T–S Fuzzy Logic Based Modeling and Robust Control for Burning-Through Point in Sintering Process

Chen, Xin; Hu, Jie; Wu, Min; Cao, Weihua

doi:10.1109/tie.2017.2708004

Cited by 31 publications

(14 citation statements)

References 32 publications

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“…Because Γ(k) satisfies the constraint condition (13), inequality (16) is handled in two cases: ‖dx(k + 1)‖ ≠ 0 and ‖dx(k + 1)‖ = 0. Case 1: ‖dx(k + 1)‖ ≠ 0 By the S-procedure method, 36 inequality (16) with the first condition of constraint (13) is equivalent to the following inequality:…”

Section: Stabilization For a Class Of Nonlinear Systemsmentioning

confidence: 99%

“…For the control of a production process, generally, it is difficult or impossible to build its accurate model. Therefore, data‐driven–based methods were used for modeling or control . However, most of works focused on modeling or control with respect to the real parameters (eg, the actual state or output of the system) instead of inherent statistical attributes implicated in these processes .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, data‐driven–based methods were used for modeling or control . However, most of works focused on modeling or control with respect to the real parameters (eg, the actual state or output of the system) instead of inherent statistical attributes implicated in these processes . In the method of pattern‐moving–based dynamics description, output pattern samples, which were extracted from working condition data, were partitioned into classes.…”

Section: Introductionmentioning

confidence: 99%

“…[15][16][17][18][19] However, most of works focused on modeling or control with respect to the real parameters (eg, the actual state or output of the system) instead of inherent statistical attributes implicated in these processes. [15][16][17][18][19] In the method of pattern-moving-based dynamics description, output pattern samples, which were extracted from working condition data, were partitioned into classes. Therefore, the statistical attributes of the concerned processes were extracted.…”

Section: Introductionmentioning

confidence: 99%

“…For the concerned complex processes, automatic control technology exists in all aspects of process control, such as production control and power control . For the control of a production process, generally, it is difficult or impossible to build its accurate model.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Stability and stabilization for a class of complex production processes via LMIs

Wang

Guo

2019

Optim Control Appl Methods

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Summary This paper deals with the problem of stability and stabilization for a non‐Newton mechanical system, and the system is described by the so‐called pattern class variable rather than a state or output variable. At the beginning of this paper, the method of pattern‐moving–based dynamics description is introduced, and it describes the dynamic properties of a concerned production process at a larger granularity. Then, a pattern‐moving–based nonlinear state space model is put forward. Moreover, a feature of output‐class partition is defined and extracted, and two constraint conditions are given for the system based on the feature. Through dealing with difference of a Lyapunov function in two cases, new methods of stability and stabilization are proposed by using the LMI and S‐procedure approaches. They guarantee the stability and robust stabilization of the system and present the relationship between the feature of output‐class partition and system stability. A sintering process and numerical examples are used to demonstrate the effectiveness and practicability of the proposed method.

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Section: Stabilization For a Class Of Nonlinear Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stability and stabilization for a class of complex production processes via LMIs

Wang

Guo

2019

Optim Control Appl Methods

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Application of grey system theory to phosphorite sinter process: From modeling to control

Toktassynova

Fourati

Suleimenov

2020

Asian Journal of Control

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The sintering process of phosphorite ore occurs with a large amount of return caused by untimely process control. The control task of phosphorite ore sintering is to regulate the parameters of the process to obtain a high quality sinter. The parameter clearly responsible for sinter quality is the temperature in the wind box. Therefore, in order to solve the control task, it is necessary to predict the highest temperature of the charge (also known as the burn through point (BTP)). In this paper, the theory of grey systems is used as a predictive model, which makes it possible to obtain an adequate model that uses a small number of initial samples of real temperature data. Based on the grey model GMC(1,n) a new optimal model is presented, which is constructed by using optimization algorithm. Optimal model predicts the BTP, and to establish an optimal regulation, a control synthesis is carried out through an optimization of the prediction according to the “particle swarm” algorithm.

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Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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The sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modelling methods in the sintering process have increasingly received the researchers' attention. But now, there is still no systematic review of the data‐driven modelling approaches in the sintering process. Therefore, in this article, we conduct a comprehensive overview and prospects on the data‐driven models for the purpose of intelligent sintering. First, the mechanism and characteristics of the sintering process are introduced and analyzed elaborately. Second, the detailed research status of the sintering process is illustrated from four aspects: key parameters prediction, control, optimization, and others. Finally, several challenges and promising modelling methods such as deep learning in the sintering process are outlined and discussed for future research.

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T–S Fuzzy Logic Based Modeling and Robust Control for Burning-Through Point in Sintering Process

Cited by 31 publications

References 32 publications

Stability and stabilization for a class of complex production processes via LMIs

Stability and stabilization for a class of complex production processes via LMIs

Application of grey system theory to phosphorite sinter process: From modeling to control

Data‐driven modelling methods in sintering process: Current research status and perspectives

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