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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
ESS017 - Automatic control
 
Syllabus adopted 2012-02-21 by Head of Programme (or corresponding)
Owner: TKELT
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: First-cycle
Major subject: Electrical Engineering
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: Swedish
Block schedule: D

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 5,5c Grading: TH   5,5c   28 Oct 2016 am M,  22 Dec 2016 pm M,  17 Aug 2017 pm M
0207 Design exercise + laboratory 2,0c Grading: UG   2,0c    

In programs

TKTEM ENGINEERING MATHEMATICS, Year 3 (compulsory elective)
TKELT ELECTRICAL ENGINEERING, Year 3 (compulsory)

Examiner:

Docent  Sébastien Gros


Replaces

ESS015   Automatic control


Eligibility:

In order to be eligible for a first cycle course the applicant needs to fulfil the general and specific entry requirements of the programme(s) that has the course included in the study programme.

Course specific prerequisites

Mathematical analysis in one and several variables. Basic theory of matrices, in particular eigenvalues. Komplex numbers. Linear ordinary differential equations and transforms. Basic knowledge in mechanics, heat, electricity as well as electronics for applications. Basic course in programming as a background for use of computer aids.

Aim

The purpose of this course is to introduce the concept of a dynamic system, and to demonstrate its application to different areas of technology. Yet another key concept is feedback and in particular the assessment of the stability of such a system. The course will teach theory and techniques for the design of both PID-controllers and state feedback controllers. Feedforward control will be adressed as well.

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

* Understand and explain the function of a linear control system, and be able to define basic control terminology.
* Describe and explain the most important properties of linear dynamical systems.
* Formulate models for dynamical systems, frequently encountered in a technical context. The models take the form of transfer functions as well as state equations.
* Transform in both directions between linear state equations and transfer functions, especially for single-input single-output systems. Linearize nonlinear state equations.
* Analyse feedback dynamical systems, emphasizing stability assessment based on the Nyquist criterion. Formulating solutions to state equations, using transition matrices.
* Describe and explain the principle of P-, I-, PI-, PD- and PID-controllers in a control loop, as well as being able to carry out design for such controllers, in particular by use of Bode plot techniques.
* Analyse feedback systems, using sensitivity functions, particularly to estimate how large modelling errors a control system can handle without risking instability.
* Describe and explain the principle of feedforward, cascade control and dead time compensation.
* Explain and apply the concepts of controllability and observability, and to carry out design of state feedback controllers and observers, using the pole placement method.
* Discretize analog controllers, explain the function of a computerized control system, and explain the sampling principle.

Content

The course can be described as a fundamental course in dynamics and control of linear continuous time systems. Formulation of state space models for linear and nonlinear systems. Linearization of state equations and obtaining transfer functions.

Analysis of linear dynamical systems. Analysis of feedback systems. The Nyquist criterion. Root locus. P-, I-, PI-, PD- and PID-controllers and their most important properties. Bodediagrams. Nichols charts. Non-minimum phase systems. Design of control systems, particularly using compensation in frequency domain. Sensitivity functions and robustness.

Feedforward control, cascade control and dead time compensation.

Linear state space methodology. Stability. Transition matrices. Controllability and observability. State feedback controllers and observers. Output control.

Computer control, sampling, and time discretization.

A very short introduction to linear quadratic control and Kalman filtering.

Organisation

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

Literature

Will be notified on the course homepage.

Examination

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


Page manager Published: Thu 04 Feb 2021.