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48th Annual Conference of the IEEE industrial Electronics Society, IECON 2022

作者： Ma, Kai Dong, Yufei Zhao, Peng Yang, Jie Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Yanshan University School of Electrical Engineering Qinhuangdao China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The consensus based distributed frequency control strategy makes all measurements and control units in microgrids vulnerable to false data injection (FDI) attacks. This paper presents an observer-based resilient frequency control for estimating and compensating for FDI attacks on sensors and actuators in AC microgrids. Firstly, attacks of sensors and actuators are estimated online simultaneously by observers. Then a distributed H∞ output feedback protocol is used to ensure that the frequency consensus tracking is achieved with cooperative uniform ultimate boundedness. The control strategy is completely distributed and does not require any quantitative information about attacks. Furthermore, there is no limit to the number and location of attacked units. Several case studies are provided to verify the effectiveness of the proposed resilient frequency control strategy. © 2022 IEEE.

关键词： Frequency estimation

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Optimal Resource Allocation Scheme for RIS-Assisted Vehicular Networks

Optimal Resource Allocation Scheme for RIS-Assisted Vehicula...

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第43届中国控制会议

作者： Wenxuan Chen Jiawei Su Zhixin Liu the School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Deploying reconfigurable intelligent surfaces(RIS)in vehicular networks can effectively improve the quality of wireless channel and increase network throughout,which is realized by optimizing the phase ***,the dynamic network environment and complex multi-user interference seriously affect network *** transmission powers blindly cannot solve the interference problem but also reduces the transmission efficiency of the RIS-based vehicular *** address the above challenges,this paper proposed a joint optimization scheme of transmission beamforming and phase *** on the block coordinate descent(BCD) theory,this paper decomposed the scheme into two sub-problems:transmission beamforming optimization and RIS phase shift optimization,and the joint optimization algorithm was *** results have demonstrated that the transmission rate has been improved with the assistance of RIS,with the multi-user interference effect being well-depressed.

关键词： Vehicular networks RIS Interference management Phase shift optimization

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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm 13

Trajectory Optimization of Manipulator for Loading and Unloa...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Zhao, Dongliang Fang, Yiming Yang, Chen Xin, Jingliang Yanshan University Key Lab of Industrial Computer Control Engineering Hebei Province Qinhuangdao China Eng. Res. Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Province Qinhuangdao China Hebei Iron and Steel Group Hebei Province Tangshan China

ISBN: (纸本)9798350315196

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter. © 2023 IEEE.

关键词： Trajectories

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Fault Tolerant control Based on GA-DRE for a Manipulator with Actuator Fault

Fault Tolerant Control Based on GA-DRE for a Manipulator wit...

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第43届中国控制会议

作者： Jiongzhi Qiu Yiming Fang Donliang Zhao Le Liu Nuan Shao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Department of Environmental Engineering Hebei University of Environmental Engineering

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To solve the trajectory tracking control problem of a rigid manipulator system in the case of unexpected actuator failure,considering the uncertainty of system parameters and bounded disturbance,an adaptive fault-tolerant tracking control strategy is proposed in this ***,an adaptive strategy is designed to estimate and compensate the effects of actuator failure and system uncertainty,and the dynamic regression matrix is extended and mixed to improve the accuracy of parameter estimation without persistency of excitation(PE),which will enhance the trajectory tracking ***,in the framework of sliding mode fault-tolerant control,combined with standard Gradient Adaptive and Dynamic Regressor Extension(GA-DRE) parameter estimation technology,a composite adaptive fault-tolerant controller is ***,theoretical analysis and numerical simulation results show that the proposed method can perform accurate parameter estimation in a finite time under the premise of matching the new conditions based on DRE additional terms,and the trajectory tracking errors of the expected positions and velocities of each joint of the manipulator can asymptotically converge to zero.

关键词： Fault-tolerant control Gradient Adaptive and Dynamic Regressor Extension(GA-DRE) Manipulator Parameter estimation Persistence of excitation(PE)

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An Improved NSGAII Algorithm for Flexible Job-shop Scheduling Problem Based on A New Decoding Mechanism

An Improved NSGAII Algorithm for Flexible Job-shop Schedulin...

