Lyapunov based controller design for trajectory tracking of an under-actuated autonomous underwater vehicle(AUV)
Addressing the combined problem of trajectory planning and tracking control for under-actuated autonomous underwater vehicle (AUV).
This project implements an advanced control system using a Neural Network-Fuzzy Logic-based Self-tuned PID Controller to optimize the performance and stability of an Autonomous Underwater Vehicle (AUV).
This repository showcases a hybrid control system combining Reinforcement Learning (Q-Learning) and Neural-Fuzzy Systems to dynamically tune a PID controller for an Autonomous Underwater Vehicle (AUV). The implementation aims to enhance precision, adaptability, and robustness in underwater environments.
MATLAB and Simulink simulation for underwater lumped-mass-spring cable model.
Complete simulation, control and design of navigation filter of an AUV during a lawn-mowe survey in a unknow area
Part of the design of a lawn-mover underwater robot for a simulated bathymetry task.
Simulation of variable blade geometries and braking strategies for the advancement of an AUV
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