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Aero-optic imaging deviation prediction based on ISSA-ELM

Optoelectronics Letters 2023年第7期19卷 425-431页

作者： XU Liang ZHAO Shiwei WANG Tao Tianjin Key Laboratory for Control Theory&Applications in Complicated Systems School of Electrical Engineering and AutomationTianjin University of TechnologyTianjin 300384China School of Intelligent Engineering Sun Yat-sen UniversityGuangzhou 510275China

When the aircraft is moving at high speed in the atmosphere,aero-optical imaging deviation will appear due to the influence of aero-optical *** order to achieve real-time compensation during the flight of the aircraft,it is necessary to analyze and predict the obtained imaging deviation *** order to improve the search speed and accuracy of the prediction algorithm and the ability to jump out of local optimum,in this paper,an improved sparrow search algorithm optimized extreme learning machine(ISSA-ELM) neural network model is proposed to predict the aero-optical imagine ***,the performance of ISSA-ELM,ELM neural network and SSA-ELM neural network was *** results showed that compared with ELM and SSA-ELM algorithms,the convergence speed of ISSA-ELM was significantly enhanced,and the accuracy of data prediction was also significantly improved.

关键词： effect. deviation extreme

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Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1096-1098页

作者： Wenkai Hu Xiangxiang Zhang Jiandong Wang Guang Yang Yuxin Cai School of Automation China University of GeosciencesWuhan 430074Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm ***,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm ***,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via *** effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

关键词： Word Alarm DTW

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Trajectory Tracking of AUV under Current Disturbance Based on Adaptive LADRC 2

Trajectory Tracking of AUV under Current Disturbance Based o...

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2nd International Conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Ji, Daxiong Wang, Xinwei Xu, Minghui Zhang, Qiang Sharma, Sanjay Sutton, Robert Institute of Marine Electronics and Intelligent Systems Ocean Collage Zhejiang University Donghai Lab Zhoushan China School of Engineering Computing and Mathematics University of Plymouth Intelligent Autonomous Control Systems Plymouth United Kingdom

ISBN: (纸本)9798350308341

In consideration of the trajectory tracking problem of autonomous underwater vehicle (AUV) under external current disturbance, an adaptive linear active disturbance rejection control (LADRC) method is proposed. Unlike the conditional LADRC using fixed parameters for extended state observer, the proposed controller makes use of the error of estimation and automatically adjusts the parameter online. As shown in the simulation, the adaptive LADRC estimates the external current disturbance effectively, and tracks the 3D trajectory better compared with traditional LADRC. © 2023 IEEE.

关键词： Disturbance rejection

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Event-triggered adaptive NN practical finite-time control with its application to MPCVD reactors

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Journal of the Franklin Institute 2023年第3期360卷 1870-1883页

作者： Yu, Xinghu Yang, Jiaxing Meng, Xinbo Zheng, Xiaolong Yang, Xuebo College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Ningbo Institute of Intelligent Equipment Technology Co. Ltd Ningbo315201 China Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology Harbin150001 China

The practical finite-time control problem of uncertain nonlinear systems is investigated in this paper. To address the uncertain nonlinearities of the system, neural networks are introduced to approximate the lumped nonlinearities containing the system unknown functions. On the other hand, to alleviate the signal transmission pressure of the system, an improved event-triggered mechanism is presented to reduce the controller update frequency without degrading the control performance of the system. By using practical finite-time stability, it is obtained that the system tracking errors are practical finite-time stable without Zeno behavior. Finally, the effectiveness of the proposed method is verified by the simulation results of its application to a microwave plasma chemical vapor deposition (MPCVD) reactor system. © 2022 The Franklin Institute

关键词： control nonlinearities

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Observer-based Sliding Mode control of Non-commensurate Fractional-Order Flexible Manipulator Against Actuator Attack

Observer-based Sliding Mode Control of Non-commensurate Frac...

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2023 China Automation Congress, CAC 2023

作者： Bi, Yannan Liao, Mingrui Wang, Tong Qiu, Jianbin Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Aerospace System Engineering Shanghai Shanghai China

ISBN: (纸本)9798350303759

This article addresses the sliding mode secure control problem for fractional-order flexible manipulator subject to actuator attack. By designing a fractional-order super-twisting sliding mode extended state observer (FOSTSM-ESO), a sliding mode control scheme based on the estimated system states and unknown actuator attack is proposed. The system stability is analyzed based on the fractional Lyapunov function. Numerical simulations are finally provided to demonstrate the validity of the proposed secure control method. © 2023 IEEE.

