Courses

Below are some suggested courses along with when they are (usually) offered.

Controls

  • AERSP 460: Aerospace Control Systems (Fall) (can replace ME 455 for ME graduate students)
  • ME 455 Automatic Control Systems (Spring): introductory controls for students who have almost no control experience
  • ME 554 Digital Process Control (Spring of odd numbered years): Analysis and design of digital control system, Kalman Filter, LQG Control, System Identification, Prerequisite: ME455 or equivalent
  • ME 555 Linear Systems Theory (Spring): typically the first grad-level controls course students take. Prerequisite: ME455 or equivalent.
  • ME 558 / EE 584 Robust Control Theory (Spring) (offered yearly, alternate years by ME and EE): Prerequisite: ME555 or EE580 or equivalent. Note: temporarily not offered Spring 2022.
  • ME 559 / EE 587 Nonlinear Control and Stability (Fall) (offered yearly, alternate years by ME and EE): Prerequisite: ME455 or equivalent
  • EE 580  Linear Control System
  • EE 581 Optimal Control
  • EE 582  Adaptive and Learning Systems

Robotics and Mechatronics

  • ME 454 Undergraduate Mechatronics (Fall and Spring): required course for undergrads at PSU and thus does not count for MS credits. However, first-year graduate students with no experience may consider taking this course.
  • ME 456 Introduction to Robotics (Fall): introductory robotics course appropriate for seniors and first-year graduate students with no robotics experience
  • ME 545 Mechatronics (Spring) a graduate-level mechatronics course, assumes students have finished ME 454 but students might be able to jump into this class, with difficulty.
  • ME 556 Robotic Concepts (Fall): more advanced robotics, assumes knowledge of ME 456 material
  • ME 597 Locomotion in biological and robotic systems (special topic course)

Kinematics and Dynamics

  • ME 452 Vehicle Dynamics (Spring): first course in ground vehicle dynamics. Note: temporarily NOT offered in Spring 2022, but may be offered in Summer 2022.
  • ME 480 Mechanism Design and Analysis (Fall): basic mechanism design
  • ME 481 Computational Machine Dynamics (???):
  • ME 581 Simulation of Mechanical Systems (Spring): 3D analysis of mechanisms
  • EMCH 520 Advanced Dynamics (Fall): graduate-level Lagrangian dynamics

Math

  • ME 550 Foundations of Engineering Systems Analysis (Fall)
  • ME 597.2 Engineering Math (Fall)
  • MATH/CMPSC 451 (???): algorithms and methods for numerical integration, linear systems, error analysis
  • STAT 418 Introduction to Stochastic Processes and Probability for Engineering (???)
  • STAT 500 Applied Statistics (Fall and Spring): introductory graduate-level statistics
  • STAT 511  Regression Models

Other

  • ME 444 Engineering Optimization (???)
  • ME 597 Materials that Compute, Learn, and Decide (???) synaptic plasticity and learning, Hodgkin-Huxley equations, models of neural circuits, biology of learning, smart materials, signal processing, modeling dynamic systems, nonlinear dynamical materials and systems, oscillation in mechanical and electrical systems, chaos, and more
  • ME 597.1 Academic Writing (Fall): course to help students write a thesis, paper, or comprehensive exam
  • AERSP 425 : Theory of Flight (???)
  • AERSP 565 System Identification (???): theory of system identification and state-of-the-art computation methods for system identification.
  • AERSP 566: Applied Optimal Estimation (???): theory and methods suitable for nonlinear estimation and filtering in aerospace problems such as system identification, navigation, and attitude determination.
  • ESC 527 Brain Computer Interfaces (BCI) (Spring): lab-focused course which uses EEG and fundamentals in signal processing, feature
    extraction, and classification to create a BCI
  • IE562: Smart Systems Design (???): a graduate-level AI course
  • IE582: Advanced Engineering Analytics (???): machine learning course
  • IE 597 Special Topics section 005: Human Factors in Transportation Safety(???): the methods appropriate for studying humans, vehicles, and their interaction, with strong emphasis on human/automation interactions
  • EE 453 : Digital Signal Processing
  • EE 552 Pattern Recognition
  • EE 560 – Rand Var and Stoc Proc
  • EE 585 – Convex Optimization
  • ME 497 – Machine Learning (special topic course)