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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Kun Luo Gang Xiong Yuxiao Wang Bin Hu Sheng Liu Shichao Chen The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences Beijng China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Beijng China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China

Regarding human-machine collaborative task, generated trajectories of a robot manipulator should be able to avoid joint limits and singularities, but the 6 Degrees of Freedom(DoF) or lower DoF manipulators encounter difficulties in many schemes. In this paper, research is implemented on the methods of trajectory planning and motion control on the newly human-machine collaborative 7-DoF Sawyer manipulator. We try to pay more attention to these approaches and find a better combination between trajectory planners and motion controllers for Sawyer manipulator. First of all, comparison is proposed between the methods about trajectory planning in task space and joint space, drawing a conclusion that the latter has the advantage of quickness and smoothness on trajectories in real time. Next, we choose to plan trajectory in the joint space by interpolating positions and orientations. Three approaches, i.e., trapezoidal velocity trajectory, cubic polynomial trajectory and basic spline trajectory, are applied to interpolate positions. Experimental results show that it results in the smoothest trajectory using basic spline trajectory planning. We also compare linear and nonlinear orientation interpolations to address the angle wrapping issue, which leads to difficulties in smooth orientation interpolation between several waypoints. Combining polynomial trajectory planning methods with the proportional-derivative (PD) motion controller and the computed-torque motion controller, we focus on the performance of the two different combinations. It is found that trajectories with computed-torque controller have shorter path with a congruous convergence on joint position, and more accurate trajectories with less overshoots in position than others.

关键词： Interpolation Trajectory planning Collaboration Aerospace electronics Manipulators Trajectory Task analysis

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Trajectory Planning and Tracking control for Positioning of Planar Three-link Underactuated Manipulator

Trajectory Planning and Tracking Control for Positioning of ...

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Asian control Conference

作者： Zixin Huang Xuzhi Lai Pan Zhang Min Wu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Automation China University of Geosciences Wuhan China

ISBN: (纸本)9781728102634

This paper is concerned with the position control problem of the planar active-passive-active (APA) underactuated manipulator, whose dynamic constraint is mainly caused by the second-order nonholonomic constraint. First, the dynamic model of the system is established and the target angles of the three links are calculated by using differential evolution algorithm. Then, we present a trajectory planning and tracking control strategy to realize the control objective, which is to stabilize the end-point from any initial position to the target position. We plan two stage trajectories, the first of which is planned for the active links. When the the active links track the first stage trajectories, they can reach to their target angles. The second one is planned for the first active link based on the differential evolution algorithm. When the first active link tracks the second stage trajectory, it will back to its target angle eventually. Meantime, the passive link will be controlled to its target angle. Then, we design sliding mode variable structure controllers to make active links track the planned trajectories, which can make the all links reach to the their target angles. Finally, numerical simulation results illuminate the effectiveness of the proposed strategy.

关键词： Trajectory Manipulators Target tracking Position control Planning Heuristic algorithms Optimization

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K-Means Clustering-based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10250-10254页

作者： Luefeng Chen Kuanlin Wang Min Wu Witold Pedrycz Kaoru Hirota 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 Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada Tokyo Institute of Technology Yokohama 226-8502 Japan

Emotion is an important part of human interaction. Emotional recognition can greatly promote human-centered interaction techniques. On this basis, multimodal feature fusion can effectively improve the emotion recognition rate. However, in the multimodal feature fusion at the feature level, most of the methods do not consider the intrinsic relationship between different modes. Only the fusion of analysis and transformation of the feature matrices of different modes does not make better use of modal differences to improve the recognition rate. This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA). Clustering makes the classification of features not classified by mode, but by the degree of influence on emotional labels, thus positively affecting the results of KCCA. The experimental results obtained on the Savee database show that the proposed K-Means based KCCA improves overall classification performance and produces higher recognition rate than that of the state of art methods, such as the Informed Segmentation and Labeling Approach.

关键词： Emotion Recognition K-Means Clustering Kernel Canonical Correlation Analysis Feature Fusion

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Robust disturbance rejection for a fractional-order system based on equivalent-input-disturbance approach

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Science China(Information Sciences) 2018年第7期61卷 200-211页

作者： Ruijuan LIU Jinhua SHE Min WU Fenfang ZHU Zhuoyun NIE School of Applied Mathematics Xiamen University of Technology School of Automation China University of Geosciences School of Engineering Tokyo University of Technology Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Information Science and Engineering National Huaqiao University

This paper presents a disturbance rejection method that is based on the equivalent-inputdisturbance approach for uncertain fractional-order(FO) systems. An FO observer is used to reconstruct the plant states and estimate the disturbances. A disturbance estimator is incorporated in the construction of the FO control system to actively compensate for the entire disturbance. A robust stability condition for the control system and the parameters of the controller are derived using an indirect Lyapunov *** presented method effectively rejects disturbances and handles modeling uncertainties without requiring prior knowledge of the disturbance. Comparison simulations on both numerical and practical examples demonstrate the effectiveness of the proposed method.

关键词： fractional-order system disturbance rejection equivalent-input-disturbance uncertain system Lyapunov method state observer stability

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Multi-agent System Motion Planning under Temporal Logic Specifications and control Barrier Function

Multi-agent System Motion Planning under Temporal Logic Spec...

