Multirate State Estimation Using Moving Horizon Estimation

Krämer, Stefan; Gesthuisen, R.

doi:10.3182/20050703-6-cz-1902.00654

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…For the multi-rate estimation, the problem has been widely studied based on EKF framework [38]. Moving horizon estimation (MHE) [39][40][41] and other state observer design [37,42,43] have also been investigated. However, most of the EKF and MHE-based methods will not account for the inter-sample dynamic behavior and are incapable of giving estimation for asynchronously sampled measurements.…”

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confidence: 99%

Modeling and Observer-Based Monitoring of RAFT Homopolymerization Reactions

et al. 2019

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Reversible addition–fragmentation chain–transfer (RAFT) polymerization of methyl methacrylate (MMA) is modeled and monitored using a multi-rate multi-delay observer in this work. First, to fit the RAFT reaction rate coefficients and the initiator efficiency in the model, in situ 1 H nuclear magnetic resonance (NMR) experimental data from small-scale (<2 mL) NMR tube reactions is obtained and a least squares optimization is performed. 1 H NMR and size exclusion chromatography (SEC) experimental data from large-scale (>400 mL) reflux reactions is then used to validate the fitted model. The fitted model accurately predicts the polymer properties of the large-scale reactions with slight discordance at late reaction times. Based on the fitted model, a multi-rate multi-delay observer coupled with an inter-sample predictor and dead time compensator is designed, to account for the asynchronous multi-rate measurements with non-constant delays. The multi-rate multi-delay observer shows perfect convergence after a few sampling times when tested against the fitted model, and is in fair agreement with the real data at late reaction times when implemented based on the experimental measurements.

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confidence: 99%

Modeling and Observer-Based Monitoring of RAFT Homopolymerization Reactions

et al. 2019

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“…Several different structures have been proposed for the multi-rate MHE; Some approaches align the two measurements by filling in empty values for the slow measurement with a zero-order hold method (Kramer et al, 2005), or a model prediction (Liu et al, 2016). In contrast, in a variable structure only existing slow measurements are considered (Krämer and Gesthuisen, 2005). The approach of the distributed MHE regards the fast measurements from the fast dynamics as algebraic equations and solves the MHE only for the slow subsystem (Yin and Liu, 2017).…”

Section: Multi-rate Measurements and Ill-conditioning Of The Estimati...mentioning

confidence: 99%

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Kim¹,

Krausch²,

Aizpuru³

et al. 2022

Preprint

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We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput microbioreactor platform at 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with an automated liquid handling and online and atline analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges which need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and atline measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can

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“…Despite the fact that fairly good estimation results have been achieved, stability analysis of a multirate multidelay observer remained open. Other multirate estimation methods considering measurement delays, such as extended Kalman filter, and moving horizon estimation, have also been investigated. In addition, a number of results are available in the literature regarding controller design for nonlinear systems with delays in states, actuators, and measurements …”

Section: Introductionmentioning

confidence: 99%

A dead time compensation approach for multirate observer design with large measurement delays

Ling¹,

Kravaris

2018

AIChE Journal

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In our previous work, we developed a multirate observer design method in linear systems with asynchronous sampling based on a Luenberger observer design coupled with inter‐sample predictors. In this article, the problem of multirate multidelay observer design is addressed where both asynchronous sampling and possible measurement delays are accounted for. The proposed observer adopts an available multirate observer design in the time interval between two consecutive delayed measurements. A dead time compensation approach is developed to compensate for the effect of delay and update past estimates when a delayed measurement arrives. The stability and robustness properties of the multirate observer will be preserved under nonconstant, arbitrarily large measurement delays. A mathematical example and a gas‐phase polyethylene reactor example demonstrate good performance of the proposed observer in the presence of nonuniform sampling and nonconstant measurement delays. © 2018 American Institute of Chemical Engineers AIChE J, 65: 562–570, 2019

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Multirate State Estimation Using Moving Horizon Estimation

Cited by 12 publications

References 10 publications

Modeling and Observer-Based Monitoring of RAFT Homopolymerization Reactions

Modeling and Observer-Based Monitoring of RAFT Homopolymerization Reactions

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

A dead time compensation approach for multirate observer design with large measurement delays

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