| 1. |
- Barenthin, Märta, et al.
(author)
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Gain estimation for Hammerstein systems
- 2006
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In: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 784-789
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Conference paper (peer-reviewed)abstract
- In this paper, we discuss and compare three different approaches for L2- gain estimation of Hammerstein systems. The objective is to find the input signal that maximizes the gain. A fundamental difference between two of the approaches is the class, or structure, of the input signals. The first approach involves describing functions and therefore the class of input signals is sinusoids. In this case we assume that we have a model of the system and we search for the amplitude and frequency that give the largest gain. In the second approach, no structure on the input signal is assumed in advance and the system does not have to be modelled first. The maximizing input is found using an iterative procedure called power iterations. In the last approach, a new iterative procedure tailored for memoryless nonlinearities is used to find the maximizing input for the unmodelled nonlinear part of the Hammerstein system. The approaches are illustrated by numerical examples.
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| 2. |
- Eng, Frida, 1977-, et al.
(author)
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Bias Compensated Least Squares Estimation of Continuous Time Output Error Models in the Case of Stochastic Sampling Time Jitter
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 612-617
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Conference paper (peer-reviewed)abstract
- This work investigates how stochastic unmeasureable sampling jitternoise affects the result of system identification, and proposes a modification ofknown approaches to mitigate the effects of sampling jitter. By just assumingconventional additive measurement noise, the analysis shows that the identifiedmodel will get a bias in the transfer function amplitude that increases for higherfrequencies. A frequency domain approach with a continuous time system modelallows an analysis framework for sampling jitter noise. This leads to a biascompensated (weighted) least squares algorithm. A continuous time output errormodel is used for numerical illustration.
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| 3. |
- Enqvist, Martin, 1976-
(author)
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Identification of Hammerstein Systems Using Separable Random Multisines
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 768-773
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Conference paper (peer-reviewed)abstract
- The choice of input signal is very important in identification of nonlinear systems. In this paper, it is shown that random multisines with a flat amplitude spectrum are separable. The separability property means that certain conditional expectations are linear and it implies that random multisines easily can be used to obtain accurate estimates of the linear time-invariant part of a Hammerstein system. This is illustrated in a numerical example.
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| 4. |
- Gerdin, Markus, 1977-
(author)
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Local Identifiability and Observability of Nonlinear Differential-Algebraic Equations
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 802-807
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Conference paper (peer-reviewed)abstract
- Identifiability is important to guarantee convergence in system identification applications, and observability is important in applications such as control and diagnosis. In this paper, recent results on analysis of nonlinear differential-algebraic equations are used to derive criteria for local identifiability and local weak observability for such models. The criteria are based on rank tests. Examples show the relationship between the new criteria and standard methods for state-space systems.
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| 5. |
- Gerdin, Markus, 1977-, et al.
(author)
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On Identifiability of Object-Oriented Models
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 820-825
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Conference paper (peer-reviewed)abstract
- When estimating unknown parameters, it is important that the model is identifiable so that the parameters can be estimated uniquely. For nonlinear differential-algebraic equation models with polynomial equations, a differential algebra approach to examine identifiability is available. This approach can be slow, so the present paper describes how this method can be modularized for object-oriented models. A characteristic set of equations is computed for components in model libraries, and stored together with the components. When an object-oriented model is built using such models, identifiability can be examined using the stored equations.
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| 6. |
- Gerdin, Markus, 1977-
(author)
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Using DAE Solvers to Examine Local Identifiability for Linear and Nonlinear Systems
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 808-813
-
Conference paper (peer-reviewed)abstract
- If a model structure is not identifiable, then it is not possible to uniquely identify its parameters from measured data. This contribution describes how solvers for differential-algebraic equations (DAE) can be used to examine if a model structure is locally identifiable. The procedure can be applied to both linear and nonlinear systems. If a model structure is not identifiable, it is also possible to examine which functions of the parameters that are locally identifiable.
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| 7. |
- Gillberg, Jonas, et al.
(author)
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Robust Frequency Domain ARMA Modelling
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 380-385
-
Conference paper (peer-reviewed)abstract
- In this paper a method for the rejection of frequency domain outliers is proposed. The algorithm is based on the work by Huber on M-estimators and the concept of influence function introduced by Hampel. The estimation takes placein the context of frequency domain continuous-time ARMA modelling, but the method can be also be applied to the discrete time case.
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| 8. |
- Ljung, Lennart, 1946-, et al.
(author)
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An Integrated System Identification Toolbox for Linear and Nonlinear Models
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 931-936
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Conference paper (peer-reviewed)abstract
- The paper describes additions to the MATLAB system identification toolbox, that handle also the estimation of nonlinear models. Both structured grey-box models and general, flexible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface should be as close as possible to the linear case.
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| 9. |
- Ljung, Lennart, 1946-, et al.
(author)
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Frequency-Domain Identification of Continuous-Time Output Error Models from Non-Uniformly Sampled Data
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 214-218
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Conference paper (peer-reviewed)abstract
- This paper treats direct identification of continuous-time autoregressive moving average (CARMA) time-series models. The main result is a method for estimating the continuous-time power spectral density from non-uniformly sampled data. It is based on the interpolation (smoothing) using the Kalman filter. A deeper analysis is also carried out for the case of uniformly sample ddata. This analysis provides a basis for proceeding with the non-uniform case. Numerical examples illustrating the performance of the method are also provided both, for spectral and subsequent parameter estimation.
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| 10. |
- Ljung, Lennart, 1946-, et al.
(author)
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Identification of Wiener System with Monotonous Nonlinearity
- 2006
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In: Proceedings of the 14th IFAC Symposium on System Identification. - 9783902661029 ; , s. 166-171
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Conference paper (peer-reviewed)abstract
- A Wiener system is composed of a linear dynamic subsystem followedby a static nonlinearity. It is well known in the literature that the identifcationof the linear subsystem of a Wiener system can be separated from that of theoutput nonlinearity, if the input signal is a Gaussian noise. In order to deal withthe non Gaussian input case, two new algorithms are proposed in this paper fordirect identifcation of the linear subsystem, regardless of any parametrization ofthe output nonlinearity. The essential assumption required in this paper is thestrict monotonousness of the output nonlinearity.
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