An Improved Bee Colony Algorithm based Parameter Optimization Method for Automatic Carrier Landing System

The automatic carrier landing system provides carrier-based aircraft with the capability to autonomously execute approach and landing procedures in all weather conditions, eliminating the need for manual control. By integrating critical subsystems—including the aircraft’s flight control, thrust management, inertial navigation, and tracking radar—the system ensures real-time monitoring and accurate state tracking throughout the landing phase, even in complex weather environments. This integration significantly enhances both the reliability and safety of approach and landing operations. To address the intricacies of multi-parameter design and the complex interdependencies within the flight control system during landing, this paper proposes a hybrid optimization strategy. Specifically, it combines the gradient-based iterative tuning method with the global search capability of heuristic algorithms, leveraging the local refinement strengths of traditional iterative techniques. A hybrid particle swarm optimization algorithm is introduced for the tuning of system parameters. This approach streamlines the parameter adjustment process and accelerates convergence toward solutions that satisfy performance design criteria more efficiently. The research presents a detailed examination of the key challenges involved in parameter optimization for landing control systems and offers innovative methodologies and insights to support the improved design and planning of automated carrier landing solutions.