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Related Graduate Cources

Please note that these are just course guidelines. The actual syllabi are decided by the respective course instructors for each semester. You are encouraged to contact the instructor of your chosen course for further details. You are also encouraged to look into the undergraduate and graduate catalogs. Please consult the UIC Timetable to see whether the course you are interested in is offered in the upcoming semester.


ECE 451 Control Engineering

State-space representation of systems; realization theory; stability; performance; modern control design techniques, including: fuzzy, learning, adaptive and nonlinear control.
Instructor: Dr. Miloš Žefran

ECE 452 Robotics: Algorithms and Control

Kinematic and dynamic modeling of robots; configuration space; motion planning algorithms; control of robots; sensors and perception; reasoning; mobile robots.
Instructor: Dr. Miloš Žefran

ECE 550 Linear Systems Theory and Design

State variable description, linear operators, impulse response matrix, controllability, observability, reducible and irreducible realizations, state feedback, state observers and stability.
Instructor: Dr. Derong Liu

ECE 551 Optimal Control

Optimal control of dynamic systems in continuous and discrete time, maximum principle, dynamic programming and constraints, learning systems.
Instructor: Dr. Miloš Žefran

ECE 552 Nonlinear Control

Nonlinear phenomena, linear and piecewise linear approximations, describing functions, servomechanisms, phase plane, limit cycles, Lyapunov's stability theory, bifurcation, bilinear control, vibrational control, learning systems.
Instructor: Dr. Miloš Žefran

ECE 553 System Identification

On-line and off-line identification of control systems in frequency and time domain, considering noise effects, nonlinearities, nonstationarities and distributed parameters.

CS 594 Special Topics: Probabilistic Robotics

Recursive state estimation, Markov localization, Monte Carlo localization, simultaneous localization and mapping, planning using partially observable Markov decision processes (POMDPs), approximate solutions to POMDPs.
Instructors: Dr. Piotr J. Gmytrasiewicz and Dr. Miloš Žefran


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