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Spherical robot: A novel robot for exploration in harsh unknown environments

IET Cyber-Systems and Robotics 2023年第4期5卷 1-15页

作者： Wei Ren You Wang Haoxiang Liu Song Jin Yixu Wang Yifan Liu Ziang Zhang Tao Hu Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhouChina

The authors propose a complete software and hardware framework for a novel spherical robot to cope with exploration in harsh and unknown *** proposed robot is driven by a heavy pendulum covered by a fully enclosed spherical shell,which is strongly protected,amphibious,anti-overturn and has a *** for location and perception,planning and motion control are comprehensively *** the one hand,the authors fully consider the kinematic model of a spherical robot,propose a positioning algorithm that fuses data from inertial measurement units,motor encoder and Global Navigation Satellite System,improve global path planning algorithm based on Hybrid A*and design an instruction planning controller based on model predictive control(MPC).On the other hand,the dynamic model is built,linear MPC and robust servo linear quadratic regulator algorithm is improved,and a speed controller and a direction controller are *** addition,based on the pose and motion charac-teristics of a spherical robot,a visual obstacle perception algorithm and an electronic image stabilisation algorithm are ***,the authors build physical systems to verify the effectiveness of the above algorithms through experiments.

关键词： mobile robots robot control robot perception robot planning

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Interval Weight state Update Approach with Deep Reinforcement Learning for Dynamic Load Change Tasks in Air Separation Processes 13

Interval Weight State Update Approach with Deep Reinforcemen...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Qiu, Ruiyu Yang, Guanghui Xu, Zuhua Shao, Zhijiang College of Control Science and Engineering Zhejiang University State Key Lab of Industrial Control Technology Hangzhou310027 China HuZhou Research Institute of Industrial Control Technology China

ISBN: (纸本)9798350361674

The air separation unit (ASU) is important for producing high-purity oxygen, nitrogen, and argon, and dynamic load change operation helps meet the needs of downstream industries. The ASU has an extremely long delay, and the requirements for this operation are strict, and model predictive control (MPC) is usually used for this task. However, the burden of online MPC computation is a key issue in improving production and economic efficiency. In this paper, a framework based on Reinforcement Learning (RL) is proposed to solve the long-delayed task in ASU. The state and reward function are specially designed for this task. First, training increases the sampling time of the original model to create a low resolution environment, which makes training more efficient. As a sacrifice, the control precision is reduced in the original high resolution environment. Then, an algorithm called Interval Weighted state Update (IWSU) is proposed to restore the agent and keeps the action accuracy in the original environment. The dynamic load change task works as the case in this paper. The performance of RL is compared with that of MPC, and results show that RL is almost exactly the same as MPC in key flow indicators, and can well meet the remaining content and purity range requirements. IWSU shows good performance compared with directly applying the agent to the original high resolution environment. © 2024 IEEE.

关键词： Deep reinforcement learning

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Unmanned aerial vehicle orthogonal laser localization by Gaussian mixture model‐based map representation

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IET Cyber-Systems and Robotics 2023年第3期5卷 37-44页

作者： Zeyu Wan Changjian Jiang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina

Localization is a core problem in mobile robot *** localization and mapping(SLAM)costs much for an unmanned aerial vehicle(UAV).This research aims to design an orthogonal laser scan device for localization and to save computation *** on disturbance analysis,residual influences on sensor state are quantitative,and they are related to uncertainty and *** research applied the residual selection method to a *** feature point detection utilises multi‐scale and Gaussian model fitting techniques to guarantee true *** map is represented by Gaussian Mixture Models(GMM)with lower memory *** orthogonal laser scan device is composed and placed on a UAV for real‐time three‐dimensional localization,whose er-rors are at the centimeter level.

