Teaching and Courses
Michael Lemmon, University of Notre Dame
Introduction to Electrical Engineering - EE20224:
This lab manual was based on a book
originally written by Paul H. Dietz (A
Pragmatic Introduction to the Art of Electrical Engineering)
using the Parallax BasicStamp.
I modified these labs to work with Technological Arts MicroStamp11,
a module based on the Motorola 68HC11 micro-controller that is
programmed using “C”. Since 2000 this document has been the lab
manual for the sophomore level circuit's lab at the University of
Notre Dame.
Robust Control –
EE60555
This course studies the design of robust optimal controllers for linear
continuous-time systems. Topics include: normed linear signal/system
spaces, matrix fraction descriptions, uncertain systems, robust
stability and performance, loopshaping, and the use of linear
fractional transformations in solving the generalized regulator
problem.
This course is a rigorous introduction to the classical theory of optimal control. The topics covered in this course include optimization of static functions, the calculus of variations, Pontragin's principle, dynamic programming, linear quadratic optimal control, non-cooperative differential games with applications to control theory, and price-based control of decentralized dynamical systems.
Nonlinear
Control - EE60580
This course studies the analysis and design of nonlinear feedback
control systems using Lyapunov and passivity methods. Topics include:
classical Lyapunov stability theory, input-to-state stability,
uniform ultimate boundedness, passivity methods, feedback designs for
stabilization and disturbance rejection, exact feedback
linearization, nonlinear H-infinity control, sliding mode control
Linear Systems Theory
- EE60550
State variable descriptions of linear dynamical systems. Solution of
state equations for continuous-time and discrete-time
systems. Input-output descriptions: impulse response and transfer
function. Controllability, observability, canonical forms,
stability. Realizations of input-output descriptions. State feedback
and state observers. Polynomial matrix and matrix fraction
descriptions of linear, time-invariant systems.
Applied State Estimation - EE67033
This course covers techniques used in estimating the state of a dynamical system. The course reviews basic concepts in linear systems, Bayesian estimation, and minimum mean-square estimation followed by the introduction of the conventional Kalman filter in both discrete-time and continuous-time formats. The course examines extensions of the Kalman filter that include the extended and unscented Kalman filter as well as the H-infinity filter. The course may also cover some advanced topics in Multi-target tracking, state estimation over networks, and the use of Markov Chain Monte Carlo (MCMC) methods.
Special Studies in Networked Control Systems - EE67598
These lectures review recent results on networked control system and cyber-physical systems.