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Fixed-Time Cooperative control of Vehicles with Connectivity Preservation

IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Ge Zhang, Qian Zhou, Zeng-Di Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Northeastern University College of Information Science and Engineering Shenyang110819 China

This article studies the lateral and longitudinal cooperative control problem of a collection of vehicles with limited communication capacity. A distributed two-layered control framework is established. Therein, the upper layer provides a pseudo velocity deviation command to achieve spacing error optimal consensus, incorporating a log-barrier penalty function to maintain communication connectivity. The lower layer contains a command-filtered tracking controller for the vehicle to follow the provided command in a predefined time. Together, they ensure the fixed-time stability of the vehicle group. Simulations are performed to validate the availability and feasibility of the methodology. © 1967-2012 IEEE.

关键词： Autonomous vehicles connectivity preservation cooperative control distributed control fixed-time

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Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis

arXiv

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arXiv 2025年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

In this paper, two model-free optimal output tracking frameworks based on policy iteration for discrete-time multiagent systems are proposed. First, we establish a framework of stabilizing policy iteration that can start from any initial feedback control policy, relaxing the dependence of traditional policy iteration on the initial stabilizing control policy. Then, another efficient and equivalent Q-learning policy iteration framework is developed, which is shown to require only less system data to get the same results as the stabilizing policy iteration. Both frameworks obtain stabilizing control policy by iterating the stabilizing virtual closed-loop system step-by-step to the actual closed-loop system. Multiple explicit schemes for the iteration step-size/coefficient are designed and their stability during the above iterations is analyzed. By using the generated closed-loop stabilizing control policy and two frameworks, the optimal feedback control gain is obtained. The approximate solution of the regulator equations is found by model-free iteration, which leads to the optimal feedforward gain. Finally, the cooperative optimal output tracking is realized by a distributed feedforward-feedback controller. The proposed algorithms are validated by simulation. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback control

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IF-Based Trajectory Planning and Cooperative control for Transportation System of Cable Suspended Payload With Multi UAVs

IF-Based Trajectory Planning and Cooperative Control for Tra...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yu Zhang Jie Xu Cheng Zhao Jiuxiang Dong the State Key Laboratory of Synthetical Automation for Process Industries and the Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education College of Information Science and Engineering Northeastern University Shenyang P.R. China

In this paper, we tackle the control and trajectory planning problems for the cooperative transportation system of cable-suspended payload with multi Unmanned Aerial Vehicles (UAVs). Firstly, a payload controller is presented considering the dynamic coupling between the UAV and the payload to accomplish the active suppression of payload swing and the complex payload trajectory tracking. Secondly, different from the simplification of obstacles in most approaches, we propose three Insetting Formation (IF) algorithms for the complete obstacle shape to generate collision-free waypoints for the cooperative transportation system. An IF strategy is proposed by integrating three IF algorithms to improve the success rate of obstacle avoidance and reduce the algorithm complexity for performing the aggressive flight. Finally, we verify the robustness and high performance of the proposed algorithm through benchmark comparison and real-world experiments. Moreover, our source code is released as an open-source ros package.

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Small Object Detection Algorithm Based on RPANet and Positional Convolution Attention Mechanism 5

Small Object Detection Algorithm Based on RPANet and Positio...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tang, Zongbing Yang, Dan Qu, Junsuo School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an Robertic Intelligent Systems International Science and Technology Cooperation Base Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

With the development of deep learning, small object detection has a significant role in application fields such as smart factories and remote sensing images. In order to address the problem of difficult and low accuracy detection of small objects due to small pixel scale and little feature information. In this paper, we present a path aggregation network with residual characteristic RPANet on YOLOv3 algorithm, which can twice use the feature information of the backbone network to enhance the small object feature information, and also offer a positional convolution attention mechanism module PCAM to thoroughly learn and extract the small object feature information as well as reduce the unnecessary feature information in the background, so as to further enhance the detection capability of the model for small objects. The experimental results demonstrate that the improved YOLOv3 algorithm is more effective for small object detection. © 2022 ACM.

