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An Indicator-Based Evolutionary Algorithm for Large-Scale Constrained Multi-objective Optimization

IEEE Transactions on Evolutionary Computation 2025年

作者： Zhong, Xiaoyu Yao, Xiangjuan Qiao, Kangjia Gong, Dunwei Jin, Yaochu China University of Mining and Technology School of Mathematics Xuzhou221116 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao266061 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Westlake University School of Engineering Hangzhou310030 China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of diversity. To enhance the efficiency of tackling LSCMOPs, this paper proposes an indicator-based evolutionary algorithm, referred to as ILCMO. In ILCMO, two complementary indicators are proposed to assess the contribution of each individual to feasibility, convergence, and diversity. The first is a feasibility-oriented indicator designed to drive the population towards the feasible regions. The second is an infeasibility-assisted dynamic indicator, which comprises two relaxed constraint boundaries. Theoretical studies demonstrate that this dynamic indicator can effectively guide the population to focus on evenly searching the infeasible regions around feasible solutions to enhance local diversity. In addition, a variable grouping-based differential evolution (VGDE) strategy, which includes a group-based intra-learning operator and a group-based inter-learning operator, is devised to improve the quality of reproduction in large-scale search spaces. The effectiveness of the proposed algorithm is validated through comprehensive experiments on four benchmarks and a microgrid dispatch problem against seven state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Stochastic Event-Triggered Estimation With Smart Sensors Over Packet-Dropping Links

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IEEE Transactions on Control of Network Systems 2025年

作者： Deng, Yahan Yang, Nachuan Li, Yuzhe Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay SAR Hong Kong999077 Hong Kong

The event-triggered scheme (ETS) has been widely used for sensor data scheduling in cyber-physical systems (CPS). Existing literature on the design of ETSs for packet drops deals with the issue of non-Gaussianity of the a posteriori distribution in the system state. On the one hand, the Gaussian assumption only derives an approximate result, while on the other hand, exact results can be obtained by numerical integration but with excessive computational complexity. To this end, in this paper, we propose a stochastic ETS based on acknowledgment (ACK) information for remote state estimation with smart sensors and packet drops. The transmission decision is jointly driven by the holding time at the remote end and the accumulated innovative information. Then, we inductively derive the exact probability density function of the augmented innovative information vector by the Bayesian rule, which is used to obtain the explicit form of the estimation error covariance. These exact theoretical results mean that the design of scheduling parameter sequences no longer relies on experience. Finally, numerical simulations are provided to demonstrate that the empirical results agree with the theoretical results. © 2014 IEEE.

关键词： Packet loss

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Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks

arXiv

引用

arXiv 2025年

作者： Li, Weitao Zhang, Xinru Wang, Dianhui Tong, Qianqian Chai, Tianyou The Department of Electric Engineering and Automation Hefei University of Technology Hefei230009 China The Institute of Artificial Intelligence China University of Mining and Technology Xuzhou221116 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110004 China

To address the issues of a weak generalization capability and interpretability in working condition recognition model of a fused magnesium furnace, this paper proposes an interpretable working condition recognition method based on deep convolutional stochastic configuration networks (DCSCNs). Firstly, a supervised learning mechanism is employed to generate physically meaningful Gaussian differential convolution kernels. An incremental method is utilized to construct a DCSCNs model, ensuring the convergence of recognition errors in a hierarchical manner and avoiding the iterative optimization process of convolutional kernel parameters using the widely used backpropagation algorithm. The independent coefficient of channel feature maps is defined to obtain the visualization results of feature class activation maps for the fused magnesium furnace. A joint reward function is constructed based on the recognition accuracy, the interpretable trustworthiness evaluation metrics, and the model parameter quantity. Reinforcement learning (RL) is applied to adaptively prune the convolutional kernels of the DCSCNs model, aiming to build a compact, highly performed and interpretable network. The experimental results demonstrate that the proposed method outperforms the other deep learning approaches in terms of recognition accuracy and interpretability. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges

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Green Energy and Intelligent Transportation 2023年第4期2卷 69-80页

作者： Zirui Li Cheng Gong Yunlong Lin Guopeng Li Xinwei Wang Chao Lu Miao Wang Shanzhi Chen Jianwei Gong Chair of Traffic Process Automation “Friedrich List”Faculty of Transport and Traffic SciencesTU Dresden01069 DresdenGermany School of Mechanical Engineering Beijing Institute of Technology100081 BeijingChina Transport and Planning Civil Engineering and GeosciencesDelft University of Technology2628 CD Delft School of Engineering and Materials Science Queen Mary University of LondonE14NS LondonUK Baidu Inc Beijing 100085China State Key Laboratory of Wireless Mobile Communications China Information and Communication Technology Group Co.Ltd.(CICT)China

Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving ***,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ***,a comprehensive review of the state-of-the-art DBL is ***,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new *** a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning *** proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case ***,future works,potential challenges and emerging trends in this area are highlighted.

