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Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method

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Journal of Systems Engineering and Electronics 2020年第4期31卷 761-769页

作者： WANG Hongwei FENG Penglong School of Electrical Engineering Xinjiang UniversityUrumqi 830047China School of Control Science and Engineering Dalian University of TechnologyDalian 110024China

Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

关键词： multirate sampled data nonlinear system fuzzy model multi-model

Local multi-model integrated soft sensor based on just-in-time learning for mechanical properties of hot strip mill process

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Journal of Iron and Steel Research(International) 2021年第7期28卷 830-841页

作者： Jie Dong Ying-ze Tian Kai-xiang Peng Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China National Engineering Research Center for Advanced Rolling Technology University of Science and Technology BeijingBeijing 100083China

The mechanical properties of hot rolled strip are the key index of product quality,and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process.To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately,a soft sensor based on ensemble local modeling was proposed.Firstly,outliers of process data are removed by local outlier factor.After standardization and transformation,normal data that can be used in the model are obtained.Next,in order to avoid redundant variables participating in modeling and reducing performance of models,feature selection was applied combing the mechanism of hot rolling process and mutual information among variables.Then,features of samples were extracted by supervised local preserving projection,and a prediction model was constructed by Gaussian process regression based on just-in-time learning(JITL).Other JITL-based models,such as support vector regression and gradient boosting regression tree models,keep all variables and make up for the lost information during dimension reduction.Finally,the soft sensor was developed by integrating individual models through stacking method.Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.

关键词： Soft sensor Just-in-time learning multi-model Hot rolling

The China multi-model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018

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Journal of Meteorological Research 2019年第3期33卷 540-552页

作者： Hong-Li REN Yujie WU Qing BAO Jiehua MA Changzheng LIU Jianghua WAN Qiaoping LI Xiaofei WU Ying LIU Ben TIAN Joshua-Xiouhua FU Jianqi SUN Laboratory for Climate Studies & China Meteorological Administration-Nanjing University Joint Laboratory for Climate Prediction Studies National Climate Center China Meteorological Administration Beijing 100081 State Key Laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 National Climate Center China Meteorological Administration Beijing 100081 School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu 610225 Institute of Atmospheric Sciences Fudan University Shanghai 200433

multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly-seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June-August) starting from March 2018 and evaluated the 1991-2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Nino-Southern Oscillation (ENSO) in the tropical central-eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June-July-August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March-August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central-eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation

关键词： multi-model ensemble China multi-model ensemble prediction system (CMME) real-time forecast skill assessment

multi-model Communication and Data Assimilation for Mitigating model Error and Improving Forecasts

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Chinese Annals of Mathematics,Series B 2019年第5期40卷 689-720页

作者： Yian CHEN Samuel N.STECHMANN Department of Mathematics University of Wisconsin-MadisonMadisonWI 53706USA Department of Statistics University of ChicagoChicagoIL 60637USA Department of Mathematics and Department of Atmospheric and Oceanic Sciences University of Wisconsin-MadisonMadisonWI 53706USA

models for weather and climate prediction are complex, and each model typically has at least a small number of phenomena that are poorly represented, such as perhaps the Madden-Julian Oscillation(MJO for short) or El Nino-Southern Oscillation(ENSO for short) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather-climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model’s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather-climate system. As an initial investigation, the method is examined here using a simplified scenario of linear models, involving a system of stochastic partial differential equations(SPDEs for short)as an imperfect tropical climate model and stochastic differential equations(SDEs for short) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the lowdimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself.

关键词： MJO multi-model communication Data assimilation Kalman filter algorithm

Steam Pressure Control of 1000 MW Ultra-Supercritical Coal-Fired Power Unit Based on multi-model Predictive Control

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Journal of Shanghai Jiaotong university(Science) 2019年第1期24卷 86-93页

作者：王国良丁宝苍阎威武 School of Electronic and Electrical Engineering Shanghai University of Engineering Science School of Electronic and Information Engineering Xi'an Jiaotong University Department of Automation Shanghai Jiao Tong University

Ultra-supercritical(USC) coal-fired unit is more and more popular in these years for its advantages.But the control of USC unit is a difficult issue for its characteristic of nonlinearity, large dead time and coupling among inputs and outputs. In this paper, model predictive control(MPC) method based on multi-model and double layered optimization is introduced for coordinated control of USC unit running in sliding pressure mode and fixed pressure mode. Three inputs(i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs(i.e. output power, main steam temperature and main steam pressure). The step responses for the dynamic matrix control(DMC) are constructed using the three inputs by the three outputs under both pressure control mode. Piecewise models are built at selected operation points. In simulation, the output power follows load demand quickly and main steam temperature can be controlled around the setpoint closely in load tracking control. The simulation results show the effectiveness of the proposed methods.

