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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset 20

Performance Comparison of Lightweight CNN Models for Drone C...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Zufar, Rifqi Nabila Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering 254 Phayathai Road Bangkok Pathumwan10330 Thailand

ISBN: (纸本)9798350310467

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work. © 2023 IEEE.

关键词： Drones

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound

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无人系统（英文） 2024年第1期12卷 19-28页

作者： Xiaoling Xu Damián Marelli Wei Meng Qianqian Cai Minyue Fu School of Automation Guangdong University of TechnologyGuangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou 510006P.R.China School of Automation Guangdong University of TechnologyGuangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou 510006P.R.China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Engineering University of Newcastle NSW 2308Australia

This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor *** goal of the problem is to maximize the detection probability within the minimum search *** propose a two-stage strategy to achieve this *** the first stage,a greedy approach is used to define a set of grid cells with the aim of maximizing the detection *** the second stage,an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is *** results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

关键词： Multiagent system source search submodularity branch-and-bound indoor exploration

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Research on Mixing of Virtual and Actual Reality for Motor Augmented Assembly 23

Research on Mixing of Virtual and Actual Reality for Motor A...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Zhang, Haowei Shao, Xuqiang Wu, Xuan School of Control and Computer Engineering North China Electric Power University China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education China

ISBN: (纸本)9781450398572

The effect of the mixing of virtual and actual reality will directly affect the realism and immersion of the augmented assembly system. The correct occlusion relationship and collision interaction between virtual and real objects are crucial to the fusion effect. The existing virtual-real occlusion handling methods cannot be applied to the augmented assembly system with complex contours and changeable scenes. Therefore, an occlusion handling method based on "virtual avatar"of motor equipment is proposed, which has strong robustness and real-time performance. Aiming at the problem that the number of wrong matching pairs of feature points is too large in the 3D registration process, we combine the method of Cosine similarity measurement on the basis of ORB algorithm to improve the accuracy of feature point matching. The current virtual and real object collision detection methods have large computation and low detection accuracy. We propose an octree hierarchical collision detection optimization algorithm based on model surface voxelization representation and volume difference, which improves the detection accuracy on the basis of ensuring computational efficiency. The experimental results show that the proposed methods have high real-time performance and accuracy, and greatly improve the realism and immersion of the augmented assembly system. © 2023 ACM.

关键词： Augmented reality

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Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments

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无人系统（英文） 2024年第2期12卷 429-441页

作者： Zhaohan Feng Jie Chen Wei Xiao Jian Sun Bin Xin Gang Wang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing 100081P.R.China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120 P.R.China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing 100081P.R.China Department of Control Science and Engineering Tongji University Shanghai 201804P.R.China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing 100081P.R.China

In recent years,drones have found increased applications in a wide array of real-world *** predictive control(MPC)has emerged as a practical method for drone flight control,owing to its robustness against modeling errors/uncertainties and external ***,MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown envi-ronmental *** paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown *** paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental *** derive these decision variables,a novel policy search framework aimed at training a high-level Gaussian policy is ***,we harness the power of neural network policies,trained on data gathered through the repeated execution of the Gaussian policy,to provide real-time decision *** effectiveness of hyMPC is validated through numerical simulations,achieving a 100％success rate in 20 drone flight tests traversing a swinging gate,demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.

关键词： Model predictive control reinforcement learning trajectory planning unmanned aerial vehicle

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Competitive Meta-Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1902-1904页

作者： Boxi Weng Jian Sun Gao Huang Fang Deng Gang Wang Jie Chen National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081 Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China School of Automation Tsinghua UniversityBeijing 100084China Department of Control Science and Engineering Tongji UniversityShanghai 201804 National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China IEEE

Dear Editor,This letter is concerned with developing meta-learning models for fast,stable,and effective few-shot learning across tasks over a few training ***,deep and reinforcement learning(RL)is widely used in auton... 详细信息

关键词： robot autonomous concerned

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Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multiobjective Approach

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IEEE Internet of Things Journal 2025年第11期12卷 17753-17764页

作者： Chu, Kai-Fung Chen, Tianlun Xie, Yue Lam, Albert Y. S. Song, Yue Iida, Fumiya University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Department of Geography Hong Kong Fano Labs Hong Kong Tongji University Department of Control Science and Engineering Shanghai201804 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai200120 China

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this article, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multiobjective mixed-integer program (MIP). To solve the optimization problem, we develop a hybrid heuristic approach based on nondominated sorting genetic algorithm II (NSGA-II) and branch-and-bound (BnB) algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors. © 2014 IEEE.

关键词： Vehicle-to-grid

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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

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control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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Identification and Prediction of Microvascular Invasion in Hepatocellular Carcinoma Based on Multiple Sequence Fusion Images 43

Identification and Prediction of Microvascular Invasion in H...

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43rd Chinese control Conference, CCC 2024

作者： Lu, Mingguang Zhu, Yuan China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

Hepatocellular carcinoma (HCC) is one of the most difficult diseases to be treated fundamentally in the medical field in today's world, and the recurrence rate of HCC has been increasing year by year in recent years. The study on the identification and prediction of microvascular invasion phenomenon (MVI) in hepatocellular carcinoma is beneficial to the postoperative prevention of hepatocellular carcinoma patients, which can greatly improve the survival probability of patients after surgery. In this study, a new MVI prediction method (MSF-GWO-GPR) was proposed, which combines the multi-sequence image features of imaging histology, sifts out the unimportant features through the T-test method and the dimensionality reduction of Lasso regression, and establishes the multi-sequence Gaussian process regression (GPR) prediction model for data fusion, and combined with the Gray Wolf Optimization (GWO) algorithm to design a suitable optimization objective function to optimize the hyper-parameters in the GPR, in order to get better prediction results. Compared with the existing methods, the data from different sequences of MRI images are selected for fusion in this study, which makes the feature data richer and more complete, and the model applicability is better;moreover, by optimizing the hyperparameters in the model through the GWO, the model can be made more accurate, reduce the risk of misidentified prediction, and improve the accuracy rate. The experimental results show that MSF-GWO-GPR outperforms widely used machine learning methods in terms of accuracy, F1 score, precision, recall and AUC metrics. This may provide a new idea for the identification and prediction of MVI in hepatocellular carcinoma. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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