关键词： Driver behaviours Connected vehicles Continual learning Machine learning Intelligent transportation systems

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Safety-Critical Control of Euler-Lagrange Systems Subject to Multiple Obstacles and Velocity Constraints

arXiv

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arXiv 2024年

作者： Liu, Zhi Wu, Si Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

This paper studies the safety-critical control problem for Euler-Lagrange (EL) systems subject to multiple ball obstacles and velocity constraints in accordance with affordable velocity ranges. A key strategy is to exploit the underlying inner-outer-loop structure for the design of a new cascade controller for the class of EL systems. In particular, the outer-loop controller is developed based on quadratic programming (QP) to avoid ball obstacles and generate velocity reference signals fulfilling the velocity limitation. Taking full advantage of the conservation-of-energy property, a nonlinear velocity-tracking controller is designed to form the inner loop. One major difficulty is caused by the possible non-Lipschitz continuity of the standard QP algorithm when there are multiple constraints. To solve this problem, we propose a refined QP algorithm with the feasible set reshaped by an appropriately chosen positive basis such that the feasibility is retained while the resulting outer-loop controller is locally Lipschitz. It is proved that the constraint-satisfaction problem is solvable as long as the ball obstacles satisfy a mild distance condition. The proposed design is validated by numerical simulation and an experiment based on a 2-link planar manipulator. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Temporal Graph Convolutional Network-Based Fault Detection Method for the Two-Wheeled Self-Balancing Robot

Temporal Graph Convolutional Network-Based Fault Detection M...

引用

International Conference on Industrial Artificial Intelligence (IAI)

作者： Yuxiang Hu Peng Yue Hui Liu Jiajun Kang Dongliang Cui Xuewu Dai State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle-upon-Tyne U.K.

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

The fault detection method based on graph neural networks has been widely applied. However, few of these methods consider the dynamic characteristics and prior knowledge of control systems, resulting in limited usage for fault detection in control systems. To address this, this article proposes a fault detection method based on temporal graph convolutional neural network (T-GCN) and multi-layer perceptron (MLP), which is applicable to general linear dynamic control systems. The proposed method first constructs a relational graph based on prior knowledge of the control system and utilizes T-GCN to extract spatio-temporal relationships between various state variables and control input signals within the system. Then, a MLP integrates the extracted information to detect whether the control system has experienced faults. Finally, a two-wheeled self-balancing vehicle is utilized to test the effectiveness of the proposed method. The results demonstrate that the proposed approach could achieve high-precision detection of sensor faults within the system.

关键词： Knowledge engineering Machine learning algorithms Convolution Fault detection Heuristic algorithms Control systems Robot sensing systems Graph neural networks Dynamical systems Vehicle dynamics

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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...

引用

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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Product Line Expansion for Quality Differentiated Manufacturers considering Product Sharing

Product Line Expansion for Quality Differentiated Manufactur...

引用

第34届中国控制与决策会议

作者： Yu Zhang Min Huang College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

In supply chain management,product line expansion is the core operation method of traditional *** the sharing market,to eliminate the negative impact of product sharing,manufacturers expand the product line to attract consumers by providing rental products(lower quality than retail products).We study whether quality differentiated manufacturers will expand the product *** results show that the low-quality manufacturer will expand the product line and whether the high-quality manufacturer expands the product line will be affected by quality *** the quality difference between retail products and the quality difference between retail products and rental products are low,the high-quality manufacturer expands the product line;otherwise,it does not expand the product ***,compared to manufacturers not expanding the product line,the equilibrium leads to a Pareto *** both manufacturers expand the product line,they will gain more *** only the low-quality manufacturer expands its product line,the low-quality manufacturer gains more profit and the high-quality manufacturer' s profit remain unchanged.

关键词： Supply chain management Product sharing Product lines Quality differentiation

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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...

引用

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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Discrete Fast Terminal Sliding Mode Control for Yaw Stability and Energy-Saving of FWID-EVs With Input Delay and Lumped Disturbances

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年

作者： Zhao, Jing Li, Renbin Yang, Kaiheng Liang, Zhongchao Pi, Dawei Yang, Zhixin Wong, Pak Kin State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Internet of Things for Smart City University of Macau Macau999078 China

This work focuses on the yaw stability and energy-saving for the four-wheel-independent-drive electric vehicle (FWID-EV) with the constrained active front steering (AFS) and torque vectoring control (TVC) subject to input delay and lumped disturbances. First, a coordination control supervisor is developed to solve the conflicts between the AFS and the TVC, in which an identification model for the boundary of instability is established based on the phase plane of vehicle sideslip angle and yaw rate. Second, an input-delay compensator is developed to reduce the negative effect of input delay from controller area network bus. Third, a discrete fast terminal sliding mode and active disturbance rejection control strategy is proposed to guarantee yaw stability of the FWID-EV by overcoming the lumped disturbances. Both the software-in-the-loop and the hardware-in-the-loop tests are performed to verify the effectiveness and practicability of the proposed method, respectively. © 2024 IEEE.

关键词： Disturbance rejection

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