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第40届中国控制会议

作者： Fan Qin Shu-ya Yan Yi-ming Fang Jian-xiong Li 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 view of the problem that the state estimation contains fault values when detecting sensor faults containing noise based on the Kalman filter algorithm,a sensor fault detection method based on adaptive weighted fusion is proposed in this *** adaptive weighted fusion algorithm gives different weights to several groups of measurement output data to generate the measurement output without ***,the composite embedded cubature Kalman filter algorithm is used to get the state estimation without faults,which will make the mean or variance of the generated residuals change,so as to realize fault ***,the single-link direct drive manipulator system driven by permanent magnet brush DC motor is simulated and *** results show that,compared with the CECKF algorithm alone,the proposed method can eliminate the faults contained in the Kalman filter state estimation,and detect all the output sensor faults more effectively.

关键词： Sensor fault detection Adaptive weighted fusion State estimation Kalman filter

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Super-twisting Finite Time control for Third Order Nonlinear Systems

Super-twisting Finite Time Control for Third Order Nonlinear...

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Chinese control Conference (CCC)

作者： Xinjian Liu Jianxiong Li Zhi Li Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Qinhuangdao China The Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

A novel super-twisting control method is proposed for a class of third-order systems with matched Lipschitz disturbance in this paper. The proposed method ensures finite-time convergence to the fourth-order sliding set, by using a homogeneous, continuously differentiable and strict Lyapunov function. Finally, numerical simulation results are shown to illustrate the effectiveness of the proposed method.

关键词： Uncertain systems Numerical simulation Nonlinear systems Sliding mode control Convergence Lyapunov methods

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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Steering motion control of a snake robot via a biomimetic approach

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Frontiers of Information Technology & Electronic engineering 2019年第1期20卷 32-44页

作者： Wenjuan OUYANG Wenyu LIANG Chenzui LI Hui ZHENG Qinyuan REN Ping LI State Key Laboratory of Industrial Control Technology Zhejiang University Department of Electrical and Computer Engineering National University of Singapore Zhejiang University of Science and Technology

We propose a biomimetic approach for steering motion control of a snake robot. Inspired by a vertebrate biological motor system paradigm, a hierarchical control scheme is adopted. In the control scheme, an artificial central pattern generator(CPG) is employed to generate serpentine locomotion in the robot. This generator outputs the coordinated desired joint angle commands to each lower-level effector controller, while the locomotion can be controlled through CPG modulation by a higher-level motion controller. The motion controller consists of a cerebellar model articulation controller(CMAC) and a proportional-derivative(PD) controller. Because of the fast learning ability of the CMAC, the proposed motion controller can drive the robot to track the desired orientation and adapt to unexpected perturbations. The PD controller is employed to expedite the convergence speed of the motion controller. Finally, both numerical studies and experiments proved that the proposed approach can help the snake robot achieve good tracking performance and adaptability in a varying environment.

关键词： Snake robot Central pattern generator Cerebellar model articulation controller

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Enhancing object detection with large kernel convolution and cross convolution

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Digital Signal Processing 2025年 167卷

作者： Yaqian Li Guoping Liu Haibin Li Wenming Zhang Xiaoyang Shen College of Electrical Engineering Yanshan University Qinhuangdao Hebei 066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao Hebei 066000 China

Existing object detection models often struggle with detecting small objects due to their limited ability to capture sufficient contextual information. In this paper, we introduce a lightweight object detection model that leverages large kernel convolution with attention (LKA) and a hierarchical feature fusion group (HFFG) to address this issue. The LKA module employs large kernel convolution to capture long-range dependencies and contextual information, combined with depthwise separate convolution to maintain a lightweight design. An incorporated attention mechanism further enables the modal to adaptively focus on key areas, thereby improving detection performance for small objects. The HFFG module, which integrates Cross Convolution Blocks, explores and retains structural information across different scales. By effectively extracting structural details, our model exhibits enhanced performance on object of various sizes. Extensive experiments on the VisDrone2019 and PASACAL VOC datasets demonstrate that our model achieves an outstanding mAP of 23.4 %, surpassing the baseline YOLOX-s model by +1.5 %. These results not only validate the effectiveness but also demonstrate its robustness and generalization capability.

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Sinusoidal-Assisted Synchrosqueezing Transform: Algorithms and Biomedical Applications

SSRN

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SSRN 2022年

作者： Chen, Tao Zheng, Qian Xie, Lei Su, Hongye State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Flexolink Technology Co. Ltd. Hangzhou China

Intermittent oscillation signals (IOSs) exist widely in biomedical systems. They are always contaminated by various randomness like noise and artifacts. Compared with traditional time-frequency analysis (TFA) methods, adaptive TFA methods show the significant advantages in separating IOSs. However, these methods are implemented by assuming that the input signal is in a weakly noisy environment and have narrow-band limitation for extracting IOSs. Besides, it is difficult for these methods to alleviate the noise in the gap among times where IOSs happen. Therefore, in this paper, a new method called the sinusoidal-assisted synchrosqueezing transform (SASST) is proposed for separating IOSs in strongly noisy environment. The SASST method locates the IOSs with the aid of a sinusoidal signal. Such a sinusoidal signal fills the energy blank in the gap between time where IOSs happen. This enables that the ridge detection approach, which is the necessary tool for mode retreival of SST-based methods, focuses on the assisted sinusoidal signal, rather than noise. Thus, the background noise in the gap is suppressed. As a result, SASST is capable of extracting wide-band IOSs in a strongly noisy environment. The effectiveness and advantages of the proposed algorithm are verified by both numerical simulations and real-world cases. keywords: Sinusoidal-assisted, Time-frequency analysis, Synchrosqueezing, Biomedical © 2022, The Authors. All rights reserved.

