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Unconstrained Nonlinear State Estimation for Tennessee Eastman Challenge Process

机译：田纳西州伊斯特曼挑战过程的无约束非线性国家估计

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Tennessee Eastman challenge process (TE) has been well known in process control literature as a challenge problem in estimation and control. Complexities in this nonlinear process and corresponding measurement models lead to non-Gaussian densities and estimators which can accommodate the non-Gaussianity can give better state estimates. In this work, we compare and demonstrate the utility of recently developed Sum of Gaussians based Unscented Gaussian Sum Filter (UGSF) for performing nonlinear state estimation of the process. UGSF uses sigma point concept to capture process nonlinearities coupled with a Sum of Gaussians approximation to achieve an accurate representation of non-Gaussian prior densities evolved from the process. The performance of UGSF is compared with the well known Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) approaches, for one of the operating modes of TE process. Results demonstrate the utility of using UGSF as a state estimator for the large-dimensional and highly nonlinear TE process.
机译：田纳西州伊斯曼挑战过程（TE）在过程控制文献中众所周知，作为估计和控制的挑战问题。该非线性过程中的复杂性和相应的测量模型导致非高斯密度和估算器，其可以适应非高斯度，可以提供更好的状态估计。在这项工作中，我们比较并展示最近基于Gaussian的高斯的高斯和滤波器（UGSF）的高斯的高斯和UGSF）的实用性，以执行该过程的非线性状态估计。 UGSF使用Sigma点概念来捕获与高斯近似的过程非线性耦合，以实现从过程中演变的非高斯的先前密度的准确表示。将UGSF的性能与众所周知的扩展卡尔曼滤波器（EKF）进行比较，以及TE过程的一个操作模式之一的众所周知的扩展卡尔曼滤波器（EKF）和Unscented Kalman滤波器（UKF）方法。结果证明了使用UGSF作为大型和高度非线性TE过程的状态估计的效用。

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