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Stochastic Model Predictive Control

By Stephen Boyd - Stanford
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Lecture Description

Stochastic Model Predictive Control, Causal State-Feedback Control, Stochastic Finite Horizon Control, 'Solution' Via Dynamic Programming, Independent Process Noise, Linear Quadratic Stochastic Control, Certainty Equivalent Model Predictive Control, Stochastic MPC: Sample Trajectory, Cost Histogram, Simple Lower Bound For Quadratic Stochastic Control, Branch And Bound Methods, Methods For Nonconvex Optimization Problems, Branch And Bound Algorithms, Comment: Example Problem

Course Description

Related Resources

Transcript   |  Assignment 6   |  Assignment 6 Solutions

Course Index

  1. Basic Rules for Subgradient Calculus
  2. Recap: Subgradients
  3. Convergence Proof, Stopping Criterion
  4. Project Subgradient For Dual Problem
  5. Stochastic Programming
  6. Addendum: Hit-And-Run CG Algorithm
  7. Example: Piecewise Linear Minimization
  8. Recap: Ellipsoid Method
  9. Comments: Latex Typesetting Style
  10. Decomposition Applications
  11. Sequential Convex Programming
  12. Recap: 'Difference Of Convex' Programming
  13. Recap: Conjugate Gradient Method
  14. Methods (Truncated Newton Method)
  15. Recap: Example: Minimum Cardinality Problem
  16. Model Predictive Control
  17. Stochastic Model Predictive Control
  18. Recap: Branch And Bound Methods, Basic Idea, Unconstrained, Nonconvex Minimization