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Pillonetto, G., Schenato, L. & Varagnolo, D. (2019). Distributed multi-agent Gaussian regression via finite-dimensional approximations. IEEE Transaction on Pattern Analysis and Machine Intelligence, 41(9), 2098-2111
Open this publication in new window or tab >>Distributed multi-agent Gaussian regression via finite-dimensional approximations
2019 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 41, no 9, p. 2098-2111Article in journal (Refereed) Published
Abstract [en]

We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather M noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the M input locations and measurements and then invert an M×M matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first E orthonormal eigenfunctions obtained from the Karhunen-Loève (KL) expansion of the chosen kernel, where typically E≪M. The benefits are that the computation and communication complexities scale with E and not with M, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Gaussian processes, sensor networks, distributed estimation, kernel-based regularization, nonparametric estimation, average consensus
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-69896 (URN)10.1109/TPAMI.2018.2836422 (DOI)000480343900005 ()2-s2.0-85048622267 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-08-28 (johcin)

Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2019-08-28Bibliographically approved
Bof, N., Carli, R., Notarstefano, G., Schenato, L. & Varagnolo, D. (2019). Multiagent Newton–Raphson Optimization Over Lossy Networks. IEEE Transactions on Automatic Control, 64(7), 2983-2990
Open this publication in new window or tab >>Multiagent Newton–Raphson Optimization Over Lossy Networks
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2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 7, p. 2983-2990Article in journal (Refereed) Published
Abstract [en]

In this work, we study the problem of unconstrained convex optimization in a fully distributed multiagent setting, which includes asynchronous computation and lossy communication. In particular, we extend a recently proposed algorithm named Newton-Raphson consensus by integrating it with a broadcast-based average consensus algorithm, which is robust to packet losses. We show via the separation of time-scale principle that under mild conditions (i.e., persistency of the agents activation and bounded consecutive communication failures), the proposed algorithm is provably locally exponentially stable with respect to the optimal global solution. Finally, we complement the theoretical analysis with numerical simulations and comparisons based on real datasets.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Distributed algorithms, optimization, packet loss, asynchronous communication, wireless networks
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-75543 (URN)10.1109/TAC.2018.2874748 (DOI)000473489700031 ()2-s2.0-85054493313 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-08-15 (johcin)

Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-15Bibliographically approved
Guay, M. & Atta, K. (2018). An extremum-seeking control observer design technique for nonlinear systems (ed.). In: (Ed.), 2017 IEEE 56th Conference on Decision and Control, CDC: . Paper presented at 56th IEEE Conference on Decision and Control, CDC 2017, Melbourne, VIC, Australia , 12-15 December 2017. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>An extremum-seeking control observer design technique for nonlinear systems
2018 (English)In: 2017 IEEE 56th Conference on Decision and Control, CDC, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper, Published paper (Refereed)
Abstract [en]

In this study, we propose an extremum-seeking control approach for the design on nonlinear observers for a general class of detectable nonlinear systems. The extremum-seeking control approach provides a mechanism to compute an observer gain that minimizes the squared output error. The technique extends and generalizes an earlier extremum seeking control observer design approach that was limited to systems in observer normal form with a linear state to output map. The analysis provides conditions for the application of the technique and establishes a semi-global practical stability property of the error dynamics. Two simulation examples are presented to demonstrate the effectiveness of the technique

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE Conference on Decision and Control, E-ISSN 0743-1546
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-67650 (URN)10.1109/CDC.2017.8263960 (DOI)000424696902012 ()2-s2.0-85046282748 (Scopus ID)978-1-5090-2873-3 (ISBN)978-1-5090-2874-0 (ISBN)
Conference
56th IEEE Conference on Decision and Control, CDC 2017, Melbourne, VIC, Australia , 12-15 December 2017
Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-05-14Bibliographically approved
Ebadat, A., Varagnolo, D., Bottegal, G., Wahlberg, B. & Johansson, K. H. (2018). Application-oriented input design for room occupancy estimation algorithms (ed.). In: (Ed.), 2017 IEEE 56th Conference on Decision and Control, CDC: . Paper presented at 56th IEEE Conference on Decision and Control, CDC 2017, Melbourne, VIC, Australia , 12-15 December 2017 (pp. 3417-3424). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Application-oriented input design for room occupancy estimation algorithms
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2018 (English)In: 2017 IEEE 56th Conference on Decision and Control, CDC, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3417-3424Conference paper, Published paper (Refereed)
Abstract [en]

We consider the problem of occupancy estimation in buildings using available environmental information. In particular, we study the problem of how to collect data that is informative enough for occupancy estimation purposes. We thus propose an application-oriented input design approach for designing the ventilation signal to be used while collecting the system identification datasets. The main goal of the method is to guarantee a certain accuracy in the estimated occupancy levels while minimizing the experimental time and effort. To take into account potential limitations on the actuation signals we moreover formulate the problem as a recursive nonlinear and nonconvex optimization problem, solved then using exhaustive search methods. We finally corroborate the theoretical findings with some numerical examples, which results show that computing ventilation signals using experiment design concepts leads to occupancy estimator performing 4 times better in terms of Mean Square Error (MSE).

