Distributed output feedback control for multi-unit system with delayed multirate measurements

Ravi, Arvind; Kaisare, Niket S.

doi:10.1016/j.compchemeng.2020.106879

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Liu et al 18 developed a distributed model predictive controller for processes where state measurements are available infrequently and asynchronously with delays. Ravi et al 19 used the sampled state augmentation approach and designed a distributed EKF that efficiently uses delayed measurements to improve state estimates.…”

Section: Introductionmentioning

confidence: 99%

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

2023

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Effective control and monitoring of a process usually require frequent and delay-free measurements of important process output variables. However, these measurements are often either not available or available infrequently with significant time delays. This article presents a method that allows for improving the performance of distributed state estimators implemented on large-scale manufacturing processes. The method uses a sample state augmentation approach that permits using delayed measurements in distributed state estimation. The method can be used with any state estimator, including unscented Kalman filters, extended Kalman filters, and moving horizon state estimators. The method optimally handles the tradeoff between computational time and estimation accuracy in distributed state estimation implemented using a computer with parallel processors. Its implementation and performance are shown using a few simulated examples.

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Section: Introductionmentioning

confidence: 99%

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

2023

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“…In contrast to classical single-loop control methods, MPC can effectively deal with multiple-input and multiple-output models, while considering global cooperative and optimal control problems. 18,19 Furthermore, DLMPC is superior in handling disturbances, providing more reasonable steady-state targets than MPC. Therefore, DLMPC achieves the functions of the VPC system and brings higher economic benefits.…”

Section: Introductionmentioning

confidence: 99%

A double-layer model predictive control algorithm for valve position control systems

Wang

Zou

Zheng

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Valve position control systems have the characteristics of combining static and dynamic, as well as fast and slow, which enables the system’s quick response while maintaining some economic performance. However, there is further potential to optimize the economic performance of the valve position control system under conventional control algorithms. This article proposes an improved double-layer model predictive control algorithm for the valve position control system to exploit the potential economic performance while satisfying control goals. In the steady-state optimization layer of the improved double-layer structural model predictive control, the ideal residence point of the key manipulated variable is calculated from the set point of the controlled variable and the steady-state model to characterize the coupling relationship between the primary and secondary manipulated variables in the valve position control system, which realizes the successive actuation feature between the manipulated variables. In order to improve the response speed of the system, the dynamic control layer adopts a two-mode switching strategy, where the mode switching symbol is the time at which the controlled variable first approaches a given steady-state threshold value. Based on the feedback from the dynamic control layer, the steady-state optimization layer optimizes set points of controlled and manipulated variables that can handle disturbances entered into the process at any time to improve the economic performance of valve position control systems. This article implements classical control methods via advanced process strategies to consider the optimal control of processes from a superior perspective.

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Simultaneous state‐estimator tuning and parameter estimation for systems with nonstationary disturbances, multi‐rate data, and measurement delays

Liu,

McAuley

2024

Can J Chem Eng

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Model‐based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state‐estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time‐varying parameters, multi‐rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state‐estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum‐of‐squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more‐advanced state estimators that require the same tuning information as EKFs.

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Distributed output feedback control for multi-unit system with delayed multirate measurements

Cited by 7 publications

References 34 publications

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

A double-layer model predictive control algorithm for valve position control systems

Simultaneous state‐estimator tuning and parameter estimation for systems with nonstationary disturbances, multi‐rate data, and measurement delays

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