Pattern-based State Feedback Stabilization for a Class of Processes

Wang, Mushu; Lu, Yanrong; Zhang, Yungang

doi:10.1109/cac51589.2020.9327079

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…Lyapunov stability theory was used to study the asymptotic stability of control systems, and the relationship between system output classification, system matrix and system asymptotic stability was established. 12,13 In engineering and physics, the dynamic stability of systems is a widely concerned topic, and various approaches are used from several perspectives. 14,15 In stochastic dynamics, when the probability density function Fokker-Planck equation is used as the auxiliary condition system for extreme value search, 16 the process based on entropy maximum is very important.…”

Section: Introductionmentioning

confidence: 99%

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Robust stabilization for a class of complex production process systems based on multi-dimensional moving pattern

Liu

et al. 2023

Measurement and Control

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This paper studies robust stabilization problem and state feedback controller design problem for a class of complex production process systems by pattern class variable instead of state variable or output variable. Firstly, the maximum entropy clustering method is introduced and modeling method based on multi-dimensional moving pattern is given. Nonlinear state space models of systems with input-delay are established by using the trajectories of pattern class variable. Then sufficient conditions for robust stabilization of the system are obtained by Linear Matrix Inequality. In addition, a state feedback controller is designed based on this condition. Finally, the effectiveness of the methods is illustrated by taking the actual working conditions data as an example.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust stabilization for a class of complex production process systems based on multi-dimensional moving pattern

Liu

et al. 2023

Measurement and Control

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“…And based on the measurement methods, system dynamical properties were described, and controllers were designed (Li et al, 2020(Li et al, , 2021Xu and Wang, 2016;Xu et al, 2013Xu et al, , 2018. Moreover, system stability and influence of output-class partition were analyzed (Wang et al, 2019(Wang et al, , 2020Xu et al, 2019Xu et al, , 2020. However, the common characteristic of these works was that the description method was composed of three parts: a measurement mapping, an initial model, and a classification mapping.…”

Section: Introductionmentioning

confidence: 99%

“…So, the description method could be simplified. Most of the initial models were linear (Wang et al, 2019(Wang et al, , 2020Xu et al, 2019Xu et al, , 2020. But it was difficult to estimate their structure and parameters of the initial model for some complex dynamical properties.…”

Section: Introductionmentioning

confidence: 99%

An improved pattern-based prediction model for a class of industrial processes

Wang

Pan

2021

Transactions of the Institute of Measurement and Control

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For the problem of simplifying pattern-based modeling procedures, an improved pattern-based modeling method is put forward via pattern classification for a class of complex processes. It is a pure data-driven modeling method using statistical attributes of the processes. At the beginning of the paper, a method of system dynamics description based on pattern moving is introduced. Then, an improved method of pattern-moving-based prediction modeling is put forward, and it simplifies the pattern-moving-based modeling method by integrating an initial model and a classification mapping. It consists of two parts: system pattern construction and pattern classification. And a constructive classification neural network (CCNN) is designed to describe system dynamics by classifying the system pattern, and its generalization is discussed. Finally, simulations using data of an actual production process demonstrate the feasibility of the proposed modeling method, and the effectiveness of the CCNN is verified using comparison experiments.

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Pattern-based State Feedback Stabilization for a Class of Processes

Cited by 2 publications

References 6 publications

Robust stabilization for a class of complex production process systems based on multi-dimensional moving pattern

Robust stabilization for a class of complex production process systems based on multi-dimensional moving pattern

An improved pattern-based prediction model for a class of industrial processes

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