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Publications (10 of 131) Show all publications
Mueller, M. I., Milosevic, J., Sandberg, H. & Rojas, C. R. (2018). A Risk-Theoretical Approach to H-2-Optimal Control under Covert Attacks. In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at 57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL (pp. 4553-4558). IEEE
Open this publication in new window or tab >>A Risk-Theoretical Approach to H-2-Optimal Control under Covert Attacks
2018 (English)In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 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)000458114804034 ()978-1-5386-1395-5 (ISBN)
Conference
57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL
Projects
CERCES
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-12Bibliographically approved
Ha, H., Welsh, J. S., Rojas, C. R. & Wahlberg, B. (2018). An analysis of the SPARSEVA estimate for the finite sample data case. Automatica, 96, 141-149
Open this publication in new window or tab >>An analysis of the SPARSEVA estimate for the finite sample data case
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 96, p. 141-149Article in journal (Refereed) Published
Abstract [en]

In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) estimation error in a general scheme, i.e., when the cost function is strongly convex and the regularized norm is decomposable for a pair of subspaces. We show how this general bound can be applied to a sparse regression problem to obtain an upper bound of the estimation error for the traditional I-1 SPARSEVA problem. Numerical results are used to illustrate the effectiveness of the suggested bound. 

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2018
Keywords
SPARSEVA estimate, Estimation error, Upper bound, Finite sample data
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-235563 (URN)10.1016/j.automatica.2018.06.046 (DOI)000444659500015 ()2-s2.0-85049567510 (Scopus ID)
Note

QC 20181001

Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-11-23Bibliographically approved
Gonzalez, R. A., Rojas, C. R. & Welsh, J. S. (2018). An asymptotically optimal indirect approach to continuous-time system identification. In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at 57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL (pp. 638-643). IEEE
Open this publication in new window or tab >>An asymptotically optimal indirect approach to continuous-time system identification
2018 (English)In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 638-643Conference paper, Published paper (Refereed)
Abstract [en]

The indirect approach to continuous-time system identification consists in estimating continuous-time models by first determining an appropriate discrete-time model. For a zero-order hold sampling mechanism, this approach usually leads to a transfer function estimate with relative degree 1, independent of the relative degree of the strictly proper real system. In this paper, a refinement of these methods is developed. Inspired by the indirect prediction error method, we propose an estimator that enforces a fixed relative degree in the continuous-time transfer function estimate, and show that the estimator is consistent and asymptotically efficient. Extensive numerical simulations are put forward to show the performance of this estimator when contrasted with other indirect and direct methods for continuous-time system identification.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords
System identification, Continuous-time systems, Parameter estimation, Sampled data
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-245010 (URN)000458114800089 ()978-1-5386-1395-5 (ISBN)
Conference
57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-05Bibliographically approved
Valenzuela, P. E., Rojas, C. R. & Hjalmarsson, H. (2018). Analysis of averages over distributions of Markov processes. Automatica, 98, 354-357
Open this publication in new window or tab >>Analysis of averages over distributions of Markov processes
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 98, p. 354-357Article in journal (Refereed) Published
Abstract [en]

In problems of optimal control of Markov decision processes and optimal design of experiments, the occupation measure of a Markov process is designed in order to maximize a specific reward function. When the memory of such a process is too long, or the process is non-Markovian but mixing, it makes sense to approximate it by that of a shorter memory Markov process. This note provides a specific bound for the approximation error introduced in these schemes. The derived bound is then applied to the proposed solution of a recently introduced approach to optimal input design for nonlinear systems.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
System identification, Input design, Markov chains
National Category
Materials Engineering
Identifiers
urn:nbn:se:kth:diva-239469 (URN)10.1016/j.automatica.2018.09.016 (DOI)000449310900039 ()2-s2.0-85053735276 (Scopus ID)
Note

QC 20181126

Available from: 2018-11-26 Created: 2018-11-26 Last updated: 2018-11-26Bibliographically approved
Abdalmoaty, M. ., Rojas, C. R. & Hjalmarsson, H. (2018). Identication of a Class of Nonlinear Dynamical Networks. In: : . Paper presented at 18th IFAC Symposium on System Identification.
Open this publication in new window or tab >>Identication of a Class of Nonlinear Dynamical Networks
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

Series
IFAC-PapersOnLine
Keywords
System Identication, Dynamical Networks, Stochastic Systems, Block-Oriented Models, Prediction Error Method.
National Category
Signal Processing Control Engineering
Research subject
Electrical Engineering; Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233639 (URN)
Conference
18th IFAC Symposium on System Identification
Funder
EU, European Research Council, 267381Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Note

