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Publications (10 of 298) Show all publications
Abdalmoaty, M. . & Hjalmarsson, H. (2018). Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem. In: 18th IFAC Symposium on System Identification: . Paper presented at 18th IFAC Symposium on System Identification, July 9-11, 2018. Stockholm, Sweden.
Open this publication in new window or tab >>Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem
2018 (English)In: 18th IFAC Symposium on System Identification, 2018Conference paper, Published paper (Refereed)
Abstract [en]

The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presentedand discussed.

Series
IFAC-PapersOnLine
Keywords
Nonlinear system identication, Stochastic systems, Wiener-Hammerstein, Benchmark problem.
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233635 (URN)
Conference
18th IFAC Symposium on System Identification, July 9-11, 2018. Stockholm, Sweden
Funder
Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079EU, European Research Council, 267381
Note

QC 20180828

Available from: 2018-08-27 Created: 2018-08-27 Last updated: 2018-08-29Bibliographically approved
Abdalmoaty, M. . & Hjalmarsson, H. (2018). Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors. In: : . Paper presented at 57th IEEE Conference on Decision and Control, Miami Beach, FL, USA.
Open this publication in new window or tab >>Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors
2018 (English)Conference paper, Published paper (Refereed)
Keywords
Nonlinear system identification, Multiple-inputs multiple outputs, Wiener Model, Stochastic System, Consistency, Prediction Error Method
National Category
Control Engineering Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233826 (URN)
Conference
57th IEEE Conference on Decision and Control, Miami Beach, FL, USA
Funder
Swedish Research Council, 2015-05285; 2016-06079
Note

QCR 20180904

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2018-10-19Bibliographically 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
Abdalmoaty, M. . & Hjalmarsson, H. (2018). Linear Prediction Error Methods for Stochastic Nonlinear Models. Automatica
Open this publication in new window or tab >>Linear Prediction Error Methods for Stochastic Nonlinear Models
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Article in journal (Refereed) Submitted
Abstract [en]

The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Parameter estimation; System identification; Stochastic systems; Nonlinear models; Prediction error methods.
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-235340 (URN)
Funder
Swedish Research Council, 2015-05285 : 2016-06079
Note

QC 20180921

Available from: 2018-09-21 Created: 2018-09-21 Last updated: 2018-11-13
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
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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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-3079

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