The least squares algorithm, parametric system identification and bounded noise

Akçay, Hüseyin; Khargonekar, Pramod P.

doi:10.1016/0005-1098(93)90017-n

Cited by 29 publications

(4 citation statements)

References 9 publications

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“…We have also shown the robust convergence of the least squares parameter estimation algorithm in the presence of worst case bounded noise [22].…”

Section: Robust Identification or Identification For Robust Controlmentioning

confidence: 86%

The Design and Analysis of Robust Feedback Systems

Khargonekar¹

1992

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“…We have also shown the robust convergence of the least squares parameter estimation algorithm in the presence of worst case bounded noise [22].…”

Section: Robust Identification or Identification For Robust Controlmentioning

confidence: 86%

The Design and Analysis of Robust Feedback Systems

Khargonekar¹

1992

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“…for some μ T , μ S ∈ R. Moreover, assume that there exists a matrix Z ∈ E T ∩ E S (+ ) such that f T ( Z) and f ( Z) are both nonsingular. 2 Then, there exist P 0, β > 0, τ S ≥ 0, τ T ≥ 0 satisfying (17) if and only if there exist P 0, β > 0, and K satisfying (16b).…”

Section: A Disturbance-free Source Systemmentioning

confidence: 99%

Data-Based Transfer Stabilization in Linear Systems

Li,

De Persis,

Tesi

et al. 2024

IEEE Trans. Automat. Contr.

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We present a novel framework for transferring the knowledge from one system (source) to design a stabilizing controller for a second system (target). Our motivation stems from the hypothesis that abundant data can be collected from the source system, whereas the data from the target system are scarce. We consider both cases where data collected from the source system is noiseless and noisy. For each case, by leveraging the data collected from the source system and a priori knowledge on the maximum distance of the two systems, we find a suitable, and relatively small, compact set of systems that contains the actual target system, and then provide a controller that stabilizes the compact set. In particular, the controller can be obtained by solving a set of linear matrix inequalities (LMIs). Feasibility of those LMIs is discussed in details. We complement our theoretical findings by two numerical case studies of low-order and high-order systems.

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“…While solving the LS identification problem under the worst-case noise there are new problems. By Akcay and Khargonekar (1993) it is shown that this method robustly converges for estimating of the finite impulse response (FIR) models of systems. The result which demonstrates a divergence of LS ∞ Hidentification of the infinite impulse response (IIR) models under the bounded noise, was obtained by Akcay and Hjalmarson (1994).…”

Section: Introductionmentioning

confidence: 99%