关键词： indoor positioning mobile robots unmanned aerial vehicle

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Feature-Aligned Stacked Autoencoder: A Novel Semisupervised Deep Learning Model for Pattern Classification of industrial Faults

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 592-601页

作者： Zhang, Xinmin Zhang, Hongyi Song, Zhihuan Zhejiang University College of Control Science and Engineering State Key Laboratory Industrial Control Technology Hangzhou310027 China

Autoencoder is a widely used deep learning method, which first extracts features from all data through unsupervised reconstruction, and then fine-tunes the network with labeled data. However, due to the limited number of labeled data samples, the network may lack sufficient generalization ability and is prone to overfitting. This article proposes a new semisupervised deep learning method called feature-aligned stacked autoencoder (FA-SAE). FA-SAE takes advantage of the unlabeled data during the fine-tuning process by aligning the feature of both labeled and unlabeled data. In FA-SAE, a new training loss function is designed by integrating the Sinkhorn distance measure of the difference between the features extracted from labeled and unlabeled data through the neural network into the cross-entropy classification loss. The effectiveness of the proposed FA-SAE is verified through its application to two industrial processes, and the application results demonstrated that the proposed FA-SAE has better generalization ability and higher fault classification accuracy as compared to the state-of-the-art methods. © 2020 IEEE.

关键词： Deep learning

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Augmented industrial Data-Driven Modeling Under the Curse of Dimensionality

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1445-1461页

作者： Xiaoyu Jiang Xiangyin Kong Zhiqiang Ge the State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China the Peng Cheng Laboratory Shenzhen 518000China

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional *** recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to *** address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial *** paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big ***,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is *** data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and *** approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial ***,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the *** results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

关键词： Index Terms—Curse of dimensionality data augmentation data-driven modeling industrial processes machine learning

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QTFN:A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series

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Big Data Mining and Analytics 2024年第3期7卷 905-919页

作者： Tao Chen Yang Jiao Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China Shenzhen Key Laboratory of Intelligent Bioinformatics and College of Computer Science and Control EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonstationary time series are ubiquitous in almost all natural and engineering *** the time-varying signatures from nonstationary time series is still a challenging problem for data *** Time-Frequency Distribution(TFD)provides a powerful tool to analyze these ***,they suffer from Cross-Term(CT)issues that impair the readability of ***,to achieve high-resolution and CT-free TFDs,an end-to-end architecture termed Quadratic TF-Net(QTFN)is proposed in this *** by classic TFD theory,the design of this deep learning architecture is heuristic,which firstly generates various basis functions through ***,more comprehensive TF features can be extracted by these basis ***,to balance the results of various basis functions adaptively,the Efficient Channel Attention(ECA)block is also embedded into ***,a new structure called Muti-scale Residual Encoder-Decoder(MRED)is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder ***,although the model is only trained by synthetic signals,both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.

关键词： Time-Frequency Analysis(TFA) Multi-scale Residual Encoder-Decoder(MRED) quadratic Time-Frequency Distribution(TFD)

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MND-GAN: A Research on Image Deblurring Algorithm Based on Generative Adversarial Network 42

MND-GAN: A Research on Image Deblurring Algorithm Based on G...

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42nd Chinese control Conference, CCC 2023

作者： Peng, Jiaheng Guan, Tong Liu, Feihu Liang, Jun Zhejiang University State Key Lab of Industrial Control Technology Hangzhou China

ISBN: (纸本)9789887581543

Aiming at the image blurring problem that often occurs in machine vision applications in intelligent transportation and unmanned driving scenarios, an image blurring algorithm MobileNetDeblur-GAN (MND-GAN) based on generative adversarial network is proposed in this paper. The algorithm has the following characteristics: the least square generative adversarial model (LS-GAN) is used for network training to solve the gradient disappearance problem in the traditional GAN network training process;the generator structure is designed based on MobileNet and two scales of input images are used to improve the authenticity of image restoration at different scales;the weights of nonlinear layers at different scales are shared, which greatly reduces the overall reasoning time of the deblurring algorithm;a global discriminator and a local discriminator are combined to construct a dual discriminator structure to improve the adaptability of the model to different scenarios;the loss function uses the combination of perception loss and structure loss to make the deblurring effect more consistent with the visual characteristics of clear images. The performance of MND-GAN algorithm is compared on multiple datasets, and good deblurring effect is obtained. At the same time, the reasoning speed of the network exceeds that of other comparison algorithms, and it can meet the real-time requirements of intelligent transportation and unmanned driving scenarios. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Generative adversarial networks

