| 1. |
- Agüero, Juan C., et al.
(författare)
-
Accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation
- 2012
-
Ingår i: Automatica. - 0005-1098. ; 48:4, s. 632-637
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study the accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation. We present a frequency-domain representation for the information matrix for general linear MIMO models. We show that the variance of estimated parametric models for linear MIMO systems satisfies a fundamental integral trade-off. This trade-off is expressed as a multivariable 'water-bed' effect. An extension to spectral estimation is also discussed.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Alberer, Daniel, et al.
(författare)
-
System Identification for Automotive Systems : Opportunities and Challenges
- 2012
-
Ingår i: Identification for Automotive Systems. - Springer London. - 978-144712220-3 ; s. 1-10
-
Bokkapitel (övrigt vetenskapligt)abstract
- Without control many essential targets of the automotive industry could not be achieved. As control relies directly or indirectly on models and model quality directly influences the control performance, especially in feedforward structures as widely used in the automotive world, good models are needed. Good first principle models would be the first choice, and their determination is frequently difficult or even impossible. Against this background methods and tools developed by the system identification community could be used to obtain fast and reliably models, but a large gap seems to exist: neither these methods are sufficiently well known in the automotive community, nor enough attention is paid by the system identification community to the needs of the automotive industry. This introduction summarizes the state of the art and highlights possible critical issues for a future cooperation as they arose from an ACCM Workshop on Identification for Automotive Systems recently held in Linz, Austria.
|
|
| 6. |
- Auvert, Marine, et al.
(författare)
-
On Router Control for Congestion Avoidance
- 2002
-
Konferensbidrag (refereegranskat)abstract
- This short paper deals with active queue management for computer networks. The goal is to develop control mechanisms for routers in heterogeneous networks that reduce traffic fluctuations. The proposed control strategy operates with local information (such as estimated arrival rates) and actively use the buffers to smooth traffic, and thus it avoids the buildup and propagation of traffic bursts.
|
|
| 7. |
|
|
| 8. |
- Barenthin, Märta, et al.
(författare)
-
Applications of mixed H2 and H-infinity H´input design in identification
- 2005
-
Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - Prague. ; s. 458-463
-
Konferensbidrag (refereegranskat)abstract
- The objective of this contribution is to quantify benefits of optimal input design compared to the use of standard identification input signals, e.g. PRBS signals for some common, and important, application areas of system identification. Two benchmark problems taken from process control and control of flexible mechanical structures are considered. We present results both when the design is based on knowledge of the true system (in general the optimal design depends on the system itself) and for a practical two step procedure when an initial model estimate is used in the design instead of the true system. The results show that there is a substantial reduction in experiment time and input excitation level. A discussion on the sensitivity of the optimal input design to model estimates is provided.
|
|
| 9. |
- Barenthin, Märta, et al.
(författare)
-
Data-driven methods for L2-gain estimation
- 2009
-
Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - Saint-Malo. ; s. 1597-1602
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present and discuss some data-driven methods for estimation of the L2-gain of dynamical systems. Partial results on convergence and statistical properties are provided. The methods are based on multiple experiments on the system. The main idea is to directly estimate the maximizing input signal by using iterative experiments on the true system. We study such a data-driven method based on a stochastic gradient method. We show that this method is very closely related to the so-called power iteration method based on the power method in numerical analysis. Furthermore, it is shown that this method is applicable for linear systems with noisy measurements. We will also study L2-gain estimation of Hammerstein systems. The stochastic gradient method and the power iteration method are evaluated and compared in simulation examples. © 2009 IFAC.
|
|
| 10. |
- Barenthin, Märta, et al.
(författare)
-
Gain Estimation for Hammerstein Systems
- 2006
-
Ingår i: Proceedings of the 14th IFAC Symposium on System Identification. - 978-3-902661-02-9 ; s. 784-789
-
Konferensbidrag (refereegranskat)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 forthe unmodelled nonlinear part of the Hammerstein system. The approaches are illustrated by numerical examples.
|
|