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE Conference on Decision and Control, E-ISSN 0743-1546
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-67654 (URN)10.1109/CDC.2017.8264159 (DOI)000424696903050 ()2-s2.0-85046132323 (Scopus ID)978-1-5090-2873-3 (ISBN)978-1-5090-2874-0 (ISBN)
Conference
56th IEEE Conference on Decision and Control, CDC 2017, Melbourne, VIC, Australia , 12-15 December 2017
Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2019-05-06Bibliographically approved
Alhashimi, A., Del Favero, S., Varagnolo, D., Gustafsson, T. & Pillonetto, G. (2018). Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures. In: 2018 European Control Conference (ECC): . Paper presented at European Control Conference, Cyprus, Limasson, 12-15 June, 2018 (pp. 2447-2453). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures
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2018 (English)In: 2018 European Control Conference (ECC), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 2447-2453Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72550 (URN)10.23919/ECC.2018.8550201 (DOI)000467725302079 ()2-s2.0-85059819837 (Scopus ID)978-3-9524-2698-2 (ISBN)978-1-5386-5303-6 (ISBN)
Conference
European Control Conference, Cyprus, Limasson, 12-15 June, 2018
Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-06-18
Knorn, S., Varagnolo, D., Oliver-Chiva, E., Melles, R. & Dewitte, M. (2018). Data-driven modelling of pelvic floor muscles dynamics. IFAC-PapersOnLine, 51(27), 321-326
Open this publication in new window or tab >>Data-driven modelling of pelvic floor muscles dynamics
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2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 27, p. 321-326Article in journal (Refereed) Published
Abstract [en]

This paper proposes individualized, dynamical and data-driven models that describe pelvic floor muscle responses in women that use vaginal dilation. Specifically, the models describe how the volume of an inflatable balloon inserted at the vaginal introitus dynamically affects the aggregated pressure exerted by the pelvic floor muscles of the person. The paper inspects the approximation capabilities of different model structures, such as Hammerstein-Wiener and NARX, for this specific application, and finds the specific model structures and orders that best describe the recorded measurement data. Hence, although the current dataset is drawn from a sample of healthy volunteers, this paper is an initial step towards better understanding women’s responses to vaginal dilation and facilitating individualised medical vaginal dilation treatment.

Keywords
female sexual dysfunction, black box, nonlinear models, system identification
National Category
Control Engineering
Research subject
Control Engineering; Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72983 (URN)10.1016/j.ifacol.2018.11.621 (DOI)
Note

Konferensartikel i tidskrift

Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-02-21Bibliographically approved
Varagnolo, D., Knorn, S., Oliver-Chiva, E., Melles, R. & Dewitte, M. (2018). Data-driven modelling of subjective pain/pleasure assessments as responses to vaginal dilation stimuli. IEEE Control Systems Letters, 2(3), 423-428
Open this publication in new window or tab >>Data-driven modelling of subjective pain/pleasure assessments as responses to vaginal dilation stimuli
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2018 (English)In: IEEE Control Systems Letters, E-ISSN 2475-1456, Vol. 2, no 3, p. 423-428Article in journal (Refereed) Published
Abstract [en]

