Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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09 Jul 2007
TL;DR: The problem of deriving MIMO parameter- dependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balanced state space realisations.
Abstract: The problem of deriving MIMO parameter- dependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balanced state space realisations. Simulation examples are used to demonstrate the performance of the proposed approach.
99 citations
TL;DR: An identification algorithm for ship manoeuvring mathematical models has been developed in this paper, which is based on the classic genetic algorithm used for minimizing a distance between the reference and recovered time histories.
99 citations
TL;DR: A method is proposed to approximately identify normalized coprime plant factors from closed loop data that leads to identified models that are specifically accurate around the bandwidth of the closed loop system.
99 citations
TL;DR: In this article, an integrated identification method is presented to consider the uncertainty effect on modal parameters for output-only system, which is based on the time-frequency characteristics of the wavelet transform (WT) and the capabilities of the bootstrap distribution in statistical estimation.
99 citations
TL;DR: An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters.
Abstract: Accurate estimation of power capacity is critical to ensure battery safety margins and optimize energy utilization. Power capacity estimators based on online identified equivalent circuit model have been widely investigated due to the high accuracy and affordable computing cost. However, the impact of noise corruption which is common in practice on such estimators has never been investigated. This paper scrutinizes the effect of noises on model identification, state of charge (SOC) and power capacity estimation. An online model identification method based on adaptive forgetting recursive total least squares (AF-RTLS) is proposed to compensate the noise effect and attenuate the identification bias of model parameters. A Luenberger observer is further used in combination with the AF-RTLS to estimate the SOC in real time. Leveraging the estimated model parameters and SOC, a multiconstraint analytical method is proposed to online estimate the power capacity. Simulation and experimental results verify that the proposed method is superior in terms of estimation accuracy and the robustness to noise corruption.
99 citations