Teaching

Classical feedback control for linear systems: transfer-function modeling, root locus, frequency response, and the design of PID, lead–lag, and loop-shaping compensators, with an emphasis on the physical principles behind feedback and the trade-offs among stability, transient response, disturbance rejection, and robustness.

Added new in-class demonstrations, exercises, and experiments to make abstract mathematical concepts concrete.

Learning and control of autonomous systems: representations of control systems, state estimation, state feedback, the separation principle, robust control, adaptive control, and reinforcement learning, applied to autonomous vehicles, drones, robots, manufacturing, human sensorimotor control, biomolecular systems, and economic models.

Designed and launched this course in 2023. Class projects use a natural-language interface for designing and interacting with controllers and a competitive gaming platform in which student-designed controllers compete against other autonomous agents.

Design of embedded controllers for aerospace, automotive, and manufacturing systems: modeling and simulation of dynamic physical systems, sampling and switching control, PWM, PID and state-feedback design, state estimation, and laboratory work with microcontrollers, motors, and automatic code generation.

Developed new lecture content and a new class project.

Mathematical foundations and computational tools for continuous- and discrete-time signals: linear time-invariant systems, impulse and frequency response, convolution, filtering, sampling, and the Fourier transform, as the entry point to signal processing, communications, and control.