关键词： Sliding mode control

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Graph-Based Deep Reinforcement Learning Approach for Alliance Formation Game based Robot Swarm Task Assignment 63

Graph-Based Deep Reinforcement Learning Approach for Allianc...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Lv, Yang Lei, Jinlong Yi, Peng Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Tongii University Department of Control Science and Engineering Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University 200092 China

ISBN: (纸本)9798350316339

This paper explores the robot swarm task allocation problem based on alliance formation game theory, which treats each robot as an autonomous agent capable of forming strategic alliances for task completion, with a focus on optimizing overall system revenue and mutual benefits. To resolve the problem, we introduce a unique graphbased Deep Reinforcement Learning (DRL) framework named AFGNet_DDQN. Firstly, we construct an Allocation Feature Graph (AFG) that intricately maps the complex interactive relationships and allocation features among robots and tasks, and develop the AFGNet architecture to efficiently extract features from the graph nodes. Then thorugh reconstructing a Markov Decision Process (MDP) within this graph, we implement an advanced version of Double Deep Q-Networks (DDQN) algorithm, adapted for our graph-based framework. This setup allows for the effective learning and optimization of task allocation strategies through localized interactions among the robots. Finally, our empirical results demonstrate the superiority of our framework over traditional methods, especially in terms of scalability and robustness. © 2024 IEEE.

关键词： Markov processes

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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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Development of a High-Frequency Power Source Dedicated to DBD Reactors for Ozone Production 3

Development of a High-Frequency Power Source Dedicated to DB...

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3rd International Conference on Advanced Electrical engineering, ICAEE 2024

作者： Bechekir, Seyf Eddine Belabed, Meriem Brahami, Mostefa Jbilou, Mokhtaria Nemmich, Said Marref, Mohemed Amine Djillali Liabes University of Sidi Bel-Abbes Intelligent Control and Electrical Power Systems Laboratory Department of Electrical Engineering Algeria Algeria Djillali Liabes University of Sidi Bel-Abbes Intelligent Control and Electrical Power Systems Laboratory Algeria Institute of Applied Sciences and Technologies University of Sciences and Technology of Oran Mb Algeria

ISBN: (纸本)9798350349351

This paper presents the implementation of a half-bridge resonant inverter to power a dielectric barrier discharge (DBD) ozone generator. We use the PIC16F876A microcontroller-based control circuit to vary the inverter frequency. We use a step-up transformer to obtain the high voltage required for the DBD. We conducted an experimental study to investigate the impact of frequency and voltage on ozone production and power consumption. The results show that these two parameters have a strong influence. This study demonstrates the advantages of using a resonant inverter combined with optimum electrical parameter adjustment to achieve excellent energy efficiency for the DBD ozone generator. The choice of a resonant half-bridge topology aims to maximize energy efficiency, crucial in a solar-powered system. The circuit has been optimized to reduce switching losses. In addition, the development of the power source takes into account the capacitive nature of DBD reactors, using a series resonant circuit formed by the DBD capacitance and the step-up transformer inductance. The frequency range from 14 kHz to 17.5 kHz has been specifically chosen and optimized for the DBD reactor, taking into account the constraints of the PV source and the characteristics of the discharge. © 2024 IEEE.

关键词： Resonant circuits

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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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Efficient Mobile Deployment of Lightweight CNN-Transformer Model for Crop Disease Diagnosis in Smart Agriculture 4

Efficient Mobile Deployment of Lightweight CNN-Transformer M...

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4th Asia Conference on Information engineering, ACIE 2024

作者： Lv, Zirong China University of Geosciences Hubei Key Lab. of Adv. Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350371925

Crop disease diagnosis is of great significance for crop yield and agricultural production. Deep learning methods have become a major research direction for solving crop disease diagnosis. However, recent studies have shown that deploying complex neural network models on mobile devices poses a significant challenge to mobile hardware performance. To address this issue, this paper proposes a lightweight network model based on the Transformer and deploys it on mobile devices. Compared to other lightweight network models, our approach significantly reduces computational complexity using Transformer modules and exhibits a particular advantage in recognition accuracy. It achieves a recognition rate of 97.76% for ten categories of tomato diseases, while the model size is only 3.86 MB. Furthermore, we deploy the model on embedded devices and apply it to tomato crop recognition, enabling farmers to monitor crop conditions in real-time and realizing smart agriculture. © 2024 IEEE.

关键词： Deep learning

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