Detection of model-plant mismatch in closed-loop control system

Ling, Dan; Zheng, Ying; Zhang, Hong; Yang, Weidong; Tao, Bo

doi:10.1016/j.jprocont.2017.06.008

Cited by 20 publications

(5 citation statements)

References 35 publications

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“…Currently, the theories, models, and methods of decision-making based on fuzzy data have been developed [ [2], [3], [4], [5], [6]]. The mismatch of the model and the topology of real objects is important in modeling and optimizing technological processes [ [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]]. In this paper, this problem is considered when modeling the technological process of oil pumping.…”

Section: Introductionmentioning

confidence: 99%

Mismatch problem of the model and topology of oil pumping facilities

Bekibayev¹,

Bossinov²,

Zhapbasbayev³

et al. 2023

KIMS/CUMR/MShKP

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The mismatch of the model and the topology of real objects is important in modeling technological processes, which is the purpose of this paper. The problem is considered when modeling hot oil pumping in the "Kasymov–Bolshoy Chagan" oil pipeline. In this problem, the topology of objects consists of the linear part of the pipeline and technological equipment (pumps and heating furnaces) of the stations. The accuracy of the simulation results is determined by the calculations of pressure and temperature in the oil pipeline. The pressure in the pipeline is created by pumps at the stations and is determined by the dependence of the pressure and efficiency of the pump on the oil flow rate. These characteristics change depending on the service life of the pump. The identification of the actual dependences of the pressure and efficiency of the pump on the oil flow rate was carried out by the regression analysis of experimental data. The pressure in the linear part is determined by the hydraulic resistance of the pipeline. The actual dependence of the hydraulic resistance coefficient on the Reynolds number and wall roughness was obtained by regression analysis of experimental data. The temperature in the oil pipeline is created at the stations by heating furnaces. The identification of the actual characteristics of the heating furnace was also found by regression analysis of the experimental data. The temperature distribution in the linear part is determined by the heat transfer of oil with the surrounding environment. An undefined parameter for calculating heat transfer is the soil thermal conductivity, which depends on the type of rock and the degree of soil moisture. The soil thermal conductivity is determined in such a way that at a given oil flow rate, oil temperatures at the beginning of the section and soil at the section, the calculated oil temperature at the end of the section has the smallest discrepancy with the actual one. Thus, the determination of the actual dependencies of the objects makes it possible to increase the accuracy of the results of hot pumping modeling and eliminates the mismatches of the model and the topology of the objects.

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Section: Introductionmentioning

confidence: 99%

Mismatch problem of the model and topology of oil pumping facilities

Bekibayev¹,

Bossinov²,

Zhapbasbayev³

et al. 2023

KIMS/CUMR/MShKP

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show abstract

“…Some detection technologies are based on an orthogonal projection method [24] to analyze the extent and calculate the index of MPM problems [25]. However, detecting MPM would be a hot issue to address while locating the position of MPM would be another.…”

Section: Introductionmentioning

confidence: 99%

Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

Ming

Xie

2021

Processes

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In closed-loop control systems, the model accuracy exerts large influences on the controllability, stability and quality of the whole process. Among all the faults that could affect the system performance, Model Plant Mismatch (MPM) is the one that not only directly threatens the system stability but also deteriorates the controller performance. Meanwhile, MPM has a major influence on the qualities of outputs about industrial products. In this work, a new detection method based on Granger Causality is proposed to detect and locate the MPM in multiple input multiple output systems. Causality can reflect the relations between the mismatch fault and its negative effects on model predictive control(MPC) systems. With the assistance of disturbance transfer function models, the causality method can further be used to locate the mismatch positions and get the correct channels of each kind of mismatches. The proposed method was examined and validated in the Wood-Berry process in contrast to the decussation location method under model predictive controller.

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“…13 Tian et al 14 proposed an intelligent approach for the performance assessment of a multivariate MPC controller by comparing the covariance of the monitored operation data with the user-specified benchmark. Ling 15 proposed a model evaluation index, which is the ratio between the variance of the disturbance innovation and that of the model quality variable to detect process model-plant mismatch in a closed-loop system. Badwe et al 16 quantified the influence of model-plant mismatch on controller performance and then separated the effect of model mismatch when system performance deteriorated.…”

Section: Introductionmentioning

confidence: 99%

Assessment of dynamic matrix control controller parameters via estimating Markov parameters of disturbance

LiLijuan

ChenXingyu

YangShipin

2020

Adaptive Control & Signal

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For an industrial control system, controller parameters are important factors that affect the control system performance. This article introduces a controller parameter performance assessment method based on disturbance characteristic variation for a dynamic matrix control system. Assuming that the process model is accurate and the setpoint is constant, disturbance characteristic variation without adjusting controller parameters leads to the degradation of system performance. Hence, the Markov parameters of a disturbance model, which inflect the variation of disturbance characteristics, are used to assess the controller parameter performance. These Markov parameters can be obtained from closed-loop data using the subspace identification method. The differences between the Markov parameters in an actual running state and those in a well-regulated state are used to design the assessing index of controller parameters. The simulation results in the Wood-Berry model and TE process show the validity of the proposed index.

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Detection of model-plant mismatch in closed-loop control system

Cited by 20 publications

References 35 publications

Mismatch problem of the model and topology of oil pumping facilities

Mismatch problem of the model and topology of oil pumping facilities

Detection and Location of Model-Plant Mismatch in Multiple Input Multiple Output Systems under Model Predictive Controller Using Granger Causality Method

Assessment of dynamic matrix control controller parameters via estimating Markov parameters of disturbance

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