Control engineering — Lernleitfaden
Distinguish feedback, estimation and optimization, and test responses in PID and Kalman experiments.
Ein strukturierter Lernweg durch Robotik, Regelung, kontrollierte Landwirtschaft, Energie- und Leistungselektronik.
Before you start Basic functions, calculus and vectors are used. Topic prerequisites link to state-space and closed-loop explanations.
1. Feedback control
Read how PID works, then vary delay and saturation.
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PID control
Background: Closed-loop feedback
2. Estimating state from observations
Separate prediction and update, then vary noise assumptions.
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Kalman filtering
Background: State-space model
3. Choosing inputs with costs and constraints
Move from LQR weights to MPC constraints and repeated optimization.
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LQR
Background: State-space model
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Model predictive control
Background: Closed-loop feedback · State-space model
4. Timing a motion
Distinguish path geometry from velocity and acceleration timing.
5. Following a path
Move from geometric lookahead tracking to speed regulation and front-axle error steering on one shared vehicle model.
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Pure Pursuit and Adaptive Pure Pursuit
Background: Trajectory timing
- Lab · Run in browserPure-Pursuit-Labor – Vergleich der Pfadverfolgung mit fester Vorausschau
- Lab · Run in browserAdaptive-Pure-Pursuit-Labor – Vorausschau mit der Geschwindigkeit skalieren
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Regulated Pure Pursuit speed regulation
Background: Pure Pursuit and Adaptive Pure Pursuit
- Lab · Run in browserRegulated-Pure-Pursuit-Labor – Geschwindigkeit in Kurven und nahe dem Ziel
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Stanley control
Background: Pure Pursuit and Adaptive Pure Pursuit
- Lab · Run in browserStanley-Labor – Lenkung aus Querabweichung und Kursfehler an der Vorderachse
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Path tracking with MPC
Background: Stanley control · Model predictive control
- Lab · Run in browserMPC-Labor – Krümmungsfolge im Horizont und innerhalb der Lenkgrenzen neu lösen
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Comparing path trackers
Background: Regulated Pure Pursuit speed regulation · Path tracking with MPC