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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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Pedestrian Trajectory Prediction by Modeling the Interactions using Social LSTM Extensions

Pedestrian Trajectory Prediction by Modeling the Interaction...

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Chinese automation Congress (CAC)

作者： Zeze Si Hongxia Zhao Haina Tang Fenghua Zhu Yisheng Lv Hao Lu State Key Laboratory for Management and Control of Complex Systems Institute of Autamation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction.

关键词： automation Aggregates Predictive models Prediction algorithms Trajectory Convolutional neural networks Task analysis

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jialiang Luo Shichao Chen Yisheng Lv Wenqiao Sun Fan Yang Ruijie Xu The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University Jinan China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong China

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field.

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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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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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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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Online Adaptive Critic Robust control of Discrete-Time Nonlinear systems With Unknown Dynamics ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1696-1701页

作者： Hao Fu Xin Chen Min Wu 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

This paper concerns the optimal model reference adaptive control problem for unknown discrete-time nonlinear systems. For such problem, it is challenging to improve online learning efficiency and guaranteeing robustness to the uncertainty. To this end, we develop an online adaptive critic robust control method. In this method, a critic network and a new supervised action network are constructed to not only improve the real-time learning efficiency, but also obtain the optimal control performance. By combining the designed compensation control term, robustness is further guaranteed by compensating the uncertainty. The comparative simulation study is conducted to show the superiority of our developed method.

关键词： Approximate dynamic programming (ADP) unknown nonlinear systems neural network (NN) supervised learning model reference adaptive control (MRAC) robust control

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Fault detection for geological drilling processes using multivariate generalized Gaussian distribution and Kullback Leibler divergence ⁎

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IFAC-PapersOnLine 2020年第2期53卷 164-169页

作者： Yupeng Li Weihua Cao Wenkai Hu Chao Gan Min Wu 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

The presence of downhole faults compromises the safety and also leads to increased maintenance costs in complex geological drilling processes. In order to achieve timely and accurate detection of downhole faults, a systematic fault detection method is proposed based on the Multivariate Generalized Gaussian Distribution (MGGD) and the Kullback Leibler Divergence (KLD). Uncorrelated components are obtained from the original drilling process signals using the principle component analysis; then, the distribution of components is estimated using the MGGD; afterwards, the KLD is calculated based on a deduced analytic formula; last, the downhole faut is detected by comparing the calculated KLD with the alarm threshold obtained from normal data. The effectiveness and practicality of the proposed method are demonstrated by application to a real drilling process.

关键词： Geological drilling processes fault detection Kullback Leibler Divergence multivariate generalized Gaussian distribution

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A comprehensive evaluation method for states adjustment priority of drilling process ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11956-11961页

作者： Yang Zhou Xin Chen Min Wu Weihua Cao 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

Drilling is an important means of obtaining resources. It is important to determine appropriate drilling states adjustment priority to guide operation of the drilling. However, the priority of drilling states adjustment is difficult to determine because of the influence of multiple parameters. In this paper, a priority comprehensive evaluation method is developed to solve this problem. Firstly, support vector regression (SVR) method and long short-term memory (LSTM) neural network are introduced to build rate of penetration (ROP) prediction model and mud pit volume (MPV) prediction model, respectively. Then, the comprehensive evaluation vector is obtained by fuzzy comprehensive evaluation method based on analysis of formation drillability, rock characteristic, pump pressure variation, ROP and MPV fluctuations. Finally, the drilling states adjustment priority is determined by the principle of maximum membership and comprehensive analysis method. The simulation based on actual drilling data indicates that the proposed method can determine the adjustment priority and guide the operation of the drilling process.

关键词： Drilling process adjustment priority fuzzy comprehensive evaluation

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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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