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第三十九届中国控制会议

作者： Xuan Gong Xin Chen Xu Jian Wenpeng He School of Automation China University of Geoscience Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The identification and positioning of insulator shed feature is of vital significance for insulator cleaning. However,there exist few texture features the insulator and its surface is reflective, which makes it difficult to identify and locate the convex points on the surface of the insulator shed. In order to identify and locate the insulator shed, we propose a method to extract the convex points of the insulator skirt based on the binary image of the insulator and the position of the central axis. Binocular vision technology is applied to obtain the depth change curve of the insulator on its axis, with the help of the depth threshold method and principal component analysis（PCA） method. At the same time, the pixel coordinates on the central axis are used to traverse the depth on the central axis to extract the shed bumps, thereby achieving the spatial positioning of the insulator shed bumps. The experiments show that the bumps on the insulator’s central axis can be well marked and used in the insulator cleaning robot system.

关键词： Insulator cleaning Binocular vision Principal component analysis Shed bump Cleaning robot system

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A Generalized Probabilistic Monitoring Model with Both Randomand Sequential Data

arXiv

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arXiv 2022年

作者： Yu, Wanke Wu, Min Huang, Biao Lu, Chengda The School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been studied. In this study, a generalized probabilistic monitoring model (GPMM) is developed with both random and sequential data. Since GPMM can be reduced to various probabilistic linear models under specific restrictions, it is adopted to analyze the connections between different monitoring methods. Using expectation maximization (EM) algorithm, the parameters of GPMM are estimated for both random and sequential cases. Based on the obtained model parameters, statistics are designed for monitoring different aspects of the process system. Besides, the distributions of these statistics are rigorously derived and proved, so that the control limits can be calculated accordingly. After that, contribution analysis methods are presented for identifying faulty variables once the process anomalies are detected. Finally, the equivalence between monitoring models based on classical multivariate methods and their corresponding probabilistic graphic models is further investigated. The conclusions of this study are verified using a numerical example and the Tennessee Eastman (TE) process. Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions. Copyright © 2022, The Authors. All rights reserved.

关键词： Process monitoring

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Dissipativity analysis of time-delayed neural networks using a vector-related zero-value-term method and generalized reciprocally convex combination techniques

Dissipativity analysis of time-delayed neural networks using...

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第三十九届中国控制会议

作者： WANG Chen-Rui HE Yong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper studies the dissipative problem of neural networks with time-varying delay and external disturbance. A suitable augmented Lyapunov-Krasovskii functional(LKF) is constructed by taking full advantage of the delay information of the system and the conditions of the excitation function. Then by employing auxiliary function inequalities, the reciprocally convex combination and a vector zero-value method to deal with the derivative of the LKF, a less conservative delay-dependent dissipative criterion is obtained. Finally, a numerical example is given to show the effectiveness of this criterion.

关键词： Neural networks Time delay Dissipativeness Lyapunov-Krasovskii functional

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Identification of drilling stick-slip vibration conditions based on Empirical Mode Decomposition threshold denoising and Support Vector Machine

Identification of drilling stick-slip vibration conditions b...

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第三十九届中国控制会议

作者： Ke Zhang Weihua Cao Chao Gan Wenkai Hu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The characteristics of the normal and stick-slip vibration signals reflect the drilling conditions, which is significant in recognizing them. In this paper, a new method that combines the Empirical Mode Decomposition(EMD) threshold denoising and Support Vector Machine(SVM) is proposed to classify these characteristics. First, the EMD threshold denoising method is introduced to denoise the raw signals of the drill string vibration. Second, the features of these characteristics are selected by the Intrinsic Mode Function(IMF) energy entropy and marginal spectral energy. Last, the drilling conditions are classified and identified by the Support Vector Machine(SVM). The simulation results show that the identification accuracy of the proposed method is higher than the conventional methods.

关键词： Stick-slip Empirical Mode Decomposition Threshold denoising Intrinsic Mode Function energy entropy Marginal spectral energy Support Vector Machine

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High Precision Waveform Reproduction of Shaking Table Based on Linear Active Disturbance Rejection control

High Precision Waveform Reproduction of Shaking Table Based ...

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第三十九届中国控制会议

作者： Wang Sheng Lei Qi School of Automation Central South University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Seismic simulation shaking table plays a key role in many industrial structural dynamic ***-precision waveform repetition has a great impact on the experimental accuracy of structural dynamic *** equipment of seismic simulation shaker table has obvious uncertainties in the modeling process,so it is difficult to describe the seismic simulation shaker table with a more accurate mathematical ***,this paper adopt the Linear Active Disturbance Rejection control（LADRC） to achieve high-precision reproduction of the waveform of the seismic simulation *** method will all the model uncertainty and external disturbance as total disturbance,using the extended state observer（ESO） to estimate the total disturbance,and by using state feedback control law of generalized disturbance compensation,to get better performance of resistance to *** method does not rely on the model of the specimen and does not need to obtain prior knowledge of the *** this paper,the inertial and single-degree-of-freedom elastic specimens are simulated for vibration test,and the control effect of this strategy is fully verified by comparison with the traditional PID control method.

