Probabilistic robust design of control systems for high-fidelity cyber–physical testing

Sauder, Thomas; Marelli, Stefano; Sørensen, Asgeir J.

doi:10.1016/j.automatica.2018.11.040

Cited by 16 publications

(19 citation statements)

References 24 publications

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“…In ReaTHM testing, precisely applying the calculated loads onto the marine platform is particularly important to achieve high fidelity in replicating the non-substructured ocean structure's behaviour [8,30,31]. Although there are many performance measures for CDPR setups, these are in our opinion, not appropriate for ReaTHM testing-as this application requires a different set of priorities, as elaborated next.…”

Section: Motionsmentioning

confidence: 99%

Optimal Actuator Placement for Real-Time Hybrid Model Testing Using Cable-Driven Parallel Robots

Ueland

Sauder

Skjetne

2021

JMSE

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In real-time hybrid model testing, complex ocean structures are emulated by fusing numerical modelling with traditional hydrodynamic model testing. This is done by partitioning the ocean structure under consideration into a numerical and a physical substructure, coupled in real time via a measurement and control interface. The numerically computed load vector is applied to the physical substructure by means of multiple actuated winches so that the resulting experimental platform becomes a type of cable-driven parallel robot. In this context, the placement of the actuated winches is important to ensure that the loads can be accurately and robustly transferred to the physical substructure. This paper addresses this problem by proposing a performance measure and an associated actuator placement procedure that enables accurate force tracking and ensures that the numerically calculated loads can be actuated throughout the testing campaign. To clarify the application of the proposed procedure, it is applied to the design of a test setup for a moored barge. Overall, the paper represents a guideline for robust and beneficial actuator placement for real-time hybrid model testing using cable-driven parallel robots for load-actuation.

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Section: Motionsmentioning

confidence: 99%

Optimal Actuator Placement for Real-Time Hybrid Model Testing Using Cable-Driven Parallel Robots

Ueland

Sauder

Skjetne

2021

JMSE

Self Cite

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“…This paper gathers, in a concise and accessible way, the contributions of the author about this subject published in the PhD thesis [11], and in the journal articles [12] and [13]. These contributions are the following ones.…”

Section: Definitionmentioning

confidence: 99%

“…The surrogate model used in this step is a so-called Polynomial Chaos Kriging (PCK) model. Details regarding the structure of this model, the method to determin its coefficients based on E and the corresponding values of ϕ can be found in [20] and in [13]. The key aspect of this surrogate model is that it associates, to any θ , not only an estimated value of ϕ (denoted μ K (θ )) but also an uncertainty on this value (through a variance denoted σ 2 K ).…”

Section: Definitionmentioning

confidence: 99%

Fidelity of Cyber-Physical Empirical Methods

Sauder

2020

Exp Tech

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Cyber-physical empirical methods enable to address problems that classical empirical methods alone, or models alone, cannot address in a satisfactory way. In CPEMs, the substructures are interconnected through a control system that includes sensors and actuators, having their own dynamics. The present paper addresses how the fidelity of CPEMs, that is the degree to which they reproduce the behaviour of the real system under study, is affected by the presence of this control system. We describe an analysis method that enables the designer of a CPEM to (1) identify the artefacts (such as biases, noise, or delays) that play a significant role for the fidelity, (2) define bounds for the describing parameter of these artefacts ensuring high-fidelity of the CPEM, and (3) evaluate whether probabilistic robust fidelity is achieved. The proposed method is illustrated by considering a substructured slender structure subjected to dynamic loading.

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“…In Real Time Hybrid Substructure (RTHS) Testing, a dynamical system is analyzed by splitting it into a numerically simulated and an experimentally tested part. The substructures are coupled in real-time by a so-called transfer system that exchanges displacement/velocity and force information (flow and effort) between them [1]. The idea of Hybrid Substructuring was first proposed by Hakuno et al [2] (in Japanese, briefly summarized in English in [3]).…”

Section: Introductionmentioning

confidence: 99%

Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Insam

Rixen

2021

Exp Tech

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Real-Time Hybrid Substructure (RTHS) testing is a commonly used method to investigate the dynamical influence of a component on a mechanical system. In RTHS, a part of the dynamical system is tested experimentally, while the remaining structure is simulated numerically in a co-simulation. There are several error sources in the RTHS loop that distort the test outcome. To investigate the reliability of the test, the fidelity of the test must be quantified. In many engineering applications, however, there is no reference solution available to which the test outcome can be validated against. This work reviews currently existing accuracy measures used in RTHS. Furthermore, using Artificial Neural Networks (ANN) to predict the fidelity of the RTHS test outcome when no reference solution is available is proposed. Appropriate input features for the network, such as dynamic properties of the system and existing error indicators, are discussed. ANN training was performed on a data set from a virtual RTHS (vRTHS) simulation of a dynamical system with contact. The training process was successful, meaning that the correlation between the ANN prediction and the true fidelity value was > 99 %. Then, the network was applied to data of experimental RTHS tests of the same dynamical system and achieved a correlation of 98 %, which proves that the relation found by the ANN captured the relation between the chosen input features and the error measure. The application of the trained ANN to data from a linear vRTHS test revealed that further improvement of the network and the choice of input features is necessary. This work suggests that ANNs could be a meaningful tool to predict the fidelity of the RTHS test outcome in the absence of a reference solution, especially if more data from different RTHS tests were aggregated to train them.

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Probabilistic robust design of control systems for high-fidelity cyber–physical testing

Cited by 16 publications

References 24 publications

Optimal Actuator Placement for Real-Time Hybrid Model Testing Using Cable-Driven Parallel Robots

Optimal Actuator Placement for Real-Time Hybrid Model Testing Using Cable-Driven Parallel Robots

Fidelity of Cyber-Physical Empirical Methods

Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

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