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Publications (10 of 122) Show all publications
Mueller, M. I., Valenzuela, P. E., Proutiere, A. & Rojas, C. R. (2017). A stochastic multi-armed bandit approach to nonparametric H-infinity-norm estimation. In: 2-s2.0-85046136421: . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA (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-infinity-norm estimation
2017 (English)In: 2-s2.0-85046136421, 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-infinity 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
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
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
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA
Funder
Swedish Research Council, 2015-04393; 2016-06079
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-06-04Bibliographically approved
Mattila, R., Rojas, C. R., Krishnamurthy, V. & Wahlberg, B. (2017). Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models. IEEE Signal Processing Letters, 24(12), 1813-1817
Open this publication in new window or tab >>Asymptotically Efficient Identification of Known-Sensor Hidden Markov Models
2017 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 12, p. 1813-1817Article in journal (Refereed) Published
Abstract [en]

We consider estimating the transition probability matrix of a finite-state finite-observation alphabet hidden Markov model with known observation probabilities. We propose a two-step algorithm: a method of moments estimator (formulated as a convex optimization problem) followed by a single iteration of a Newton-Raphson maximum-likelihood estimator. The two-fold contribution of this letter is, first, to theoretically show that the proposed estimator is consistent and asymptotically efficient, and second, to numerically show that the method is computationally less demanding than conventional methods-in particular for large datasets.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2017
Keyword
Hidden Markov models (HMM), maximum-likelihood (ML), method of moments, system identification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-217930 (URN)10.1109/LSP.2017.2759902 (DOI)000413962800006 ()2-s2.0-85031786373 (Scopus ID)
Note

QC 20171121

Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2017-11-21Bibliographically approved
Eckhard, D., Bazanella, A. S., Rojas, C. R. & Hjalmarsson, H. (2017). Cost function shaping of the output error criterion. Automatica, 76, 53-60
Open this publication in new window or tab >>Cost function shaping of the output error criterion
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 76, p. 53-60Article in journal (Refereed) Published
Abstract [en]

Identification of an output error model using the prediction error method leads to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because in most cases both the corresponding objective function and the search space are nonconvex. The difficulty in solving the optimization problem depends mainly on the experimental conditions, more specifically on the spectra of the input/output data collected from the system. It is therefore possible to improve the convergence of the algorithms by properly choosing the data prefilters; in this paper we show how to perform this choice. We present the application of the proposed approach to case studies where the standard algorithms tend to fail to converge to the global minimum.

Place, publisher, year, edition, pages
Elsevier, 2017
Keyword
Identification methods, Model fitting
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-202642 (URN)10.1016/j.automatica.2016.10.015 (DOI)000392788100007 ()2-s2.0-85001975561 (Scopus ID)
Note

QC 20170307

Available from: 2017-03-07 Created: 2017-03-07 Last updated: 2017-11-29Bibliographically approved
Eckhard, D., Bazanella, A. S., Rojas, C. R. & Hjalmarsson, H. (2017). Cost function shaping of the output error criterion. Automatica, 76, 53-60
Open this publication in new window or tab >>Cost function shaping of the output error criterion
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 76, p. 53-60Article in journal (Refereed) Published
Abstract [en]

Identification of an output error model using the prediction error method leads to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because in most cases both the corresponding objective function and the search space are nonconvex. The difficulty in solving the optimization problem depends mainly on the experimental conditions, more specifically on the spectra of the input/output data collected from the system. It is therefore possible to improve the convergence of the algorithms by properly choosing the data prefilters; in this paper we show how to perform this choice. We present the application of the proposed approach to case studies where the standard algorithms tend to fail to converge to the global minimum.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2017
Keyword
Identification methods, Model fitting
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-202774 (URN)10.1016/j.automatica.2016.10.015 (DOI)000392788100007 ()
Note

