On Identification of Parallel Cascade Serial Systems

Hägg, Per; Wahlberg, Bo

doi:10.3182/20110828-6-it-1002.02068

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…Thus the least amount of power in the input is needed if the two systems are equal. In system identification of structured systems it is recognized that it could be hard to identify subsystems which are equal, see [10], [11]. Here we see that in this particular scenario this is not the case.…”

Section: A Minimum Phase Systemmentioning

confidence: 65%

On optimal input design for feed-forward control

Hägg

Wahlberg

2013

52nd IEEE Conference on Decision and Control

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Abstract-This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are analytically solved and fundamental properties of the optimal input signal solution are presented. The optimal input signal contains feed-forward control and depends of the noise model and transfer function of the system in a specific way. Next, we show how to apply the partial correlation approach to closed loop optimal experiment design to the general feed-forward problem. A framework for optimal input signal design for feedforward control is presented and numerically evaluated on a temperature control problem.

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Section: A Minimum Phase Systemmentioning

confidence: 65%

On optimal input design for feed-forward control

Hägg

Wahlberg

2013

52nd IEEE Conference on Decision and Control

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“…The first is the parallel serial (cascade) structure considered in [Hägg et al, 2011], see Figure 1. Structure 1:…”

Section: Problem Formulationmentioning

confidence: 99%

“…The aim of this paper is to quantify the improvement in two specific structures. The first structure has its origin in boiler control, and has been considered in the paper [Hägg et al, 2011]; key results concern the case when there is common dynamics in the subsystems. The structure also appears, as a special case, when estimating a subsystem in a dynamic network, using the two-stage method [Van den Hof et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

Variance Results for Parallel Cascade Serial Systems

Everitt

Rojas

Hjalmarsson

2014

IFAC Proceedings Volumes

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Modelling dynamic networks is important in different fields of science. At present, little is known about how different inputs and sensors contribute to the statistical properties concerning an estimate of a specific dynamic system in a network. We consider two forms of parallel serial structures, one multiple-input-multiple-output structure and one single-inputmultiple-output structure. The quality of the estimated models is analysed by means of the asymptotic covariance matrix, with respect to input signal characteristics, noise characteristics, sensor locations and previous knowledge about the remaining systems in the network. It is shown that an additive property applies to the information matrix for the considered structures. The impact of input signal selection, sensor locations and incorporation of previous knowledge is illustrated by simple examples.

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“…Thus, several contributions have recently been provided in this area, e.g., Dankers et al (2014), Everitt et al (2014), Van den Hof et al (2013), Dankers et al (2013), Everitt et al (2013), Van den Hof et al (2012), Hägg et al (2011), Wahlberg and Sandberg (2008), .…”

Section: Introductionmentioning

confidence: 99%

A Least Squares Method for Identification of Feedback Cascade Systems**This work was supported by the European Research Council under the advanced grant LEARN, contract 267381 and by the Swedish Research Council under contract 621-2009-4017.

2015

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The problem of identification of systems in dynamic networks is considered. Although the prediction error method (PEM) can be applied to the overall system, the non-standard model structure requires solving a non-convex optimization problem. Alternative methods have been proposed, such as instrumental variables and indirect PEM. In this paper, we first consider acyclic cascade systems, and argue that these methods have different ranges of applicability. Then, for a network with feedback connection, we propose an approach to deal with the fact that indirect PEM yields a non-convex problem in that case. A numerical simulation may indicate that this approach is competitive with other existing methods.

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On Identification of Parallel Cascade Serial Systems

Cited by 9 publications

References 9 publications

On optimal input design for feed-forward control

On optimal input design for feed-forward control

Variance Results for Parallel Cascade Serial Systems

A Least Squares Method for Identification of Feedback Cascade Systems**This work was supported by the European Research Council under the advanced grant LEARN, contract 267381 and by the Swedish Research Council under contract 621-2009-4017.

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