[Spring 2024] EE581: Linear Systems Theory.
Linear systems theory is one of the most important basic theories to know before pursuing any type of research in control theory. Coverage includes linear model representations, canonical forms and transfer function models. Systems properties such as time-invariance, causality, controllability, observability, and stability are investigated. Well-known methods for control (e.g., LQR, state-feedback) and state-estimation (observer, Kalman filter) are also discussed. Motivating examples, applications, and connections to more advanced topics (robust control theory, stochastic systems) are also included.
[Fall 2023] EE488F: Learning Patterns for Autonomous Control.
The theme of this course is to identify and use patterns in intelligent autonomous systems.
By studying patterns, many control and state-estimation algorithms can be more efficient in data consumption and computation time.
Relevant motivating applications include robotic path-planning with AI, fault-tolerant control, and decision-making networks (e.g., vehicle traffic systems, UAV traffic management). This is a topics course based on my own research, and is open to all interested undergraduate, MS, and PhD students.