QC 20170307

Available from: 2017-03-07 Created: 2017-03-07 Last updated: 2017-06-29Bibliographically approved
Rallo, G., Formentin, S., Rojas, C. R., Oomen, T. & Savaresi, S. M. (2017). Data-driven H-infinity-norm estimation via expert advice. In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA. IEEE
Open this publication in new window or tab >>Data-driven H-infinity-norm estimation via expert advice
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2017 (English)In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

H-infinity-norm estimation is usually an important aspect of robust control design. The aim of this paper is to develop a data-driven estimation method exploiting iterative input design, without requiring parametric modeling. More specifically, the estimation problem is formulated as a sequential game, whose solution is derived within the prediction with expert advice framework. The proposed method is shown to be competitive with the state-of-the-art techniques.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223867 (URN)000424696901091 ()978-1-5090-2873-3 (ISBN)
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, AUSTRALIA
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-03-06Bibliographically approved
Mattila, R., Rojas, C. R., Krishnamurthy, V. & Wahlberg, B. (2017). Identification of Hidden Markov Models Using Spectral Learning with Likelihood Maximization. In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017: . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia (pp. 5859-5864). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Identification of Hidden Markov Models Using Spectral Learning with Likelihood Maximization
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 5859-5864Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we consider identifying a hidden Markov model (HMM) with the purpose of computing estimates of joint and conditional (posterior) probabilities over observation sequences. The classical maximum likelihood estimation algorithm (via the Baum-Welch/expectation-maximization algorithm), has recently been challenged by methods of moments. Such methods employ low-order moments to provide parameter estimates and have several benefits, including consistency and low computational cost. This paper aims to reduce the gap in statistical efficiency that results from restricting to only low-order moments in the training data. In particular, we propose a two-step procedure that combines spectral learning with a single Newton-like iteration for maximum likelihood estimation. We demonstrate an improved statistical performance using the proposed algorithm in numerical simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223860 (URN)10.1109/CDC.2017.8264545 (DOI)000424696905103 ()2-s2.0-85046135167 (Scopus ID)978-1-5090-2873-3 (ISBN)
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia
Funder
Swedish Research Council, 2016-06079
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-06-01Bibliographically approved
Ebadat, A., Valenzuela, P. E., Rojas, C. R. & Wahlberg, B. (2017). Model Predictive Control oriented experiment design for system identification: A graph theoretical approach. Journal of Process Control, 52, 75-84
Open this publication in new window or tab >>Model Predictive Control oriented experiment design for system identification: A graph theoretical approach
2017 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 52, p. 75-84Article in journal (Refereed) Published
Abstract [en]

We present a new approach to Model Predictive Control (MPC) oriented experiment design for the identification of systems operating in closed-loop. The method considers the design of an experiment by minimizing the experimental cost, subject to probabilistic bounds on the input and output signals due to physical limitations of actuators, and quality constraints on the identified model. The excitation is done by intentionally adding a disturbance to the loop. We then design the external excitation to achieve the minimum experimental effort while we are also taking care of the tracking performance of MPC. The stability of the closed-loop system is guaranteed by employing robust MPC during the experiment. The problem is then defined as an optimization problem. However, the aforementioned constraints result in a non-convex optimization which is relaxed by using results from graph theory. The proposed technique is evaluated through a numerical example showing that it is an attractive alternative for closed-loop experiment design.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2017
Keyword
Closed-loop identification, Optimal input design, System identification, Model Predictive Control, Constrained systems
National Category
Chemical Engineering
Identifiers
urn:nbn:se:kth:diva-207702 (URN)10.1016/j.jprocont.2017.02.001 (DOI)000399849400008 ()2-s2.0-85013678020 (Scopus ID)
Note

QC 20170530

Available from: 2017-05-30 Created: 2017-05-30 Last updated: 2017-06-30Bibliographically approved
Muller, M. I., Valenzuela, P. E. & Rojas, C. R. (2017). Risk-coherent H-2-optimal disturbance rejection under model uncertainty. In: IFAC PAPERSONLINE: . Paper presented at 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), JUL 09-14, 2017, Toulouse, FRANCE (pp. 15530-15535). ELSEVIER SCIENCE BV, 50(1)
Open this publication in new window or tab >>Risk-coherent H-2-optimal disturbance rejection under model uncertainty
2017 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2017, Vol. 50, no 1, p. 15530-15535Conference paper, Published paper (Refereed)
Abstract [en]