关键词： Bioinformatics

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Robust Multi-Modal Pedestrian Detection Using Deep Convolutional Neural Network with Ensemble Learning Model

SSRN

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SSRN 2023年

作者： Jain, Deepak Kumar Zhao, Xudong Garcia, Salvador Neelakandan, Subramani Chongqing University China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian116024 China DaSCI Andalusian Research Institute University of Granada Granada Spain Department of Computer Science and Engineering R.M.K Engineering College 601206 India

In the realm of video surveillance systems, pedestrian detection (PD) is a crucial task that enables the identification and tracking of individuals, crowd monitoring, and people counting. However, accurate PD is a challenging task due to variations in the appearance and pose of pedestrians. With the advent of deep learning (DL) techniques, particularly convolutional neural networks (CNNs), computer vision (CV) methods for object classification and detection have been revolutionized. Successful PD depends on supervised learning, which necessitates large labeled datasets. This paper proposes a Robust Multi-modal Pedestrian Detection using a Deep Convolutional Neural Network with an Ensemble Learning (RMPD-DCNNEL) model to address the challenges of PD. This model leverages computer vision and ensemble learning to achieve accurate pedestrian detection. The SimAM-EfficientNet model with a stochastic gradient descent (SGD) optimizer is utilized for feature extraction, while ensemble classification is performed using three DL models: nested long short-term memory (NLSTM), deep belief networks (DBNs), and extreme learning machine (ELM).To evaluate the effectiveness of the proposed approach, we use the INRIA dataset, and the experimental results demonstrate the superior performance of RMPD-DCNNEL over other DL approaches. The proposed technique enables robust pedestrian detection despite variations in appearance and pose, making it an effective solution for video surveillance systems in smart transportation environments. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Adaptive Decentralized Output-Constrained control of Single-Bus DC Microgrids

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 424-432页

作者： Jiangkai Peng Bo Fan Jiajun Duan Qinmin Yang Wenxin Liu the Smart Microgrid and Renewable Technology (SMRT) Research Laboratory Department of Electrical and Computer Engineering Lehigh University the State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University the GEIRI North America

A single-bus DC microgrid can represent a wide range of applications. control objectives of such systems include high-performance bus voltage regulation and proper load sharing among multiple distributed generators(DGs) under various operating conditions. This paper presents a novel decentralized control algorithm that can guarantee both the transient voltage control performance and realize the predefined load sharing percentages. First, the output-constrained control problem is transformed into an equivalent unconstrained one. Second, a two-step backstepping control algorithm is designed based on the transformed model for bus-voltage regulation. Since the overall control effort can be split proportionally and calculated with locally-measurable signals, decentralized load sharing can be realized. The control design requires neither accurate parameters of the output filters nor load measurement. The stability of the transformed systems under the proposed control algorithm can indirectly guarantee the transient bus voltage performance of the original system. Additionally, the high-performance control design is robust, flexible, and reliable. Switch-level simulations under both normal and fault operating conditions demonstrate the effectiveness of the proposed algorithm.

关键词： DC microgrids decentralized control paralleled converters output constraint

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in industrial control Through Trusted Smart Community and Lock Mechanism

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiguo Huang Yong Ding Jun Li Yujue Wang Hai Liang Changsong Yang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

关键词： Crowdsourcing Software testing Smart cities industrial control Smart contracts Decentralized applications Software Blockchains Software reliability Security

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Multi-level Coordinated Energy Management for Energy Hub in Hybrid Markets with Distributionally Robust Scheduling

arXiv

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arXiv 2022年

作者： Cao, Jiaxin Yang, Bo Zhu, Shanying Chung, Chi Yung Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China Department of Electrical and Computer Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada

Maintaining energy balance and economical operation is significant for multi-energy systems such as the energy hub (EH). However, it is usually challenged by the frequently changing and unpredictable uncertain parameters at different timescales. Under this scope, this paper investigates the coordinated energy management problem for day-ahead and intra-day conditions considering uncertainties of source-load and market prices concurrently. Note that the precise knowledge of distributions about uncertainties may be unaccessible before the decision-making in day-ahead phase. A two-stage chance-constrained model based on distributionally robust approach with ambiguous moment information is proposed to immunize scheduling strategies against the worst-case probability distributions. The first stage is dedicated to obtaining more energy arbitrage and operation flexibility by optimizing bidding strategies in day-ahead power, natural gas and carbon trading markets. The second stage focuses on the optimization of the worst-case expected operation cost. It provides a robust energy equipment and load scheduling strategy for the reference of subsequent intra-day arrangements. With respect to different variations of electrical and thermal components, an intra-day two-timescale coordination is implemented step by step. The energy scheduling is re-dispatched circularly to minimize the operation and penalty costs. The possible energy imbalance is also compensated by this way. As the energy management program is nonlinear, chance-constrained and multi-stage, some linearization and dual transformation techniques are designed to enhance tractability of the program. Experimental results show that the developed multi-level framework results in a carbon emission decrease of 37%, and reduces energy cost averagely 3% compared with corresponding contrasting cases. The obtained strategy validates a good tradeoff between robustness and optimality. Copyright © 2022, The Authors. All rights reser

关键词： Power markets

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