Designing a Ship Autopilot System for Operation in a Disturbed Environment Using the Adaptive Neural Fuzzy Inference System

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.

Automated summary

Efficient ship guidance and fuel savings can be achieved through the integration of neural networks and fuzzy logic control in an intelligent control system. This study presents a ship autopilot system using the Adaptive Neural Fuzzy Inference System (ANFIS) that outperforms the traditional PID controller in terms of stability and trajectory accuracy.