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pattern-moving-based dynamic description and optimal control for non-Newtonian mechanical systems with generalized cell mapping

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第7期103卷 3208-3229页

作者： Li, Ning Xu, Zheng Guang Zhao, Cheng Tao Li, Xiang Quan Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Beijing Foreign Studies Univ Informat Technol Ctr Beijing Peoples R China Jingdezhen Univ Sch Informat Engn Jingdezhen Peoples R China

This article focuses on the problem of modelling and control for a non-Newtonian mechanical system that may be subject to the statistical law instead of the traditional Newtonian principles mechanics. A discrete method, synthesizing the generalized cell mapping (GCM) together with dynamic programming (DP), with inverse search strategy, using pattern moving theory (PMT) framework, was proposed. The basic idea is to describe and control the system's dynamic peculiarity utilizing pattern class variable in 'pattern moving space'. First, a few prior concepts were reviewed, including PMT, cell mapping, and optimal control for this kind system. Then, we articulated a cross-mapping method to analyze the system dynamic behaviour, which takes into account the computational and statistical properties of pattern class variable simultaneously. For system optimal control, the improved GCM and cost function were performed to determine the discrete optimal control table (DOCT) in accordance with dynamic programming and inverse search. Finally, simulation results of two cases demonstrate the efficiency and practicality of the proposed approach. The study has resulted in a solution of describing and controlling based on pattern class variable for non-Newtonian mechanical systems, and its main objective was to give a different perspective in term of research into non mechanistic principles modelling and application of nonlinear systems.

关键词： generalized cell mapping inverse search optimal control pattern class variable pattern moving theory

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

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MEASUREMENT & CONTROL 2023年第7-8期56卷 1410-1421页

作者： Wu, Jinxia Li, Jianwei Liu, Chuang Yang, Weidong Liaoning Univ Technol Coll Sci Jinzhou Peoples R China Univ Sci & Technol Beijing Sch Automat Beijing Peoples R China Liaoning Univ Technol Coll Sci 169 Shiying St Jinzhou 121001 Liaoning Peoples R China

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.

关键词： Nonlinear state space model input-delay pattern class variable state feedback controller robust stabilization linear matrix inequality

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pattern-based State Feedback Stabilization for a class of Processes

Pattern-based State Feedback Stabilization for a Class of Pr...

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Chinese Automation Congress (CAC)

作者： Wang, Mushu Lu, Yanrong Zhang, Yungang Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan Peoples R China Univ Sci & Technol Sch Automat Beijing Peoples R China

ISBN: (纸本)9781728176871

For a class of production processes, a modeling method based on pattern recognition is introduced, and state feedback stabilization of the system is studied in a pattern-moving state space. At the first part of the article, the modeling method of the system is introduced, which is based on pattern moving, and the system dynamics are presented by using the so-named pattern class variable(PCV) instead of a system output or state variable. A discrete-time and discrete-state nonlinear state-space model (DNSM) is given in Euclidean space based on the modeling method. And soon a new control and stability analysis method is put forward. Finally, validity of the stabilization method is demonstrated by using data of an actual production process.

关键词： state feedback pattern recognition pattern class variable nonlinear systems process control

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Cell Mapping Model for Complex Systems with Statistical Trajectory Based on Data Mining 37

Cell Mapping Model for Complex Systems with Statistical Traj...

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37th Chinese Control Conference (CCC)

作者： Xu, Zhengguang Guo, Lingli Wang, Mushu Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chang Zhi Coll Dept Math Changzhi 046011 Peoples R China

ISBN: (纸本)9789881563958

This paper proposes a new type of modeling framework for complex systems. This kind of systems has the characteristics: it is difficult to establish the mechanism model and its dynamics obey the statistical law. Firstly, in order to extract the statistical attributes, an iterative self-organizing data analysis techniques algorithm (ISODATA) of data mining is used to separate process data into a number of classes. Based on these classes, a pattern class space in which each class is taken as an element is established. Next, to describe the system dynamic with statistical law, a pattern class variable is defined in the pattern class space. To further solve the incalculable problem of the pattern class variable, a cell space is introduced where each pattern class variable is measured by cell centers. Based on this measure, an improved controlled autoregressive moving average (CARMA) model described by the pattern class variable is obtained, and model parameters are identified by an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. The attractive of the algorithm lies in enhancing the ability of the global development and local exploration. Based on the model, the corresponding cell mapping model is derived by the state space representation, which avoid the expansion of categories. Then, the global performance of the pattern class space is analyzed via cell mappings. Finally, a simulation in the dynamic trajectory of a sintering process is given to demonstrate the feasibility of the proposed approach.

关键词： Statistical law ISODATA pattern class variable IQPSO Cell mapping

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Cell Mapping Model for Complex Systems with Statistical Trajectory Based on Data Mining

Cell Mapping Model for Complex Systems with Statistical Traj...

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第37届中国控制会议

作者： Zhengguang Xu Lingli Guo Mushu Wang School of Automation and Electrical Engineering University of Science &Technology Beijing Department of mathematics Chang Zhi College

This paper proposes a new type of modeling framework for complex *** kind of systems has the characteristics:it is difficult to establish the mechanism model and its dynamics obey the statistical ***,in order to extract the statistical attributes,an iterative self-organizing data analysis techniques algorithm(ISODATA)of data mining is used to separate process data into a number of *** on these classes,a pattern class space in which each class is taken as an element is ***,to describe the system dynamic with statistical law,a pattern class variable is defined in the pattern class *** further solve the incalculable problem of the pattern class variable,a cell space is introduced where each pattern class variable is measured by cell *** on this measure,an improved controlled autoregressive moving average(CARMA) model described by the pattern class variable is obtained,and model parameters are identified by an improved quantum-behaved particle swarm optimization(IQPSO) *** attractive of the algorithm lies in enhancing the ability of the global development and local *** on the model,the corresponding cell mapping model is derived by the state space representation,which avoid the expansion of ***,the global performance of the pattern class space is analyzed via cell ***,a simulation in the dynamic trajectory of a sintering process is given to demonstrate the feasibility of the proposed approach.

