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This course will be offered during the third AND fourth quartile.
After the course the student is able to...
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Describe the relations for signals and (dynamic) systems in time and frequency domain as well as conversions between continuous time and discrete time descriptions.
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Explain non-parametric system identification in time and frequency domain and comment on the validity of the obtained impulse response functions (IRF) and frequency response functions (FRF).
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Estimate parameters in models that are linear-in-the-parameters and explain the matrix formulation for the least squares estimate (LSE) and its solution with the pseudo-inverse.
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Describe system identification with subspace identification techniques and use these techniques to obtain models.
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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 estimate models.
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Design an experiment to identify a set-up, collect the data and estimate a model of the system.
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Estimate parameters in more advanced models and explain the sources for errors in the estimates.
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Explain the approaches for identification of closed-loop systems in time and frequency domain and implement an algorithm to estimate models from frequency domain data.
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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 play a role. By setting 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 so-called 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.
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