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Publications (10 of 294) Show all publications
Bombois, X., Korniienko, A., Hjalmarsson, H. & Scorletti, G. (2018). Optimal identification experiment design for the interconnection of locally controlled systems. Automatica, 89, 169-179
Open this publication in new window or tab >>Optimal identification experiment design for the interconnection of locally controlled systems
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 89, p. 169-179Article in journal (Refereed) Published
Abstract [en]

This paper considers the identification of the modules of a network of locally controlled systems (multi-agent systems). Its main contribution is to determine the least perturbing identification experiment that will nevertheless lead to sufficiently accurate models of each module for the global performance of the network to be improved by a redesign of the decentralized controllers. Another contribution is to determine the experimental conditions under which sufficiently informative data (i.e. data leading to a consistent estimate) can be collected for the identification of any module in such a network. 

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Experiment design, Identification for control, Interconnected systems, Control systems, Electrical engineering, Large scale systems, Controlled system, Decentralized controller, Experimental conditions, Global performance, Multi agent systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223124 (URN)10.1016/j.automatica.2017.12.014 (DOI)000427210200020 ()2-s2.0-85039159596 (Scopus ID)
Funder
Swedish Research Council, 2016-06079 2015-05285
Note

 QC 20180327

Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-04-05Bibliographically approved
Bottegal, G., Hjalmarsson, H. & Pillonetto, G. (2017). A new kernel-based approach to system identification with quantized output data. Automatica, 85, 145-152
Open this publication in new window or tab >>A new kernel-based approach to system identification with quantized output data
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 85, p. 145-152Article in journal (Refereed) Published
Abstract [en]

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the so-called Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectation-maximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the state-of-the-art kernel-based methods when these are employed in system identification with quantized data. (C) 2017 Elsevier Ltd. All rights reserved.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-218225 (URN)10.1016/j.automatica.2017.07.053 (DOI)000414818100016 ()2-s2.0-85027880897 (Scopus ID)
Note

QC 20171128

Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2017-11-28Bibliographically approved
Risuleo, R. S., Bottegal, G. & Hjalmarsson, H. (2017). A nonparametric kernel-based approach to Hammerstein system identification. Automatica, 85, 234-247
Open this publication in new window or tab >>A nonparametric kernel-based approach to Hammerstein system identification
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 85, p. 234-247Article in journal (Refereed) Published
Abstract [en]

Hammerstein systems are the series composition of a static nonlinear function and a linear dynamic system, In this work, we propose a nonparametric method for the identification of Hammerstein systems. We adopt a kernel-based approach to model the two components of the system. In particular, we model the nonlinear function and the impulse response of the linear block as Gaussian processes with suitable kernels. The kernels can be chosen to encode prior information about the nonlinear function and the system. Following the empirical Bayes approach, we estimate the posterior mean of the impulse response using estimates of the nonlinear function, of the hyperparameters, and of the noise variance. These estimates are found by maximizing the marginal likelihood of the data. This maximization problem is solved using an iterative scheme based on the expectation-conditional maximization, which is a variation of the standard expectation maximization method for solving maximum-likelihood problems. We show the effectiveness of the proposed identification scheme in some simulation experiments.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-218227 (URN)10.1016/j.automatica.2017.07.055 (DOI)000414818100027 ()2-s2.0-85027880897 (Scopus ID)
Note

QC 20171128

Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2017-11-28Bibliographically approved
Gerencser, L., Hjalmarsson, H. & Huang, L. (2017). Adaptive Input Design for LTI Systems. IEEE Transactions on Automatic Control, 62(5), 2390-2405, Article ID 7574358.
Open this publication in new window or tab >>Adaptive Input Design for LTI Systems
2017 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 5, p. 2390-2405, article id 7574358Article in journal (Refereed) Published
Abstract [en]

