Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 292) Show all publications
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, 145-152 p.Article 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, 234-247 p.Article 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, 2390-2405 p., 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
Keyword
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, 31-56 p.Article 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, 53-60 p.Article 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, 53-60 p.Article 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
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, 572-581 p.Article 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
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, 14058-14063 p.Conference 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
Keyword
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)
Conference
The 20th IFAC World Congress
Note

QC 20171024

Available from: 2017-10-22 Created: 2017-10-22 Last updated: 2017-12-04Bibliographically approved
Özkan, L., Bombois, X., Ludlage, J. H. A., Rojas, C. R., Hjalmarsson, H., Modén, P. E., . . . Van den Hof, P. M. J. (2016). Advanced autonomous model-based operation of industrial process systems (Autoprofit): Technological developments and future perspectives. ANNUAL REVIEWS IN CONTROL, 42, 126-142.
Open this publication in new window or tab >>Advanced autonomous model-based operation of industrial process systems (Autoprofit): Technological developments and future perspectives
Show others...
2016 (English)In: ANNUAL REVIEWS IN CONTROL, ISSN 1367-5788, Vol. 42, 126-142 p.Article, review/survey (Refereed) Published
Abstract [en]

Model-based operation support technology such as Model Predictive Control (MPC) is a proven and accepted technology for multivariable and constrained large scale control problems in process industry. Despite the growing number of successful implementations, the low level of operational efficiency of MPC is an existing problem, specifically the lack of advanced maintenance technology. To this end, within the EU FP 7 program, a project (Autoprofit (1)) has been executed to advance the level of autonomy and automated maintenance of MPC technology. Taking linear model-based technology as a starting point, in the project a philosophy has been developed for autonomous performance monitoring, diagnosis, experiment design, model adaptation and controller re-tuning, that is driven by economic criteria in each step, working towards an operation support system in which effective maintenance and adaptation of MPC controllers becomes feasible. In this development, challenging research questions have been addressed in the areas of on-line performance monitoring and diagnosis, least costly experiment design, automated adaptation of models, and auto-tuning, and new fundamental techniques have been developed. Although a full fledge and industrially proven (semi-)automated system is not yet realised, parts of the on-line system have been implemented and validated on real life cases provided by the industrial partners, showing that the formulated objectives are within reach.

Place, publisher, year, edition, pages
Elsevier, 2016
Keyword
Model based operation support system, Autonomous maintenance, Performance diagnosis, Hypothesis testing, System identification, Experiment design, Randomized algorithms, (Auto)Tuning, Controller matching, Extremum seeking
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-200044 (URN)10.1016/j.arcontrol.2016.09.015 (DOI)000389388800009 ()2-s2.0-84995653361 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 257059
Note

QC 20170126

Available from: 2017-01-26 Created: 2017-01-20 Last updated: 2017-01-26Bibliographically approved
Larsson, C. A., Hägg, P. & Hjalmarsson, H. (2016). Generation of signals with specified second-order properties for constrained systems. International journal of adaptive control and signal processing (Print), 30(3), 456-472.
Open this publication in new window or tab >>Generation of signals with specified second-order properties for constrained systems
2016 (English)In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 3, 456-472 p.Article in journal (Refereed) Published
Abstract [en]

This contribution considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identification, firstly, because the quality and accuracy of the identified model are highly dependent on the excitation signal used during the experiment and secondly, because on real processes, it is often important to constrain the input and output of the process because of actuator saturation and safety considerations. The signal generation is formulated as a model predictive controller with probabilistic constraints to make the algorithm robust to model uncertainties and process noise. The corresponding optimization problem is then solved with tools from scenario-based stochastic optimization. To reduce the model uncertainties, the method is made adaptive where a new model of the system and its uncertainties are reidentified. The algorithm is successfully applied to a simulation example and in a practical experiment for the identification of a quadruple tank lab process.

Keyword
signal generation, excitation signals, input design, constrained systems, uncertain systems, stochastic optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-185376 (URN)10.1002/acs.2586 (DOI)000372359800003 ()2-s2.0-84959887129 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 257059
Note

QC 20160418

Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2017-11-30Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9368-3079

Search in DiVA

Show all publications