EduController is a progressive and practical learning project that systematically covers classical control theory, modern control, and next-generation AI-based control.
It uses Python-based simulation and visualization to support intuitive understanding and real-world control system design, including LLM-integrated architectures.
The EduController curriculum consists of 9 parts, divided into the following two tracks:
π Control Theory Track (Part 01β05)
Covers classical to modern control theory, digital implementation, and hands-on practice.
π€ AI-based Control Track (Part 06β09)
Covers neural networks, reinforcement learning, data-driven control, and LLM-integrated hybrid control.
Each chapter is independently accessible, but AI-based control assumes basic knowledge of control theory.
| Part | Directory | Overview |
|---|---|---|
| Part 01 | part01_classical | Classical Control (PID, frequency response, stability) |
| Part 02 | part02_modern | Modern Control (State-space, LQR, Kalman filter) |
| Part 03 | part03_adaptive | Adaptive and Robust Control (MRAC, Hβ, L1) |
| Part 04 | part04_digital | Digital Control and DSP (Z-transform, FFT, implementation) |
| Part 05 | part05_practical | Hands-on Practice (Python, ROS, FPGA examples) |
| Part | Directory | Overview |
|---|---|---|
| Part 06 | part06_nn_control | Neural Network Control (NN-PID, inverse modeling) |
| Part 07 | part07_rl_control | Reinforcement Learning Control (Q-learning, DDPG, PPO) |
| Part 08 | part08_data_driven | Data-Driven Control (Koopman, matrix identification) |
| Part 09 | part09_llm_hybrid | LLM-Integrated Hybrid Control (FSM Γ PID Γ LLM) |
control, scipy, matplotlib, torch, gymnasium, stable-baselines3EduController is integrated with the following related educational and control projects:
An educational project for semiconductor design. Covers process, circuit, and layout design along with Python-based automation and SoC implementation.
Its special topics section aligns with LLM-integrated hybrid control and AITL-H concepts.
A hierarchical intelligent control framework for humanoid systems.
Combines FSM (instinct) + PID (reason) + LLM (intelligence) and is directly connected to Part 09 of EduController.
Education: M.E. in Electrical and Electronic Engineering, Shinshu University Graduate School
MIT License Β© 2025 Shinichi Samizo
This project is open for educational, research, and personal use.
π¬ Join the discussion on EduController β GitHub Discussions