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Chinese Control and Decision Conference, CCDC

作者： Zhenyu Guo Yu Fu Kang Li Guobin Zou Qingkai Wang BGRIMM Technology Group State Key Laboratory of Process Automation in Mining and Metallurgy Beijing China College of Information Science and Engineering Northeastem University Shenyang China

The roller mill is a crucial production equipment for the nonferrous metal beneficiation process, responsible for crushing ores. Over time, the roller tyre of the roller mill gradually wears down, ultimately impacting the crushing efficiency. Therefore, Prognostics and Health Management (PHM) of the roller tyre is crucial to enhancing the production efficiency of the roller mill. In this study, a PHM method based on Dual Autoencoder (DUAE) is proposed to predict the grinding roller tyre’s service life. The effectiveness of the proposed PHM method is validated using actual data from a domestic concentrator. This research demonstrates that the PHM of roller tyres can be effectively conducted.

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Observer-Based Adaptive Neural Predefined Performance Control of Non-Affine Multi-Agent Systems with Input Delay

Observer-Based Adaptive Neural Predefined Performance Contro...

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Chinese automation Congress (CAC)

作者： Xin Li Dakuo He College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang China

In this paper, observer-based adaptive neural pre-defined performance control is studied for non-affine multi-agent systems with input delay. Compared with existing results, a variable parameters predefined performance control method for multi-agent system is proposed. In addition, Pade approximation and mean value theorem are used to solve the obstacles of input delay and non-affine term. Different from the dynamic surface control(DSC) method, the command filter (CF) method can also solve the problem of computational explosion in the backstepping design, and the filter error generated is well eliminated. By utilizing adaptive backstepping technique and Lyapunov stability theorem, it is proved that all signals in multi-agent systems are cooperative semi-global uniform and ultimately bounded (CSUUB). The observer estimation errors and the tracking errors within a predefined range converge on a small neighborhood. Simulation results are provided to demonstrate the effectiveness of the proposed method.

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Collision/Obstacle Avoidance Dynamic Formation Reconfiguration of High-Order Nonlinear Multi-Agent Systems

Collision/Obstacle Avoidance Dynamic Formation Reconfigurati...

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第35届中国控制与决策会议

作者： Liwei An Weinan Gao Chao Deng Wei-Wei Che College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Institute of Advanced Technology Nanjing University of Posts and Telecommunications Department of Automation Qingdao University

Multi-Agent formation involves the motion conflicts of large numbers of physical systems,which leads to significant safety challenges in terms of collision/obstacle *** paper studies the problem of collision/obstacle avoidance formation tracking for a class of high-order strict-feedback nonlinear multiagent systems and presents a dynamic formation reconfiguration ***,a smooth and analytic reference trajectory is constructed,which consists of a convex combination of preset trajectory and delayed target trajectory with its coefficient adaptively adjusted by two barrier functions to avoid ***,a distributed backstepping control scheme with highorder filtering for barrier functions is ***,all agents adaptively reconfigure their formation shape within the barrier function's coverage to avoid collisions with each other and obstacles,while quickly forming a pre-defined shape of formation following the target trajectory outside the barrier function's *** results are given to illustrate the effectiveness of the proposed scheme.

关键词： Formation reconfiguration collision/obstacle avoidance multi-agent systems barrier functions

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A Decomposition-Based Evolutionary Algorithm With Clustering and Hierarchical Estimation for Multi-Objective Fuzzy Flexible Jobshop Scheduling

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, Xuwei Liu, Shixin Zhao, Ziyan Yang, Shengxiang State Key Laboratory of Synthetical Automation for Process Industries and College of Information Science and Engineering Northeastern University Shenyang China School of Computer Science and Informatics Institute of Artificial Intelligence De Montfort University Leicester U.K

As an effective approximation algorithm for multi-objective jobshop scheduling, multi-objective evolutionary algorithms (MOEAs) have received extensive attention. However, maintaining a balance between the diversity and convergence of non-dominated solutions while ensuring overall convergence is an open problem in the context of solving Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs). To address it, we propose a new MOEA named MOEA/DCH by introducing a hierarchical estimation method, a clustering-based adaptive decomposition strategy, and a heuristic-based initialization method into a basic MOEA based on decomposition. Specifically, a hierarchical estimation method balances the convergence and diversity of non-dominant solutions by integrating Pareto dominance and scalarization function information. A clustering-based adaptive decomposition strategy is constructed to enhance the population’s ability to approximate a complex Pareto front. A heuristic-based initialization method is developed to provide high-quality initial solutions. The performance of MOEA/DCH is verified and compared with five competitive MOEAs on widely-tested benchmark datasets. Empirical results demonstrate the effectiveness of MOEA/DCH in balancing the diversity and convergence of non-dominated solutions while ensuring overall convergence. IEEE

