Control engineering — Guía de aprendizaje
Distinguish feedback, estimation and optimization, and test responses in PID and Kalman experiments.
Una ruta estructurada de robótica, control, agricultura en entornos controlados, energía y electrónica de potencia.
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 browserLab de Pure Pursuit: comparar el seguimiento de trayectoria con lookahead fijo
- Lab · Run in browserLab de Adaptive Pure Pursuit: escalar el lookahead con la velocidad
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Regulated Pure Pursuit speed regulation
Background: Pure Pursuit and Adaptive Pure Pursuit
- Lab · Run in browserLab de Regulated Pure Pursuit: velocidad en curvas y cerca de la meta
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Stanley control
Background: Pure Pursuit and Adaptive Pure Pursuit
- Lab · Run in browserLab de Stanley: dirección a partir del error de eje delantero y rumbo
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Path tracking with MPC
Background: Stanley control · Model predictive control
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Comparing path trackers
Background: Regulated Pure Pursuit speed regulation · Path tracking with MPC