Sensor-Only Noncausal Blind Identification of Pseudo Transfer Functions

D’Amato, Anthony; Brzezinski, Adam J.; Holzel, Matthew S.; Ni, Jun; Bernstein, Dennis S.

doi:10.3182/20090706-3-fr-2004.00282

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…In many applications of system identification, the system is driven by external signals that are not measured. In this situation, blind identification techniques are used to obtain useful estimates of the system dynamics 7 . Since the input signal is unknown, its statistical properties are usually assumed to be known in order to compensate for lack of knowledge of its time history.…”

Section: B Sensor-only Noncausal Blind Identification (Sonbi) -Intermentioning

confidence: 99%

Structural Health Determination and Model Refinement for a Deployable Composite Boom

D’Amato

Arritt

Banik

et al. 2009

50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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The Air Force Research Laboratory Space Vehicles Directorate previously developed a novel composite boom that enables simplified on-orbit deployment for a class of space structures. These composite members are self deploying, reducing the need for hinge and complex motor mechanisms and resulting in decreased weight and structural complexity. Due to its unique capabilities, NASA chose to incorporate this boom architecture into their Nano-Sail D experiment. This composite boom technology was also chosen as the candidate for an investigation into structural health monitoring (SHM) for space structures. Generally, health monitoring has been used on civil and aeroelastic structures for maintenance applications. It is proposed that SHM concepts can be used on space structures to determine health and indirectly predict changes in structural dynamics, which may be crucial for high precision pointing, maneuvering, and life-prediction applications. To begin investigating this topic, a testbed, capable of cyclically damaging the composite booms with a high degree of repeatability, is constructed. As the composite booms are progressively damaged, a series of dynamic interrogations are used to assess the boom. The goal of this research is to correlate SHM features with dynamic properties, leading to an ability to determine a component's dynamic characteristics purely from SHM data. Using data gathered for SHM testing, the concept of adaptively updating structural models is demonstrated.

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Section: B Sensor-only Noncausal Blind Identification (Sonbi) -Intermentioning

confidence: 99%

Structural Health Determination and Model Refinement for a Deployable Composite Boom

D’Amato

Arritt

Banik

et al. 2009

50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…Some measurements are used as output while other measurements are used as input to what is called a pseudo transfer function. Unfortunately, dynamics that is shared, in the transfer functions from the unknown input to the measurements, will cancel, but more importantly, so does the unknown input (Aljanaideh, Ali, Holzel, Kukreja, & Bernstein, 2012;D'Amato, Brzezinski, Holzel, Ni, & Bernstein, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Note that the framework of this paper allows τ O and τ D to be correlated and that extra consideration have to be taken in this case.Söderström (2007) gives a survey for EIV problems andMahata and Garnier (2006) deals with one method of estimating the model in a continuous-time EIV problem. (c) Finally, if n I = dim(u) and hence, that there are only indirectly measured inputs, then the resulting model is a sensor-to-sensor system identification problem (S2SID) and the resulting transfer function is called a pseudo transfer function(D'Amato et al, 2009). Note that the principal structure of the S2SID problem shown inFigure 2(c)is misleadingly similar to the EIV problem shown inFigure 2(b).…”

mentioning

confidence: 99%

Identification of systems with unknown inputs using indirect input measurements

Linder

Enqvist

2016

International Journal of Control

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A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modeling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modeling framework it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework.

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“…For example, because the transfer function between sensors does not arise as the forced response of a state space model, a sensor-to-sensor transfer function is not a transfer function in the usual sense. Therefore, we adopt the terminology pseudo-transfer function (PTF) and transmissibility operator to refer to a dynamic model relating sensor signals, which are called the pseudo-input and pseudo-output [15][16][17]. To use a PTF identified under healthy conditions for fault detection, it is necessary to show that the identified PTF is independent of both the initial condition and the input to the system.…”

Section: Introductionmentioning

confidence: 99%

“…Generically, the poles of the plant play no role in the numerator and denominator of a PTF, whose roots are the zeros of the original transfer functions. These issues are discussed in [15] and applied to structural vibration in [16,17].…”

Section: Introductionmentioning

confidence: 99%