Kadali Ramesh, Huang Biao
Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Canada.
ISA Trans. 2002 Oct;41(4):521-37. doi: 10.1016/s0019-0578(07)60107-4.
This paper proposes a new method for obtaining a linear quadratic Gaussian (LQG) benchmark in terms of the variances of process input and output from closed-loop data, for assessing the controller performance. LQG benchmark has been proposed in the literature to assess controller performance since the LQG tradeoff curve represents the limit of performance in terms of input and output variances. However, an explicit parametric model is required to calculate the LQG benchmark. In this work, we propose a data driven subspace approach to calculate the LQG benchmark under closed-loop conditions with certain external excitations. The optimal LQG-benchmark variances are obtained directly from the subspace matrices corresponding to the deterministic inputs and the stochastic inputs, which are identified using closed-loop data with setpoint excitation. These variances are used for assessing the controller performance. The method proposed in this paper is applicable to both univariate and multivariate systems. Profit analysis for the implementation of feedforward control to the existing feedback-only control system is also analyzed under the optimal LQG performance framework. The proposed method is illustrated through a simulation example and an application on a pilot scale process.
本文提出了一种新方法，可根据闭环数据中的过程输入和输出方差来获取线性二次高斯（LQG）基准，以评估控制器性能。由于LQG权衡曲线代表了输入和输出方差方面的性能极限，文献中已提出LQG基准来评估控制器性能。然而，计算LQG基准需要一个明确的参数模型。在这项工作中，我们提出了一种数据驱动的子空间方法，用于在具有特定外部激励的闭环条件下计算LQG基准。最优LQG基准方差直接从与确定性输入和随机输入对应的子空间矩阵中获得，这些矩阵是使用带有设定值激励的闭环数据识别出来的。这些方差用于评估控制器性能。本文提出的方法适用于单变量和多变量系统。在最优LQG性能框架下，还对在现有仅反馈控制系统中实施前馈控制的效益分析进行了分析。通过一个仿真示例和一个中试规模过程中的应用对所提出的方法进行了说明。
