Improved State Estimation using a Combination of Moving Horizon Estimator and Particle Filters

Rajamani, Murali R.; Rawlings, James B.

doi:10.1109/acc.2007.4283068

Cited by 10 publications

(9 citation statements)

References 2 publications

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“…These categories, along with the most popular estimators in each category, are the following: (1) purely recursive: extended Kalman filter (EKF), 20 unscented Kalman filter (UKF), [21][22][23][24][25] particle filter (PF), [26][27][28][29][30][31] and cell filter (CF), 32,33 (2) optimization-based: Moving Horizon Estimation (MHE), [34][35][36][37][38] and (3) hybrid methods combining (1) and (2): PF-MHE. 39, 40 Rawlings and Bakshi 39 present an overview of many of these methods. Purely recursive state estimators may not be robust to the presence of data outliers, process disturbances and model errors.…”

Section: Introduction and Prior Workmentioning

confidence: 99%

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

Lima

Rawlings

2011

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com).State estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high-quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time-varying autocovariance least-squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter.

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Section: Introduction and Prior Workmentioning

confidence: 99%

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

Lima

Rawlings

2011

AIChE Journal

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“…For example, in [1], the EKF Gaussian approximation is used as the importance distribution for PF; Merwe et al follows the similar idea, using the Unscented Kalman filter (UKF) as the importance distribution [2]; Rajamani and Rawlings proposes to combine moving horizon estimation (MHE) with PF to improve the robustness of the algorithm [3]. All of the mentioned strategies require the uncertainties expressed in terms 1 X. Shao is with Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, T6G 2V4, Canada xinguang at ualberta.ca of stochastic models.…”

Section: Introductionmentioning

confidence: 99%

Ellipsoidal set based robust particle filtering for recursive Bayesian state estimation

Shao

Zhao

Liu

et al. 2013

2013 10th IEEE International Conference on Control and Automation (ICCA)

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Particle filters have become an increasingly useful tool for recursive Bayesian state estimation, especially for nonlinear and non-Gaussian problems. Despite the large number of papers published on particle filters in recent years, one issue that has not been addressed to any significant degree is the robustness. This paper presents a deterministic approach that has emerged in the area of robust filtering, and incorporates it into particle filtering framework. In particular, an ellipsoidal set membership approach is used to define a feasible set for particle sampling that contains the true state of the system, and makes the particle filter robust against unknown but bounded uncertainties. Simulation results show that the proposed algorithm is more robust than the regular particle filter and its variants such as the extended Kalman particle filter.

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“…Recently, the advanced step MHE 32 was proposed to efficiently solve the MHE optimization problem considering gross measurement errors, but applied to address CSTR and distillation column systems. Finally, the last group of estimators, called hybrid methods, represents the combination of recursive and optimization-based methods (for example, the MHE with a particle filter, 33 which incorporates the advantages of each of the basic methods). In a recent review dealing with stochastic filters, 34 the mentioned techniques can be found, as well as other formulations that address differential algebraic systems, multirate sampling, and delayed measurements.…”

Section: Introductionmentioning

confidence: 99%

The Autocovariance Least-Squares Method for Batch Processes: Application to Experimental Chemical Systems

Rincón

Roux

Lima

2014

Ind. Eng. Chem. Res.

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Chemical engineering processes need careful monitoring in order to ensure product specification (i.e., composition, quality, etc.). Physicochemical analytical techniques can be applied for characterization, but those techniques can be time-consuming and become impractical for chemical processes for which online monitoring is needed. Calorimetry is frequently used to monitor and control batch and semibatch reactions. State and covariance estimation for batch processes employing advanced techniques, such as moving horizon estimation (MHE) for state and autocovariance least-squares (ALS) for covariance, have not been studied together. In this paper, the ALS technique is extended for batch processes and validated using laboratory data of three experimental case studies, with focus on polymerization processes. The problem of the simultaneous online estimation of states and parameters in these systems, employing recursive, extended Kalman filter (EKF) and unscented Kalman filter (UKF), and optimization-based (MHE) estimators with covariances estimated by the ALS, is examined. The results of the implementation of the proposed approach show accuracy of the estimated variables for the polymerization processes considered. Thus, this work provides a systematic procedure for the successful determination of covariances and states for experimental batch cases, including systems under repeated perturbations.

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Improved State Estimation using a Combination of Moving Horizon Estimator and Particle Filters

Cited by 10 publications

References 2 publications

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

Ellipsoidal set based robust particle filtering for recursive Bayesian state estimation

The Autocovariance Least-Squares Method for Batch Processes: Application to Experimental Chemical Systems

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