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

作者： Man Li Kai Ma Shiliang Guo Shuai Li Bin Yang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

The flexible job-shop scheduling problem (FJSP) is a famous combinatorial optimization problem. FJSP is widely used in process manufacturing industry, so it is of great significance to study FJSP to improve production efficiency. In this paper, a non-dominated sorting genetic algorithm II based on a new decoding mechanism (NSGAII_NDM) is proposed. Firstly, the opposite-based learning strategy is introduced on the basis of NSGAII, and the sufficiency of global search is improved by generating the opposite value. Secondly, an improved adaptive crossover and mutation probability strategy is added, which can dynamically adjust the inherent crossover and mutation probability with the number of iterations and improve the convergence speed of the algorithm. Furthermore, based on fully active scheduling strategy, a new decoding mechanism is proposed, which can get a better scheduling scheme by arranging the machines and starting time of each process more reasonably. Finally, the improved algorithm is tested in a standard test case, which verifies the superiority of NSGAII_NDM in solving the flexible job-shop scheduling problem.

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Relaxed Stability Analysis and Event-Triggered controller Design for Discrete-Time Positive T-S Fuzzy Networked control Systems

Relaxed Stability Analysis and Event-Triggered Controller De...

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Annual Conference of industrial Electronics Society

作者： Xiaoxiao Wang Xiaomiao Li Zhiyong Bao Fucai Liu Yuehao Du Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

This paper deals with the relaxation stability analysis and event-triggered controller design of discrete-time positive Takagi-Sugeno (T-S) fuzzy networked control systems (FNCSs). The constraints of positive conditions in NCSs bring significant challenges to stability analysis and controller design. A 1-norm event-triggered scheme is proposed for the positive system, which can effectively reduce the frequency of network communication. Since the event-triggered T-S fuzzy controller (ETTSFC) can only observe the latest transmission state after the triggering action, the ETTSFC selects the premise variables different from the T-S fuzzy model, improving design and implementation flexibility. Using the linear copositive Lyapunov function (LCLF), the design criteria of the event-triggered controller to ensure the positivity and stability of the closed-loop system are derived. To relax the sum-of-squares (SOS) based conservative condition, we adopt the Taylor series membership functions (TSMFs) and introduce TSMFs and their approximation error into the stability conditions. Finally, an example is given to illustrate the effectiveness of the design.

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An Improved Seagull Optimization Algorithm for Flexible Job-shop Scheduling Problem

An Improved Seagull Optimization Algorithm for Flexible Job-...

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

作者： Kai Ma Shijie Yao Shiliang Guo Bin Yang Shuai Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

Job-shop scheduling problem is the core link and key technology to realize smart factory and develop smart manufacturing technology. Among them, the flexible job-shop scheduling problem (FJSP) which is more consistent with the actual production and processing mode has been focused. This paper proposes an improved seagull optimization algorithm(ISOA) for solving the FJSP. The ISOA uses chaos initialization and reverse learning to optimize initial population, and introduces Q-learning to change the fitting parameters to balance global search and local search. In addition, we propose a local search model that is adaptive iterative process to enhance the algorithm's optimization search capability. The results of the experiment proved that the algorithm is superior in terms of optimization speed and scheduling results.

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Improved Lightweight YOLOv5 Based on GhostNet and Its Application in Target Board Detection for Ladle Nozzle Positioning 43

Improved Lightweight YOLOv5 Based on GhostNet and Its Applic...

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43rd Chinese control Conference, CCC 2024

作者： Si, Fengfei Fang, Yiming Ju, Hao Zhao, Dongliang Xin, Jingliang Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Eng. Res. Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Qinhuangdao066004 China Tangshan Branch Hbis Company Limited Tangshan063016 China

