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Linear-Quadratic-Gaussian Problem

Linear-Quadratic-Gaussian Problem. Study Guide for ES205 Yu-Chi Ho Jonathan T. Lee Feb. 5, 2001. Outline. Linear-Quadratic Problem Calculus of Variation Approach Dynamic Programming Approach Kalman Filter Linear Feedback Control. N. Linear-Quadratic Problem.

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Linear-Quadratic-Gaussian Problem

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  1. Linear-Quadratic-Gaussian Problem Study Guide for ES205 Yu-Chi Ho Jonathan T. Lee Feb. 5, 2001

  2. Outline • Linear-Quadratic Problem • Calculus of Variation Approach • Dynamic Programming Approach • Kalman Filter • Linear Feedback Control

  3. N Linear-Quadratic Problem subject to linearsystem dynamics given the initial state x(0) where x(i) is the state variables at time i u(i) is the control variable at time i A(i) and B(i) are the cost matrices at time i

  4. N Calculus of Variation Approach • Let

  5. N Dynamic Programming Approach • Cost-to-gowithwhereand

  6. N DP Approach (cont.) • Set • We have • LetandThen

  7. N DP Approach (cont.) • By induction, we have the optimal solution to bewhereand with boundary conditionwith

  8. N Static State subject to state transitionwith initial conditionwhereand  is noise

  9. N Static State (cont.) • Set • Thus,

  10. N Static State (cont.) • New Estimate = Old Estimate + Confidence Factor  Correction Term

  11. N Dynamic State subject to state transition with disturbance (i)where and  is noise

  12. N Dynamic State (cont.) • Cost-to-go • Setwhere • We have

  13. N Dynamic System (cont.) • Cost-to-go • Substitute

  14. N Dynamic System (cont.) • Set • Let Then, we have

  15. N Dynamic System (cont.) • Cost-to-go • Substitute

  16. N Dynamic System (cont.) • Set • Let and Then, we have

  17. N With Control subject to state transitionwith disturbance (i)where and  is noise

  18. N With Control (cont.) • Cost-to-go • Set • We have

  19. N With Control (cont.) • Cost-to-go • Substitute

  20. N With Control(cont.) • Set • Let Then, we have

  21. N With Control(cont.) • Cost-to-go • Substitute

  22. N With Control(cont.) • Set • Let andThen, we have

  23. N Linear Feedback Control • The predicted statewith initial statebased on the estimator

  24. N Linear Feedback Control (cont.) • Optimal control based on the estimated state:whereandwith boundary conditionwith

  25. N Applications • Apollo program: control of the space craft. • Airplane controllers: autopilot. • Neighboring Optimal Control for Non-Linear Systems • Chemical process controller • Guidance and control

  26. References: • Bryson, Jr., A. E. and Y.-C. Ho, Applied Optimal Control: Optimization, Estimation, and Control, Taylor & Francis, 1975. • Ho, Y.-C., Lecture Notes, Harvard University, 1997.

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