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Constraints in Identification of Multi-Loop Feedforward Human Control Models

机译：多回路前馈人为控制模型的识别约束

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Abstract: The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three central issues that complicate this objective. First, the identification method should not require prior assumptions regarding the dynamics of the feedforward and feedback components. Second, severe biases might be introduced by high levels of noise in the data measured under closed-loop conditions. To address the first two issues, we will consider two identification methods that make use of linear ARX models: the classic direct method and the two-stage indirect method of van den Hof and Schrama (1993). Third, model complexity should be considered in the selection of the ‘best’ ARX model to prevent ‘false-positive’ feedforward identification. Various model selection criteria, that make an explicit trade-off between model quality and model complexity, are considered. Based on computer simulations with a HC model, we conclude that 1) the direct method provides more accurate estimates in the frequency range of interest, and 2) existing model selection criteria do not prevent false-positive feedforward identification. Copyright ?2016 IFAC.
机译：摘要：人机控制器（HC）除了对反馈信号进行反馈外，还可以通过对目标信号进行前馈操作来大大提高目标跟踪性能。系统识别方法用于确定正确的HC模型结构：纯反馈或前馈/反馈组合模型。在本文中，我们研究了使这一目标复杂化的三个主要问题。首先，识别方法不应要求有关前馈和反馈组件动态的事先假设。其次，在闭环条件下测量的数据中的高噪声水平可能会引入严重的偏差。为了解决前两个问题，我们将考虑使用线性ARX模型的两种识别方法：van den Hof和Schrama（1993）的经典直接方法和两阶段间接方法。第三，在选择“最佳” ARX模型时应考虑模型的复杂性，以防止“假阳性”前馈识别。考虑了各种模型选择标准，这些标准在模型质量和模型复杂性之间做出了明确的权衡。基于使用HC模型的计算机仿真，我们得出的结论是：1）直接方法可在感兴趣的频率范围内提供更准确的估计值，以及2）现有的模型选择标准不会防止假阳性前馈识别。版权所有？2016 IFAC。

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