ISBN: (纸本)9789887581581

The detection of the target board of ladle nozzle positioning is the basis of automatic loading and unloading of ladle nozzle by ladle robot in continuous casting. Due to the complex situation of high temperature, dust, backlight and so on, the target board of ladle nozzle positioning is prone to false detection or low detection accuracy, and it is difficult to take into account the contradiction between rapidity and detection accuracy. Therefore, this paper proposes an improved lightweight algorithm GVP-YOLOv5 (YOLOv5 with GhostNet, VoVGSCSP and Pruning), which takes into account both detection accuracy and rapidity, to detect the target board for ladle nozzle positioning. Firstly, Ghost module is used to improve the backbone network of YOLOv 5 as the backbone feature extraction network to reduce the number of parameters and calculation of the backbone. Then in the neck, the GSConv module is used to design a thin neck structure, which can ensure the multi-scale feature fusion capability and further reduce the complexity of the network. Finally, the model pruning technology is used to prune the whole model, and the channels with small scaling factor are removed to compress the model to the greatest extent, so as to meet the requirements of industrial field application deployment. The experimental results show that the lightweight GVP-YOLOv5 algorithm proposed in this paper can complete the real-time detection of the target board in complex industrial scenarios. Compared with the original YOLOv5s algorithm, the number of parameters in the model is reduced by 82.6 %, and the size of the model is significantly reduced to 19.4 %. At the same time, the detection accuracy is maintained at more than 95 % on mAP @ 50: 95 and the detection speed is effectively improved. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Continuous casting

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G-Anomaly: A Pyramid Graph Transformer-based Vision-Language Model for General industrial Anomaly Detection

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IEEE Transactions on Automation Science and engineering 2025年

作者： Li, Jiaqi Wen, Shuhuan Fang, Bin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Beiing University of Posts and Telecommunications Department of Artificial intelligence Beijing China

Visual-language alignment is crucial for enhancing the domain adaptability of industrial anomaly detection models. However, the existing methods overlook the importance of structured image representation, fail to further distinguish topological differences between anomalies and the inherent textures of products, which reduces the accuracy of semantic matching. To address this problem, we propose a novel industrial anomaly detection model G-Anomaly, to preserve the topological structure of the sample images and further enhance the model’s domain adaptability. We designed Pyramid Graph Transformer as a visual encoder to extract multi-scale visual features, which can directly preserve the structural relationships between different regions of the image, and also optimize the over-smoothing issue present in deep graph networks, thereby retaining the distinguishability of anomalous nodes. Additionally, we design a Multi-level Domain Adapter that ensures semantic consistency of anomalous features across different scales and contexts by performing visual-language matching at various resolutions and levels of abstraction. This enhances the model’s domain adaptability for anomaly detection for a wide range of industrial products. We collect and craft an actual solar panel dataset PV_actual AD, and conduct extensive experiments on the public dataset MVTec AD as well as the actual solar panel dataset PV_actual AD. This has demonstrated that G-Anomaly not only performs well in standard testing environments but also exhibits robustness and domain adaptability for anomaly detection tasks in real-world scenarios. © 2025 IEEE. All rights reserved.

关键词： Semantics

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Hierarchical Energy Management Strategy of Hybrid Electric Truck Fleet under Connected Environment

Hierarchical Energy Management Strategy of Hybrid Electric T...

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第43届中国控制会议

作者： Qizhe Lu Wenbin Tang Zitong Pang Yahui Zhang Xiaohong Jiao School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University School of Mechanical Engineering Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Considering the slow-moving and energy-consuming issues that tend to occur when truck fleets drive,this paper proposes a hierarchical energy management strategy(EMS) for the hybrid electrical truck(HET) fleet,including speed planning and energy *** speed threshold range that allows the whole fleet to pass through the traffic light is computed in the upper-layer speed *** the model predictive control framework,a multi-objective optimization function is constructed for speed planning to find the optimum *** the lower-layer energy management,an offline model is established for power allocation between the Integrated Starter Generator and the Electric Motor and gear shift selection of automated manual transmission to reduce the dimensionality of the control ***,an adaptive equivalent consumption minimum strategy(A-ECMS) is designed based on the fleet's optimal speed *** the offline part,the transformation relationship between the co-state and equivalence factor is established based on Pontryagin's minimum principle(PMP),and PMP obtains the globally optimal control sequence in the historical driving cycle to train a back-propagation neural network(BPNN).The BPNN model predicts the current optimal co-state in the online part,transformed into a real-time optimal equivalence factor to guide the A-ECMS online *** simulation results indicate that the fuel economy of the proposed EMS is comparable to that of the global optimization method PMP and superior to that of Fixed EF-ECMS and ***,it enables the HET fleet to pass through traffic lights without stopping,thereby ensuring queue continuity and improving the passage efficiency of the fleet.

关键词： hybrid electric trucks equivalent consumption minimization strategy fleet speed planning back-propagation neural network model predictive control

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