Q-Learning based intelligent level controller for Spherical tank system

This project explores a novel approach for level control in non-linear spherical tanks by integrating machine learning with mathematical modelling. Spherical tanks exhibit complex non-linear behavior due to their geometry, which complicates traditional control methods. We begin by developing a detailed open loop model of the spherical tank, capturing the intricate relationship between the liquid height and the tank’s volume, as well as the dynamics of fluid inflow and outflow. This model forms the basis for understanding how variations in flow rates affect the liquid level within the tank. To address the non-linearity and enhance control performance, we incorporate machine learning techniques. By training a machine learning model on historical data, including inflow rates, outflow rates, and observed liquid levels, we develop a predictive tool that accurately reflects the system’s behavior. This model is integrated into a Q-learning framework, which uses it to forecast future states and optimize control actions accordingly. Simulation results show that this Q-learning based control strategy significantly improves the accuracy and stability of the liquid level control compared to traditional methods, demonstrating its effectiveness in managing the complexities of non-linear spherical tanks.