Syllabus

Objectives

  • Nonlinear Optimization
  • Investigate Key Basic Control Concepts and Extend to Advanced Algorithms (MPC)
  • Linear Quadratic Regulators and Compensators
  • MATLAB® Implementation

Approximate Number of Lectures by Topic


LECTURES TOPICS
2 Nonlinear Optimization
3 Dynamic Programming
3 Calculus of Variations: General
4 Calculus of Variations: Control
5 LQR/LQG - Stochastic Optimization
4 On-line Optimization and Control (MPC)

Grading


ACTIVITIES PERCENTAGES
Homework 20%
Two Midterms (25% each) 50%
Final Exam 30%

Prerequisites

This course assumes a good working knowledge of linear algebra and differential equations. New material will be covered in depth in the class, but a strong background will be necessary. A solid background in control design is best to fully understand this material, but not essential. Course material and homework assume a good working knowledge of MATLAB®.

Policies

You are encouraged to discuss the homework and problem sets; however, your submitted work must be your own. Late homework will not be accepted unless prior approval is obtained from the instructor. Grade on all late homework will be reduced 25% per day. No homework will be accepted for credit after the solutions have been handed out.