关键词： ultra-supercritical coordinated control multi-model model predictive control steam pressure control

A multi-model Approach to Design a Robust SVC Damping Controller Using Convex Optimization Technique to Enhance the Damping of Inter-Area Oscillations Considering Time Delay

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Energy and Power Engineering 2017年第12期9卷 750-771页

作者： Abdlmnam Abdlrahem Hani Albalawi Holcombe Department of Electrical and Computer Engineering Clemson University Clemson USA Department of Electrical and Computer Engineering University of Tabuk Tabuk Saudi Arabia

This paper introduces a multi-model approach to design a robust supplementary damping controller. The designed fixed-order supplementary damping controller adjusts the voltage reference set point of SVC. There are two main objectives of the controller design, damping low frequencies oscillations and enhancing power system stability. This method relies on shaping the closed-loop sensitivity functions in the Nyquist plot under the constraints of these functions. These constraints can be linearized by choosing a desired open-loop transfer function. The robust controller is designed to minimize the error between the open-loop of the original plant model and the desired transfer functions. These outcomes can be achieved by using convex optimization methods. Convexity of the problem formulation ensures global optimality. One of the advantages of the proposed approach is that the approach accounts for multi-model uncertainty. In contrast to the methods available in the literature, the proposed approach deals with full-order model (i.e., model reduction is not required) with lower controller order. The issue of time delay of feedback signals has been addressed in this paper for different values of time delay by applying a multi-model optimization technique. The proposed approach is compared to other existing techniques to design a robust controller which is based on H2 under pole placement. Both techniques are applied to the 68-bus system to evaluate and validate the robust controller performance under different load scenarios and different wind generations.

关键词： H∞ Nyquist Diagram Inter-Area Modes multi-model Oscillations Robust Control Wind Generations SVC

A DFIM Sensor Faults multi-model Diagnosis Approach Based on an Adaptive PI multiobserver—Experimental Validation

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International Journal of Modern Nonlinear Theory and Application 2015年第2期4卷 161-178页

作者： Abid Aicha Benhamed Mouna Sbita Lassaad National Engineering School of Gabes Tunisia Photovoltaic Wind and Geothermal Systems Research Gabes Tunisia

This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM’s outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov’s based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.

关键词： Diagnosis Doubly Fed Induction Motor multi-model Approach Adaptive PI multi-Observer

Splitting and Merging Based multi-model Fitting for Point Cloud Segmentation

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Journal of Geodesy and Geoinformation Science 2019年第2期2卷 78-89页

作者： Liangpei ZHANG Yun ZHANG Zhenzhong CHEN Peipei XIAO Bin LUO The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430072 China

This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.

关键词： machine vision 3D conformation point cloud segmentation splitting and merging multi-model fitting

Induction Motor modeling Based on a Fuzzy Clustering multi-model—A Real-Time Validation

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International Journal of Modern Nonlinear Theory and Application 2015年第2期4卷 153-160页

作者： Abid Aicha Bnhamed Mouna Sbita Lassaad National Engineering School of Gabes Photovoltaic Wind and Geothermal Systems Research Gabes Tunisia

This paper discusses a comparative study of two modeling methods based on multimodel approach. The first is based on C-means clustering algorithm and the second is based on K-means clustering algorithm. The two methods are experimentally applied to an induction motor. The multimodel modeling consists in representing the IM through a finite number of local models. This number of models has to be initially fixed, for which a subtractive clustering is necessary. Then both C-means and K-means clustering are exploited to determine the clusters. These clusters will be then exploited on the basis of structural and parametric identification to determine the local models that are combined, finally, to form the multimodel. The experimental study is based on MATLAB/SIMULINK environment and a DSpace scheme with DS1104 controller board. Experimental results approve that the multimodel based on K-means clustering algorithm is the most efficient.

关键词： multi-model modeling C-means Clustering Algorithm K-Means Clustering Algorithm Induction Motor (IM) Experimental Validation