关键词： LADRC ESO seismic simulation shaking table

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Global Automatic Recognition and Localization of Multi Screws Based on Image Mosaic

Global Automatic Recognition and Localization of Multi Screw...

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第三十九届中国控制会议

作者： Hang Yin Yan Yuan Chaolong Yao Xiaoqi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In the tuning system based on vision sensor, the object recognition and localization is very important. In order to meet the practical tuning of the microwave cavity filter by the mechanical arm, a global recognition and localization method of multiple adjustable screws is proposed, which combines image mosaic and template matching. A high-definition global image of microwave cavity filter is composed of multiple local images according to feature point registration, and then all adjustable screws in the global image are recognized by template matching algorithm based on shape feature, which solves the problem of limited field of vision of a single industrial camera. It realizes the recognition, localization and numbering of all tuning objects in the whole tuning range, and transmits the coordinate information corresponding to the number to the mechanical arm. This method can be used to tune various types of microwave cavity filters, and it has certain versatility. The experimental results show that the recognition effect of the screws is favorable when the above method is applied to the actual tuning of 12-step microwave cavity filter with the hand-eye integrated four-axis mechanical arm. And it meets the requirements of tuning accuracy.

关键词： image mosaic microwave cavity filter image localization template matching

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Semi-global Periodic Event-triggered Output Regulation for Nonlinear Multi-agent systems

arXiv

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arXiv 2022年

作者： Zheng, Shiqi Shi, Peng Zhang, Huiyan the School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China the School of Electrical and Electronic Engineering The University of Adelaide AdelaideSA5005 Australia the College of Engineering and Science Victoria University MelbourneVIC8001 Australia the National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing400067 China

This study focuses on periodic event-triggered (PET) cooperative output regulation problem for a class of nonlinear multi-agent systems. The key feature of PET mechanism is that event-triggered conditions are required to be monitored only periodically. This approach is beneficial for Zeno behavior exclusion and saving of battery energy of onboard sensors. At first, new PET distributed observers are proposed to estimate the leader information. We show that the estimation error converges to zero exponentially with a known convergence rate under asynchronous PET communication. Second, a novel PET output feedback controller is designed for the underlying strict feedback nonlinear multi-agent systems. Based on a state transformation technique and a local PET state observer, the cooperative semi-global output regulation problem can be solved by the proposed new control design technique. Simulation results of multiple Lorenz systems illustrate that the developed control scheme is effective. © 2022, CC BY.

关键词： Multi agent systems

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A New Model of Associative Memory Neural Network Based on An Improved Memristor

A New Model of Associative Memory Neural Network Based on An...

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第三十九届中国控制会议

作者： Geliang Wan Leimin Wang Huayu Zou Shan Jiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In order to simulate biological associative learning, extensive studies have been conducted on associative memory networks. This paper designs a learning forgetting model with an improved forgetting memristor based on associative memory neural network and the corresponding circuit implementation is presented. The entire circuit includes a neuron module and a synapse module. The neuron module circuit has two inputs and one output. The synapse module circuit is mainly composed of resistance and memristor. In complete circuit, associative learning and the three forgetting processes together form a full functional associative memory model. Then, the new designed memory model reflects that people learn faster during secondary learning by adjusting the speed of memory. Besides, this model is able to take longer to forget during secondary forgetting,which can simulate young people's and elderly people's abilities to learn and forget. The simulation results of PSPICE show the effectiveness of the designed circuit. The memory model proposed in this article provides the possibility to better understand the memory function of human brain.

关键词： Forgetting memristor memristive neural network associative memory circuit learning forgetting model

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Outdoor 3D reconstruction method based on multi-line laser and binocular vision ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9554-9559页

作者： Xu Jian Xin Chen Wenpeng He Xuan Gong School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China

Three-dimensional (3D) reconstruction of substation fittings is of great significance for live working robots. However, the key problem is that active 3D cameras cannot work in outdoor strong light environment, and the passive 3D cameras cannot extract features of weak texture targets. This paper proposes an outdoor 3D reconstruction method based on multi-line laser and binocular vision. To solve the problem that weak texture target has few features, we use multi-line laser to create artificial features. To reduce the interference of natural light on the laser in the images, the frame-difference method is proposed for natural light filtering. Then we use the gray-centroid method to position the multi-line laser accurately. Finally, the binocular vision model is used to complete 3D reconstruction of the target. The experiments show that, compared with traditional 3D reconstruction methods, our 3D reconstruction method can realize 3D reconstruction of outdoor weak texture targets effectively.

关键词： Live working robot outdoor 3D reconstruction binocular vision frame-difference method laser positioning

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