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arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Fault-Tolerant control for Uncertain Sub-Fully Actuated Systems

Fault-Tolerant Control for Uncertain Sub-Fully Actuated Syst...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Mengtong Gong Li Sheng Donghua Zhou Department of Automation Tsinghua University Beijing P. R. China College of Control Science and Engineering China University of Petroleum (East China) Qingdao P. R. China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper presents a fault-tolerant stabilization method for a class of uncertain high-order sub-fully actuated systems (sub-FASs) with actuator faults. As one of the important branches of the fully actuated system theory, sub-FAS control is more sophisticated and challenging due to the singularity problem. To deal with the fault tolerance issue of uncertain sub-FASs, a high-order nonlinear system model with both multiplicative and additive actuator faults is introduced. Motivated by the idea of FAS approach, a robust adaptive tracking controller is proposed to make system states track specific reference signals and avoid singularity under certain feasibility constraints. Then, a sufficient condition to ensure the feasibility constraints are satisfied is further derived. By appropriately selecting reference signals, the faulty sub-FAS is ultimately uniformly bounded. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

关键词： Fault tolerance Actuators Sufficient conditions Additives Uncertainty Adaptive systems Fault tolerant systems

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Improved Generative Adversarial Network with Hybrid Attention Mechanism for Path Planning

Improved Generative Adversarial Network with Hybrid Attentio...

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第42届中国控制会议

作者： Yi-Fan Zhang Tao Sun Song Ma Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology School of Control Science and Engineering Dalian University of Technology Department of Automation Tsinghua University

Multi-agent path planning is one of the most challenging problems in the field of artificial intelligence. In this paper,an improved generative adversarial network model with hybrid attention mechanism for data-driven path planning is ***, by introducing spatial attention mechanism, positional attention mechanism and channel attention mechanism, a hybrid attention module is designed. This module not only establishes feature relationship between different locations, but also provides adaptive feature weights to highlight key information. Next, by embedding the hybrid attention module into the generative adversarial network, an improved generative adversarial network model is developed. The proposed model can capture effective feature information in the image and ensures that the generated path can evade obstacles accurately. Finally, some simulation results are provided to verify the superiority and accuracy of the model in path planning.

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Preassigned-Time Stabilization of Memristive Chaotic Circuit via Switching control

Preassigned-Time Stabilization of Memristive Chaotic Circuit...

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Neuromorphic Computing (ICNC), International Conference on

作者： Qiming Wang Leimin Wang Wenzhao Qin School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Mathematics and Information Engineering Dongchang College of Liaocheng University Liaocheng China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

In this paper, we propose a 3D memristive chaotic circuit, in which both its dynamical behaviors and the preassigned-time stability are analyzed. First, we design a chaotic circuit utilizing the KNOWM memristor model, and confirm the presence of chaos through phase portrait. Subsequently, we develop a switching controller, which successfully achieves the preassigned-time stability of the memristive chaotic circuit. Ultimately, the validity of our theoretical findings is substantiated through rigorous numerical simulations.

关键词： Three-dimensional displays Memristors Switches control systems Stability analysis Circuit stability Numerical models Integrated circuit modeling Numerical stability Switching circuits

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A Sensorless Method based on Zero-sequence Back-EMF for Five-phase Open-end Winding PMSM with Deadtime Compensation

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IEEE Transactions on Energy Conversion 2025年

作者： Jiang, Chunyang Wang, Yingjie Liu, Hongchen Wheeler, Pat Bao, Danyang Northeast Forestry University College of Computer and Control Engineering Harbin150001 China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin150001 China University of Nottingham Department of Electrical and Electronics Engineering NottinghamNG7 2RD United Kingdom Shenzhen Polytechnic University School of Mechanical and Electrical Engineering Shenzhen518000 China

