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.

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).

Lyapunov based controller design for trajectory tracking of an under-actuated autonomous underwater vehicle(AUV)

Dynamic Model of a 6DOF AUV

Addressing the combined problem of trajectory planning and tracking control for under-actuated autonomous underwater vehicle (AUV).

The repository has a matlab implementation of SMC based controller on an AUV.

The simulation of various types of robot control systems is conducted by using Simulink, focusing on robot configuration design, kinematics and dynamics modeling, and controller design.

Repository for Modeling and control of underwater vehicle: Sparus

Compute the added mass coefficients of an underwater vehicle that can be approximated by a prolate spheroid.

A new model for decelerating AUVs

Simulation of variable blade geometries and braking strategies for the advancement of an AUV

This GitHub repository holds the complete Git history associated with the development of my thesis. It encompasses all steps, changes, and iterations that contributed to the final result.