Texas A&M UniversityDepartment of Aerospace Engineering
ISRLIntelligent Systems Research Laboratory

Teaching

Lecture notes for control, estimation, and machine learning courses in Aerospace Engineering at Texas A&M.

Lecture notes

AERO 422Active Control for Aerospace Vehicles

  1. Introduction
  2. Dynamic Response
  3. Basic Feedback Analysis & Design
  4. PID Control Design
  5. Root Locus
  6. Frequency Domain

AERO 632Design of Advanced Flight Control Systems

  1. Preliminaries
  2. Signal Norms
  3. Linear Quadratic Regulator (Part I)
  4. Introduction to Linear Matrix Inequalities
  5. Full-State Feedback: LQR (Part II)
  6. Full-State Feedback: Optimal
  7. Full-State Feedback: Optimal
  8. Estimator Design
  9. Output Feedback
  10. Uncertainty Modeling
  11. Design Problems

AERO 489/689Introduction to Machine Learning for Aerospace Engineers

  1. Introduction
  2. Linear Regression
  3. Logistic Regression
  4. Clustering & PCA
  5. Dimensionality Reduction
  6. Neural Networks

TutorialsUncertainty Quantification

  1. Polynomial Chaos & Karhunen–Loève Expansions
  2. Uncertainty Propagation with the Frobenius–Perron Operator
  3. New Nonlinear Filtering Algorithms