the potential risk degrees due to the subjective prediction of the pilots. The coefficients of the landing risk field are established based on a pilot behavioral model. Third, a concept of compensating states is proposed by this paper. On one hand, the compensating states of the air wake disturbance are estimated from the nonlinear and reference models. On the other hand, the compensating states of the landing risk are composed of the high-dimension risk field. Fourth, automatic carrier landing control law is built with the compensating states of air wake and landing risk, nonlinear dynamic inversion, and feedback signals of the flight states. In the rolling optimization progress, the landing risk, air wake, and nonlinear factors of flight states are dynamically introduced to control the state deviations and suppress landing risk. Test results based on a semi-physical simulation platform indicate that the proposed algorithm brings about an excellent landing performance and an ability to eliminate landing risk and air wake.

Keywords: carrier landing; automatic carrier; compensating states; risk; landing risk

Journal Title: IEEE Access
Year Published: 2019

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