🎛 Control Theory
Based on 
Wikipedia articles, licensed CC BY-SA 4.0.
How to make systems do what you want: measure the output, compare it to the goal, adjust the input. Ten chapters from feedback loops to Lyapunov stability.
| Chapter | |||
|---|---|---|---|
| 1. | Feedback | Open-loop guesses; closed-loop measures and corrects | 🎛 |
| 2. | Transfer Functions | Laplace transforms turn differential equations into algebra | 🎛 |
| 3. | PID Control | Three knobs that handle most real-world control problems | 🎛 |
| 4. | Stability | A system is stable when bounded inputs produce bounded outputs | 🎛 |
| 5. | Root Locus | Watch the poles migrate as you turn up the gain | 🎛 |
| 6. | Frequency Response | Bode plots show how a system amplifies or attenuates each frequency | 🎛 |
| 7. | State Space | Replace one high-order ODE with a system of first-order equations | 🎛 |
| 8. | Optimal Control | Minimize a cost function to find the best control law | 🎛 |
| 9. | Kalman Filter | Optimally fuse noisy measurements with a model prediction | 🎛 |
| 10. | Lyapunov Stability | Prove stability by finding an energy function that always decreases | 🎛 |
📺 Video lectures: Brian Douglas: Control Systems Lectures
Neighbors
- ∫ Calculus — differential equations are the language of control
- 📐 Linear Algebra — state space representation is matrix algebra
- 🎰 Probability — the Kalman filter is a Bayesian filter
- 🤖 Machine Learning — reinforcement learning is optimal control with learned dynamics