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.
Power System Analysis - EE 30372
The objective of this course is to provide students with an overview of interconnected 3-phase power system operation. The student will learn how to model the basic components of such systems; transmission lines, transformers, generators, and loads. The student will learn about power flow analysis and its role in economic dispatch and generation control. Additional topics include transient stability and short circuit protection.
Power Electronics - EE40442
This course studies the use of electronics in the conversion of electrical power. The topics covered in this course include modeling, analysis, and control techniques for power electronics; the design of power circuits including inverters, rectifiers, and DC-DC converters; feedback control principles, and characteristics of real components. Students will assist in the design and construction of an autonomous PV power system.
Advanced Control Systems - EE 60655
This is graduate level course introducing advanced concepts in the feedback control of linear and nonlinear dynamical processes to students that may not necessarily specialize in control. So one might think of this as a "grand tour" of the major accomplishments in control theory. Prerequisites for this course are graduate level course work in linear systems theory and random processes.
Robust Control –
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.
Optimal Control – EE60565
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.
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
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- Forecasting Regime-Shifts in Complex Dynamical Processes- EE67598
Regime shifts occur when a dynamical system's state shifts irreversibly from the neighborhood of a nominal equilibrium to that of an alternative equilibrium. This course will examine an approach for regime shift prediction that makes use of data analytics and formal dynamics system models. In particular, we use existing data records of the physical system to identify a nominal mathematical model and then use that model to estimate the likelihood of a regime shift occurring in the near future. Preliminary work has shown that barrier certificate methods can be used to compute a system's sensitivity to regime shifts when the nominal system model is known. These methods can also be used to identify regime shift indices that provide real-time probabilistic forecasts of a regime shift occurring in the near future. The course will introduce several computational tools used to automate this approach to regime shift analysis in the context of two applications involving 1) voltage collapse in power grids and 2) regime shifts in exploited aquatic ecosystems.