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Multi-objective optimization for 10-kW rated power dynamic wireless charging systems of electric vehicles

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Science China(Information Sciences) 2022年第10期65卷 178-195页

作者： Ze ZHOU Zhitao LIU Hongye SU Liyan ZHANG State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University School of Automation Wuhan University of Technology

In this paper, we propose a method to optimize the output power, efficiency, and cost of the dynamic wireless charging(DWC) system of electric vehicles by using the transmitting coil spacing as the decision variable. In a set of transmitting equipment, we adopt a structure with two transmitting networks in parallel and derive loss models. The expressions of the output power and efficiency of the DWC system are obtained by data fitting. In addition, combined with the proposed cost function, we construct a multiobjective optimization problem on output power, efficiency, and cost. Due to the complexity of the objective function, it is difficult to solve the problem by the analytic method, and thus we propose a constrained adaptive particle swarm optimization(CAPSO) algorithm with high accuracy to solve the problem. Finally,the simulation results verify the feasibility of the proposed model. The advantages of the proposed CAPSO algorithm and the optimization results under different weight combinations are presented.

关键词： dynamic wireless charging electric vehicles multi-objective optimization constrained adaptive particle swarm optimization

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Distributed asynchronous double accelerated optimization for ethylene plant considering delays

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Chinese Journal of Chemical Engineering 2025年第2期78卷 245-250页

作者： Ting Wang Zhongmei Li Wenli Du State Key Laboratory of Industrial Control Technology East China University of Science and TechnologyShanghai 200237China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.

关键词： Asynchronous distributed optimization Plant-wide optimization Heavy ball Nesterov Inequality constraints

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Matrix Coding Enabled Impact Mitigation against Primary False Data Injection Attacks in Cyber-Physical Microgrids

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IEEE Transactions on Power Systems 2025年第4期40卷 3144-3159页

作者： Liu, Mengxiang Zhang, Xin Zhao, Chengcheng Deng, Ruilong University of Sheffield School of Electrical and Electronic Engineering Sheffield United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou China

The impact mitigation against false data injection attacks (FDIAs) has become a prevailing topic in enhancing the cyber resilience of microgrids. In particular, the primary FDIA (PFDIA) injecting biases into the sensor channel of the primary controller can fake the real physical states and result in devastating control commands to the power conversion device. Nevertheless, existing impact mitigation schemes cannot handle the PFDIA due to the primary control's strict real-time requirement. Therefore, this paper proposes a time- and cost-efficient impact mitigation scheme against the PFDIA by alternately encoding the transmitted measurement with an invertible coding matrix. Specifically, when the PFDIA is detected by unknown input observers (UIOs), two additional half-downsampled UIOs, which only require simple multiplication, addition, and subtraction operations within each control cycle, will be triggered to obtain the residuals under encoded and unencoded data. The complete bias vector can be then reconstructed recursively from these two residuals, and the bias will be removed from the compromised data to eliminate the malicious attack impact. Based on the theoretical analysis of reconstruction performance, the coding matrix is optimised to minimise the system noises' impact on reconstruction accuracy subject to the reconstruction stability and the encoding's hiddenness from the adversary. Finally, extensive experimental studies are conducted to validate the effectiveness, superiority, robustness, and lightweightness of the proposed impact mitigation scheme. © 1969-2012 IEEE.

关键词： Encoding (symbols)

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