This paper proposes a sensorless method using the zero-sequence back electromotive force (ZSB-EMF) for five-phase open-end winding permanent magnet synchronous machine (OEW-PMSM) with common dc bus. The output of the zero-sequence controller, which is applied to suppress the zero-sequence current (ZSC), is used to reconstruct the ZSB-EMF. Then the position of the rotor is extracted from the output signal of the zero-sequence controller by single phase locked loop. A compensation of the deadtime is proposed to eliminate the negative effect for the position estimation and increase the effective modulation index. The proposed method needs no parameters of the motor, like the resistance, d-q inductance, which are vulnerable and might affect the accuracy of the estimation for the conventional sensorless methods based on the ZSB-EMF. It is easy to implement and the resistance network and complex calculation of conventional methods are also avoided. Experimental results on the five-phase open-end winding PMSM drive system are shown to validate the proposed sensorless method. © 1986-2012 IEEE.

关键词： Permanent magnets

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on control, Mechatronics and automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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Sensor Fusion for Glucose Monitoring Systems

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IFAC-PapersOnLine 2023年第2期56卷 11527-11532页

作者： Mohamad Al Ahdab Karim Davari Benam Hasti Khoshamadi Anders Lyngvi Fougner Sebastien Gros Section of Automation and Control Department of Electronic Systems Aalborg University Aalborg Øst Denmark Department of Engineering Cybernetics Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) O. S. Bragstads Plass 2D 7034 Trondheim Norway

A fully automated artificial pancreas (AP) requires accurate blood glucose (BG) readings. However, many factors can affect the accuracy of commercially available sensors. These factors include sensor artifacts due to the pressure on surrounding tissues, connection loss, and poor calibration. The AP may administer an incorrect insulin bolus due to inaccurate sensor data when the patient is not supervising the system. The situation can be even worse in animal experiments because animals are eager to play with the sensor and apply pressure. In this study, we propose and derive a Multi-Model Kalman Filter with Forgetting Factor (MMKFF) for the problem of fusing information from redundant subcutaneous glucose sensors. The performance of the developed MMKFF was assessed by comparing it against other Kalman Filter (KF) strategies on experimental data obtained in two different animals. The developed MMKFF was shown to provide a reliable fused glucose reading. Additionally, compared to the other KF approaches, the MMKFF was shown to be better able to adjust to changes in the accuracy of the glucose sensors.

关键词： Developments in measurement signal processing Diabetes

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Short-Term Load Forecasting for Buildings Based on Dynamic Weighted edRVFL Model

Short-Term Load Forecasting for Buildings Based on Dynamic W...

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Chinese automation Congress (CAC)

作者： Junwei Xie Meng Yuan Zhezhuang Xu Chi Liu Yuxiong Xia Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou China Fujian Huading Intelligent Manufacturing Technology Co. Ltd Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering.

关键词： Adaptation models Load forecasting Heuristic algorithms Buildings Clustering algorithms Predictive models Prediction algorithms Vectors Forecasting Load modeling

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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Congress on Evolutionary Computation

作者： Michalis Mavrovouniotis Changhe Li Danial Yazdani Diofantos Handjimitsis ERATOSTHENES Centre of Excellence Limassol Cyprus Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum.

关键词： Schedules Satellites Processor scheduling Heuristic algorithms Evolutionary computation Traveling salesman problems Search problems

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Research on Obstacle Recognition Methods of Unmanned Vehicles in Off-road Environment 6

Research on Obstacle Recognition Methods of Unmanned Vehicle...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Dong, Qingyu Wang, Jingyao Guo, Jinghua Jilin University State Key Laboratory of Automotive Simulation and Control Changchun China Xiamen University Department of Mechanical and Electrical Engineering Xiamen China Xiamen University Department of Automation Xiamen China Shenzhen Research Institute of Xiamen University Shenzhen China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

The off-road environment is complex, and the accurate obstacle detection of unmanned vehicles in the off-road environment is difficult. In order to identify the obstacles in the off-road environment with low cost, real-time image recognition and high efficiency, the Faster R-CNN optimized by 4 methods is designed in this paper. Comparing with the original model, SSD and YOLO, our optimized model shows obvious advantages in obstacle identification in the off-road environment. © 2022 IEEE.

关键词： Roads and streets

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