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Input design for Bayesian frequency response identification via convex programming
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DOI：10.1016/j.automatica.2021.110023.png)
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Abstract
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This paper proposes a novel approach for the input design of the Bayesian frequency response identification. The input design problem is first formulated by considering deterministic inputs, which leads to a non-convex optimization problem. Restricting inputs to be periodic makes the problem convex but introduces conservativeness. The stochastic consideration facilitates the input design problem to be presented as a convex problem whose decision variables are a finite number of autocorrelation coefficients. A multiplied sampled input turns out to be the good input with a certain probability. Then, the optimal input is selected to be the minimum energy one among the good inputs. Simulations results demonstrate the effectiveness of the proposed method. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
System identification
Bayesian estimation
Experiment design
Frequency response identification
Convex optimization
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5.9
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1.2W
Citations:
5.2W
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AUTOMATICA
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AUTOMATICAIF5.9
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