A control algorithm for a non-stationary batch service production system using Kalman filter

Yousefnejad, Helia; Monfared, Mohammadali Saniee

doi:10.1016/j.eswa.2022.117916

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…satisfying x ∈ R n . A detailed study on the application of DA in discrete manufacturing can be found in [75][76][77]. In the next section, we will derive the specific implementation algorithms for each block in the architecture.…”

Section: Da Architecture For Dtmentioning

confidence: 99%

A generalized data assimilation architecture of digital twin for complex process industrial systems

Zhao,

Jiang,

Cai

et al. 2024

Meas. Sci. Technol.

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As one of the critical cores of digital twin (DT), data assimilation (DA) can maintain consistency and synchronization between DT and physical system. Kalman filtering is a common DA method, but its estimation performance is deteriorated by factors such as model inaccuracy and time-varying noise covariance in practical applications. The errors caused by these multiple uncertainties are all coupled in the measurements, which augments the difficulty for DT to obtain physical system information. In order to tackle the DA problem with multiple uncertainties, this paper proposes a generalized DA architecture of DT for sophisticated process industry. First, combining Stein variational gradient descent (SVGD) and nonlinear Bayesian filtering paradigm, a recursive estimation framework is established, which has higher accuracy in estimating the noise covariance compared to traditional methods. Second, to effectively deal with model inaccuracy by using the filtering residual containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on modified wavelet technique, the filtering residual denoising (FRD) built in NNMW-MEC can better cope with time-varying noise compared to existing wavelets, and extract the low-frequency signal involving model error information from noisy residual smoothly. In addition, because of the neural network-based state-compensation (NNSC) subblock, NNMW-MEC has more outstanding ability in compensating the state deviations with large changing range. Finally, we take the boiler system in a coal-fired power plant as an example to verify the effectiveness of our architecture. Experimental results show that the DA architecture proposed in this paper can improve the estimation performance of DT under inaccurate model and uncertain noise statistics.

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Section: Da Architecture For Dtmentioning

confidence: 99%

A generalized data assimilation architecture of digital twin for complex process industrial systems

Zhao,

Jiang,

Cai

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…A manufacturing ontology is adopted to formalize raw data into structured information while rule-based control approaches are being followed in both. A Markov decision-making process for scheduling in semiconductor fabrication is employed in [26], while a control algorithm based on Kalman filters is discussed in [27] for non-stationary batch service production, with promising results. In [28] they quantify the production recovery of SMEs in the post COVID-19 era according to their openness to Industry 4.0 technologies, indicating the importance of adaptive production planning and control in the management of the disruptive effect generated by the COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case

2022

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This work describes an approach for the Digital Transformation (DT) of a manufacturing SME in the mold production industry. The phases for changing from manual and non-adding value labor-intensive practices to digital and smart manufacturing configurations are described. Initially, the needs of the SME are presented in terms of improving the planning and monitoring capabilities. Following the requirements analysis, a set of digital functionalities are proposed and mapped to the specific needs. The digital solution presented includes technologies such as the Internet of Things, data management, intelligent production planning and web technologies. The industrial pilot case has showed how the use of open-source off-the-shelf technology, integrated with the facility’s legacy systems can reduce daily production management tasks and ease the burden of planning and monitoring. The engineers and management team of the SME have acquired first-hand experience on the benefits digitalization of process can bring to the organization that is valuable for proceeding with the DT of the organization.

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“…At present, most methods of estimating the road adhesion coefficient are based on a KF algorithm. A common KF algorithm can obtain the best estimation and a better tracking effect under the conditions of a linear Gaussian model (Yousefnejad and Monfared, 2022); however, the actual system nonlinear factors cannot be ignored. The UKF algorithm can realize the state parameter estimation of a vehicle dynamics model containing nonlinear factors, which improves the accuracy and stability of the estimation system.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of the unscented Kalman filter and particle filter estimation methods for the measurement of the road adhesion coefficient

Fan²,

Li³

2022

Mech. Sci.

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Abstract. The measurement of the road adhesion coefficient is of great significance for the vehicle active safety control system and is one of the key technologies for future autonomous driving. With a focus on the problems of interference uncertainty and system nonlinearity in the estimation of the road adhesion coefficient, this work adopts a vehicle model with 7 degrees of freedom (7-DOF) and the Dugoff tire model and uses these models to estimate the road adhesion coefficient in real time based on the particle filter (PF) algorithm. The estimations using the PF algorithm are verified by selecting typical working conditions, and they are compared with estimations using the unscented Kalman filter (UKF) algorithm. Simulation results show that the road adhesion coefficient estimator error based on the UKF algorithm is less than 7 %, whereas the road adhesion coefficient estimator error based on the PF algorithm is less than 0.1 %. Thus, compared with the UKF algorithm, the PF algorithm has a higher accuracy and control effect with respect to estimating the road adhesion coefficient under different road conditions. In order to verify the robustness of the road adhesion coefficient estimator, an automobile test platform based on a four-wheel-hub-motor car is built. According to the experimental results, the estimator based on the PF algorithm can realize the road surface identification with an error of less than 1 %, which verifies the feasibility and effectiveness of the algorithm with respect to estimating the road adhesion coefficient and shows good robustness.

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A control algorithm for a non-stationary batch service production system using Kalman filter

Cited by 5 publications

References 54 publications

A generalized data assimilation architecture of digital twin for complex process industrial systems

A generalized data assimilation architecture of digital twin for complex process industrial systems

Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case

A comparative study of the unscented Kalman filter and particle filter estimation methods for the measurement of the road adhesion coefficient

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