
After the course the student is able to...
 Describe the relations for signals and (dynamic) systems in time and frequency domain as well as conversions between continuous time and discrete time descriptions.
 Explain nonparametric system identification in time and frequency domain and comment on the validity of the obtained impulse response functions (IRF) and frequency response functions (FRF).
 Estimate parameters in models that are linearintheparameters and explain the matrix formulation for the least squares estimate (LSE) and its solution with the pseudoinverse.
 Describe system identification with subspace identification techniques and use these techniques to obtain models.
 Explain system identification with prediction error identification methods (PEM), use these methods to obtain models, explain the approximate behaviour of these methods, evaluate and validate estimated models.
 Design an experiment to identify a setup, collect the data and estimate a model of the system.
 Estimate parameters in more advanced models, explain the identifiability of the parameters and explain the sources for errors in the estimates.
 Explain the approaches for identification of closedloop systems in time and frequency domain and implement an algorithm to estimate models from frequency domain data.



In system modelling the choice of the model structure plays an important role. This model structure specifies the mathematical expressions to describe the system and the parameters that are considered to be relevant. By identifying correct values for the parameters, it is possible to optimise the agreement between the behaviour of the model and system.
Topics of this course are: The selection of the model structure, parameter estimation and the design of identification experiments for that purpose. One part is about socalled system identification, where mathematical models are used. Usually the parameters do not have a physical meaning. The focus is on a limited number of standard model structures for linear systems. In addition, attention will be paid to more general parameter estimates in time and frequency domain. Nonlinear systems are also tackled and the parameters usually have a physical meaning.
Examination: The final grade is composed from two parts:
 An individual written examination about a part of the course material. This contributes 50% to the final grade and a pass grade is required. The exact scope of the course material tested during this exam is communicated via Canvas. The test is scheduled once in the exam weeks at the end of block 2A and a resit is offered at the end of block 2B.
 Answering standard assignments or solving another identification problem in the second part of the course. This part may include a practical assignment and contributes the remaining 50% to the final grade. These assignments are available in block 2A such that students can complete the full course at the end of that block. Alternatively, it is also allowed to make the assignments later, e.g. in block 2B. A handin and grading schema is published on Canvas.




 VoorkennisMandatory: 201700125 Module Dynamic Systems, onderdeel System analysis (of equivalent).
Recommended: 201500322 Module Mechatronica, onderdeel Systeem en Regeltechniek (of equivalent). 
Master Mechanical Engineering 
Master Systems and Control 
  Verplicht materiaalCourse material“System Identification and Parameter Estimation” (in PDF) available on Canvas. 

 Aanbevolen materiaalWerkvormenToetsenWritten/Oral Exam + Practic. Assignments


 