AlinaBaber/ReinforcementLearning-QLearning-based-self-tuned-PID-controller-for-AUV-MatLab
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 helps operations engineers and robotics researchers enhance the precision and stability of Autonomous Underwater Vehicles (AUVs). It takes data from an AUV's dynamics and environment, and through a hybrid control system, outputs dynamically adjusted controller parameters to improve navigation and energy efficiency. It's designed for professionals managing or developing robotic systems for complex, unpredictable environments.
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Use this if you need to improve the adaptive control and robustness of an AUV or similar robotic system in dynamic conditions.
Not ideal if you are looking for a plug-and-play solution for non-robotics applications or do not have access to MATLAB and its specialized toolboxes.
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Nov 23, 2024
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