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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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A Semi-Supervised Root Cause Identification Method for Alarm Floods via Graph-Based Manifold Regularization 43

A Semi-Supervised Root Cause Identification Method for Alarm...

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43rd Chinese control Conference, CCC 2024

作者： Chen, Ning Hu, Wenkai Liu, Zhe Wang, Fei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Petrochemical Company Limited Wuhan430082 China

ISBN: (纸本)9789887581581

An alarm flood is a situation where the alarm rate is too high and exceeds the operator's management ability. The occurrence and propagation of an alarm flood cause difficulties for the operators to respond to critical alarms in time, which may make the situation further deteriorate into an industrial accident. In this paper, a semi-supervised method is proposed for root cause identification of alarm floods via graph-based manifold regularization. A vector representation method for industrial alarm event data is presented based on word embedding for alarm textual encoding. A semi-supervised method is proposed to identify root causes of alarm floods based on both labeled and unlabeled alarm flood sequences. The effectiveness of the proposed method is demonstrated by a case study with data generated by a public industrial simulation model. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Semi-supervised learning

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A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

A discrete moth-flame optimization algorithm for multiple au...

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作者： Zeng, Junhai Xie, Wei Pan, Mi School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangdong Guangzhou510641 China Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing South China University of Technology Guangdong Guangzhou510641 China Department of Civil and Environmental Engineering University of Macau 999078 China

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more

关键词： Materials handling

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Estimation of Bottom-Hole Pressure Based on Bayesian Optimization and Bidirectional Long Short-Term Memory Neural Network 43

Estimation of Bottom-Hole Pressure Based on Bayesian Optimiz...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zihan Lu, Chengda Wang, Yawu Zhou, Yang Wu, Min She, Jinhua China University of Geosciences School of Future Technology Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji192-0982 Japan

ISBN: (纸本)9789887581581

Bottom-hole pressure (BHP) plays a crucial role in a drilling process. The accurate estimation of BHP ensures safe and efficient drilling operations. Artificial neural networks can predict BHP indirectly by analyzing the non-linear relationship with wellhead variables. This paper introduces a BHP estimation model that leverages Bayesian optimization and a bidirectional long short-term memory neural network (BO-BiLSTM). The model employs Bayesian optimization to determine optimal hyperparameters, including the number of hidden layers, initial learning rate, and L2 regularization parameter. The BO-BiLSTM model achieves a mean absolute percentage error of 2.056% and an R-squared score of 0.94596. The results demonstrate that the presented model enhances estimation precision and outperforms other methods examined in this study. This method facilitates indirect measurement of BHP using wellhead data, and contributes to the exploration of deep-sea resources in complex environments. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Bottom hole pressure

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Adaptive Fuzzy control of Autonomous Surface Vehicles for Fast Accurate Trajectory Tracking

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International Journal of Fuzzy systems 2023年第6期25卷 2411-2422页

作者： Zhang, Xuefeng Su, Shun-Feng Chen, Yang-Quan College of Sciences Northeastern University Shenyang110819 China Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Embedded Systems and Automation Laboratory School of Engineering University of California at Merced MercedCA95343 United States

This paper investigates the trajectory tracking control problem for the autonomous surface vehicles with unknown dynamics and environmental disturbances. A novel adaptive fuzzy constrained control approach is put forward to deal with this problem. Instead of the exponentially decaying functions, a new-type performance envelop is designed to offer a way to explicitly and freely preassign the settling time and the accuracy of trajectory tracking. Moreover, in lieu of involving the approach angle or azimuth angle, a new idea for tackling underactuation is proposed that yields a singularity-free continuous control solution. The fuzzy logic systems are employed to address the unknown and lumped nonlinearities in the closed-loop system. The resulting control system possesses inherent robustness to the external disturbances and the second derivative of the reference trajectory, thus without the need for employing disturbance observers or assuming the available derivative information. It is demonstrated by the constraint analysis based on dialectic by contradiction that the developed control achieves arbitrarily fast and accurate trajectory tracking for the vehicle. Finally, the effectiveness and superiority of our approach are verified by a comparative simulation. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy systems Association.

关键词： Fuzzy logic

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A Feature Difference Network for Defect Detection on the Surface with Complex Pattern of Ceramic Tile 42

A Feature Difference Network for Defect Detection on the Sur...

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42nd Chinese control Conference, CCC 2023

作者： Xiong, Jianru An, Jianqi Wang, Xing-Ao Ou, Huimin Chen, Xin 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

ISBN: (纸本)9789887581543

The surface defects of ceramic tile greatly affect the service life of ceramic tile. At present, many detection methods of ceramic tile surface defects are mostly used for ceramic tiles with monochrome background or simple texture. However, many tiles with complex and irregular surface patterns are used in practical applications, but many methods cannot effectively detect surface defects in such tiles. This paper presents a double input feature difference network structure to overcome the limitation. First, a double input channel is constructed to extract features from the template image and the defect image respectively. Next, a method of feature difference is performed at different depths to suppress the background interference and prevent misclassification between different defect categories. Then a parameter-free attention module is embedded in the backbone to improve the ability of feature extraction. Experimental results show that this model effectively improves the mean average accuracy of 8.3% and the recall rate of 11.7%. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Surface defects

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Semantic Segmentation of Historical Landslide Based on Improved U-Net 42

Semantic Segmentation of Historical Landslide Based on Impro...

