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Model Predictive Control

By Stephen Boyd - Stanford
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Lecture Description

Model Predictive Control, Linear Time-Invariant Convex Optimal Control, Greedy Control, 'Solution' Via Dynamic Programming, Linear Quadratic Regulator, Finite Horizon Approximation, Cost Versus Horizon, Trajectories, Model Predictive Control (MPC), MPC Performance Versus Horizon, MPC Trajectories, Variations On MPC, Explicit MPC, MPC Problem Structure, Fast MPC, Supply Chain Management, Constraints And Objective, MPC And Optimal Trajectories, Variations On Optimal Control Problem

Course Description

Related Resources

Transcript   |  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