🎛️ EduController: Educational Framework for Control Theory and AI Control
🔗 Official Links
| Language | GitHub Pages 🌐 | GitHub 💻 |
|---|---|---|
| 🇺🇸 English |  |
 |
| 🇯🇵 Japanese |  |
 |
📘 Overview
EduController is a step-by-step, practical educational project covering classical control, modern control, and AI-based next-generation control.
It supports a wide range of learning, from intuitive understanding of control theory using Python to HDL coding and LLM-integrated design.
🧭 Structure Overview
| Track | Description |
|---|---|
| 🎓 Classical Control (Part 01–05) | Classical control, state-space, digital control, practical implementation |
| 🤖 AI-based Control (Part 06–08) | Neural networks, reinforcement learning, data-driven control |
| 🧠 Hybrid & Applied Control (Part 09–10) | LLM-integrated control, inverted pendulum control |
📚 Chapter Structure
🎓 Classical & Modern Control
| Chapter | Directory | Overview |
|---|---|---|
| Part 01 | part01_classical | PID control, Bode plot, stability |
| Part 02 | part02_modern | State-space, LQR, Kalman filter |
| Part 03 | part03_adaptive | Adaptive & robust control (MRAC, H∞, L1) |
| Part 04 | part04_digital | Digital control, Z-transform, DSP implementation |
| Part 05 | part05_practical | Python implementation, ROS exercises, FPGA-based control |
🤖 AI-based Control
| Chapter | Directory | Overview |
|---|---|---|
| Part 06 | part06_nn_control | Neural network control (NN-PID, inverse model) |
| Part 07 | part07_rl_control | Reinforcement learning control (Q-learning, DDPG, PPO) |
| Part 08 | part08_data_driven | Data-driven control (Koopman, system identification) |
🧠 Hybrid & Applied Control
| Chapter | Directory | Overview |
|---|---|---|
| Part 09 | part09_llm_hybrid | LLM-integrated hybrid control (FSM×PID×LLM) |
| Part 10 | part10_pendulum | Integrated control of inverted pendulum (PID / LQR / DDPG / HDL) |
🔩 Implementation Toolkit
| Directory | Overview |
|---|---|
| matlab_tools/ | Visualization of PID/state-space control in Simulink, C code generation, HDL design |
| SoC_DesignKit_by_ChatGPT/ | Templates for FSM, PID, LLM control; Verilog generation via ChatGPT; testbench verification |
🔗 Related Projects
| Project | Summary |
|---|---|
| 🎓 Edusemi-v4x 💻 GitHub |
Semiconductor design & process education (Python, sky130, OpenLane) |
| 🤖 AITL-H 💻 GitHub |
Three-layer control framework (FSM×PID×LLM) |
| 🧠 SamizoGPT 💻 GitHub |
Prompt design templates for ChatGPT (design assistance) |
👤 Author
| Item | Details |
|---|---|
| Name | Shinichi Samizo |
| Education | M.Eng., Electrical & Electronic Engineering, Shinshu University |
| Career | Former engineer, Seiko Epson Corp. (1997–) |
| Expertise | Semiconductor devices (logic, memory, high-voltage mixed-signal), inkjet thin-film piezo actuators, PrecisionCore productization, BOM management, ISO training |
| Contact | ✉️ shin3t72@gmail.com 🐦 https://x.com/shin3t72 💻 https://samizo-aitl.github.io/ |
📄 License
| Item | Details |
|---|---|
| Type | MIT License |
| Usage | Free to use, modify, and redistribute |
| Recommended Uses | Education, research, corporate training |