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1.	Hjalmarsson, Håkan, 1962-, et al. (author) Identification for control: L-2 and L-infinity methods 2001 In: PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL. - 0780370619 ; , s. 2701-2706 Conference paper (peer-reviewed)abstract Identification of restricted complexity models for linear time-invariant systems is considered. A method, with ties to model reduction and the extended instrumental variable method, which uses a cost function based on cross-correlations between the prediction error and some auxiliary variable such as the input is presented. In open loop and under linear feedback, this method is able to asymptotically approximate the underlying system in L-2 or L-infinity norm without an explicit noise model under noisy conditions. Arbitrary frequency weighting, also depending on the true frequency function, can be used. The method is applied to identification of models suited for control design. It is shown that some model free methods can be fit into this framework. It is also pointed out that closed loop stability is not taken into account in the bias tuning due to the fact that L-norms are used.

Hjalmarsson, Håkan, 1962-, et al. (author)
Identification for control: L-2 and L-infinity methods
2001
In: PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL. - 0780370619 ; , s. 2701-2706
Conference paper (peer-reviewed)abstract
- Identification of restricted complexity models for linear time-invariant systems is considered. A method, with ties to model reduction and the extended instrumental variable method, which uses a cost function based on cross-correlations between the prediction error and some auxiliary variable such as the input is presented. In open loop and under linear feedback, this method is able to asymptotically approximate the underlying system in L-2 or L-infinity norm without an explicit noise model under noisy conditions. Arbitrary frequency weighting, also depending on the true frequency function, can be used. The method is applied to identification of models suited for control design. It is shown that some model free methods can be fit into this framework. It is also pointed out that closed loop stability is not taken into account in the bias tuning due to the fact that L-norms are used.

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