Set‐membership methods applied to FDI and FTC

Combastel, Christophe; Puig, Vicenç; Raïssi, Tarek; Álamo, T.

doi:10.1002/acs.2660

Cited by 11 publications

(6 citation statements)

References 15 publications

(30 reference statements)

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“…Besides, most of these approaches require initial robot position to be known at least approximately, hence cannot solve the lost robot problem. We will develop a more robust and effective alternative, which is offered by set membership estimation and data fusion techniques that work in the bounded-error framework [5], [6]. These techniques can operate efficiently with sparse, asynchronous and heterogeneous measurements while being robust to the presence of non-consistent measurements, inaccuracy in environment modelling, and drift and inaccuracy in the robot evolution model.…”

Section: B Internet-based Indoors Localization Technologiesmentioning

confidence: 99%

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

Ramdani¹,

Panayides²,

Karamousadakis³

et al. 2019

2019 20th IEEE International Conference on Mobile Data Management (MDM)

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Hospitals are rightfully considered a field of indoor logistic robotics of high commercial potential. However, today, only a handful of mobile robotic solutions for hospital logistics exist that have failed to trigger widespread acceptance by the market. This is because existing systems require costly infrastructure installation, they do not easily integrate to corporate IT solutions, are not adequately shielded from cybersecurity threats, and as a result, they do not fully automate procedures and traceability of the items they carry. Moreover, existing systems are limited on scope, focusing only on delivery services, and hence do not provide any other type of support to the medical and nursing staff. ENDORSE system will address the aforementioned technical challenges and functional limitations by pursuing four innovation pillars: (i) infrastructure-less multirobot indoor navigation; (ii) advanced Human-Robot Interaction (HRI) for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; (iii) deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions, complying with GDPR data security requirements; (iv) reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring on a fleet of mobile robots, facilitating connectivity to cloud-based Electronic Health Records (EHR), and validated in an operational hospital environment for realistic assessment.

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Section: B Internet-based Indoors Localization Technologiesmentioning

confidence: 99%

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

Ramdani¹,

Panayides²,

Karamousadakis³

et al. 2019

2019 20th IEEE International Conference on Mobile Data Management (MDM)

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“…induced by faulty time synchronization within one beacon, the 6 hyperbola are no longer consistent, i.e., the set (7) becomes empty. To provide robustness w.r.t such a case, one uses the q−relaxed intersection as in (7) to compute the solution set consistent with all but q data. Because TDoA computes time differences, the presence of synchronization errors in one beacon will impact the three TDoA where the beacon is involved.…”

Section: Multilateration With Interval Datamentioning

confidence: 99%

“…Such description of the errors gathers both systematic and probabilistic errors, provided that the support domain of the probability distribution is bounded (see e.g. [7], and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Towards Robust Methods for Indoor Localization using Interval Data

Ramdani

Zeinalipour-Yazti

Karamousadakis

et al. 2019

2019 20th IEEE International Conference on Mobile Data Management (MDM)

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Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than stateof-the-art methods.

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“…In the passive robust FDI methods (mainly the set-based approaches), the effects of uncertain factors are propagated to the residuals by considering theirs bounds. There exist three types of set-based approaches, which use set invariance theory [14]- [16], interval observers [17], [18], and set-membership estimation [19]- [21], respectively. By propagating the effects of uncertain factors into the residuals, they generate fixed (invariant set-based approaches) or adaptive (interval observerbased approaches and set-membership estimation approaches) thresholds for the residuals.…”

Section: Introductionmentioning

confidence: 99%

Mixed Active/Passive Robust Fault Detection and Isolation Using Set-Theoretic Unknown Input Observers

Tan

Wang

et al. 2018

IEEE Trans. Automat. Sci. Eng.

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This paper proposes a robust fault detection and isolation (FDI) approach that combines active and passive robust FDI approaches. Standard active FDI approaches obtain robustness by using the unknown input observer (UIO) to decouple unknown inputs from residuals. Differently, standard passive FDI approaches achieve robustness by using the set theory to bound the effect of uncertain factors (disturbances and noises). In this paper, we combine the UIO-based and the set-based approaches to produce a mixed robust FDI, which can mitigate the disadvantages and exert the advantages of the two robust FDI approaches. In order to emphasize the role of set theory, the UIO design based on the set theory is named as the set-theoretic UIO (SUIO). A quadrotor subsystem is used to illustrate the effectiveness of the proposed FDI approach.Note to Practitioners-The proposed SUIO-based approach achieves robustness in FD against disturbances, noises, and modeling uncertainties (e.g., linearization errors) either by decoupling or by bounding their effect on the residuals. Moreover, this approach allows FI by generating a set of structured residuals decoupling the effect of faults. The authors think that this method can be used for processes that operate around equilibrium points. In this case, we can linearize the nonlinear systems into linear systems with bounded unknown inputs and then apply the proposed method. Moreover, an important feature of this method is that it can overcome the design condition of the standard UIO approach, which means that it could be applied to a larger number of applications.Index Terms-Active and passive decoupling, robust fault detection and isolation (FDI), set theory, unknown input observer (UIO).

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Set‐membership methods applied to FDI and FTC

Cited by 11 publications

References 15 publications

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

Towards Robust Methods for Indoor Localization using Interval Data

Mixed Active/Passive Robust Fault Detection and Isolation Using Set-Theoretic Unknown Input Observers

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