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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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DSTAnet: A Trajectory Distribution-aware Spatio-Temporal Attention Network for Vehicle Trajectory Prediction

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Taoying Sun, Xulei Dong, Quanyu Guo, Ge Dalian Maritime University School of Maritime Economics and Management Dalian116026 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

Trajectory prediction is a crucial task of autonomous driving and benefits vehicles travel safely in complex traffic environments. However, most existing trajectory prediction methods suffer from low accuracy issue due to the highly complex and dynamic interactions between vehicles, as well as the uncertainty of driver intentions in real-world scenarios. To address these challenges, we propose a trajectory Distribution-aware Spatio-Temporal Attention network (DSTAnet) for vehicle trajectory prediction. The proposed DSTAnet begins with a temporal feature extractor that combines Transformer Encoder Layers (TEL) and Gated Recurrent Units (GRU), extracting both long-term teporal dependencies and short-term variations in vehicle trajectories. Next, Graph Attention Networks (GAT) are introduced to extract spatial features in dynamically changing interactions, and focus on the most noteworthy vehicles, which are beneficial for modeling interactions between vehicles. Meanwhile, Mixed Density Network (MDN) is used to learn potential trajectory distributions from spatiotemporal features, which enriches the features related to driver intentions, providing a probabilistic understanding of possible future trajectories. By predicting multiple trajectories through random sampling, DSTAnet captures the inherent uncertainty of driver intentions. Finally, the INTERACTION dataset is adopted to estimate the model performance and results indicate that the proposed method outperforms others. © 1967-2012 IEEE.

关键词： Distribution transformers

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Fixed-time Sliding Mode Formation control of AUVs Based on a Disturbance Observer

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 539-545页

作者： Zhenyu Gao Ge Guo School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819 School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China(

In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external *** external disturbances due to the wind,waves,and ocean currents are combined with the model parameter uncertainties as a compound *** a disturbance observer(DO)is introduced to estimate the compound disturbance,which can be achieved within a finite time independent of the initial estimation *** on a DO,a novel fixed-time sliding control scheme is developed,by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling *** effectiveness and performance of the method are demonstrated by numerical simulations.

关键词： Autonomous underwater vehicles(AUVs) disturbance observer(DO) sliding mode control formation control

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Stability Analysis and Impulsive controller Design for Positive Interval Type-2 Polynomial Fuzzy Systems with Input Saturation

Stability Analysis and Impulsive Controller Design for Posit...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Meng Han Yongjie Huang Ge Guo Zhengsong Wang School of Control Engineering Northeastern University at Qinhuangdao Qinhuang-dao China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

This paper designs an impulsive controller for nonlinear positive systems with uncertain parameters and input saturation constraints. To address the issue of modeling errors caused by parameter uncertainties, system modeling is done using an interval type-2 (IT2) polynomial fuzzy model. Also, a premise mismatched controller design strategy is employed to improve controller design flexibility. In addition, to attenuate the conservatism of the analytical results, a novel impulse-time-dependent discretized polynomial copositive Lyapunov function (IDDPCLF) is employed, and the obtained non-convex resultant conditions are handled by the proposed convexification method. Finally, the usefulness of the impulsive controller design approach and the convexification method are verified through an example.

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Analysis of Equilibrium Point and Genetic Algorithm Design of Observer-Based PID controller for Membrane Bioreactor

Analysis of Equilibrium Point and Genetic Algorithm Design o...

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2021 International Automatic control Conference, CACS 2021

作者： Changsila, Yanita Banjerdpongchai, David Intelligent Control Automation of Process Systems Research Unit Dept. of Electrical Engineering Faculty of Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9781665444125

The operation of most wastewater treatment systems is an open-loop system. There is limited feedback control of membrane bioreactors (MBR) which makes it impossible to regulate the quality of the treated water. The objectives of this paper are twofold. First, we analyze the equilibrium point of the MBR nonlinear model. Second, we utilize output feedback control with state observer and design optimal PID controllers. We determine the optimum equilibrium point and derive a linearized model of MBR. Then, we design the state observer to estimate the non-measurable state using the pole placement method. Both the measurable output and estimated state are fed back through PID controller. The objectives of PID control consist of stabilization and performance improvement. Genetic Algorithm is applied to find the optimal PID parameters that minimize the integral of absolute error and satisfy the specified time-domain criteria. We apply the observer-based PID controller to the linearized model of MBR. Numerical results demonstrate the design of PID controller and the improvement of the closed-loop response and quality of the treated water. © 2021 IEEE

关键词： Bioreactors

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Indistinguishability analysis and observer design for size-structured cell populations

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IFAC-PapersOnLine 2023年第2期56卷 581-586页

作者： P. Jerono J.J. Winkin A. Vande Wouwer A. Schaum Digital Process Engineering Group (DPE) Karlsruhe Institute of Technology Germany Department of Mathematics and Institute for Complex Systems (naXys) University of Namur Belgium Systems Estimation Control and Optimization (SECO) University of Mons Belgium Chair of Automation and Control (ACON) Kiel University Germany

The observability property of population balance equations with biomass measurements is addressed within the framework of indistinguishable trajectories. The population balance equation is described by a partial integro-differential equation which is coupled with an ordinary differential equation for the nutrient dynamics. For the semi-discretized model equations, the dynamics of indistinguishable trajectories are derived and evaluated for the special case of equal partitioning at cell division revealing that the observability property is guaranteed as long as the nutrient concentration is not depleted. Based on these results, an observer is designed and tested in simulation to estimate the cell population distribution using biomass measurements in a batch bioreactor for the case of a bi-structured population.