QC 20180829

Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-29Bibliographically approved
Mattila, R., Rojas, C. R., Krishnamurthy, V. & Wahlberg, B. (2018). Inverse Filtering for Linear Gaussian State-Space Models. In: 2018 IEEE Conference on Decision and Control  (CDC): . Paper presented at 57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beac hMiami; United States; 17 December 2018 through 19 December 2018 (pp. 5556-5561). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8619013.
Open this publication in new window or tab >>Inverse Filtering for Linear Gaussian State-Space Models
2018 (English)In: 2018 IEEE Conference on Decision and Control  (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 5556-5561, article id 8619013Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers inverse filtering problems for linear Gaussian state-space systems. We consider three problems of increasing generality in which the aim is to reconstruct the measurements and/or certain unknown sensor parameters, such as the observation likelihood, given posteriors (i. e., the sample path of mean and covariance). The paper is motivated by applications where one wishes to calibrate a Bayesian estimator based on remote observations of the posterior estimates, e. g., determine how accurate an adversary's sensors are. We propose inverse filtering algorithms and evaluate their robustness with respect to noise (e. g., measurement or quantization errors) in numerical simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-245114 (URN)10.1109/CDC.2018.8619013 (DOI)000458114805022 ()2-s2.0-85062188998 (Scopus ID)978-1-5386-1395-5 (ISBN)
Conference
57th IEEE Conference on Decision and Control, CDC 2018; Centre of the Fontainebleau in Miami Beac hMiami; United States; 17 December 2018 through 19 December 2018
Note

QC 20190306

Available from: 2019-03-06 Created: 2019-03-06 Last updated: 2019-03-06Bibliographically approved
Bjurgert, J., Valenzuela, P. E. & Rojas, C. R. (2018). On Adaptive Boosting for System Identification. IEEE Transactions on Neural Networks and Learning Systems, 29(9), 4510-4514
Open this publication in new window or tab >>On Adaptive Boosting for System Identification
2018 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 9, p. 4510-4514Article in journal (Refereed) Published
Abstract [en]

In the field of machine learning, the algorithm Adaptive Boosting has been successfully applied to a wide range of regression and classification problems. However, to the best of the authors' knowledge, the use of this algorithm to estimate dynamical systems has not been exploited. In this brief, we explore the connection between Adaptive Boosting and system identification, and give examples of an identification method that makes use of this connection. We prove that the resulting estimate converges to the true underlying system for an output-error model structure under reasonable assumptions in the large sample limit and derive a bound of the model mismatch for the noise-free case.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Adaptive algorithms, adaptive boosting, dynamical systems, orthonormal basis functions, system identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-235117 (URN)10.1109/TNNLS.2017.2754319 (DOI)000443083700049 ()29035231 (PubMedID)2-s2.0-85052677886 (Scopus ID)
Note

QC 20180919

Available from: 2018-09-19 Created: 2018-09-19 Last updated: 2018-09-19Bibliographically approved
Rallo, G., Formentin, S., Rojas, C. R. & Savaresi, S. M. (2018). Robust Experiment Design for Virtual Reference Feedback Tuning. In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at 57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL (pp. 2271-2276). IEEE
Open this publication in new window or tab >>Robust Experiment Design for Virtual Reference Feedback Tuning
2018 (English)In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 2271-2276Conference paper, Published paper (Refereed)
Abstract [en]

This paper deals with robust experiment design for the Virtual Reference Feedback Tuning (VRFT) approach, a non-iterative control design method aimed to tune fixed-order controllers directly from experimental data, without the need for a model of the plant. In a previous contribution, it has been shown that the spectrum of the optimal input depends on the frequency response of the controller achieving the desired performance. In this work, a robust input design procedure is proposed, which requires only mild prior knowledge about the optimal controller. The solution is obtained analytically via constrained min-max optimization. Simulation results on a benchmark case study for digital control systems show the effectiveness of the proposed 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-245003 (URN)000458114802022 ()978-1-5386-1395-5 (ISBN)
Conference
57th IEEE Conference on Decision and Control (CDC), DEC 17-19, 2018, Miami Beach, FL
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-05Bibliographically approved
Oomen, T. & Rojas, C. R. (2018). Sparse Iterative Learning Control (SPILC): When to Sample for Resource-Efficiency?. In: 2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC): . Paper presented at 15th IEEE International Workshop on Advanced Motion Control (AMC), MAR 09-11, 2018, Shibaura Inst Technol, Toyosu Campus, Tokyo, JAPAN (pp. 497-502). IEEE
Open this publication in new window or tab >>Sparse Iterative Learning Control (SPILC): When to Sample for Resource-Efficiency?
2018 (English)In: 2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC), IEEE, 2018, p. 497-502Conference paper, Published paper (Refereed)
Abstract [en]

Iterative learning control enables the determination of optimal command inputs by learning from measured data of previous tasks. The aim of this paper is to address the negative impact of trial-varying disturbances that contaminate these measurements, both in terms of resource-efficient implementations and performance degradation. The proposed method is an optimal framework for ILC that enforces sparsity and related structure on the command signal. This is achieved through a convex relaxation relying on l(1) regularization. The approach is demonstrated on a benchmark motion system, confirming substantial extensions compared to earlier results.

Place, publisher, year, edition, pages
IEEE, 2018
Series
International Workshop on Advanced Motion Control, ISSN 1943-6572
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-232932 (URN)000436348700078 ()2-s2.0-85048787229 (Scopus ID)978-1-5386-1946-9 (ISBN)
Conference
15th IEEE International Workshop on Advanced Motion Control (AMC), MAR 09-11, 2018, Shibaura Inst Technol, Toyosu Campus, Tokyo, JAPAN
Note

QC 20180807

Available from: 2018-08-07 Created: 2018-08-07 Last updated: 2018-08-07Bibliographically approved
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0355-2663

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