000			01779nam a22001577a 4500
082			_a629.8
100			_aAhmad, Shakeel _928263
245			_aControl of Flywheel Inverted Pendulum Using Reinforcement Learning / _cShakeel Ahmad
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a67p. _bSoft Copy _c30cm
500			_aBalancing an inverted pendulum is a classic control problem that traditionally requires precise system modeling for effective controller design. Reinforcement Learning (RL) offers a model-free alternative but requires extensive training, which is impractical and risky when performed directly on physical hardware. Existing methods typically rely on simulation environments built on accurate models, which are often difficult to obtain. In this work, we use RL to balance flywheel inverted pendulum by constructing an approximate model of the system through parameter estimation. Despite its inaccuracies, the model proved sufficient for training RL agents in simulation. We developed a simulation environment based on the estimated model and trained agents using Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Discrete Soft Actor-Critic (SAC) algorithms. The trained policies were deployed on real hardware without any additional fine-tuning. All agents achieved successful swing-up and stabilization, with SAC achieving the fastest swing-up time (1.65 s) and lowest steady-state error (0.0220 rad), demonstrating that RL can tolerate model imperfections and still perform effectively on real systems.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Khawaja Fahad Iqbal _9125661
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/54335
942			_2ddc _cTHE
999			_c614608 _d614608