A control design procedure for disturbance rejection, when the disturbance model is uncertain, is proposed. We use the probabilistic information about the process disturbance model to design a controller to account for the uncertainty by using a risk-theoretical approach. By introducing the notion of coherent measures of risk, we analyze standard approaches to account for this uncertainty, and we intend to show that the conditional value-at-risk (CVaR) is an appropriate function to measure the uncertainty in the disturbance model. We also derive a convex formulation for the controller design problem when the Youla parameter is linearly parametrized. A numerical example illustrates the main discussion of this article.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2017
Keyword
disturbance rejection (linear case), control problems under conflict and uncertainties, output feedback control (linear case), H-2-control
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-224095 (URN)10.1016/j.ifacol.2017.08.2137 (DOI)000423965400108 ()
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC), JUL 09-14, 2017, Toulouse, FRANCE
Note

QC 20180314

Available from: 2018-03-14 Created: 2018-03-14 Last updated: 2018-03-14Bibliographically approved
Oomen, T. & Rojas, C. R. (2017). Sparse iterative learning control with application to a wafer stage: Achieving performance, resource efficiency, and task flexibility. Mechatronics (Oxford), 47, 134-147
Open this publication in new window or tab >>Sparse iterative learning control with application to a wafer stage: Achieving performance, resource efficiency, and task flexibility
2017 (English)In: Mechatronics (Oxford), ISSN 0957-4158, E-ISSN 1873-4006, Vol. 47, p. 134-147Article in journal (Refereed) Published
Abstract [en]

Trial-varying disturbances are a key concern in Iterative Learning Control (ILC) and may lead to inefficient and expensive implementations and severe performance deterioration. The aim of this paper is to develop a general framework for optimization-based ILC that allows for enforcing additional structure, including sparsity. The proposed method enforces sparsity in a generalized setting through convex relaxations using el norms. The proposed ILC framework is applied to the optimization of sampling sequences for resource efficient implementation, trial-varying disturbance attenuation, and basis function selection. The framework has a large potential in control applications such as mechatronics, as is confirmed through an application on a wafer stage. 

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2017
Keyword
Iterative learning control, Motion control, Feedforward, Sparse optimization, Resource-efficient control
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:kth:diva-217738 (URN)10.1016/j.mechatronics.2017.09.004 (DOI)000414107500013 ()2-s2.0-85030700202 (Scopus ID)
Note

QC 20171122

Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-22Bibliographically approved
Ottersten, J., Wahlberg, B. & Rojas, C. R. (2016). Accurate Changing Point Detection for l(1) Mean Filtering. IEEE Signal Processing Letters, 23(2), 297-301
Open this publication in new window or tab >>Accurate Changing Point Detection for l(1) Mean Filtering
2016 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 23, no 2, p. 297-301Article in journal (Refereed) Published
Abstract [en]

It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data while trend filtering finds a piece-wise linear trend. When the signal is noisy, the main difficulty is finding the changing points in the data that mark the transition points when the mean or the trend changes. Previously proposed methods based on l(1) filtering suffer from the occurrence of false changing points in the estimate. This is known as the staircase effect. The main contribution in this paper is incorporating a technique to remove these false changing points to a fast mean filtering algorithm, referred to as the taut-string method, resulting in an efficient procedure with accurate change point detection and thus the removal of the stair-case effect.

Keyword
l(1) mean filtering, stair-case effect, taut-string algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-186015 (URN)10.1109/LSP.2016.2517605 (DOI)000373742000009 ()2-s2.0-84962280942 (Scopus ID)
Note

QC 20160509

Available from: 2016-05-09 Created: 2016-04-29 Last updated: 2017-11-30Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0355-2663

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