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

Dynamic Optimization