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第三十九届中国控制会议

作者： Xinyuan Huang Li Li Jie Chen IEEE IFAC Chinese Academy of Engineering Tongji University the State Key Laboratory of Intelligent Control and Decision of Complex Systems the Chinese Association of Automation the Chinese Artificial Intelligence Society College of Electronics and Information Engineering Tongji University Shanghai Reserach Institute for Intelligent Autonomous Systems

In this paper, we provide a novel scheme to solve the motion planning problem of multi-agent systems under highlevel task specifications. First, linear temporal logic is applied to express the global task specification. Then an efficient and decentralization algorithm is proposed to decompose it into local tasks. Finally, we use control barrier function to synthesize the local controller for each agent under the LTL motion plan safety constraint. Simulation results prove the effectiveness and efficiency of our proposed method.

关键词： temporal logic multi-agent system formal methods control barrier function

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Multi-scale Protein complex Discovery based on Graph Wavelet

Multi-scale Protein Complex Discovery based on Graph Wavelet

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第三十九届中国控制会议

作者： Wang Yue Ran Xu Peng Xiao-yu Yi Ming Zhu Yuan School of Science Huazhong Agricultural University School of Mathematics and Statistics Huazhong University of Science and Technology School of Mechanical Engineering Wuhan Institute of Shipbuilding Technology School of Mathematics and Physics China University of Geosciences School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Identifying protein complexes from protein interaction networks is an important task in the post-genomic era. With the development of high-throughput sequencing technology, the acquisition of protein interaction network data is becoming more and more convenient. The current protein complex recognition algorithms cannot well balance the hierarchical and overlapping properties of protein networks. In order to overcome these limitations, this paper proposes a Graph Wavelet based Protein complex Discovery algorithm, GW-PCD. The method exploits the clustering analysis of protein interaction networks to find a modular mechanism by characterizing the distance between graph wavelet coefficients of protein pairs. Comparative experimental results show that GW-PCD enhances the performance of identifying protein complexes, which will provide valuable clues for further exploration of the unknown functions of proteins at the multiscale perspective.

关键词： Graph wavelet Protein-protein interaction network Multiresolution analysis Protein complex Community discovery

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Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

Semi-autonomous greenhouse environment control by combining ...

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Chinese automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

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Graph Attention Model Embedded With Multi-Modal Knowledge For Depression Detection

Graph Attention Model Embedded With Multi-Modal Knowledge Fo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi’an Jiaotong University Xi’an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

With more than 300 million people depressed worldwide annually, depression is a global problem. The goal of depression detection is to improve diagnostic accuracy and availability, leading to faster intervention. The most important and challenging problem here is to design an effective and robust depression detection model. To this end, there are two challenges to overcome: 1) Multi-modal (audio, image, text, etc.) information must be jointly considered to make accurate inferences. 2) Existing deep learning-based work suffers from multi-modal data sufficiency problem. To address these issues, we propose a graph attention model embedded with multi-modal knowledge for depression detection. This approach learns not only reasonable embeddings for nodes in the knowledge graph, but also exploits medical knowledge to improve the performance of classification and prediction with the knowledge attention mechanism. Experimental results on the real-world datasets show that the proposed approach significantly improves the classification and prediction performance compared with other major state-of-the-art approaches, with guaranteed the robustness with each modality of multi-modal data. Overall, this paper shows how multi-modal knowledge attention mechanism and deep-learning-based networks can be combined to assist mental health patients and practitioners.

关键词： Biomedical imaging Robustness Data models Machine learning Task analysis Knowledge engineering

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Reputation-based Distributed Filtering Over Sensor Networks Subject to Stochastic Nonlinearity and Network-Induced Quantization

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IEEE Transactions on Network Science and Engineering 2025年

作者： Jia, Chaoqing Wang, Zidong Hu, Jun Dong, Hongli Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Northeast Petroleum University Artificial Intelligence Energy Research Institute Daqing163318 China Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Daqing163318 China

In sensor networks, due to inevitable sensor faults, malfunctions, or deliberate attacks, sensors may transmit erroneous, inaccurate, or misleading data, thereby degrading overall system performance. To address this issue, an effective approach is to assign reputation scores to sensors based on their trustworthiness, historical performance, or reliability. In this paper, the reputation-based distributed filtering (RBDF) problem is considered for a class of stochastic nonlinear systems over sensor networks with network-induced quantization. A reputation mechanism is employed to mitigate the adverse effects caused by noisy, faulty, or malicious sensors. Specifically, reputations are allocated by each sensor to the data received from its neighbors, ensuring that abnormal data are assigned smaller reputation values and may even be discarded. For the first time, a recursive RBDF algorithm is proposed, wherein an upper bound of the filtering error covariance (UBFEC) is derived by solving two matrix equations. Subsequently, the filter gain is determined by minimizing the trace of UBFEC at each step. Furthermore, a sufficient condition is presented to ensure the uniform boundedness of the filtering error dynamics. Finally, a simulation example is provided to verify the feasibility and validity of the developed RBDF algorithm. © 2013 IEEE.

关键词： Digital arithmetic

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Coverage Path Planning Design of Mapping UAVs Based on Particle Swarm Optimization Algorithm

Coverage Path Planning Design of Mapping UAVs Based on Parti...

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第三十八届中国控制会议

作者： Shouqi Wang Yongqiang Bai Chao Zhou School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems

Mapping UAVs have played a more important role in regional mapping than ever before, and coverage path planning is an important part of mapping tasks. However, there are few studies on single-region path planning in complex polygonal region. In this paper, we proposed a method that searches the optimal yaw-angle and flight height of mapping UAVs based on particle swarm optimization algorithm. Compared with traditional algorithms such as cattle farming algorithm, there are less redundant coverage and turning, and the flight path planned by the algorithm in this paper is more reasonable and convenient for photographic processing.

关键词： mapping UAVs coverage path planning particle swarm optimization

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