| 1. |
- Björklund, Svante, et al.
(author)
-
An Improved Phase Method for Time-Delay Estimation
- 2009
-
In: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 45:10, s. 2467-2470
-
Journal article (peer-reviewed)abstract
- A promising method for estimation of the time-delay in continuous-time linear dynamical systems uses the phase of the all-pass part of a discrete-time model of the system. We have discovered that this method can sometimes fail totally and we suggest a method for avoiding such failures.
|
|
| 2. |
- Enqvist, Martin, 1976-, et al.
(author)
-
Linear Approximations of Nonlinear FIR Systems for Separable Input Processes
- 2005
-
In: Automatica. - Linköping : Elsevier. - 0005-1098 .- 1873-2836. ; 41:3, s. 459-473
-
Journal article (peer-reviewed)abstract
- Nonlinear systems can be approximated by linear time-invariant (LTI) models in-many ways. Here, LTI models that are optimal approximations in the mean-square error sense are analyzed. A necessary and sufficient condition on the input signal for the optimal LTI approximation of an arbitrary nonlinear finite impulse response (NFIR) system to be a linear finite impulse response (FIR) model is presented. This condition says that the in ut should be separable of a certain order, i.e., that certain conditional expectations should be,P linear. For the special case of Gaussian input signals, this condition is closely related to a generalized version of Bussgang's classic theorem about static nonlinearities. It is shown that this generalized theorem can be used for structure identification and for the identification of generalized Wiener-Hammerstein systems.
|
|
| 3. |
- Enqvist, Martin, 1976-, et al.
(author)
-
LTI Approximations of Slightly Nonlinear Systems : Some Intriguing Examples
- 2005
-
Reports (other academic/artistic)abstract
- Approximations of slightly nonlinear systems with linear time-invariant (LTI) models are often used in applications. Here, LTI models that are optimal approximations in the mean-square error sense are studied. It is shown that these models can be very sensitive to small nonlinearities. Furthermore, the significance of the distribution of the input process is discussed. From the examples studied here, it seems that LTI approximations for inputs with distributions that are Gaussian or almost Gaussian are less sensitive to small nonlinearities.
|
|
| 4. |
- Fujimore, Atsushi, et al.
(author)
-
Model Identification of Linear Parameter Varying Aircraft Systems
- 2006
-
In: Proceedings of the Institution of Mechanical Engineers. Part G, Journal of Aerospace Engineering. - 0954-4100 .- 2041-3025. ; 220:4, s. 337-346
-
Journal article (peer-reviewed)abstract
- This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expressing the behaviours of the LPV aircraft system than the LTI model from the viewpoints of the time and the frequency responses. The SCDs of the initial polytopic model are adjusted so as to fit the model output to the output data obtained from the LPV aircraft system.
|
|
| 5. |
- Fujimori, Atsushi, et al.
(author)
-
Parameter Estimation of Polytopic Models for a Linear Parameter Varying Aircraft System
- 2005
-
Reports (other academic/artistic)abstract
- This paper presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system that has flight velocity as the varying parameter and stability and control derivatives (SCDs) as the model parameters. In an identification simulation, the polytopic model is more suitable for expressing the behaviors of the LPV aircraft system than the LTI model from the viewpoints of time and frequency responses. The SCDs of the initial polytopic model are adjusted to fit the model output to the output-data obtained from the LPV aircraft system.
|
|
| 6. |
- Fujimori, Atsushi, et al.
(author)
-
Parameter Estimation of Polytopic Models for a Linear Parameter Varying Aircraft System
- 2006
-
In: Transactions of the Japan Society for Aeronautical and Space Sciences. - : Japan Society for Aeronautical and Space Sciences. - 0549-3811. ; 49:165, s. 129-136
-
Journal article (peer-reviewed)abstract
- This paper presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system that has flight velocity as the varying parameter and stability and control derivatives (SCDs) as the model parameters. In an identification simulation, the polytopic model is more suitable for expressing the behaviors of the LPV aircraft system than the LTI model from the viewpoints of time and frequency responses. The SCDs of the initial polytopic model are adjusted to fit the model output to the output-data obtained from the LPV aircraft system.
|
|
| 7. |
- Gerdin, Markus, et al.
(author)
-
Global Identifiability of Complex Models, Constructed from Simple Submodels
- 2007
-
In: Modeling, Estimation and Control. - Linköping : Linköping University Electronic Press. - 9783540735694 ; , s. 123-133
-
Reports (other academic/artistic)abstract
- It is a typical situation in modern modeling that a total model is built up from simpler submodels, or modules, for example residing in a model library. The total model could be quite complex, while the modules are well understood and analysed. A procedure to decide global parameter identifiability for such a collection of model equations of differential-algebraic nature is suggested. It is shown how to make use of the natural modularization of the model structure. Basically, global identifiability is obtained if and only if each module is identifiable, and the connecting signals can be retrieved from the external signals, without knowledge of the values of the parameters.
|
|
| 8. |
- Gerdin, Markus, 1977-, et al.
(author)
-
On Parameter and State Estimation for Linear Differential-Algebraic Equations
- 2007
-
In: Automatica. - Linköping : Elsevier. - 0005-1098 .- 1873-2836. ; 43:3, s. 416-425
-
Journal article (peer-reviewed)abstract
- The current demand for more complex models has initiated a shift away from state-space models towards models described by differential-algebraic equations (DAEs). These models arise as the natural product of object-oriented modeling languages, such as Modelica. However, the mathematics of DAEs is somewhat more involved than the standard state-space theory. The aim of this work is to present a well-posed description of a linear stochastic differential-algebraic equation and more importantly explain how well-posed estimation problems can be formed. We will consider both the system identification problem and the state estimation problem. Besides providing the necessary theory we will also explain how the procedures can be implemented by means of efficient numerical methods.
|
|
| 9. |
- Gerdin, Markus, 1977-, et al.
(author)
-
Well-Posedness of Filtering Problems for Stochastic Linear DAE Models
- 2005
-
In: Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference. - Linköping : Linköping University Electronic Press. - 0780395670 ; , s. 350-355
-
Conference paper (peer-reviewed)abstract
- Modern modeling tools often give descriptor or DAE models, i.e., models consisting of a mixture of differential and algebraic relationships. The introduction of stochastic signals into such models in connection with filtering problems raises several questions of well-posedness. The main problem is that the system equations may contain hidden relationships affecting variables defined as white noise. The result might be that certain physical variables get infinite variance or contain formal differentiations of white noise. The paper gives conditions for well-posedness in terms of certain subspaces defined by the system matrices.
|
|
| 10. |
- Gillberg, Jonas, et al.
(author)
-
Frequency-Domain Identification of Continuous-Time ARMA Models from Non-Uniformly Sampled Data
- 2005
-
Reports (other academic/artistic)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 fromnon-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 sampled data. 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.
|
|
