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🚀 Advancing Motor Control with Deep Reinforcement Learning (TD3)
I am excited to share my latest project: Speed Control of a Three-Phase Induction Motor using the TD3 (Twin Delayed DDPG) Reinforcement Learning agent.
Unlike standard controllers, this RL-based approach adapts to dynamic environments by learning the optimal control policy directly from the system's state.
Key Technical Highlights:
Agent: TD3 (Twin Delayed Deep Deterministic Policy Gradient) to reduce overestimation bias.
Architecture: Customized neural network for the actor/critic.
Output Layer: Integrated a tanh (Hyperbolic Tangent) activation function at the final layer to bound the control actions efficiently.
Environment: High-fidelity Simulink model featuring a 3-phase inverter and induction machine.
The results show excellent convergence and robust tracking of the speed setpoint!

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