关键词： Job shop scheduling

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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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A Dual Mutation Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization With Undetectable Changes

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Liu, Yuanchao Tang, Lixin Ding, Jinliang Chen, Qingda Liu, Kanrong Liu, Jianchang College of Information Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Most of the current research on dynamic multi-objective optimization problems (DMOPs) assumes that environmental changes can be detectable. However, undetectable changes are frequently encountered in real-world applications, which pose a serious challenge for the existing methods. Because undetectable changes can lead to the failure of change detection techniques, thereby making it difficult to adapt to environmental changes for most algorithms. Therefore, to effectively deal with DMOPs with undetectable changes, this work proposes a dual mutation based dynamic multi-objective evolutionary algorithm (DM-DMOEA). The proposed DM-DMOEA incorporates the following two main components. First, based on the exploration level of the population, an adaptive selection strategy is proposed, which enables the adaptive identification of individuals for mutation. Second, a dual mutation scheme is developed, utilizing both the polynomial mutation and the Gaussian mutation. These mutation operations are applied on the selected individuals to generate the mutated individuals, allowing for diverse exploration in the search space. After conducting the above two strategies, the population will evolve by the evolutionary criterion of multi-objective optimization. As a result, the algorithm can effectively adapt to undetectable changes in the environment. Comprehensive empirical studies are conducted on different benchmark functions and a real-world application to evaluate the performance of DM-DMOEA. Experimental results have demonstrated that DM-DMOEA is competitive in tracking the Pareto front over time when facing undetectable changes. IEEE

关键词： Multiobjective optimization

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Singular Perturbation: When the Perturbation Parameter Becomes a State-Dependent Function

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IFAC-PapersOnLine 2023年第1期56卷 294-300页

作者： Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China Department of Electrical and Computer Engineering New York University 370 Jay Street Brooklyn NY 11201 USA

This paper introduces a new class of singularly perturbed systems in which the small, but constant, perturbation coefficient in standard singular perturbation theory is replaced by a state-dependent function. This generalization is aimed at broadening the applicability of singular perturbation theory in practice. For this class of singularly perturbed systems, it is assumed that the boundary-layer subsystem is globally asymptotically stable (GAS) at the origin and the reduced subsystem is input-to-state stable (ISS) with respect to the state of the boundary-layer subsystem. Under a mild monotonicity condition, sufficient conditions on the perturbation functions are given under which the singularly perturbed system is GAS at the origin. ISS and nonlinear small-gain techniques are exploited in the stability analysis. The efficacy of the proposed theoretical result is validated via its applications to tackling integral control and feedback optimization problems.

关键词： Singular perturbation perturbation functions global asymptotic stability

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A Lyapunov-Based Small-Gain Theorem for Interconnected Finite-Time Input-to-State Stable Systems

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IFAC-PapersOnLine 2023年第2期56卷 1865-1870页

作者： Huanhuan Li Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China Department of Electrical and Computer Engineering New York University 370 Jay Street Brooklyn NY 11201 USA

This paper presents a Lyapunov formulation of the small-gain theorem for the finite-time input-to-state stability (FTISS) of an interconnected system composed of FTISS subsystems. In addition, an FTISS-Lyapunov function for the entire system is constructed from the FTISS-Lyapunov functions of the subsystems. With respect to the previously developed nonlinear, Lyapunov-based small-gain theorem restricted to input-to-state stability, a new power-function-based scaling technique is proposed to deal with the challenge that a nonlinearly scaled FTISS-Lyapunov function may not retain a decreasing rate as a power function with a positive power less than one.

关键词： Finite-time input-to-state stability small-gain theorem Lyapunov functions

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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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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Huarong Shan, Jinjun Li, Xing Yu, Hongnian School of Internet of Things Engineering Jiangnan University Wuxi214122 China York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan523000 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9781665410205

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches. © 2024 IEEE.

关键词： Sliding mode control

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