Course Schedule

Week

Subject

Assignments

Due Fridays

Arduino Lab

Due Mondays

Wk1

Introduction to Dynamic Optimization, Basics, and Formulation

Data Science Modules

A - Reservoirs

Lab A - SISO Model

Wk2

Collocation, Initialization, and Simulation

B - Collocation

Lab B - MIMO Model

Wk3

Dynamic Data and Moving Horizon Estimation

C - Parameter and State Estimation or Machine Learning

Lab C - Parameter Estimation

Wk4

Estimator Objectives and Tuning

D - HIV Estimation and Estimator Tuning

Lab D - Empirical Model Estimation

Wk5

Estimation Tuning for Improved Control

E - Dynamic Optimization Benchmarks

Lab E - Hybrid Model Estimation

Wk6

Review for Mid-Term Exam

Mid-term Exam


Week

Subject

Assignments

Lab / Project

Wk7

Create Project Proposals and Evaluate Resources

Project Proposals

Model Predictive Control Introduction

Wk8

Dynamic Control Introduction

F - Cruise Control and Crane Pendulum

Lab F - Linear Model Predictive Control

Wk9

Model Predictive Control, Nonlinear MPC, and Objective Functions for Control

G - Linear MPC of CSTR

Lab G -Nonlinear Model Predictive Control

Wk10

Controller Tuning and Mixed Integer Nonlinear Programming

H - Nonlinear MPC of CSTR

Lab H - Estimation with Model Predictive Control

Wk11

Multi-Objective Optimization and Group Projects

Progress Report #1: Dynamic Model

Multi-Objective and Discrete Control

Wk12

Review and Example Problems

Final Exam


Week

Subject

Assignments

Project

Wk13

Projects

Progress Report #2: Estimation and Data

Project Work Sessions

Wk14

Projects

Progress Report #3: Control

Project Work Sessions

Wk15

Final Project Presentations

Report and Oral Presentation

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