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Ramírez-Chavarría, R. G., Müller, M. I., Mattila, R., Quintana-Carapia, G. & Sánchez-Pérez, C. (2019). A framework for high-resolution frequency response measurement and parameter estimation in microscale impedance applications. Measurement, 148, Article ID 106913.
Open this publication in new window or tab >>A framework for high-resolution frequency response measurement and parameter estimation in microscale impedance applications
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2019 (English)In: Measurement, ISSN 0263-2241, Vol. 148, article id 106913Article in journal (Refereed) Published
Abstract [en]

Electrical impedance spectroscopy (EIS) is a tool for characterizing the electrical behavior of matter. Nevertheless, most of the work is focused on purely experimental results, leading aside alternative measurement and estimation techniques. In this paper, we introduce a framework for spectral measurements and parameter estimation applied to EIS. There are two methods in the proposal running independently: frequency response function based non-parametric estimation, and parametric recursive estimation. The former provides consistent estimates even in the presence of noise and works with batches of data. Whilst the latter gives consistent parametric estimates under the right model structure. The proposed platform is designed around a reconfigurable device, which comprises minimal hardware design and digital signal processing. We test the system with a multisine signal by measuring calibration circuits and colloidal samples at microscale. Results show that this method outperforms the state-of-the-art techniques for impedance measurement applications, exhibiting low uncertainty and physical interpretation.

National Category
Bioengineering Equipment
Identifiers
urn:nbn:se:kth:diva-256301 (URN)10.1016/j.measurement.2019.106913 (DOI)000487930000027 ()2-s2.0-85070930515 (Scopus ID)
Note

QC 20190820

Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-10-28Bibliographically approved
Rojas, C. R. & Müller, M. I. (2019). Algorithms for data-driven H∞-norm estimation. In: Carlo Novara and Simone Formentin (Ed.), DATA-DRIVEN FILTER AND CONTROL DESIGN: Methods and applications (pp. 145-163). IET Digital Library
Open this publication in new window or tab >>Algorithms for data-driven H-norm estimation
2019 (English)In: DATA-DRIVEN FILTER AND CONTROL DESIGN: Methods and applications / [ed] Carlo Novara and Simone Formentin, IET Digital Library, 2019, p. 145-163Chapter in book (Refereed)
Abstract [en]

In this chapter, the problem of estimating in a model-free manner the H norm of a linear dynamic system is discussed at a tutorial level. Two recently developed methods for addressing this problem are presented, namely the power iterations method and a class of multi-armed bandit (MAB) algorithms. Due to reasons of space, many details are omitted, but references are provided to complement this exposition.

Place, publisher, year, edition, pages
IET Digital Library, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-257879 (URN)10.1049/PBCE123E (DOI)9781785617126 (ISBN)
Note

QC 20190909

Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2019-09-09Bibliographically approved
Müller, M. I. & Rojas, C. R. (2019). Gain estimation of linear dynamical systems using Thompson Sampling. In: Kamalika Chaudhuri, Masashi Sugiyama (Ed.), Proceedings of Machine Learning Research: . Paper presented at The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) (pp. 1535-1543). , 89
Open this publication in new window or tab >>Gain estimation of linear dynamical systems using Thompson Sampling
2019 (English)In: Proceedings of Machine Learning Research / [ed] Kamalika Chaudhuri, Masashi Sugiyama, 2019, Vol. 89, p. 1535-1543Conference paper, Published paper (Refereed)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-256045 (URN)
Conference
The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)
Note