Women affected by pain during penetrative sexual intercourse are often treated using fixed-size vaginal dilators that are regularly perceived as uncomfortable and leading to premature treatment drop-outs. These dilators could be improved by making them adaptive, i.e., able to exert dynamically different pressures on the vaginal duct to simultaneously guarantee comfort levels and achieve the medical dilation objectives. Implementing feedback control would then benefit from models that connect the patients’ comfort levels with their experienced physiological stimuli. Here we address the problem of data-driven quantitative modelling of pain/pleasure self-assessments obtained through medical trials. More precisely, we consider time-series records of Pelvic Floor Muscles (PFM) pressure, vaginal dilation, and pain/pleasure evaluations, and model the relations among these quantities using statistical analysis tools. Besides this, we also address the important issue of the individualization of these models: different persons may respond differently, but these variations may sometimes be so small that it may be beneficial to learn from several individuals simultaneously. We here numerically validate the previous claim by verifying that clustering patients in groups may lead, from a data-driven point of view, to models with a significantly improved statistical performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-69289 (URN)10.1109/LCSYS.2018.2841186 (DOI)
Available from: 2018-06-11 Created: 2018-06-11 Last updated: 2018-06-13Bibliographically approved
Varagnolo, D. (2018). Detecting and modelling air flow overprovisioning / underprovisioning in air-cooled datacenters. In: IECON 2018: 44th Annual Conference of the IEEE Industrial Electronics Society. Paper presented at 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018, October 21-23 2018, Washington D.C., USA..
Open this publication in new window or tab >>Detecting and modelling air flow overprovisioning / underprovisioning in air-cooled datacenters
2018 (English)In: IECON 2018: 44th Annual Conference of the IEEE Industrial Electronics Society, 2018Conference paper, Published paper (Refereed)
Identifiers
urn:nbn:se:ltu:diva-73022 (URN)10.1109/IECON.2018.8592930 (DOI)2-s2.0-85061557374 (Scopus ID)
Conference
44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018, October 21-23 2018, Washington D.C., USA.
Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-02-26
Fjällström, E., Knorn, S., Staffas, K. & Varagnolo, D. (2018). Developing Concept Inventory Tests for Electrical Engineering: Extractable Information, Early Results, and Learned Lessons. In: 2018 UKACC 12th International Conference on Control (CONTROL): . Paper presented at 12th UKACC International Conference on Control, Sheffield, UK, 5-7 September 2018 (pp. 436-441). , Article ID 8516766.
Open this publication in new window or tab >>Developing Concept Inventory Tests for Electrical Engineering: Extractable Information, Early Results, and Learned Lessons
2018 (English)In: 2018 UKACC 12th International Conference on Control (CONTROL), 2018, p. 436-441, article id 8516766Conference paper, Published paper (Refereed)
Abstract [en]

This paper suggests a method for developing, implementing and assessing a concept inventory test for electrical engineering students (CITE). The aim of this test is to help students better understand and learn core concepts, plus increase their awareness about links between the different courses and other themes of the program. Our and other experiences show that students often struggle to understand and use fundamental concepts, and how these relate to the various courses. This issue is probably due to the fact that traditional exams mainly focus on assessing procedural tasks (e.g., directly solving specific problems following step-by-step approaches). The investigated programs at Uppsala University (UU) and Luleå Uni-versity of Technology (LTU), nonetheless, have no tool for collecting quantitative data on how students develop conceptual knowledge throughout the programs, and thus no means to obtain an holistic view about their learning process. The here proposed methodology thus describes how to develop tests that would not only provide students with valuable feedback on their progression, but also equip teachers and program boards with high-end data for pedagogical and course development purposes. Besides illustrating the developmental methodology, the paper includes reactions and remarks from students on what the tests would provide and what would motivate them to take it.

Keywords
Concept inventory tests, progression, Courses-concept matrix (CCM), deep learning
National Category
Control Engineering Didactics
Research subject
English and Education; Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72222 (URN)10.1109/CONTROL.2018.8516766 (DOI)000454605000095 ()2-s2.0-85056874958 (Scopus ID)978-1-5386-2864-5 (ISBN)
Conference
12th UKACC International Conference on Control, Sheffield, UK, 5-7 September 2018
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2019-01-29Bibliographically approved
Modolo, V., Varagnolo, D. & Carli, L. (2018). Distributed formation control using robust asynchronous and broadcast-based optimization schemes. IFAC-PapersOnLine, 51(23), 385-390
Open this publication in new window or tab >>Distributed formation control using robust asynchronous and broadcast-based optimization schemes
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 23, p. 385-390Article in journal (Refereed) Published
Abstract [en]

We consider the problem of letting a network of mobile agents distributedly track and maintain a formation while using communication schemes that are asynchronous, broadcasts based, and prone to packet losses. To this purpose we revisit and modify an existing distributed optimization algorithm that corresponds to a distributed version of the Newton Raphson (NR) algorithm. The proposed scheme uses then robust asynchronous ratio consensus algorithms as building blocks, and employs opportune definitions for the local cost functions to achieve the desired coordination objective. In our algorithm, indeed, we code the position of the to-be-followed target as the minimum of a shared global cost, and capture the desired inter-robots behaviors through dedicated distances-based potential barriers. We then check the effectiveness of the strategy using field tests, and verify that the scheme achieves the desired goal of introducing robustness to changes in the agents positions due to unexpected disturbances. More precisely, if an agent breaks the formation, then the update mechanism embedded in our scheme make that agent move back to a meaningful position as soon as some packets are successfully received by the misplaced agent. 

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Newton Raphson Consensus, packet losses, target tracking
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72431 (URN)10.1016/j.ifacol.2018.12.066 (DOI)2-s2.0-85058443288 (Scopus ID)
Note

Konferensartikel i tidskrift

Available from: 2019-01-03 Created: 2019-01-03 Last updated: 2019-04-23Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4310-7938

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