A comparative study of classical, symbolic, and reinforcement learning control for mobile robots and manipulators.
We made a Automatic Self Balance ball in midpoint of rail with PID Tuning using Classical Controls theory as a team.
Deep RL implementations in PyTorch — REINFORCE, A2C with GAE, DQN with replay/target network on CartPole-v1 and LunarLander-v3 with full training telemetry and checkpointing.
Selected control systems reference notes compiled for long-term use in modeling, simulation, and embedded control work.
Written by Brian Lesko, the repository contains Matlab scripts demonstrating controls theories largely originating from the book, Control of Mechatronic Systems, by Dr. Levent Guvenc.
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