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Chinese control and Decision Conference, CCDC

作者： Hao Guo Cheng-Lin Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper investigates the platooning problem of vehicles modeled by double integrators, where the time delay is taken into consideration. A novel difference-compensated consensus protocol is proposed by treating the platooning problem as the consensus problem. The internal stability and string stability are analyzed respectively by using the frequency-domain method, and the sufficient conditions for the asymptotic consensus convergence are gained. Through theoretical analysis, we find that the internal stability of the platooning has no relationship with the time delay, but the string stability is affected by the time delay. Finally, numerical simulation results are carried out to show the effectiveness and performance of the proposed consensus strategy.

关键词： Delay effects Vehicle dynamics Asymptotic stability Numerical stability Stability criteria Protocols

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A Novel Method Combining Leader-Following control and Reinforcement Learning for Pursuit Evasion Games of Multi-Agent Systems

A Novel Method Combining Leader-Following Control and Reinfo...

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International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Zhe-Yang Zhu Cheng-Lin Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

In the existing literature, many methods have been utilized in solving the pursuit evasion game. However, most of these methods have one thing in common: all pursuers have to know the evader's position. This paper presents a method combining the multi-agent leader-following control and reinforcement learning, which aims at addressing a pursuit evasion game only when partial pursuers have the knowledge of the position of the evader who moves randomly in two-dimensional grid space. Through decomposing the team task, pursuers realize the encirclement of the evader. Simulation experiment validates the effectiveness of our method.

关键词： Task analysis Games Reinforcement learning Multi-agent systems Robot sensing systems Markov processes automation

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Average-consensus Tracking for First-order Multi-agent Systems with Systems with Quantized Data

Average-consensus Tracking for First-order Multi-agent Syste...

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Chinese control and Decision Conference, CCDC

作者： Yu Zhang Cheng-Lin Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper addresses the average-consensus tracking problem of first-order multi-agent systems with constant reference signals via exchanging quantized data, where the topology is balanced and strongly connected. By transforming the quantized consensus problem into the stability problem, a necessary and sufficient condition is obtained by using matrix analysis theory to guarantee that the multi-agent systems achieve the quantized consensus, and the final consensus state is the average value of constant reference signals. Numerical simulation examples are provided to illustrate the correctness of the results.

关键词： Quantization (signal) Protocols Eigenvalues and eigenfunctions Multi-agent systems Topology Delays Linear matrix inequalities

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Lexicographic Multi-objective Order Picking Optimization for Robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hanying Wang Ziyan Zhao Jiaqi Liang Xingyang Li Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China Département de mathématiques et génie industriel Polytechnique Montréal Montréal Qc Canada Faculty of Robot Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao Hebei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice.

关键词： Intelligent automation Adaptation models Mechatronics Metaheuristics Search problems Scheduling Resource management Internet of Things Electronic commerce Robots

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Resilient control of Networked control Systems With Hidden DoS Attacks and Unknown Observation Probability

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IEEE Transactions on control of Network Systems 2025年

作者： Wang, Yuan Yan, Huaicheng Park, Ju H. Zhang, Hao Shen, Hao East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China Weifang University College of Machinery and Automation Weifang261061 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of Tongji University Department of Control Science and Engineering Shanghai200092 China Anhui University of Technology School of Electrical and Information Engineering Ma'anshan243002 China

This paper systematically studies the problem of resilient controller design for networked control systems under imperfect communication environments. The denial of service (DoS) attacks in the case of an imperfect network are remodeled. The semi-Markov chain is introduced in this paper to provide a suitable description of random attacks. On this basis, the double-layer stochastic process is constructed, which consists of an attack sequence and an observation one. Second, since the attack modes are inaccessible, a controller is constructed based on the observation sequence by virtue of the emission probability to stabilize the system under random attacks. Due to the complexity of the network, the probability that an observer can accurately detect an attack when it occurs is considered unknown. Based on the introduced double-layer stochastic process, a mode-associated Lyapunov function is constructed to facilitate the stability analysis. Building on this foundation, sufficient conditions that can ensure the δ-error mean square stability of the closed-loop system are derived. In the end, a simulation of the unmanned ground vehicle is presented, showcasing the feasibility and validity of the proposed control scheme. © 2025 IEEE.

