| 1. |
- Hägg, Per, et al.
(författare)
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The Transient Impulse Response Modeling Method for Non-parametric System Identication
- 2016
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 68, s. 314-328
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Tidskriftsartikel (refereegranskat)abstract
- A method for the nonparametric estimation of the Frequency Response Function (FRF) was introduced in [5] and latercalled Transient Impulse Response Modeling Method (trimm). We present here a slightly improved version of the originalmethod and, more importantly, we thoroughly analyze the method in terms of bias and variance errors. This analysis leads toguidelines for the choice of the design parameters of the trimm method. Our theoretical expressions for the bias and varianceerrors are validated by simulations which, at the same time, highlight the eect of the design parameters on the performanceof the method.
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| 2. |
- Larsson, Christian A., et al.
(författare)
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Generation of signals with specified second-order properties for constrained systems
- 2016
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Ingår i: International journal of adaptive control and signal processing (Print). - : Wiley. - 0890-6327 .- 1099-1115. ; 30:3, s. 456-472
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Tidskriftsartikel (refereegranskat)abstract
- This contribution considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identification, firstly, because the quality and accuracy of the identified model are highly dependent on the excitation signal used during the experiment and secondly, because on real processes, it is often important to constrain the input and output of the process because of actuator saturation and safety considerations. The signal generation is formulated as a model predictive controller with probabilistic constraints to make the algorithm robust to model uncertainties and process noise. The corresponding optimization problem is then solved with tools from scenario-based stochastic optimization. To reduce the model uncertainties, the method is made adaptive where a new model of the system and its uncertainties are reidentified. The algorithm is successfully applied to a simulation example and in a practical experiment for the identification of a quadruple tank lab process.
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