In recent years, the research on drones has increased rapidly because of its high applicability in many fields and its great development potential. In the maritime sector too, especially marine rescue, a Drone with a compact size and fast flight speed is an effective solution in search and surveillance, giving quick results and being very convenient. When operating at sea, marine rescue drones are often affected by the environment, especially wind, which leads to turbulence that causes the drone to deviate from its predetermined flight trajectory. To overcome the above problem, the author has proposed the application of a Neural-Fuzzy controller for unmanned marine rescue aircraft presented in this paper introduces a controller that combines neural networks and fuzzy controllers that enhance the efficiency of the drone’s trajectory tracking. The paper presents the mathematics of a quadcopter described by the Newton-Euler equations. Presentation on stable flight control and trajectory control of marine rescue drones. In this paper, Matlab/Simulink is used to describe the operation of the Drone, and the characteristics obtained after using the simulation are used to compare, test, and analyze the system. The obtained results show that the Neural-Fuzzy controller is much more sensitive, more resistant to turbulence, and can be used on different sizes, weights, and configurations of drones without adjusting PID gain.

Efficient ship guidance, fuel savings, and reduced human control have long been a key focus in developing intelligent controllers. The integration of neural networks and fuzzy logic control offers numerous advantages, creating a robust and adaptive system capable of handling complex dynamics and uncertainties. This intelligent control system learns from its environment and adjusts behavior, making it effective in challenging situations. Additionally, it improves system efficiency, reduces energy consumption, and minimizes human intervention, enhancing safety and reducing errors. This study presents an intelligent control approach, titled “Designing a Ship Autopilot System for Operation in a Disturbed Environment using the Adaptive Neural Fuzzy Inference System”, combining a neural network and fuzzy logic control to steer ships. A 6DOF dynamic model is constructed, simulating ship operations with noise signals. The ANFIS controller comprises six layers, with a distinct composition rule expressing conclusions as linear equations of input variables. Layer 1 has two input signals, layer 2 represents fuzzy rules with six nodes, and layers 3, 4, and 5 contain nine nodes each. Layer 6 combines output signals from layer 5, following the first-order Takagi–Sugeno fuzzy logic control model. Simulation results using MATLAB/Simulink demonstrate the superiority of the ANFIS controller over the PID controller, significantly improving stability and trajectory accuracy.

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