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42nd Chinese control Conference, CCC 2023

作者： Qu, Hanqi Lu, Chengda Pan, Yutong Zeng, Yuxuan Wu, Min 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

ISBN: (纸本)9789887581543

Landslide disasters are extremely destructive. Accurate identification of landslides plays an important role in disaster assessment, loss control and post-disaster reconstruction. This paper proposes a semantic segmentation landslide identification method based on improved U-Net. The deep convolution neural network and jump connection method is used for end-to-end semantic segmentation to achieve deep feature extraction and fusion of different receptive fields, thus enriching feature information. SENet modules are adopted to enhance the ability of the model to extract important features, so as to further improve the accuracy of model recognition. Extensive experiments show that our improved U-Net achieves better performance than the original algorithm on our landslide datasets. The results of Iou are improved by 4.12% which demonstrates our work is of great significance for the research of landslide area identification. Finally, the model is deployed to the web and applied to the geological hazard intelligent monitoring system to realize the landslide identification task. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Landslides

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Development of a High-Frequency Power Source Dedicated to DBD Reactors for Ozone Production 3

Development of a High-Frequency Power Source Dedicated to DB...

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3rd International Conference on Advanced Electrical engineering, ICAEE 2024

作者： Bechekir, Seyf Eddine Belabed, Meriem Brahami, Mostefa Jbilou, Mokhtaria Nemmich, Said Marref, Mohemed Amine Djillali Liabes University of Sidi Bel-Abbes Intelligent Control and Electrical Power Systems Laboratory Department of Electrical Engineering Algeria Algeria Djillali Liabes University of Sidi Bel-Abbes Intelligent Control and Electrical Power Systems Laboratory Algeria Institute of Applied Sciences and Technologies University of Sciences and Technology of Oran Mb Algeria

ISBN: (纸本)9798350349351

This paper presents the implementation of a half-bridge resonant inverter to power a dielectric barrier discharge (DBD) ozone generator. We use the PIC16F876A microcontroller-based control circuit to vary the inverter frequency. We use a step-up transformer to obtain the high voltage required for the DBD. We conducted an experimental study to investigate the impact of frequency and voltage on ozone production and power consumption. The results show that these two parameters have a strong influence. This study demonstrates the advantages of using a resonant inverter combined with optimum electrical parameter adjustment to achieve excellent energy efficiency for the DBD ozone generator. The choice of a resonant half-bridge topology aims to maximize energy efficiency, crucial in a solar-powered system. The circuit has been optimized to reduce switching losses. In addition, the development of the power source takes into account the capacitive nature of DBD reactors, using a series resonant circuit formed by the DBD capacitance and the step-up transformer inductance. The frequency range from 14 kHz to 17.5 kHz has been specifically chosen and optimized for the DBD reactor, taking into account the constraints of the PV source and the characteristics of the discharge. © 2024 IEEE.

关键词： Resonant circuits

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A Pipeline Leak Detection Method Based on MGD-KLD and On-Delay Timer 42

A Pipeline Leak Detection Method Based on MGD-KLD and On-Del...

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42nd Chinese control Conference, CCC 2023

作者： He, Jiaqi Yuan, Yan Hu, Wenkai Cao, Weihua 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

ISBN: (纸本)9789887581543

Leaks in natural gas pipelines can cause very serious safety accidents, and timely detection and remedial action can greatly reduce the losses. In recent years, pipeline leak detection has received extensive studies. Most methods use pressure sensors or acoustic sensors to detect pipelines, but there are certain limitations on the usage scenarios and detection time delays. On this basis, this paper selects maglev vibration detector to detect the vibration signal of pipelines. The difficulty lies in that, sudden changes in vibration signals due to external disturbances, may lead to false alarms. Therefore, this paper proposes a pipeline leak detection method using Multivariate Gaussian Distribution based Kullback-Leibler Divergence (MGD-KLD) and on-delay timer to reduce false alarms during the detection process. In this paper, by constructing a simulated pipeline platform for leak experiments and applying the above method to process the experimental data, the false alarm rate of pipeline leak detection can be effectively reduced. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Leak detection

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Image Super-Resolution of Residual-Dense Connected Networks in Unmanned Deep-Sea Exploration Equipment 42

Image Super-Resolution of Residual-Dense Connected Networks ...

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42nd Chinese control Conference, CCC 2023

作者： Chu, Jiajun Cao, Weihua Gan, Chao Yang, Yulong 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

ISBN: (纸本)9789887581543

Deep-sea unmanned exploration equipment is an important tool for exploring and developing the resources in the ocean, and it can survey the deep-sea environment more visually with the help of visual images. However, the complex and variable environment and the low resolution of the underwater lens lead to the poor resolution of the images acquired by the equipment. In this paper, we propose a residual-dense connected method applied to unmanned deep-sea exploration equipment to improve it's image resolution. The method uses dense connections within the residual structure to improve the model detail information acquisition to ensure accuracy and model stability of model. Secondly, through the study of the model performance, a high precision residual-dense connected model with less computational effort is designed. Finally, the model is trained and tested using environmental images in deep-sea conditions, and it is demonstrated that the method can be applied to deep-sea unmanned exploration equipment for fast, accurate, and stable image super-resolution processing. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Image resolution

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