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Teaching > Courses
EECE 360 (3) Systems and Control
Instructor: Mihai Huzmezan (huzmezan@ece.ubc.ca)
Calendar Description: Continuous and discrete time system analysis by Laplace and Z transforms; system modeling by transfer function and state space methods; feedback, stability and sensitivity; control design; digital filtering.
Pre-requisite: EECE 253. [3-0-0]
Text book: Modern Control Systems (Eighth Edition), Dorf & Bishop, Addison Wesley

 
 
 
EECE 361 (2) Signals and Systems
Instructor: Mihai Huzmezan (huzmezan@ece.ubc.ca)
Calendar Description: The course is composed of two sections, Communications and Controls. It focuses on the analysis and design software, spectral analysis, modulation, servo and regular system design. For laborartory experiments, the total number of students will be divided into 8 groups.
Prerequisite(s): ELEC 359 or 360
Pre-Lab Exercises: At the end of a laboratory, each student should hand in the pre-lab exercises, which can be found in the handouts. Be sure to operate these pre-lab before coming to the lab.
Lab Reports: Lab Reports: After completing a laboratory, each group will have to prepare one Lab Report according to the directions given in the handouts. Lab Reports are due one week after the lab.

 
 
 
EECE 530 Robust Multivariable Control
Instructor: Mihai Huzmezan (huzmezan@ece.ubc.ca)
Description: This course will gradually introduce the use of advanced mathematics in a control-engineering framework, having as final goal the design and analysis of robust multivariable controllers.
Aims: To introduce recent techniques used for the control of multivariable systems.
Prerequisites: It is recommended that the candidates have basic knowledge on Linear Algebra, Modelling, State Space Methods, Continuous and Discrete Time Systems and Classical Control Systems Design.

 
EECE 540 (3) Linear and Nonlinear Multivariable Predictive Control
Instructor: Mihai Huzmezan (huzmezan@ece.ubc.ca)
Description: The goal of this course is to provide the students with an understanding of the important problems associated with MIMO predictive control. The use of advanced mathematics will be made to attend the design, implementation and analysis of MIMO predictive controllers. Tuning of such controllers will not be neglected and as a result hands on experinece with a commercial product will be given. A full design cycle on a MIMO plant will certify the student's understanding of this complex problem.
Aims: To introduce recent techniques used in industry and beyond for controlling multivariable systems.
Prerequisites: It is recommended that the candidates have basic knowledge on Linear Algebra, Modelling, State Space Methods, Continuous and Discrete Time Systems and Classical Control Systems Design.