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← 학습 가이드

Control engineering — 학습 가이드

Distinguish feedback, estimation and optimization, and test responses in PID and Kalman experiments.

기초 원리부터 실제 응용까지 체계적인 학습 경로를 따라갑니다.

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.

  1. PID control

    Background: Closed-loop feedback

2. Estimating state from observations

Separate prediction and update, then vary noise assumptions.

  1. Kalman filtering

    Background: State-space model

3. Choosing inputs with costs and constraints

Move from LQR weights to MPC constraints and repeated optimization.

  1. LQR

    Background: State-space model

  2. Model predictive control

    Background: Closed-loop feedback · State-space model

4. Timing a motion

Distinguish path geometry from velocity and acceleration timing.

  1. Trajectory timing

5. Following a path

Move from geometric lookahead tracking to speed regulation and front-axle error steering on one shared vehicle model.

  1. Pure Pursuit and Adaptive Pure Pursuit

    Background: Trajectory timing

  2. Regulated Pure Pursuit speed regulation

    Background: Pure Pursuit and Adaptive Pure Pursuit

  3. Stanley control

    Background: Pure Pursuit and Adaptive Pure Pursuit

  4. Path tracking with MPC

    Background: Stanley control · Model predictive control

  5. Comparing path trackers

    Background: Regulated Pure Pursuit speed regulation · Path tracking with MPC