Optimal and Learning-based Control

AA203

Stanford School of Engineering


Aeronautics and Astronautics: Introduction to Optimal Control and Dynamic Optimization

Description

This course provides basic solution techniques for optimal control and dynamic optimization problems, such as those found in work with rockets, robotic arms, autonomous cars, option pricing, and macroeconomics. You will learn the theoretic and implementation aspects of various techniques including dynamic programming, calculus of variations, model predictive control, and robot motion planning. The course is especially well suited to individuals who perform research and/or work in electrical engineering, aeronautics and astronautics, mechanical and civil engineering, computer science, or chemical engineering as well as students and researchers in neuroscience, mathematics, political science, finance, and economics.

 

What you will learn

  • How to optimize the operations of physical, social, and economic processes with a variety of techniques.
  • The theoretical and implementation aspects of techniques in optimal control and dynamic optimization.
  • How to use tools including MATLAB, CPLEX, and CVX to apply techniques in optimal control.

Prerequisites

A conferred Bachelor’s degree with an undergraduate GPA of 3.5 or better.

Familiarity with linear algebra.

Topics include

  • Dynamic programming
  • Modern solution approaches including MPF and MILP
  • Introduction to stochastic optimal control
  • Calculus of variations
  • Pontryagin’s principle

Notes

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.

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