关键词： Statistical law ISODATA pattern class variable IQPSO Cell mapping

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Forecasting Model based on Multidimensional Moving pattern for a class of Complex Production Process

Forecasting Model based on Multidimensional Moving Pattern f...

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11th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Sun Changping Xu Zhengguang Gao Qiang Yu Hang Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Elect Engn Tianjin 300384 Peoples R China Univ Sci & Technol Beijing Sch Automat Beijing 100083 Peoples R China

ISBN: (纸本)9781479939794

For a class of complex production processes, in our previous work, the idea of moving pattern based modeling is proposed. But, in our previous work, the moving pattern is confined to one dimension and the one dimensional moving pattern-based modeling is studied. In this paper, moving pattern is extended to multidimensional case, and a multidimensional moving pattern based forecasting model is proposed. First, the algorithm for constructing multidimensional pattern moving space is proposed. Second, for characterizing pattern class variable quantitatively, the multidimensional interval auto-regression model (MIAR) is defined. Third, the proposed MIAR is applied to modeling the movement of pattern class variable in pattern moving space. At last, experimental results are then presented that indicate the validity and applicability of the proposed model.

关键词： Multidimensional moving pattern pattern class variable interval arithmetic interval auto-regression model

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Modeling and Optimal Control Based on Moving pattern

Modeling and Optimal Control Based on Moving Pattern

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32nd Chinese Control Conference (CCC)

作者： Xu Zhengguang Wu Jinxia Guo Lingli Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Minist Educ Adv Control Iron & Steel Proc Beijing 100083 Peoples R China

ISBN: (纸本)9781479900305

For a class of complex production process systems, in our previous work, we have proposed a modeling and control method based on moving pattern. Based on our previous work, in this paper, a new optimal control method is presented. First, based on the historical data, by constructing "pattern moving space", a dynamics description method based on moving pattern is given. Furthermore, the optimal control method is proposed based on moving pattern. In this method, by variable substitution, the system dynamics description based on moving pattern is converted to the form of state equation. Then the optimal controller is designed by using discrete-time system minimum principal. At last, some simulations based on the actual running status data collected from 400 sinter machine of Anyang iron and steel plant are given to verify the feasibility.

关键词： Moving pattern pattern Moving Space pattern class variable Modeling Optimal Control

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Data-driven pattern Moving and Generalized Predictive Control

Data-driven Pattern Moving and Generalized Predictive Contro...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Xu, Zhengguang Wu, Jinxia Univ Sci & Technol Beijing Dept Control Sci & Engn Minist Educ Key Lab Adv Control Iron & Steel Proc Beijing 100083 Peoples R China

ISBN: (纸本)9781467317146

In this paper, a generalized predictive control method based on pattern recognition technique is proposed for a class of uncertain complex systems with statistical characteristics. The system dynamics is described by working condition pattern moving rather than output equation or state space model. First, working condition data are collected around the nominal operating condition to construct "pattern moving space". The space can be updated on-line. Then "pattern class variable" is defined in this space and the prediction model based on this new variable is also constructed. At last, the generalized predictive control method is also given. This method can identify the optimal controller's parameters directly by the prediction equations and input/output data. And it can avoid many intermediate operations for online solving Diophantine equations. Some simulations based on the actual run status data collected from sintering process of Anyang iron and steel plant are given to verify the effectiveness.

关键词： pattern moving pattern moving space pattern class variable generalized predictive control

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pattern moving trajectory: a new dynamics description method

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2012年第4期17卷 370-379页

作者： Xu, Zheng-guang Wu, Jin-xia Univ Sci & Technol Beijing Sch Automat & Elect Engn Dept Control Sci & Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Minist Educ Adv Control Iron & Steel Proc Beijing 100083 Peoples R China

For a class of complex production process systems, a dynamics description method based on moving pattern is proposed. Different from traditional modelling methods, the definition of moving pattern is presented and the dynamics of system is directly described by the moving law of working condition pattern rather than differential equation or state space model. First, based on the actual run status data, 'pattern moving space' is constructed by a clustering algorithm. Then a variable called pattern class variable (which has statistical characteristic) is defined in this space to describe the moving of pattern. The prediction model based on pattern class variable is also constructed. This prediction model is different from the existing statistical modelling methods though they have the same structure formally. At last, one practical example of sintering process illustrates that the proposed strategy might provide satisfactory results without prior knowledge of the systems.

关键词： moving pattern pattern moving space pattern class variable pattern moving trajectory modelling

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Prediction Model based on Moving pattern

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JOURNAL OF COMPUTERS 2012年第11期7卷 2695-2701页

作者： Xu, Zhengguang Wu, Jinxia Qu, Shoude Univ Sci & Technol Beijing Beijing Peoples R China

For sintering process, based on the actual run status data, a new dynamics description method based on moving pattern is presented by constructing pattern moving space and defining moving variable in it. The moving variable is called as pattern class variable. And the prediction model is constructed based on pattern class variable. The feature vectors for constructing pattern moving space are extracted from the actual run status data by using partial least squares regression analysis method to cope with multicollinearity problem. Some simulations are given to verify the feasibility.

关键词： prediction model moving pattern pattern moving space pattern class variable

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