关键词： Convolution

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A Koopman Operator-Based Finite Impulse Response Filter for Nonlinear Systems

A Koopman Operator-Based Finite Impulse Response Filter for ...

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IEEE Conference on Decision and control

作者： Zhichao Pan Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China department of chemical and materials engineering University of Alberta Edmonton Alberta Canada

This paper proposes a novel Koopman operator-based finite impulse response (KFIR) filter for nonlinear dynamic systems. This filter is generalized from the minimum variance unbiased (MVU) FIR filter for linear systems by using a global linear approximation of the nonlinear dynamics obtained from Koopman operator theory and the extended dynamic mode decomposition (EDMD) algorithm. Based on the recursive linear model, a reduced-order FIR filtering structure is proposed, and the optimal gain is derived to minimize the trace of the estimation error covariance. Unlike traditional methods, the KFIR filter requires no prior knowledge of the initial state and fully utilizes the data of a moving horizon. Simulation results show that the proposed filter has excellent robustness against unexpected modeling uncertainties and inaccurate noise information, making it suitable for real applications.

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An Improved Cooperative Team Spraying control of a Diffusion process With a Moving or Static Pollution Source

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 494-504页

作者： Juan Chen Baotong Cui Yang Quan Chen Bo Zhuang Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education)and School of IoT Engineering Jiangnan UniversityWuxi 214122China Mechatronics Embedded Systems and Automation LaboratoryUniversity of CaliforniaMerced CA 95343 USA

This paper is concerned with a control problem of a diffusion process with the help of static mesh sensor networks in a certain region of interest and a team of networked mobile actuators carrying chemical *** major contribution of this paper can be divided into three parts:the first is the construction of a cyber-physical system framework based on centroidal Voronoi tessellations(CVTs),the second is the convergence analysis of the actuators location,and the last is a novel proportional integral(PI)control method for actuator motion planning and neutralizing control(e.g.,spraying)of a diffusion process with a moving or static pollution source,which is more effective than a proportional(P)control *** optimal spraying control cost function is ***,the minimization problem of the spraying amount is ***,a new CVT algorithm based on the novel PI control method,henceforth called PI-CVT algorithm,is introduced together with the convergence analysis of the actuators location via a PI control ***,a modified simulation platform called diffusion-mobile-actuators-sensors-2-dimension-proportional integral derivative(Diff-MAS2D-PID)is *** addition,a numerical simulation example for the diffusion process is presented to verify the effectiveness of our proposed controllers.

关键词： Centroidal Voronoi tessellations(CVTs) diffusion processes mobile actuator-sensor networks(MAS-Net) PI control

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Multimodal Decoupling of Stationarity and Nonstationarity with Deep Cascaded Architecture for Varying-Condition process Monitoring

Multimodal Decoupling of Stationarity and Nonstationarity wi...

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第34届中国过程控制会议

作者： Pengyu Song Chunhui Zhao Jinliang Ding Furong Gao College of Control Science and Engineering Zhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Chemical and Biological Engineering Hong Kong University of Science and Technology

Nonstationarity,i.e.,the time-varying property of the data distribution,is a typical process characteristic caused by operating condition switching,which can conceal stationarity describing time-invariant process mechanisms and thus bring challenges for explicit process *** achieve full decoupling of stationarity and nonstationarity,this study reveals the following facts:1) the coupling between stationarity and nonstationarity is not uniform but can present time-varying modes,defined as multimodal coupling here;2) diverse coupling modes possess intrinsic association since they essentially stem from definite nonstationary driving forces,namely changeable operating ***,this study proposes a multimodal decoupling representation method for learning explicit representations of stationarity and ***,to capture the basic coupling mode caused by nonstationary driving forces,an information refinement module(IRM) is designed in which a shortcut connection is incorporated into the auto-encoding *** the deep cascading of multiple IRMs,multimodal coupling modes can be fully ***,a compatibility learning algorithm is developed,which can balance the stationarity and reconstruction requirements to form a stationary feature learning module(SFLM).With compatibility learning,IRMs can be induced to capture and filter out nonstationarity and output stationary refined information for *** this way,multimodal decoupling of stationarity and nonstationarity can be fully *** effectiveness of the proposed method is illustrated through a numerical example and an actual industrial process.