Optimal input design for parameter estimation has obtained extensive coverage in the past. A key problem here is that the optimal input depends on some unknown system parameters that are to be identified. Adaptive design is one of the fundamental routes to handle this problem. Although there exist a rich collection of results on this problem, there are few results that address dynamical systems. This paper presents sufficient conditions for convergence/consistency and asymptotic optimality for a class of adaptive systems consisting of a recursive prediction error estimator and an input generator depending on the time-varying parameter estimates. The results apply to a general family of single input single output linear time-invariant systems. An important application is adaptive input design for which the results imply that, asymptotically in the sample size, the adaptive scheme recovers the same accuracy as the off-line prediction error method that uses data from an experiment where perfect knowledge of the system has been used to design an optimal input spectrum.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
Linear time-invariant (LTI), recursive prediction error (RPE), single-input single-output (SISO), Adaptive systems, Dynamical systems, Error analysis, Errors, Forecasting, Invariance, Linear systems, Nonlinear control systems, Telecommunication repeaters, Time varying control systems, Adaptive designs, Asymptotic optimality, Linear time invariant, Linear time invariant systems, Optimal input design, Recursive prediction errors, Single input single output, Time varying parameter, Parameter estimation
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-216511 (URN)10.1109/TAC.2016.2612946 (DOI)000400473800022 ()2-s2.0-85018271080 (Scopus ID)
Note

QC 20171201

Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2017-12-01Bibliographically approved
Annergren, M., Larsson, C. A., Hjalmarsson, H., Bombois, X. & Wahlberg, B. (2017). Application-Oriented Input Design in System Identification Optimal input design for control. IEEE CONTROL SYSTEMS MAGAZINE, 37(2), 31-56
Open this publication in new window or tab >>Application-Oriented Input Design in System Identification Optimal input design for control
Show others...
2017 (English)In: IEEE CONTROL SYSTEMS MAGAZINE, ISSN 1066-033X, Vol. 37, no 2, p. 31-56Article in journal (Refereed) Published
Place, publisher, year, edition, pages
IEEE, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-208262 (URN)10.1109/MCS.2016.2643243 (DOI)000398902900003 ()2-s2.0-85016139089 (Scopus ID)
Funder
Swedish Research Council, 621-2009-4017EU, FP7, Seventh Framework Programme, 257059EU, European Research Council, 267381
Note

QC 20170614

Available from: 2017-06-14 Created: 2017-06-14 Last updated: 2017-06-30Bibliographically 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
Keywords
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
Keywords
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
Fang, M., Zhu, Y. & Hjalmarsson, H. (2017). On anti-aliasing filtering and over-sampling scheme in system identification. Computers and Chemical Engineering, 106, 572-581
Open this publication in new window or tab >>On anti-aliasing filtering and over-sampling scheme in system identification
2017 (English)In: Computers and Chemical Engineering, ISSN 0098-1354, E-ISSN 1873-4375, Vol. 106, p. 572-581Article in journal (Refereed) Published
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-216593 (URN)10.1016/j.compchemeng.2017.07.010 (DOI)000412192800041 ()2-s2.0-85027989038 (Scopus ID)
Note

QC 20171116

Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2017-11-16Bibliographically approved
Fang, M., Galrinho, M. & Hjalmarsson, H. (2017). Recursive Identification Based on Weighted Null-Space Fitting. 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 >>Recursive Identification Based on Weighted Null-Space Fitting
2017 (English)In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

Algorithms for online system identification consist in updating the estimated model while data are being collected. A standard method for online identification of structured models is the recursive prediction error method (PEM). The problem is that PEM does not have an exact recursive formulation, and the need to rely on approximations makes recursive PEM prone to convergence problems. In this paper, we propose a recursive implementation of weighted null-space fitting, an asymptotically efficient method for identification of structured models. Consisting only of (weighted) least-squares steps, the recursive version of the algorithm has the same convergence and statistical properties of the off-line version. We illustrate these properties with a simulation study, where the proposed algorithm always attains the performance of the off-line version, while recursive PEM often fails to converge.

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-223849 (URN)000424696904077 ()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
Note

QC 20180306

Available from: 2018-03-06 Created: 2018-03-06 Last updated: 2018-03-06Bibliographically approved
Abdalmoaty, M. & Hjalmarsson, H. (2017). Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models. In: The 20th IFAC World Congress: . Paper presented at The 20th IFAC World Congress (pp. 14058-14063). Elsevier, 50
Open this publication in new window or tab >>Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models
2017 (English)In: The 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 14058-14063Conference paper, Published paper (Refereed)
Abstract [en]

Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
IFAC-PapersOnLine
Keywords
System identification, Nonlinear systems, Stochastic systems, Monte Carlo method
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-216419 (URN)10.1016/j.ifacol.2017.08.1841 (DOI)2-s2.0-85044304531 (Scopus ID)
Conference
The 20th IFAC World Congress
Note

QC 20171024

Available from: 2017-10-22 Created: 2017-10-22 Last updated: 2017-12-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-3079

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