Advanced Topics¶
A starting point for self-directed learning. This page gives you a bird's-eye view of which examples to tackle first based on your area of interest. Skim Examples 16, 18, and 19 along with the API Reference before diving in — that will give you the right mental model.
Jump Right In with Examples¶
The advanced topics below have hands-on examples ready to run in the examples/ folder.
| Topic | Example | Description |
|---|---|---|
| TinyML on STM32 | Ex.16 — Tiny AI Sensor Fusion | Complementary filter + 3-layer NN inference on MCU |
| Inverted-Pendulum Gait Control | Ex.15 — Inverted Pendulum | MgL·sin(θ) gravity compensation + PD stabilization |
| FSM Gait Intent Recognition | Ex.17 — FSM Gait Intent | 7-phase gait FSM + phase-specific assist torque |
| PD Control Theory | Ex.14 — PD Realtime Control | Discrete-time PD control implemented from first principles |
| System Debugging | Ex.18 — Debug Monitor | Loop profiling + health dashboard |
| Memory Patterns | Ex.19 — Memory Aware Design | Ring buffer, pool allocator, moving average |
Content requests and contributions are welcome via GitHub Issues.
Recommended Self-Directed Learning Paths¶
| Area of Interest | Recommended Starting Point | Next Steps |
|---|---|---|
| AI / ML on MCU | Ex.16 TinyAI Sensor Fusion | Ex.36 On-Device Kinesthetic Learning → AI Training Data Pipeline |
| Gait Control Algorithms | Ex.15 Inverted Pendulum → Ex.17 FSM Gait | Ex.20–31 Algorithm Series |
| Disturbance Observer + Transparent Mode | Ex.21 Gravity Comp | Ex.31 DOB Stage 1 → Ex.35 MultiLayer |
| Learning / Adaptive Control | Ex.26 ILC | Ex.27 MRAC → Ex.33 Kinesthetic Teaching |
| Data Collection / Analysis | Ex.09 CDC Stream | → AI Training Data Pipeline |
| System Diagnostics / Debugging | Ex.18 Debug Monitor | Ex.19 Memory Aware Design |
If you get stuck, check docs/troubleshooting.md or tell Claude Code "I'm stuck on Ex.XX".