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Unconstrained Nonlinear State Estimation for Tennessee Eastman Challenge Process

机译：田纳西伊士曼挑战过程的无约束非线性状态估计

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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：田纳西州伊士曼挑战过程（TE）在过程控制文献中作为估计和控制中的挑战问题而众所周知。这种非线性过程和相应的测量模型的复杂性导致非高斯密度，并且可以适应非高斯性的估计器可以提供更好的状态估计。在这项工作中，我们比较并演示了最近开发的基于高斯和的无味高斯和滤波器（UGSF）的效用，用于执行过程的非线性状态估计。 UGSF使用sigma点概念来捕获过程非线性，并结合高斯近似值和，以精确表示从过程演化而来的非高斯先验密度。对于TE处理的一种操作模式，将UGSF的性能与众所周知的扩展卡尔曼滤波器（EKF）和无味卡尔曼滤波器（UKF）方法进行了比较。结果证明了将UGSF用作状态估计器在大型和高度非线性TE过程中的实用性。

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