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Sökning: WFRF:(Hjalmarsson Håkan)

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21.
  • Annergren, Mariette, 1982- (författare)
  • Application-Oriented Input Design and Optimization Methods Involving ADMM
  • 2016
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • This thesis is divided into two main parts. The first part considers application-oriented input design, specifically for model predictive control (MPC). The second part considers alternating direction method of multipliers (ADMM) for ℓ1 regularized optimization problems and primal-dual interior-point methods.The theory of system identification provides methods for estimating models of dynamical systems from experimental data. This thesis is focused on identifying models used for control, with special attention to MPC. The objective is to minimize the cost of the identification experiment while guaranteeing, with high probability, that the obtained model gives an acceptable control performance. We use application-oriented input design to find such a model. We present a general procedure of implementing application-oriented input design to unknown, possibly nonlinear, systems controlled using MPC. The practical aspects of application-oriented input design are addressed and the method is tested in an experimental study.In addition, a MATLAB-based toolbox for solving application-oriented input design problems is presented. The purpose of the toolbox is threefold: it is used in research; it facilitates communication of research results; it helps an engineer to use application-oriented input design.Several important problems in science can be formulated as convex optimization problems. As such, there exist very efficient algorithms for finding the solutions. We are interested in methods that can handle optimization problems with a very large number of variables. ADMM is a method capable of handling such problems. We derive a scalable and efficient algorithm based on ADMM for two ℓ1 regularized optimization problems: ℓ1 mean and covariance filtering, and ℓ1 regularized MPC. The former occurs in signal processing and the latter is a specific type of model based control.We are also interested in optimization problems with certain structural limitations. These limitations inhibit the use of a central computational unit to solve the problems. We derive a distributed method for solving them instead. The method is a primal-dual interior-point method that uses ADMM to distribute all the calculations necessary to solve the optimization problem at hand.
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23.
  • Anubhab, Ghosh, et al. (författare)
  • DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks in learning an estimator. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks -- long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator. 
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24.
  • Auvert, Marine, et al. (författare)
  • On Router Control for Congestion Avoidance
  • 2002
  • Konferensbidrag (refereegranskat)abstract
    • This short paper deals with active queue management for computer networks. The goal is to develop control mechanisms for routers in heterogeneous networks that reduce traffic fluctuations. The proposed control strategy operates with local information (such as estimated arrival rates) and actively use the buffers to smooth traffic, and thus it avoids the buildup and propagation of traffic bursts.
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26.
  • Barenthin, Märta, et al. (författare)
  • Applications of mixed H2 and H∞ input design in identification
  • 2005
  • Konferensbidrag (refereegranskat)abstract
    • The objective of this contribution is to quantify benefits of optimal input design compared to the use of standard identification input signals, e.g. PRBS signals for some common, and important, application areas of system identification. Two benchmark problems taken from process control and control of flexible mechanical structures are considered. We present results both when the design is based on knowledge of the true system (in general the optimal design depends on the system itself) and for a practical two step procedure when an initial model estimate is used in the design instead of the true system. The results show that there is a substantial reduction in experiment time and input excitation level. A discussion on the sensitivity of the optimal input design to model estimates is provided.
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27.
  • Barenthin, Märta, et al. (författare)
  • Data-driven methods for L2-gain estimation
  • 2009
  • Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 1597-1602
  • Konferensbidrag (refereegranskat)abstract
    • In this paper we present and discuss some data-driven methods for estimation of the L2-gain of dynamical systems. Partial results on convergence and statistical properties are provided. The methods are based on multiple experiments on the system. The main idea is to directly estimate the maximizing input signal by using iterative experiments on the true system. We study such a data-driven method based on a stochastic gradient method. We show that this method is very closely related to the so-called power iteration method based on the power method in numerical analysis. Furthermore, it is shown that this method is applicable for linear systems with noisy measurements. We will also study L2-gain estimation of Hammerstein systems. The stochastic gradient method and the power iteration method are evaluated and compared in simulation examples. © 2009 IFAC.
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28.
  • Barenthin, Märta, et al. (författare)
  • Gain estimation for Hammerstein systems
  • 2006
  • Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). ; , s. 784-789
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we discuss and compare three different approaches for L2- gain estimation of Hammerstein systems. The objective is to find the input signal that maximizes the gain. A fundamental difference between two of the approaches is the class, or structure, of the input signals. The first approach involves describing functions and therefore the class of input signals is sinusoids. In this case we assume that we have a model of the system and we search for the amplitude and frequency that give the largest gain. In the second approach, no structure on the input signal is assumed in advance and the system does not have to be modelled first. The maximizing input is found using an iterative procedure called power iterations. In the last approach, a new iterative procedure tailored for memoryless nonlinearities is used to find the maximizing input for the unmodelled nonlinear part of the Hammerstein system. The approaches are illustrated by numerical examples.
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29.
  • Barenthin, Märta, et al. (författare)
  • Identification and control: Joint input design and H infinity state feedback with ellipsoidal parametric uncertainty
  • 2005
  • Ingår i: Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05. - 0780395670 ; , s. 6454-6459
  • Konferensbidrag (refereegranskat)abstract
    • One obstacle in connecting robust control with models generated from prediction error identification is that very few control design methods are able to directly cope with the ellipsoidal parametric uncertainty regions that are generated by such identification methods. In this contribution we present a sufficient condition for the existence of a H-infinity state feedback controller for the multi-input/single-output case which accomodates for ellipsoidal parametric uncertainty. The condition takes the form of a linear matrix inequality whose solution also provides a set of valid feedback gains. The model class considered corresponds to systems with known poles but uncertain zero locations. A second important contribution of the paper is to integrate the input design problem in system identification with this control synthesis method. This means that given H-infinity specifications on the closed loop transfer function are translated into the requirements on the input signal spectrum used to identify the process so that the ellipsoidal model uncertainty resulting from model identification using this input spectrum will be shaped such that the control specifications are satisfied for all models in the uncertainty set and hence guaranteed for the true system. The procedures are illustrated on a numerical example.
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30.
  • Barenthin, Märta, et al. (författare)
  • Identification and control: Joint input design and H-infinity state feedback with ellipsoidal parametric uncertainty via LMIs
  • 2008
  • Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 44:2, s. 543-551
  • Tidskriftsartikel (refereegranskat)abstract
    • One obstacle in connecting robust control with models generated from prediction error identification is that very few control design methods are able to directly cope with the ellipsoidal parametric uncertainty regions that are generated by such identification methods. In this contribution we present a joint robust state feedback control/input design procedure which guarantees stability and prescribed closed-loop performance using models identified from experimental data. This means that given H-infinity specifications on the closed-loop transfer function are translated into sufficient requirements on the input signal spectrum used to identify the process. The condition takes the form of a linear matrix inequality.
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