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Syllabus for

Academic year
SSY160 - Digital and multivariable control
 
Syllabus adopted 2010-02-22 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering, Electrical Engineering
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course
0107 Examination 5,0c Grading: TH   5,0c   17 Dec 2010 pm H,  28 Apr 2011 pm V,  17 Aug 2011 pm V
0207 Laboratory 2,5c Grading: UG   2,5c    

In programs

MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 2 (elective)
MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 1 
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory)

Examiner:

Bitr professor  Torsten Wik


Course evaluation:

http://document.chalmers.se/doc/1533384358


Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

A basic course in automatic control including some familiarity with state space models.

Aim

The purpose of this course is to introduce and investigate techniques to solve model based control problems by use of digital computers. As in most industrial systems, linear models are quite sufficient to obtain the desired results, the emphasis is on linear systems in this course.

Learning outcomes (after completion of the course the student should be able to)

Understand and explain the function of linear computer control systems, and discuss practical implications of this. Identify and describe the most important phenomena in sampled data dynamical systems. Describe a few applications where digital control is an essential advantage compared to analogue control in the design of feedback systems. Analyse the stability, controllability and observability properties of sampled data dynamical systems. Apply the pole placement method in the design of state feedback controllers as well as state observers. Explain and design digital state feedback controllers, based on linear quadratic optimization. Explain and design digital Kalmanfilters, and apply them for state reconstruction in state feedback stochastic control systems, so called LQG control.

Content

The course can be described as a fundamental course in the dynamics and control of linear sampled data systems. Concepts like sampling mechanisms and Z-transforms are dealt with in depth. Discrete time state space models are the most frequently used system description in this course, being the basis for state feedback controllers as well as state observers. One approach to controller and observer design is the pole placement method, which is a major topic in the course. Another approach, better suited for multivariable control is based on, so called, linear quadratic optimization. Disturbance models, including stochastic ones, are also encountered in the course. These models are used in the theory and design of optimal state estimators, most often referred to as Kalman filters. The combination of Kalman filters and linear quadratic controllers is, under the assumption of Gaussian disturbances, called LQG-control.

Organisation

The course is organised as a number of lectures and problem sessions, and a mandatory project, comprised of several analysis and design assignments and laboratory sessions.

Literature

See course homepage.

Examination

Written exam with TH grading; project with assignments and laboratory sessions (pass/fail).


Published: Mon 28 Nov 2016.