Research on Direct Lift Carrier-Based Unmanned Aerial Vehicle Landing Control Based on Performance Index Intelligent Optimization/Dynamic Optimal Allocation

Abstract

High-precision control problems for carrier-based UAVs are challenging due to the requirements for safety, high-performance operation and uncertain ocean environments. To address such problems, this paper proposes a direct lift landing control method that can ensure the safe operation of UAVs using intelligent optimization of control performance indicators and dynamic optimal allocation methods. The direct lift control (DLC) scheme is adopted to improve the operability of the control by introducing flap channels to achieve the fast correction of vertical disturbances. To improve the landing control performance, an intelligent optimization method for DLC gain is proposed, and a neural network relationship between parameter uncertainty and optimal DLC gain is established. In addition, a recursive least-squares identification method is used to estimate the uncertain parameters in real time, so that the established neural network can be used to generate the optimal DLC commands online, and the generated control commands can be assigned to multiple actuators of the carrier-based UAV through a dynamic optimal assignment algorithm. Finally, the superiority of the method is verified using simulation comparison.

Keywords

• direct lift control
• performance index optimization
• dynamic optimal allocation
• online identification