关键词： Observability indistinguishable trajectories cell population dynamics

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Supervised Dynamic Latent Variable Models for Fault Identification in Dynamic processes

Supervised Dynamic Latent Variable Models for Fault Identifi...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical processes, SAFEprocess 2021

作者： Niu, Qiqi Shen, Chengkai Lyu, Yuting Liu, Yi Zhou, Le Zhejiang University of Science Technology School of Automation and Electrical Engineering Zhejiang Hangzhou China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Zhejiang Hangzhou China

ISBN: (纸本)9781665401159

With an increasing application of the multivariate statistical process monitoring method, the fault identification becomes more and more crucial after the fault detection, which is benefited to fault troubleshooting effectively and quickly. To deal with the dynamic process and the impact of random noise on fault identification, a novel fault identification method based on supervised autoregressive dynamic latent variable is proposed in this paper. After the fault label structure is added on the unsupervised model, the relationship between fault information and dynamic latent feature is strengthened. Then, the fault cause is determined based on the fault relevance method. Finally, the validity of method is verified by a numerical example and an actual wastewater treatment case. © 2021 IEEE.

关键词： Fault detection

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Adaptive Dynamic Programming and Decentralized Optimal Output Regulation of Two-Time-Scale Interconnected Systems

Adaptive Dynamic Programming and Decentralized Optimal Outpu...

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International Conference on control, automation and Information Sciences (ICCAIS)

作者： Jianguo Zhao Chunyu Yang Linna Zhou Weinan Gao School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

In this paper, we address the decentralized optimal output regulation problem for two-time-scale (TTS) interconnected systems with unknown slow dynamics through adaptive dynamic programming (ADP). Firstly, singular perturbation theory is leveraged to decompose the TTS interconnected system into a slow subsystem and several interconnected fast time-scale subsystems. Secondly, we develop a decentralized optimal control scheme for the fast time-scale subsystems with interconnections. Also, we devise an off-policy ADP algorithm to learn the optimal control strategy for the slow time-scale subsystem with unknown dynamics. Moreover, we combine the slow and fast control strategies to design the composite decentralized optimal output regulator. Both optimality and stability of the closed-loop system are rigorously analyzed. Finally, simulation results are provided to validate the effectiveness of the proposed methodology.

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Repetitive T-S Fuzzy Model-Based Iterative Learning control Law Design 63

Repetitive T-S Fuzzy Model-Based Iterative Learning Control ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Maniarski, Robert Paszke, Wojciech Tao, Hongfeng Rogers, Eric Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 Zielona Góra65-246 Poland Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214000 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9798350316339

This paper develops an iterative learning control law for a class of nonlinear systems. The approach used to represent the nonlinear system dynamics is a Takagi-Sugeno fuzzy repetitive process that considers the two directions of information propagation. Then, the control action investigated is a state feedback control law combined with a PD-type feed-forward learning control law. Consequently, linear matrix inequality techniques can be used for control design. Furthermore, this approach allows the design of control action to satisfy the requirements on both the error convergence and the transient dynamics. Finally, an example demonstrates the properties of the new design. © 2024 IEEE.

关键词： State feedback

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Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis

arXiv

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arXiv 2023年

作者： Zhao, Jiancheng Yue, Jiaqi Feng, Liangjun Zhao, Chunhui Ding, Jinliang College of Control Science and Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Fault diagnosis is a critical aspect of industrial safety, and supervised industrial fault diagnosis has been extensively researched. However, obtaining fault samples of all categories for model training can be challenging due to cost and safety concerns. As a result, the generalized zero-shot industrial fault diagnosis has gained attention as it aims to diagnose both seen and unseen faults. Nevertheless, the lack of unseen fault data for training poses a challenging domain shift problem (DSP), where unseen faults are often identified as seen faults. In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by recombining transferable attribute features extracted from seen samples under the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with the help of generated samples, which aims to address the DSP by modeling seen categories in the knowledge space. KSS-D avoids misclassifying rare faults as seen faults and identifies seen fault samples. We conduct generalized zero-shot diagnosis experiments on the benchmark Tennessee-Eastman process, and our results show that our approach outperforms state-of-the-art methods for the generalized zero-shot industrial fault diagnosis problem. Note to Practitioners—This paper is motivated by the difficulty of fault diagnosis in practical industrial scenarios caused by the lack of fault samples for training supervised diagnosis models. The focus of this study is to develop a generalized zero-shot industrial fault diagnosis method, which can diagnose both faults with sufficient training samples and faults without training samples with the help of expert knowledge of these faults. Considering the generalized zero-shot diagnosis models often confuse unseen faults as seen faults, a knowledge spac

关键词： Failure analysis

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