Application of Fuzzy Control Allocation Approach for Coordinated Landing of Multi-Aircrafts Considering Ground Obstacles

Nikseresht, Saba

doi:10.22034/jast.2026.494043.1216

Application of Fuzzy Control Allocation Approach for Coordinated Landing of Multi-Aircrafts Considering Ground Obstacles

Document Type : Original Article

Author

Saba Nikseresht

Department of Aerospace Engineering, K. N. Toosi University of Technology, Tehran, Iran

10.22034/jast.2026.494043.1216

Abstract

Achieving agreement among agents is crucial in multi-agent systems to enable coordinated operations and synchronized path alignment. These agreements enhance safety, streamline planning, reduce interference, and improve efficiency. Coordination methods vary based on factors such as prioritization, routes, constraints, and shared information, with agreements established directly, interactively, or via a central agent. This study explores the coordinated control of multiple aircraft for synchronized landings using a fuzzy control allocation approach. A fuzzy controller is implemented and its parameters optimized through the NSGA-II genetic algorithm, ensuring effective distribution of control signals between elevator operators and thrust vector systems. This approach enables synchronized, automated landings from different initial altitudes, maintaining stability with minimal control effort across two scenarios. In the first, agents coordinate landings without ground obstacles, where one agent leads and others follow. The second scenario incorporates a ground obstacle into the coordination process. In both cases, the approach ensures synchronized landings with minimal control effort, high precision, and stable performance. Simulation results demonstrate that the optimized fuzzy controller successfully achieves coordinated automatic landings in both scenarios, handling obstacles effectively. The system ensures synchronized landings with reduced control effort, high accuracy, minimal time, and reliable stability.

Keywords

Fuzzy control allocation

Multi-agent systems

Coordinated landing

Multi-objective optimization

Genetic algorithm

Subjects

Flight Mechanics Stability & Contorl

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