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

作者： Luyuan Gao Zhihe Zhuang Shanglei Guan Hongfeng Tao Jier Qiu Wojciech Paszke Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China Tiandi (Changzhou) Automation Co. Ltd. Changzhou China Institute of Automation University of Zielona Gora Zielona Gora Poland

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In practical applications of iterative learning control (ILC), the desired trajectory may scale within different time intervals along the iteration axis, leading to the non-uniform trial length problem. To solve the problem, this paper proposes a projection-based ILC algorithm for linear time-varying systems with flexible tasks. Defining the system dynamics in Hilbert space, the performance index to be iteratively optimized is designed through a successive projection strategy. Based on this performance index, a norm optimal ILC algorithm is obtained, and the convergence analysis of the proposed algorithm is given. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation of a gantry robot.

关键词： System dynamics Heuristic algorithms Hilbert space Trajectory Performance analysis Task analysis Time-varying systems

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Distributed Fault-Tolerant Consensus control for Linear Multi-Agent System with K-Step Hierarchical Protocol

Distributed Fault-Tolerant Consensus Control for Linear Mult...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wang, Yuanwen Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

ISBN: (纸本)9789811966125

The distributed fault-tolerant consensus control using k-step hierarchical protocol for linear multi-agent system (MAS) is studied in this note. Unlike most existing fault-tolerant methods for compensating actuator fault, a hierarchical protocol consisting of a virtual layer and a actual layer that considers both fault compensation and introducing fault information into the trajectory planning process which means that the motion of the MAS does not exceed the fault-tolerant compensation range of the faulty agent is proposed. Trajectory planning is responsible for the virtual layer consisting of virtual agent system and a co-controller that introduces fault information. The actual layer, consisting of the actual agent system, actuator fault observer, and tracking trajectory controller, traces the virtual layer for consensus. It is shown that the integrity of the MAS is guaranteed. Finally, the validity of the developed protocols is illustrated by simulation examples. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Adaptive Command-filtered Finite-time control of Vehicular Platoons with Inter-Vehicle Spacing Constraints

Adaptive Command-filtered Finite-time Control of Vehicular P...

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Asian control Conference

作者： Wei Liu Zhongyang Wei Zhenyu Gao Ge Guo Northeastern University Qinhuangdao Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper investigates the problem of platoon control of vehicles with inter-vehicle spacing constraints and uncertain parameters. The prescribed performance control methodology is employed to describe the spacing constraints that de-scribed by a time-varying smooth function. Based on the smooth function, asymmetric barrier lyapunov functions, neural network technique and in the context of command filtered control, a novel finite-time control scheme is proposed, by which the platoon can be achieved with all the states stabilized in a given finite-time, while guarantee that the inter-vehicle spacing constraints are not violated during the whole operation. The effectiveness and performance of the method is demonstrated by numerical simulations.

关键词： Uncertainty Backstepping control design Simulation Numerical simulation Explosions Complexity theory

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Design of indirect-type iterative learning control for continuous-time batch processes with the repetitive process setting

Design of indirect-type iterative learning control for conti...

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Asian control Conference

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Zhihe Zhuang Institute of Automation Electronic and Electrical Engineering University of Zielona Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

An indirect iterative learning control law is proposed for a class of continuous-time batch processes with time-varying uncertainties, input delay, and disturbances. First, by designing a predictor based on a state observer we are able to estimate the future state and compensate for the input delay. Then, based on the estimated state we design a robust $H$ ∞ controller in the presence of time-varying uncertainties and load disturbances. Additionally, the differential repetitive process setting for the design of the iterative learning control scheme is applied and hence the convergence of the tracking error in trial-to-trial direction occurs. Finally, the simulation results verify the new method's effectiveness.

关键词： Uncertainty Simulation Batch production systems process control Observers Delays Iterative learning control

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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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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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Model-Free Data-Driven Prescribed-TimePerformance control for Underactuated ASV

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Ying Lu, Wenlong Guo, Ge Dalian Maritime University Marine Electrical Engineering College 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

In this paper, a model-free data-driven prescribedtime-performance (DDPTP) algorithm is developed for the underactuated autonomous surface vehicle (ASV). By defining a leader to provide the dynamic reference signal, the proposed DDPTP algorithm can achieve high-precision target tracking tasks while ensuring collision avoidance and connectivity between the leader and follower. A prescribed-time-performance function (PTPF) is designed for the ASV, featuring an adaptive shape and customizable convergence time. By using both real-time and historical data, a data-driven neural predictor (DDNP) is developed to estimate system uncertainties including unmodeled dynamics, unknown environmental disturbances and unknown input gains. An adaptive control law is employed based on the knowledge obtained from the DDNP, without relying on prior information about the motion model parameters. A switching dynamic event-triggered mechanism (SDETM) is introduced, which can switch between fixed-threshold triggering mechanism and dynamic event-triggered mechanism (DETM). Through this SDETM, not only are communication resources conserved, but frequent updates of the control signal are also mitigated. The effectiveness of the proposed algorithm is validated through verification simulations. © 1967-2012 IEEE.

关键词： Model reference adaptive control

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Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

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Journal of Central South University 2022年第12期29卷 3956-3973页

作者： LI Xian-ling ZHANG Jian-feng ZHAO Chun-hui DING Jin-liang SUN You-xian Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research InstituteWuhan 430205China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory of Synthetical Automation for Process Industries(Northeastern University) Shenyang 110819China

With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.

关键词： nonlinear fault diagnosis multiple multivariate Gaussian distributions sparse Gaussian feature learning Gaussian feature extractor

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Analysis of Operational Data and Self-learning control Rules for Mining Mill Equipment

Analysis of Operational Data and Self-learning Control Rules...

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Asian control Conference

作者： Guicheng Wang Liang Yuan Zhicheng Bai Guoliang Cai Ning Hua Xiaobin Li School of Electrical and Electronics Engineering Shanghai Institute of Technology Shanghai China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process and Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Zijin Intelligent Control (Xiamen) Technology Co. Ltd. Xiamen China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The engineering application under industrial big-data analysis is of great significance for improving the optimization effect of enterprise production process control processes. By analyzing the operating principle of the semi-automatic mill and studying its control optimization strategy, based on the operational data of the SAG mill, industrial big-data analysis is adopted to self-learning its control optimization rules. Firstly, SPSS is used to preprocess the high-dimensional process data generated during the operation of the semi-automatic mill. Four parameters, namely power, axial pressure, grinding sound, and grinding concentration, are preliminarily selected. Then, PCA is used to screen and reduce the dimensionality of multidimensional data, and K-Means clustering algorithm is used to classify and identify the reduced dataset. After simulation research, the results show that PCA can reduce the redundancy of SAG mill operation data, and the control optimization rules formed by self-learning can serve as operational guidance for improving the efficiency and energy-saving of SAG mill operation. The effectiveness can be further verified in practical engineering applications in the later stage.

关键词： Dimensionality reduction Energy consumption Technological innovation Redundancy Force process control Clustering algorithms

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A parallel intelligent system for optimizing high-speed railway rescheduling by learning 1

A parallel intelligent system for optimizing high-speed rail...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yang, Pengxin Cui, Dongliang Dai, Xuewu Lv, Yisheng Dong, Hairong Wang, Xinghao Liu, Ruiguang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9781665433372

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed. © 2021 IEEE.

关键词： Intelligent systems

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