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Objectoriented modeling and simulation 7.5 Credits

Objektorienterad modellering och simulering
First cycle, R0003E
Course syllabus valid: Spring 2018 Sp 3 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level
First cycle
Grade scale
G U 3 4 5
Control Engineering
Subject group (SCB)
Automation Technology

Entry requirements

In order to meet the general entry requirements for first cycle studies you must have successfully completed upper secondary education and documented skills in English language and Ordinary differential equations, linear algebra, laplace transforms, frequency functions, basic knowledge in physics (M0018E or E0003E). Alternative: Alternative to completed courses can be corresponding knowledge acquired through work within the processindustry or electronics sector.

More information about English language requirements


The selection is based on 1-165 credits.

Course Aim
The course aim is for students to acquire basic knowledge of feedback systems, their design and their use in control engineering applications. 
The students should have the skills and knowledge to:
  • demonstrate knowledge of basic methods and terminology of automatic control..
  • demonstrate a basic knowledge of proven methods for designing controllers.
  • demonstrate the ability to model and simulate dynamic systems based on balance equations and constitutive relationsships.
  • use an object oriented modelling language to build hierarchical models.
  • using mathematical methods to analyze the static, dynamic and frequency characteristics of dynamic systems.
  • use standard methods for designing and analyzing controllers.
  • demonstrate an ability to, in a team, design and implement controllers, as well as evaluate their performance for a real process.
  • demonstrate good ability to, both orally and in writing, report on the practical work of modeling, design and implementation of closed loop control  for a real process.
  • identify the usefulness of basic control methods and their limitations, and identify the need for more advanced methods.

Introduction to mathematical modeling of physical and other types of systems. Modeling and analysis of interconnected systems. Time and frequency properties of dynamical systems. Bond graphs. Object oriented modeling with Modelica. Scaling and model reduction. Simulation of dynamical systems. Laplace transforms and transfer functions. Properties for feedback systems. PID-control

Automatic Control is the science of controlling processes. A typical example is the cruise control in a car (In this case the car is the "process") that by varying the throttle ("input" to the process) will keep the speed ("output" of the process) constant despite hills and wind (so-called "disturbances"). Other common examples include companies in the process industry, where the aim is to control pressures and temperatures, and in communication where you want to control data rates and transmission powers.

Automatic Control is not limited to technical processes but can also be applied in areas such as economics and medicine. One example is the human body's, highly sophisticated control system that is able to keep the body temperature constant at 37 degrees Celsius despite variations in ambient temperature or to keep the body weight constant despite assiduous efforts to better nutrition and exercise.

This course is our first course in control theory and covers the classical methods of analysis and synthesis of feedback control for a wide range of technical processes. This course provides in-depth knowledge of the subject, sufficient for non-specialists in control theory to develop simple control systems. The is a necessary basis for continued studies in the subject.

During the course, the following methods and concepts will be discussed:

Introduction: Introduction to general control engineering concepts, such as static systems, dynamic systems, process, reference signal (setpoint), control signal, output signal, noise, open systems, measurement signal, the feedback, controller.

Dynamic models: Mathematical modeling of physical systems. Differential equations. Differential equations in state space. Linearization.

Simulation: Introduction to simulation of dynamic systems and the programs Regsim and Simulink.

Mathematical tools: Laplace transform and its properties. Transfer function. Static gain. Super-position principle. Block Diagrams. Specifications. Rise time. Settling time. Overshoot. Poles and Zeroes. Modeling based on emperical data.

Feedback systems: PID controller. Process disturbances. Steady state control error. Ziegler-Nichols methods. Lambda tuning. Stability Concepts.

Frequency domain: The frequency function. Frequency analysis. Bode plots. Asymptotic Bode plot. Stability. Stability Margins. Compensation. Sensitivity. Time delays.

Digital control: Approximation of continuous controllers. Sampling.

The teaching consists of lectures, problem seminars, mandatory assignments and laboratory work.

Written exam with differentiated grades and approved lab work

Thomas Gustafsson

Transition terms
The course R0003E is equal to SMR058

Literature. Valid from Spring 2017 Sp 4 (May change until 10 weeks before course start)
Gene F. Franklin, Feedback Control of Dynamic Systems, Global Edition, Upplaga 7, Pearson Education Limited, ISBN 9781292068909.

Course offered by
Department of Computer Science, Electrical and Space Engineering

0001Written exam6.0G U 3 4 5
0002Laboratory work1.5U G#

Study guidance
Study guidance for the course is to be found in our learning platform Canvas before the course starts. Students applying for single subject courses get more information in the Welcome letter. You will find the learning platform via My LTU.

Syllabus established
by the Department of Computer Science and Electrical Engineering 28 Feb 2007

Last revised
by Jonny Johansson, HUL SRT 15 Feb 2017