关键词： Stochastic systems

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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An Alternative Bearing Fault Detection Strategy for Vibrating Screen in the Case Of Random Impulsive Noise and Vibration Disturbance

SSRN

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SSRN 2022年

作者： Xu, Yuanbo Nolan, John P. Li, Yongbo Wei, Yu Qu, Junsuo School of Automation Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Xi’an Key Laboratory of Advanced Control and Intelligent Process Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Department of Mathematics and Statistics American University WashingtonDC20016 United States School of Aeronautics Northwestern Polytechnical University Shaanxi Xian710072 China

An alternative procedure for bearing fault detection from vibrating screens served in the coal industry is proposed in the work. The strategy for bearing fault signature extraction covers three main steps: (a) a new fast resampled iterative filtering (FRIF) method is utilized to remove the vibration interferences produced by the unbalanced shafts and simultaneously eliminate large amounts of background noise;(b) a signal processing method based stable distribution is designed to cancel the random impulses;(c) an improved energy operator demodulation technique is proposed to identify the bearing fault characteristics from the purified signal. © 2022, The Authors. All rights reserved.

关键词： Demodulation

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Corrections to “Practical Fixed-Time Consensus Tracking for Multiple Euler–Lagrange Systems With Stochastic Packet Losses and Input/Output Constraints”

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IEEE Systems Journal 2022年第1期16卷 1709-1709页

作者： He Li Cheng-Lin Liu Ya Zhang Yang-Yang Chen Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi China School of Automation Southeast University Nanjing China

In [1], several errors were introduced during the production process, which are corrected here.

关键词： Packet loss

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Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

arXiv

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arXiv 2022年

作者： Dai, Wei Ning, Chuanfeng Pei, Shiyu Zhu, Song Wang, Xuesong The School of Information and Control Engineering Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The School of Mathematics China University of Mining and Technology Xuzhou221116 China

As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In the light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram-Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Manifold-aware and sparsity discriminant collaborative representation model and its application to multimode process monitoring

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Knowledge-Based Systems 2025年 324卷

作者： Yuanjian Fu Fubing Xia Jinliang Ding Xue Xu School of Control Science and Engineering Tiangong University Tianjin 300387 PR China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin 300387 PR China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 PR China School of Artificial Intelligence Tiangong University Tianjin 300387 PR China

Industrial processes are generally characterized by multiple operating modes from several factors, such as changes in market demand or raw material fluctuations, which pose challenges to traditional process monitoring. Existing multimode process monitoring primarily learns the distinct characteristics in each mode without collaboratively capturing the connections encapsulated across different modes. In this study, a novel multimode process monitoring approach, called the manifold-aware and sparsity discriminant collaborative representation model is proposed, which effectively captures the collaborative representation between and among modes, providing deeper insights into multimodal processes. The between-mode and within-mode affinity matrices were designed by considering the local information of the data to characterize the correlation between modes and the uniqueness of each mode, respectively. To faithfully reveal the characteristics of multimodal processes, a shared representation matrix was constructed to learn the common information among the different modes. Furthermore, the L 1 / 2 norm was imposed on the projection matrix, enabling the proposed approach effective in selecting the key variables for monitoring tasks. On the basis of these distinct model characterizations, the proposed approach is powerful in learning informative representations of data, further enhances the process monitoring capability. In addition, contrastive clustering was introduced to divide the historical modes and identify the running-on modes of the new samples, thereby reducing the online identification time. The monitoring results for both the Tennessee Eastman process and three-phase flow facility demonstrate the effectiveness of the proposed approach, in which fault detection rate was improved by 6 %-26 % in most cases.

关键词： Discriminant analysis Multimode process monitoring Representation learning Sparse regularization

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