| TSRT62 |
Modelling and Simulation, 6 ECTS credits.
/Modellbygge och simulering/
For:
D
I
Ii
IT
KeBi
M
TB
U
Y
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Prel. scheduled
hours: 58
Rec. self-study hours: 102
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Area of Education: Technology
Main field of studies: Electrical Engineering
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Advancement level
(G1, G2, A): A
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Aim:
The course should give knowledge about methods and principles for
constructing mathematical models of dynamic systems (systems described
by differential/difference equations), and about how properties of the
models can be studied through simulation. Furthermore, the significance of dynamic properties and the limitation of static models will be studied. Students will be expected to be able to do the following after completing this course:
- Define, describe and apply basic concepts related to models, identification and simulation.
- Simplify a given model by using static relations, replacing variables by constants, using idealized assumptions and aggregation of states.
- Use scaling and dimension-free variables in order to simplify analysis of systems.
- Model (one-dimensional) mechanical, electrical, flow and thermal systems from balance and equilibrium equations. Furthermore, construct models including combinations of different domains, in DAE form and (when possible) state-space form.
- Construct bond graphs for appropriate systems from the class mentioned above. Simplify and analyze bond graphs with respect to causality. From a given bond graph, compute a corresponding state-space model.
- Compute the index for a given DAE and describe the different standard forms for linear DAE:s.
- Model and simulate (one-dimensional) mechanical and electrical systems in Simulink and Modelica, and write simple Modelica objects in code.
- Use system identification to construct a model of a real system, through appropriate choices of experiment design, post-processing of data, model structure, and careful validation.
- Compute asymptotic bias and variance properties for a given linear system identification problem.
- Describe nonlinear graybox models, local models, local linear models and nonlinear regression models (in particular neural networks), and estimate models of these types for very simple cases.
- Determine whether a given simulation method is implicit or explicit and how many steps it contains. Compute the local error and stability region for simple simulation methods.
- Produce a well-written, informative lab report.
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Statistics, Automatic Control, Basic knowledge of electrical circuits and mechanics.
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Organisation:
The course consists of lectures, lessons and laboratory work.
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Course contents:
Models and modeling: Different types of models. Continuous and discrete time models. Differential and difference equations. State-space descriptions. Principles for model building, starting from physical relations. Balance and state equations. Simplification of models. Analogies between different physical domains. Bond graphs. Differential algebraic models. Object-oriented modeling. Models with disturbances. Black-box models.
Identification: Transient-response, frequency, correlation, and spectral analysis. Parameter estimation for linear and nonlinear dynamic models. System identification as a model building tool. Model validation.
Simulation: Methods for state-space and differential algebraic models. Numerical properties and stability. The simulation languages Simulink and Modelica.
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Course literature:
Ljung L., Glad T.: Modellbygge och simulering, 2nd edition, Studentlitteratur 2004.
Exercises.
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Examination: |
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Computer examination
Labratory work
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4,5 ECTS
1,5 ECTS
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Course language is English.
Department offering the course: ISY.
Director of Studies: Johan Löfberg
Examiner: Claudio Altafini
Link to the course homepage at the department
Course Syllabus in Swedish
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