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第三十九届中国控制会议

作者： Yaxue Ren Jinfeng Lv Fucai Liu Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Normal University of Science and Technology

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering(FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares(RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.

关键词： T-S fuzzy modeling Fuzzy identification Fuzzy c-means Gaussian function

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Localizability with Range-Difference Measurements

arXiv

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arXiv 2021年

作者： Wu, Junfeng Mu, Biqiang Yi, Xinlei Wei, Jieqiang Johansson, Karl Henrik The School of Data Science The Chinese University of Hong Kong Shenzhen China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University China The Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Ericsson Torshamnsgatan 21 Stockholm16440 Sweden

The physical position is crucial in location-aware services or protocols based on geographic information, where localization is performed given a set of sensor measurements for acquiring the position of an object with respect to a certain coordinate system. In this paper, we revisit the long-standing localization methods for locating a radiating source from range-difference measurements, or equivalently, time-difference-of-arrival measurements from the perspective of least squares (LS). In particular, we focus on the spherical LS error model, where the error function is defined as the difference between the squared true distance from a signal receiver (sensor) to the source and its squared measured value, and the resulting spherical LS estimation problem. This problem has been known to be challenging due to the non-convex nature of the hyperbolic measurement model. First of all, we prove that the existence of least-square solutions is universal and that solutions are bounded under some assumption on the geometry of the sensor placement. Then a necessary and sufficient condition is presented for the solution characterization based on the method of Lagrange multipliers. Next, we derive a characterization for the uniqueness of the solutions incorporating a second-order optimality condition. The solution structures for some special cases are also established, contributing to insights on the effects of the Lagrangian multipliers on global solutions. These findings establish a comprehensive understanding of the localizability with range-difference measurements, which are also illustrated with numerical examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Time difference of arrival

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Jiajun Miao Yujun Shen Yan Zhang Lingjie Liu Ye Tian Yu Zhao Chunyan Zhou College of Computer Science and Technology Harbin Engineering University Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs.

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Integral Sliding Mode control for PMSM with Uncertainties and Disturbances via Nonlinear Disturbance Observer

Integral Sliding Mode Control for PMSM with Uncertainties an...

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第三十九届中国控制会议

作者： Jianxiong Li Hongmei Ren Songhua Wang Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University The Key Lab of Industrial Computer Control Engineering of Yanshan University

In this paper, a nonlinear disturbance observer-based(NDOB) integral sliding mode control(SMC) algorithm is proposed for speed control of permanent magnet synchronous motor(PMSM) in the presence of uncertain parameters and external time-varying load toque. Firstly, three nonlinear disturbance observers(NDOs) are constructed to estimate the lumped disturbances comprising of uncertain parameters and/or disturbances in speed loop and two current loops, respectively. Secondly, an integral sliding mode controller is developed based on the NDOs. It can be proved that the resulting closed loop system is asymptotically stable and the speed tracking error converges to zero asymptotically in theory. Finally, experiment results are given to validate the proposed control method.

关键词： PMSM sliding mode control nonlinear disturbance observer uncertain parameters time varying load torque

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QoTa-MPR: QoS-oriented and Traffic-aware Multi-path Routing Protocol for Internet of Remote Things

Research Square

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Research Square 2021年

作者： Ma, Zhongyu Liu, Yanxing Guo, Qun Dang, Xiaochao Hao, Zhanjun Tian, Ran College of Computer Science and Engineering Northwest Normal University China Gansu Internet of Things Engineering Research Center Northwest Normal University China College of Electrical and Information Engineering Lanzhou University of Technology China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou University of Technology China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology China

