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Håkan Hjalmarsson

Bio: Håkan Hjalmarsson is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: System identification & Estimation theory. The author has an hindex of 39, co-authored 380 publications receiving 10138 citations. Previous affiliations of Håkan Hjalmarsson include Université catholique de Louvain & Linköping University.


Papers
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Journal ArticleDOI
TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.

2,031 citations

Journal ArticleDOI
TL;DR: An optimization approach to iterative control design and a direct optimal tuning algorithm that is particularly well suited for the tuning of the basic control loops in the process industry, which are typically PID loops.
Abstract: We have examined an optimization approach to iterative control design. The important ingredient is that the gradient of the design criterion is computed from measured closed loop data. The approach is thus not model-based. The scheme converges to a stationary point of the design criterion under the assumption of boundedness of the signals in the loop. From a practical viewpoint, the scheme offers several advantages. It is straightforward to apply. It is possible to control the rate of change of the controller in each iteration. The objective can be manipulated between iterations in order to tighten or loosen performance requirements. Certain frequency regions can be emphasized if desired. This direct optimal tuning algorithm is particularly well suited for the tuning of the basic control loops in the process industry, which are typically PID loops. These primary loops are often very badly tuned, making the application of more advanced (for example, multivariable) techniques rather useless. A first requirement in the successful application of advanced control techniques is that the primary loops be tuned properly. This new technique appears to be a very practical way of doing this, with an almost automatic procedure.

906 citations

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TL;DR: It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy.

500 citations

Journal ArticleDOI
TL;DR: Adaptive and iterative control algorithms based on explicit criterion minimization are briefly reviewed and an overview of one such algorithm, iterative feedback tuning IFT, is presented.
Abstract: Adaptive and iterative control algorithms based on explicit criterion minimization are briefly reviewed and an overview of one such algorithm, iterative feedback tuning IFT, is presented. The basic IFT algorithm is reviewed for both single-input/single-output and multi-input/multi-output systems. Subsequently the application to non-linear systems is discussed. Stability and robustness aspects are covered. A survey of existing extensions, applications and related methods is also provided. Copyright © 2002 John Wiley & Sons, Ltd.

431 citations

Journal ArticleDOI
TL;DR: Different approximation methods are considered, and the acquired approximation experience is applied to estimation problems, and wavelet and ‘neuron’ approximations are introduced, and shown to be spatially adaptive.

416 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.

2,031 citations