Control engineering — अध्ययन मार्गदर्शिका
Distinguish feedback, estimation and optimization, and test responses in PID and Kalman experiments.
व्यवस्थित अध्ययन पथों से बुनियादी सिद्धांतों से व्यावहारिक उपयोग तक पहुँचें।
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 Lab — स्थिर-लुकअहेड पथ ट्रैकिंग की तुलना करें
- Lab · Run in browserAdaptive Pure Pursuit Lab — गति के साथ लुकअहेड को स्केल करें
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Regulated Pure Pursuit speed regulation
Background: Pure Pursuit and Adaptive Pure Pursuit
- Lab · Run in browserRegulated Pure Pursuit Lab — वक्रों और लक्ष्य के निकट गति
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Stanley control
Background: Pure Pursuit and Adaptive Pure Pursuit
- Lab · Run in browserStanley Lab — फ्रंट-एक्सल क्रॉस-ट्रैक और हेडिंग त्रुटि से स्टीयरिंग
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Path tracking with MPC
Background: Stanley control · Model predictive control
- Lab · Run in browserMPC Lab — क्षितिज और स्टीयरिंग सीमाओं के भीतर वक्रता अनुक्रम को पुनः हल करना
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Comparing path trackers
Background: Regulated Pure Pursuit speed regulation · Path tracking with MPC