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)