Internet of Remote Things (IoRT) is widely used in both military and civilian applications. However, due to the unique characteristics, which is characterized as long-distance and high-latency, the design of the routing protocol is a great challenge of IoRT. In this paper, a QoS-oriented and traffic-aware multi-path routing protocol (QoTa-MRP) is proposed for IoRT. QoTa-MRP is composed of two parts. The first part is the link traffic-aware based muti-paths source route discovery mechanism, which is used to establish multi-paths with lower link disjoint degree. The second part is path similarity and traffic priority based multi-path selection mechanism, which is used to determine the transmission mode of the traffic flows to enhance the reliability or effectiveness of the transmission. Simultaneously, the protocol is theoretically analyzed in terms of the successful transmission rate of routing transmission data packet. Finally, the dynamic source routing, which is a very representative protocol and is usually used as the baseline comparison protocol, is revisited for the performance verification of the QoTa-MRP in IoRT. It is shown in the simulation results that there are significant superiorities of QoTa-MRP than that of DSR in terms of network throughput, packet loss rate and routing packet header overhead in IoRT. © 2021, CC BY.

关键词： Routing protocols

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Whole-Body Real-Time Motion Planning for Multicopters

Whole-Body Real-Time Motion Planning for Multicopters

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shaohui Yang Botao He Zhepei Wang Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden School of Automation Nanjing Institute of Technology Nanjing China Huzhou Institute Zhejiang University Huzhou China

Multicopters are able to perform high maneuverability yet their potential have not been fully achieved. In this work, we propose a full-body, optimization-based motion planning framework that takes shape and attitude of aerial robot into consideration such that the aggressiveness of drone maneuvering improves significantly in cluttered environment. Our method takes in a series of intersecting polyhedrons that describe a range of 3D free spaces and outputs a time-indexed trajectory in real-time with full-body collision-free guarantee. The drone is modeled as a tilted cuboid, yet we argue that our framework can be freely adjusted to fit multicopters of different shapes. Guaranteeing dynamic feasibility and safety conditions, our framework transforms the original constrained nonlinear programming problem to an unconstrained one in higher dimensions which is further solved by quasi-Newton methods. Benchmark has shown that our method improves the state-of-art with orders of magnitude in terms of computation time and memory usage. Simulations and onboard experiments are carried out as validation.

关键词： Three-dimensional displays Shape Transforms Real-time systems Planning Trajectory Safety

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Novel controller design procedure for output based ILC update laws applied to discrete-time polytopic systems

Novel controller design procedure for output based ILC updat...

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IEEE Conference on Decision and control

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Tao Liu Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and the Institute of Advanced Control Technology Dalian University of Technology Dalian China Electronics and Computer Science University of Southampton Southampton UK

ISBN: (纸本)9781665436601

This paper considers the design of iterative learning control laws for discrete-time linear time-invariant systems with polytopic uncertainty. To avoid state feedback or using an observer, the control law uses static output feedback. These systems’ control law design problem is reformulated in the repetitive process setting to ensure robust stability along the trial property. Hence, monotonic convergence in the trial-to-trial direction is guaranteed over the whole uncertainty domain. This approach results in a linear matrix inequality-based design, and an illustrative example is given.

关键词： State feedback Uncertainty Robust stability Process control Observers Linear matrix inequalities Output feedback

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An improved particle swarm optimization particle filter algorithm based on harmony search

An improved particle swarm optimization particle filter algo...

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第三十九届中国控制会议

作者： Zhen-dong Liu Yi-ming Fang Le Liu Xiao-dong Zhao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University

In light of the problems that particle filter based on particle swarm optimization（PSO-PF） algorithm is easy to fall into the local optimum in the state estimation of complex nonlinear systems, an improved particle swarm optimization particle filter algorithm based on harmony search（HSPSO-PF） is proposed. The optimal value of each individual in the particle swarm algorithm is equivalent to a variable in the harmony memory（HM） in the harmony search algorithm. On the one hand, the idea of generating solutions in harmony search algorithm is used to update the matrix that is composed of particles’ optimal solutions in the iteration of PSO. And the harmony memory consideration rate is adjusted to ensure that the source of solution generation is not limited to the HM, so that the PSO can search for the global optimal solution. On the other hand, the pitch adjustment rate and the fine-tuning bandwidth are adjusted to improve the local search ability of the PSO. Simulation results under Gaussian and non-Gaussian show that HSPSO-PF can effectively improve the estimation accuracy of the particle filtering algorithm, and has extensive adaptability.

