Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 140) Show all publications
Müller, M. & 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)
Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2019-08-16
Valenzuela, P. E., Schön, T. B. & Rojas, C. R. (2019). On model order priors for Bayesian identification of SISO linear systems. International Journal of Control, 92(7), 1645-1661
Open this publication in new window or tab >>On model order priors for Bayesian identification of SISO linear systems
2019 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 92, no 7, p. 1645-1661Article in journal (Refereed) Published
Abstract [en]

A method for the identification of single input single output linear systems is presented. The method employs a Bayesian approach to compute the posterior distribution of the model parameters given the data-set. Since this distribution is often unavailable in closed form, a Metropolis Hastings algorithm is implemented to draw samples from it. To implement the sampler, the inclusion of prior information regarding the model order of the identified system is discussed. As one of the main contributions of this work, a prior over the Hankel singular values of the model is imposed. Numerical examples illustrate the method.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
System identification, Bayesian estimation, Metropolis Hastings sampler, model order prior
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-255303 (URN)10.1080/00207179.2017.1406147 (DOI)000472558100017 ()2-s2.0-85067805258 (Scopus ID)
Note

QC 20190730

Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-07-30Bibliographically approved
Galrinho, M., Rojas, C. R. & Hjalmarsson, H. (2019). Parametric Identification Using Weighted Null-Space Fitting. IEEE Transactions on Automatic Control, 64(7), 2798-2813
Open this publication in new window or tab >>Parametric Identification Using Weighted Null-Space Fitting
2019 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 7, p. 2798-2813Article in journal (Refereed) Published
Abstract [en]

In identification of dynamical systems, the prediction error method with a quadratic cost function provides asymptotically efficient estimates under Gaussian noise, but in general it requires solving a nonconvex optimization problem, which may imply convergence to nonglobal minima. An alternative class of methods uses a nonparametric model as intermediate step to obtain the model of interest. Weighted null-space fitting (WNSF) belongs to this class, starting with the estimate of a nonparametric ARX model with least squares. Then, the reduction to a parametric model is a multistep procedure where each step consists of the solution of a quadratic optimization problem, which can be obtained with weighted least squares. The method is suitable for both open- and closed-loop data, and can be applied to many common parametric model structures, including output-error, ARMAX, and Box-Jenkins. The price to pay is the increase of dimensionality in the nonparametric model, which needs to tend to infinity as function of the sample size for certain asymptotic statistical properties to hold. In this paper, we conduct a rigorous analysis of these properties: namely, consistency, and asymptotic efficiency. Also, we perform a simulation study illustrating the performance of WNSF and identify scenarios where it can be particularly advantageous compared with state-of-the-art methods.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Least squares, system identification
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-255416 (URN)10.1109/TAC.2018.2877673 (DOI)000473489700011 ()2-s2.0-85055726363 (Scopus ID)
Note

QC 20190815

Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-08-15Bibliographically approved
Müller, M. I. & Rojas, C. R. (2019). Risk-Coherent H∞-optimal Filter Design Under Model Uncertainty with Applications to MISO Control. In: : . Paper presented at 18th European Control Conference (ECC) (pp. 1461-1466).
Open this publication in new window or tab >>Risk-Coherent H-optimal Filter Design Under Model Uncertainty with Applications to MISO Control
2019 (English)Conference paper, Published paper (Refereed)
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-256077 (URN)
Conference
18th European Control Conference (ECC)
Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-19
Sadeghi, M., Rojas, C. R. & Wahlberg, B. (2018). A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization. IFAC-PapersOnLine, 51(15), 96-101
Open this publication in new window or tab >>A Branch and Bound Approach to System Identification based on Fixed-rank Hankel Matrix Optimization
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 96-101Article in journal (Refereed) Published
Abstract [en]

We consider identification of linear systems with a certain order from a set of noisy input-output observations. We utilize the fact that the system order corresponds to the rank of the Hankel matrix associated with the system impulse response. Then, the system identification problem is formulated as the minimization of the output error subject to a rank constraint on a Hankel matrix. As this problem is non-convex, we propose a branch and bound (BB) solver, which is a powerful tool for solving non-convex problems to optimality. The main ingredients of the proposed BB method are a convex relaxation problem and a local minimizer of the original non-convex problem. We illustrate the promising performance of the proposed scheme in a system identification problem. The results demonstrate the higher accuracy and stability of our method in estimating the true system compared to the standard output error (OE) algorithm.

Place, publisher, year, edition, pages
Elsevier B.V., 2018
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-247401 (URN)10.1016/j.ifacol.2018.09.097 (DOI)2-s2.0-85054351520 (Scopus ID)
Note

QC 20190322

Available from: 2019-03-22 Created: 2019-03-22 Last updated: 2019-03-22Bibliographically approved
González, R. A. & Rojas, C. R. (2018). A fully Bayesian approach to kernel-based regularization for impulse response estimation⁎. IFAC-PapersOnLine, 51(15), 186-191
Open this publication in new window or tab >>A fully Bayesian approach to kernel-based regularization for impulse response estimation⁎
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 186-191Article in journal (Refereed) Published
Abstract [en]

Kernel-based regularization has recently been shown to be a successful method for impulse response estimation. This technique usually requires choosing a vector of hyper-parameters in order to form an appropriate regularization matrix. In this paper, we develop an alternative way to obtain kernel-based regularization estimates by Bayesian model mixing. This new approach is tested against state-of-the-art methods for hyperparameter tuning in regularized FIR estimation, with favorable results in many cases.

Place, publisher, year, edition, pages
Elsevier B.V., 2018
Keywords
Bayesian estimation, kernel-based regularization, Linear system identification, model mixing, Impulse response, Linear systems, Mixing, Bayesian estimations, Bayesian model, Fully bayesian approaches, Hyper-parameter, New approaches, State-of-the-art methods, Bayesian networks
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-247500 (URN)10.1016/j.ifacol.2018.09.123 (DOI)000446599200033 ()2-s2.0-85054355409 (Scopus ID)
Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved
Müller Riquelme, M. & Rojas, C. R. (2018). A Markov Chain Approach to Compute the ℓ2-gain of Nonlinear Systems⁎. IFAC-PapersOnLine, 51(15), 84-89
Open this publication in new window or tab >>A Markov Chain Approach to Compute the ℓ2-gain of Nonlinear Systems⁎
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 84-89Article in journal (Refereed) Published
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)
Note

QC20190418

Available from: 2019-04-18 Created: 2019-04-18 Last updated: 2019-04-18Bibliographically approved
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)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 (CDC), DEC 17-19, 2018, Miami Beach, FL
Projects
CERCES
Note

QC 20190305

Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-04-11Bibliographically 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)10.1109/CDC.2018.8619141 (DOI)000458114800089 ()2-s2.0-85062184143 (Scopus ID)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: 2019-04-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0355-2663

Search in DiVA

Show all publications