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Robotic fish:Opening a new era of underwater detection

The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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An Obstacle Avoidance Path Planning Algorithm for Manipulator in Deep Crate Grasping

An Obstacle Avoidance Path Planning Algorithm for Manipulato...

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2023 China automation Congress, CAC 2023

作者： Yao, Zhuobin Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Systems of Engineering Ministry of Education NanJing China Intel Joint Research Institute on Intelligent Edge Computing Nanjing YingQi Intelligent Technology Co. Ltd. NanJing China

ISBN: (纸本)9798350303759

The deep crate grasping problem is a major challenge for manipulator in industrial applications. In order to solve the issue of deep crate grasping and ensure the path quality of the algorithm, this paper proposes a new hybrid obstacle avoidance planning algorithm based on goal-biased rapidly exploring random tree and improved artificial potential field (RRT-APF). Initially, the algorithm strategizes an efficient trajectory for the working element within the operational area. Subsequently, it dissects this trajectory through sampling, creating a sequence of sub-target points. These points serve as the basis for obstacle avoidance planning within the manipulator's configuration space, mitigating planning complexities. Moreover, the RRT algorithm is employed to address the challenge of local optima within the artificial potential field method. The stochastic nature of the RRT algorithm plays a crucial role in enabling the APF algorithm to break free from local optima. Ultimately, the efficacy of the algorithm is demonstrated through the planning of a path for obstacle avoidance on the DOBOT CR5 manipulator using MATLAB. © 2023 IEEE.

关键词： Trees (mathematics)

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Mean square stability for Markov jump Boolean networks

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Science China(Information Sciences) 2020年第1期63卷 190-199页

作者： Liqing WANG Mei FANG Zheng-Guang WU State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University College of Automation Harbin Engineering University

In this paper, one of the stability definitions of Markov jump Boolean networks(MJBNs), called mean square stability(MSS), is investigated. Some necessary and sufficient conditions are presented to guarantee the MSS of such MJBNs. Moreover, one of the necessary and sufficient conditions for MSS is obtained in terms of linear programming, which implies that MSS is equivalent to global stability with probability 1 for MJBNs. Furthermore, the construction of Lyapunov function is given and also another theorem based on the Lyapunov function is derived to ensure the MSS of MJBNs. Finally, a numerical example is provided to illustrate the profits of our results.

关键词： mean square stability MSS Boolean network BN stochastic stability Markovian jump parameters semi-tensor product STP Lyapunov function

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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A Reinforcement Learning Mission Supervisor with Memory for Human-multi-robot Coordination systems 17th

A Reinforcement Learning Mission Supervisor with Memory for ...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Mo, Zhibin Zhang, Zhenyi Chen, Yutao Huang, Jie School of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China

ISBN: (纸本)9789811663192

In this paper, a novel reinforcement learning mission supervisor (RLMS) with memory is proposed for human-multi-robot coordination systems (HMRCS). The existing HMRCS are known to suffer from long decision waiting time and large mission error caused by repeated human intervention, restricting the autonomy of multi-robot systems. The proposed supervisor elaborately integrates deep-Q-network (DQN) and long-short-term memory (LSTM) knowledge base within the null-space based behavioral control (NSBC) framework, so as to achieve optimal adjustment strategy of the behavioral priority in the presence of mission conflicts, and to reduce the frequency of human intervention. In particular, the proposed RLMS with memory first memorize human intervention history when robot systems are not confident in decision making when encountering emergencies, and then reload the history information when encountering the same situation that have been tackled by human previously. Simulation demonstrates the effectiveness of proposed RLMS with memory. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor

A Nonsmooth Observer Based on Friction Model Compensation fo...

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第42届中国控制会议

作者： Zaishan Zhang Wenwu Zhu Ke Yu Xiangyu Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Electrical Engineering and Automation Hefei University of Technology

In this paper,a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor(PMSM) to estimate the rotor position and *** is one of the main factors affecting the tracking accuracy of the motor at low speed,especially in the back and forth motion of the ***,based on the Stribeck friction model,a nonlinear friction model of the motor is ***,a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor,and the lumped disturbance of the system at the same ***,the stability analysis of this nonsmooth observer is ***,the effectiveness of the proposed method is verified by the simulation results.

