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Data Modeling Predictive Control Theory for Deriving Real-Time Models from Simulations

机译:从仿真中得出实时模型的数据建模预测控制理论

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This paper presents the mathematical framework and procedure for extracting differential equation based models from High-Fidelity Real-Time and Non Real-Time models for use in hyper-real-time simulation. Our approach captures a series of input/output scenario frames and derives analytical transfer function models from these examples. The result is a coupled set of differential equations that are integrated in real-time or analytically solved into polynomial form for Volterra type solution in real-time. The resulting model numerically yields the same answer on training inputs as the model was derived from, and yields nonlinear interpolated transfer functions in frequency space for off-nominal cases. Since the upper and lower error bounds and their variance are predictable, the derived model can maintain accreditation without implicit caveats. This allows the derived model to be executed in freeform when departures from intended uses are necessary but accreditation boundaries must not be violated.
机译:本文提出了从高保真实时模型和非实时模型中提取基于微分方程模型的模型的数学框架和过程,以用于超实时仿真。我们的方法捕获了一系列输入/输出方案框架,并从这些示例中得出了分析传递函数模型。结果是一对耦合的微分方程组,它们被实时集成或实时地解析为Volterra型解的多项式形式。所得模型在数值上对训练输入产生与从模型导出时相同的答案,并且对于非标称情况在频率空间中产生非线性插值传递函数。由于误差上下限及其方差是可以预测的,因此导出的模型可以保持认证,而不会产生隐含的警告。这样,当有必要偏离预期用途但不得违反认证界限时,可以自由形式执行导出的模型。

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