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A novel permeability index prediction method for blast furnace based on the improved extreme learning machine

A novel permeability index prediction method for blast furna...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Liu Shi-Xin Zhang Sen Sun Shao-Lun Yin Yi-Xin Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) School of Automation University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665465373

The permeability index of blast furnace reflects the operating conditions of the blast furnace and is an important basis for operators to judge whether the blast furnace is going forward. Based on K-ELM, this paper proposes a prediction model of permeability index based on KOS-ELM. Aiming at the shortcomings of KOS-ELM, such as high computational complexity and long learning time, this paper proposes an ALD-KOS-ELMF combining ALD and the sliding window. In the modeling process, taking into account the noise accompanying the blast furnace production data, this paper chooses the wavelet transform to reduce the noise inside the data. The experimental results show that the prediction model of permeability index based on ALD-KOS-ELMF not only improves the computational complexity and learning time, but also has better prediction accuracy and generalization performance than KOS-ELM. The model can meet the actual needs of relevant operators.

关键词： Wavelet transforms Computational modeling Production Predictive models Blast furnaces Prediction algorithms Data models

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Q-Learing-Based Multi-Rate Optimal control and Its Application

Q-Learing-Based Multi-Rate Optimal Control and Its Applicati...

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第34届中国控制与决策会议

作者： Zhenxing Xia Wei Dai School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

In this paper,adaptive dynamic programming(ADP)algorithm,with lifting technology,is develop to solve the multi-rate optimal control problem for discrete-time linear *** make use of the lifting technology to convert the multi-rate sample control problem to the single-rate one in the uniform *** propose a Q-Learning based approach to learn the optimal regulator by a value iteration(Ⅵ) ***,a class of continuous-time(CT) linear system with multi-timescale is ***,the convergence of a Q-Learning based algorithm is *** is proven that the iterative cost function precisely converges to the optimal value,and the control input also converges to the optimal ***,HIL system for grinding process is given to illustrate the effective performance of the proposed method.

关键词： Reinforcement learning(RL) Multi-Rate Optimal control Lifting Technology

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Automated machine learning-assisted enhanced product carbon footprint tracking and analysis in refinery industry: A graph-based life cycle assessment framework

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Journal of Cleaner Production 2025年 514卷

作者： Zhang, Tingwei Peng, Xin Zhong, Weimin Shen, Feifei Ding, Jinliang State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai200237 China State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China

Product carbon footprint assessment and tracking in the refinery industry mainly rely on the expertise of carbon management professionals, which leads to high service costs and involves significant manual intervention, reducing the accuracy of carbon footprint analysis. Accordingly, a data-driven automated product carbon footprint analysis framework based on life cycle assessment is developed in this work. Specifically, an integrated material-energy-carbon multi-scale emission accounting model is established to quantify CO2 emissions accurately. Subsequently, considering that traditional carbon emission allocation methods are generally based on fixed assumptions or simplified proportional relationships, which fail to fully account for the dynamic variations in complex production processes, the allocation results lack accuracy and reliability. Additionally, these methods lack sufficient flexibility and are unable to effectively adjust to the actual carbon emission characteristics of different production stages and products. A data-driven contribution-based carbon emission allocation method is proposed from the carbon flow supply–demand balance perspective to improve allocation transparency. Moreover, a graph-based automated recognition framework of product carbon footprint chains is developed to achieve process operation automation and eliminate manual operations. The proposed models and methods are applied to a real refinery production site for a case study. The results show that the developed product carbon footprint analysis framework can highlight high-energy consumption and high-emission links in the production process and pay more attention to the environmental costs of high-value products. Moreover, CO2 can act as a carbon flow regulation hub, effectively managing emissions through the balance of carbon flow supply and demand. Light oil has a higher contribution rate to CO2 emissions than heavy oil. The carbon footprint of petroleum coke is 3.8677 tCO2eq/t, ma

关键词： Carbon footprint

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