QC 20190820

Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2019-08-20Bibliographically approved
Müller, M. I. & Rojas, C. R. (2019). Risk-Coherent H∞-optimal Filter Design Under Model Uncertainty with Applications to MISO Control. In: 2019 18th European Control Conference, ECC 2019: . Paper presented at 18th European Control Conference, ECC 2019; Naples; Italy; 25 June 2019 through 28 June 2019 (pp. 1461-1466). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8795947.
Open this publication in new window or tab >>Risk-Coherent H-optimal Filter Design Under Model Uncertainty with Applications to MISO Control
2019 (English)In: 2019 18th European Control Conference, ECC 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1461-1466, article id 8795947Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a framework to address the problem of designing discrete-time LTI (linear and time-invariant) multiple-input and multiple-output (MIMO) filters, aiming to optimize the performance of a system when model uncertainty is considered. Additionally, we present an interesting application to control design for disturbance rejection under model uncertainty. To account for this uncertainty we employ coherent measures of risk, which are a family of measures in theory of risk. We particularly discuss which measures are suitable by comparing the conditional value-at-risk (CVaR) to other three common designs. Using a scenario approach, we derive a convex optimization problem based on linear matrix inequalities (LMIs), whose solution minimizes the risk of falling into poor mathcal{H}{ infty} performance. Finally, we present an application to multiple-input and single-output (MISO) control design under model uncertainty in the auto-covariance function of the output noise, comparing approaches minimizing different notions of risk.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-256077 (URN)10.23919/ECC.2019.8795947 (DOI)000490488301079 ()2-s2.0-85071597588 (Scopus ID)9783907144008 (ISBN)
Conference
18th European Control Conference, ECC 2019; Naples; Italy; 25 June 2019 through 28 June 2019
Note

QC 20190827

Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-11-14Bibliographically approved
Müller, M. I. & Rojas, C. R. (2018). A Markov Chain Approach to Compute the ℓ2-gain of Nonlinear Systems. In: : . Paper presented at 18th IFAC Symposium on System Identification (pp. 84-89). Elsevier B.V., 51(15)
Open this publication in new window or tab >>A Markov Chain Approach to Compute the ℓ2-gain of Nonlinear Systems
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this work the problem of computing the maximum gain of non-linear systems, also known as its ℓ2-gain, from input-output data is studied. From an input design perspective, this problem reduces to find an optimal input sequence, of bounded norm, maximizing the norm gain of the output, where our target estimation corresponds to the ratio of these quantities. The novelty of this approach lies on the fact that the input signal is a realization of a stationary process with finite memory whose range is a finite set of values. Based on recent developents on input design for nonlinear systems, our approach leads to a linear program whose optimal cost gives an approximation of the ℓ2-gain of the system. An illustrative example shows how well the algorithm performs compared to other methods approximating this quantity.

Place, publisher, year, edition, pages
Elsevier B.V., 2018
Keywords
excitation design, identification for control, Input, non-linear system identification, ℓ2-gain, Linear programming, Linear systems, Markov processes, Input-output data, Linear programs, Markov chain approaches, Stationary process, Target estimations, Nonlinear systems
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-247492 (URN)10.1016/j.ifacol.2018.09.095 (DOI)2-s2.0-85054461369 (Scopus ID)
Conference
18th IFAC Symposium on System Identification
Note

QC 20190418

Available from: 2019-04-18 Created: 2019-04-18 Last updated: 2019-08-27Bibliographically approved
Müller, M. I., Milosevic, J., Sandberg, H. & Rojas, C. R. (2018). A Risk-Theoretical Approach to H2-Optimal Control under Covert Attacks. In: 57th IEEE Conference on Decision and Control: . Paper presented at 57th IEEE Conference on Decision and Control (pp. 4553-4558). IEEE
Open this publication in new window or tab >>A Risk-Theoretical Approach to H2-Optimal Control under Covert Attacks
2018 (English)In: 57th IEEE Conference on Decision and Control, IEEE , 2018, p. 4553-4558Conference paper, Published paper (Refereed)
Abstract [en]

