Håkan Hjalmarsson

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A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, waveiet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models(More)
The links between identification and control are examined. The main trends in this research area are summarized, with particular focus on the design of low complexity controllers from a statistical perspective. 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(More)
A framework for reformulating input design problems in prediction error identification as convex optimization problems is presented. For linear time-invariant single input/single output systems, this framework unifies and extends existing results on open-loop input design that are based on the finite dimensional asymptotic covariance matrix of the parameter(More)
A key issue in system identification is how to cope with high system complexity. In this contribution we stress the importance of taking the application into account in order to cope with this issue. We define the concept ‘‘cost of complexity’’ which is a measure of the minimum required experimental effort (e.g., used input energy) as a function of the(More)
In this contribution we extend a recently developed framework for open loop input design to closed loop experiment design. More specifically, for the very common situation of a fixed controller during the identification experiment, the framework is extended to the design of an optimal reference signal spectrum and also to cope with closed loop signal(More)
A l a n ' S W e compare open loop versus closed loop identification when the identified model is used for control design, and when the system itself belongs to the model class, so that only variance errors are relevant. Our measure of controller performance (which is used as our design criterion for identification) is the variance of the error between the(More)