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Simultaneous State and Parameter Estimation using Receding-horizon Nonlinear Kalman Filter

机译：后向水平非线性卡尔曼滤波器的同时状态和参数估计

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Online estimation of internal states and parameters is often required for process monitoring, control and fault diagnosis. The conventional approach to estimate drifting parameters is to artificially model them as a random walk process and estimate them simultaneously with the states. However, tuning of the random walk model is not a trivial exercise. Recently, Valluru et al. (2017) have developed a moving window based state and parameter estimator which assumes that the parameters change slowly and remain constant within the window. Also, in another development, a moving window based recursive filter, receding horizon nonlinear Kalman (RNK) filter has been proposed by Rengaswamy et al. (2013). In this work, a novel simultaneous state and parameter estimator is proposed by combining the window based parameter variation model with RNK filter formulation. The performance of the RNK based estimator is demonstrated by conducting simulation studies on the benchmark quadruple tank system and a CSTR system. The efficacy of RNK based estimator is compared with that of the conventional simultaneous EKF approach and Moving Horizon Estimator (MHE) based state and parameter approach. Analysis of the simulation results reveals that the proposed state and parameter estimation scheme is able to generate better estimation performance than that of the simultaneous EKF and closer to that of the MHE based parameter estimator with less computational efforts.
机译：内部状态和参数的在线估计通常是过程监控，控制和故障诊断所必需的。估计漂移参数的常规方法是将它们作为随机游走过程进行人工建模，并与状态同时进行估计。但是，调整随机游走模型并不是一件容易的事。最近，Valluru等。 （2017）开发了一个基于移动窗口的状态和参数估计器，它假设参数变化缓慢并且在窗口内保持不变。另外，在另一个发展中，Rengaswamy等人提出了一种基于移动窗口的递归滤波器，即后视非线性非线性卡尔曼（RNK）滤波器。 （2013）。在这项工作中，通过将基于窗口的参数变化模型与RNK滤波器公式相结合，提出了一种新颖的同时状态和参数估计器。通过对基准四缸系统和CSTR系统进行仿真研究，证明了基于RNK的估算器的性能。将基于RNK的估计器的功效与传统的同时EKF方法和基于移动水平估计器（MHE）的状态和参数方法的功效进行了比较。对仿真结果的分析表明，所提出的状态和参数估计方案能够比同时执行的EKF产生更好的估计性能，并且与基于MHE的参数估计器相比具有更少的计算工作量。

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