We consider the control design problem of optimizing the H-2 performance of a closed-loop system despite the presence of a malicious covert attacker. It is assumed that the attacker has incomplete knowledge on the true process we are controlling. To account for this uncertainty, we employ different measures of risk from the so called family of coherent measures of risk. In particular, we compare the closed-loop performance when a nominal value is used, with three different measures of risk: average risk, worst-case scenario and conditional valueat- risk (CVaR). Additionally, applying the approach from a previous work, we derive a convex formulation for the control design problem when CVaR is employed to quantify the risk. A numerical example illustrates the advantages of our approach.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-245006 (URN)10.1109/CDC.2018.8618886 (DOI)000458114804034 ()2-s2.0-85062181179 (Scopus ID)978-1-5386-1395-5 (ISBN)
Conference
57th IEEE Conference on Decision and Control
Projects
CERCES
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-08-27Bibliographically approved
Ramírez-Chavarría, R. G., Quintana-Carapia, G., Müller, M. I., Mattila, R., Matatagui, D. & Sánchez-Pérez, C. (2018). Bioimpedance Parameter Estimation using Fast Spectral Measurements and Regularizaton. In: IFAC-PapersOnLine: . Paper presented at 18th IFAC Symposium on System Identification (SYSID), JUL 09-11, 2018,Stockholm Univ, AlbaNova Univ Ctr, Stockholm, SWEDEN (pp. 521-526). IFAC Papers Online, 51(15)
Open this publication in new window or tab >>Bioimpedance Parameter Estimation using Fast Spectral Measurements and Regularizaton
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2018 (English)In: IFAC-PapersOnLine, IFAC Papers Online, 2018, Vol. 51, no 15, p. 521-526Conference paper, Published paper (Refereed)
Abstract [en]

This work proposes an alternative framework for parametric bioimpedance estimation as a powerful tool to characterize biological media. We model the bioimpedance as an electrical network of parallel RC circuits, and transform the frequency-domain estimation problem into a time constant domain estimation problem by means of the distribution of relaxation times (DRT) method. The Fredholm integral equation of the first kind is employed to pose the problem in a regularized least squares (RLS) form. We validate the proposed methodology by numerical simulations for a synthetic biological electrical circuit, by using a multisine signal in the frequency range of 1kHz to 853kHz and considering an error in variables (EIV) problem. Results show that the proposed method outperforms the state-of-the-art techniques for spectral bioimpedance analysis. We also illustrates its potentiality in terms of accurate spectral measurements and precise data interpretation, for further usage in biological applications.

Place, publisher, year, edition, pages
IFAC Papers Online, 2018
National Category
Bioengineering Equipment
Identifiers
urn:nbn:se:kth:diva-256302 (URN)10.1016/j.ifacol.2018.09.198 (DOI)000446599200089 ()2-s2.0-85054370558 (Scopus ID)
Conference
18th IFAC Symposium on System Identification (SYSID), JUL 09-11, 2018,Stockholm Univ, AlbaNova Univ Ctr, Stockholm, SWEDEN
Note

QC 20190821

Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-06Bibliographically approved
Müller, M. I., Valenzuela, P. E., Proutiere, A. & Rojas, C. R. (2017). A stochastic multi-armed bandit approach to nonparametric H∞-norm estimation. In: 56th IEEE Conference on Decision and Control: . Paper presented at 56th IEEE Conference on Decision and Control (pp. 4632-4637). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A stochastic multi-armed bandit approach to nonparametric H-norm estimation
2017 (English)In: 56th IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 4632-4637Conference paper, Published paper (Refereed)
Abstract [en]

We study the problem of estimating the largest gain of an unknown linear and time-invariant filter, which is also known as the H norm of the system. By using ideas from the stochastic multi-armed bandit framework, we present a new algorithm that sequentially designs an input signal in order to estimate this quantity by means of input-output data. The algorithm is shown empirically to beat an asymptotically optimal method, known as Thompson Sampling, in the sense of its cumulative regret function. Finally, for a general class of algorithms, a lower bound on the performance of finding the H-infinity norm is derived.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223861 (URN)10.1109/CDC.2017.8264343 (DOI)000424696904075 ()2-s2.0-85046136421 (Scopus ID)978-1-5090-2873-3 (ISBN)
Conference
56th IEEE Conference on Decision and Control
Funder
Swedish Research Council, 2015-04393; 2016-06079
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2019-08-27Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6322-7857

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