| 1. |
- Chistiakova, Tatiana, et al.
(författare)
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Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process
- 2017
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Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963. ; 50:1, s. 3917-3922
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Tidskriftsartikel (refereegranskat)abstract
- Aeration of biological reactors in wastewater treatment plants is important to obtain a high removal of soluble organic matter as well as for nitrification but requires a significant use of energy. It is hence of importance to control the aeration rate, for example, by ammonium feedback control. The goal of this paper is to model the dynamics from the set point of an existing dissolved oxygen controller to effluent ammonia using two types of system identification methods for a Hammerstein model, including a newly developed recursive variant. The models are estimated and evaluated using noise corrupted data from a complex mechanistic model (Activated Sludge Model no.1). The performance of the estimated nonlinear models are compared with an estimated linear model and it is shown that the nonlinear models give a significantly better fit to the data. The resulting models may be used for adaptive control (using the recursive Hammerstein variant), gain-scheduling control, L2 stability analysis, and model based fault detection.
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| 2. |
- Händel, Peter, 1962-, et al.
(författare)
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Performance analysis of a correlation based single tone frequency estimator
- 1995
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Ingår i: Signal Processing. - 0165-1684 .- 1872-7557. ; 44:2, s. 223-231
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Tidskriftsartikel (refereegranskat)abstract
- This paper analyzes the frequency error variance of a low complexity single tone frequency estimator based on sample correlations of the input data. In the high SNR scenario it is analytically shown that the accuracy of a properly tuned algorithm is nearly optimal, i.e. nearly attains the Cramer-Rao lower bound. For low SNR the statistical efficiency of the algorithm is degraded, but it is analytically proven that for a large number of samples the error variance attains the lower bound for this class of estimators.
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| 3. |
- Wigren, Torbjörn, et al.
(författare)
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Harmonic signal modeling using adaptive nonlinear function estimation
- 1996
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Ingår i: Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on. - Piscataway, NJ : IEEE. ; , s. 2952-2955, s. 2952-2955
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Konferensbidrag (refereegranskat)abstract
- It is well-known that sine waves produce harmonic overtones when passed through static nonlinear functions. This paper describes a new algorithm where an arbitrary periodic signal is estimated recursively. The estimated signal model is typically parameterised as a real sine wave with unknown frequency in cascade with a piecewise linear function. A recursive Gauss-Newton prediction error identification algorithm for joint estimation of the driving frequency and the parameters of the nonlinear output function can then be derived. The approach handles colored measurement disturbances and gives a direct measure of the size of the nonlinearity that corresponds to the harmonic spectrum. The Cramer-Rao bound (CRB) is also calculated in the paper
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| 4. |
- Chistiakova, Tatiana, et al.
(författare)
-
Nonlinear system identification of the dissolved oxygen to effluent ammonium dynamics in an activated sludge process
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Aeration of biological reactors in wastewater treatment plants is important to obtain a high removal of soluble organic matter as well as for nitrification but requires a significant use of energy. It is hence of importance to control the aeration rate, for example, by ammonium feedback control. The goal of this report is to model the dynamics from the set point of an existing dissolved oxygen controller to effluent ammonium using two types of system identification methods for a Hammerstein model, including a newly developed recursive variant. The models are estimated and evaluated using noise corrupted data from a complex mechanistic model (Activated Sludge Model no.1). The performances of the estimated nonlinear models are compared with an estimated linear model and it is shown that the nonlinear models give a significantly better fit to the data. The resulting models may be used for adaptive control (using the recursive Hammerstein variant), gain-scheduling control, L2 stability analysis, and model based fault detection.
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| 5. |
- Abd-Elrady, Emad, 1970-
(författare)
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Nonlinear Approaches to Periodic Signal Modeling
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors. The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors. Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters. In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented.
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