关键词： Particle filter Harmony search Particle swarm optimization

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Deciphering circRNA-drug sensitivity associations via global-local heterogeneous matrix factorization and hypergraph contrastive learning

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Expert Systems with Applications 2025年 292卷

作者： Tiyao Liu Shudong Wang Yuanyuan Zhang Shanchen Pang Wenjing Yin Wenhao Wu Yingye Liu College of Computer Science and Technology Institute of Software China University of Petroleum Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China The School of Information and Control Engineering Qingdao University of Technology Qingdao 266525 China

Growing evidence highlights the critical role of circular RNAs (circRNAs) as regulators of cellular drug sensitivity, significantly influencing drug efficacy. While matrix factorization has proven feasible in uncovering circRNA-drug sensitivity associations, existing methods rely solely on decomposing the association matrix and fail to efficiently incorporate richer biological information. Moreover, current predictive models are limited in representing multi-perspective relationships and higher-order relationships in circRNA-drug sensitivity associations. To address these limitations, we propose a novel model based on global-local heterogeneous matrix factorization and hypergraph contrastive learning (HMFHCL). HMFHCL first calculates the global and local similarities of circRNAs and drugs, then constructs global and local circRNA-drug heterogeneous networks and performs matrix factorization of the adjacency matrices of these networks to extract information-rich feature representations. By utilizing multi-source information, HMFHCL effectively captures richer structural features and reveals potential connections in heterogeneous networks. Next, we constructed multiple circRNA and drug hypergraphs using global and local association matrices to capture higher-order interactions between nodes via hypergraph convolution. To enhance feature learning, comparative learning is applied to both global and local views of circRNA/drugs, effectively mining the similarities and differences between global structures and local details, improving the model’s ability to perceive underlying patterns and the consistency of representation learning. Finally, HMFHCL integrates circRNA and drug features from different perspectives to predict circRNA-drug associations effectively. Comprehensive experiments on three benchmark datasets demonstrate that HMFHCL outperforms state-of-the-art models, highlighting its superior ability in uncovering complex circRNA-drug sensitivity associations.

关键词： CircRNA-drug sensitivity associations Global heterogeneous network Heterogeneous matrix factorization Hypergraph contrastive learning Local heterogeneous network

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Enhance Accuracy: Sensitivity and Uncertainty Theory in LiDAR Odometry and Mapping

arXiv

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arXiv 2021年

作者： Wan, Zeyu Zhang, Yu He, Bin Cui, Zhuofan Dai, Weichen Zhou, Lipu Huang, Guoquan State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The College of Computer Science Hangzhou Dianzi University Hangzhou China Meituan 7 Rongda Road Chaoyang District Beijing100012 China University of Delaware NewarkDE19716 United States

Currently, the improvement of LiDAR poses estimation accuracy is an urgent need for mobile robots. Research indicates that diverse LiDAR points have different influences on the accuracy of pose estimation. This study aimed to select a good point set to enhance accuracy. Accordingly, the sensitivity and uncertainty of LiDAR point residuals were formulated as a fundamental basis for derivation and analysis. High-sensitivity and low -uncertainty point residual terms are preferred to achieve higher pose estimation accuracy. The proposed selection method has been theoretically proven to be capable of achieving a global statistical optimum. It was tested on artificial data and compared with the KITTI benchmark. It was also implemented in LiDAR odometry (LO) and LiDAR inertial odometry (LIO), both indoors and outdoors. The experiments revealed that utilizing selected LiDAR point residuals simultaneously enhances optimization accuracy, decreases residual terms, and guarantees real-time performance. © 2021, CC BY-NC-ND.

关键词： Mapping

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