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Detecting Defects of Wooden Boards by Improved YOLOv4-tiny Algorithm 17th

Detecting Defects of Wooden Boards by Improved YOLOv4-tiny A...

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Qiu, Yang Ai, Zhijie Lin, Ye Xu, Zhezhuang Liu, Xinxiang College of Electrical Engineering and Automation Fuzhou University Fuzhou350000 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9789811663192

Wooden boards are widely used as raw materials for furniture. Detecting defects on the wooden is significant to improve the quality of products. In this paper, we propose an improved YOLOv4-tiny target detection algorithm with feature extraction and utilization tailored for wood defect recognition. The proposed algorithm replace the residual network of the original algorithm with the Res2Net network to enhance the feature extraction ability, and increase the different scales to improve the prediction accuracy. A testbed is deployed to collect the images of wooden boards, and the performance of the proposed algorithm is evaluated on the testbed. Compared with the original YOLOv4-tiny algorithm, the mean average precision for detecting the three types of defects deadknot, insectpest and crack was 80.06% which was an increase of 5%. Compared with other methods of wood defect detection, the proposed algorithm is more accurate and faster. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Testbeds

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Robust Dynamic Latent Variable Model for Dynamic Process Monitoring With Missing Data in Cyber-Physical systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2023年 1卷 227-235页

作者： Le Zhou Lixinqian Yu Beiping Hou Hongbo Zheng Zheng-Guang Wu School of Automation and Electrical Engineering Zhejiang University of Science & Technology Hangzhou China Faculty of Engineering and IT University of Melbourne Melbourne Australia National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Although remarkable studies on dynamic process modeling and monitoring in cyber-physical systems have been conducted, there are still several limitations to these approaches, rendering them unsuitable for practical applications. Firstly, the common dynamic models are built based on complete data, which means that their modeling performance usually deteriorates when there are missing measurements. Secondly, practical data often exhibits more complex characteristics, such as non-stationary and non-Gaussian noises. Therefore, a robust dynamic latent variable model is proposed to address the challenges of dynamic process modeling and monitoring in complex measurement environments with missing values. The proposed method effectively handles non-Gaussian noises and recursively estimates missing values, thus enhancing the monitoring performance within the dynamic latent variable modeling framework. Finally, the feasibility and superiority of the proposed method are evaluated through a numerical example and a real wastewater treatment case, demonstrating its effectiveness and practicality in real-world scenarios.

关键词： Cyber-physical systems Principal component analysis Process monitoring Data models Load modeling Numerical models Loading

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Research on Path Planning Algorithm Based on D∗ Search

Research on Path Planning Algorithm Based on D∗ Search

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Leng, Jiajun Fu, Li Wang, Lingling Tang, Ning Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Beijing100036 China Tianjin Navigation Instruments Research Institute Tianjin300131 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Bauman Moscow State Technical University Moscow Department of Automatic Control Systems 105005 Russia

ISBN: (纸本)9798350382457

Path planning algorithms are current research hotspots. Heuristic algorithms that can solve dynamic environment problems are gradually becoming the mainstream research direction. The D∗ algorithm, as one of the new heuristic algorithms, has the advantages of being more computationally efficient than the A ∗ algorithm and being able to solve dynamic problems, and has played an important role in two-dimensional path planning projects such as the NASA Mars rover. But its theoretical research and practical application in 3D environment are very rare. In this context, this paper carries out an in-depth study of the path planning problem based on the D∗ algorithm, and completes the theoretical derivation and simulation process in 2D and 3D environments. In this paper, a complex random 2D map is established by random generation, and the simulation of D∗ algorithm is realized on the map with the help of Python language, which realizes the finding of the relative optimal path. Then, in order to study the feasibility of D∗ solving in 3D environment, the simulation of D∗ algorithm under 3D map is realized with the help of Python and Unity3d. The research results show that the simulation results are good, which is valuable for the theoretical research and practical application of heuristic algorithms, and plays a role in the application of robot path planning algorithms. © 2023 IEEE.

关键词： Python

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