Control engineering — Guide d’apprentissage
Distinguish feedback, estimation and optimization, and test responses in PID and Kalman experiments.
Un parcours structuré en robotique, commande, agriculture en environnement contrôlé, énergie et électronique de puissance.
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.
-
PID control
Background: Closed-loop feedback
2. Estimating state from observations
Separate prediction and update, then vary noise assumptions.
-
Kalman filtering
Background: State-space model
3. Choosing inputs with costs and constraints
Move from LQR weights to MPC constraints and repeated optimization.
-
LQR
Background: State-space model
-
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.
-
Pure Pursuit and Adaptive Pure Pursuit
Background: Trajectory timing
-
Regulated Pure Pursuit speed regulation
Background: Pure Pursuit and Adaptive Pure Pursuit
-
Stanley control
Background: Pure Pursuit and Adaptive Pure Pursuit
-
Path tracking with MPC
Background: Stanley control · Model predictive control
-
Comparing path trackers
Background: Regulated Pure Pursuit speed regulation · Path tracking with MPC