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Chinese Automation Congress (CAC)

作者： Sen Chang Xiaomiao Li Fucai Liu Zhiyong Bao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This study explores polynomial Markov positive fuzzy systems, formulating controllers for these systems by examining the prerequisites for positivity and stochastic stability. Initially, polynomials are incorporated into the Markov positive fuzzy system to represent nonlinear elements better and enhance its capacity to model nonlinear systems. Concurrently, the study discusses the non-convex scenarios encountered in the stability analysis of the closed-loop system once the positive fuzzy system controller is integrated and confirms the stability of the Markov polynomial positive fuzzy system. Ultimately, the effectiveness of the proposed approaches is demonstrated through a numerical simulation example.

关键词： Automation Stochastic processes Numerical simulation Stability analysis Polynomials Numerical models Closed loop systems Nonlinear systems Numerical stability Fuzzy systems

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Event-Triggered-Based Robust control of Heterogeneous Connected Vehicles Platoon with Communication Delay and Random Packet Loss 8

Event-Triggered-Based Robust Control of Heterogeneous Connec...

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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Liu, Chen Ru, Shuangkun Qi, Lin Fang, Jiayi Zhang, Ting Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Hebei Province Qinhuangdao066004 China School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Vehicle and Mobility Tsinghua University Beijing100081 China

ISBN: (纸本)9798331504892

With the development of vehicle autonomous driving technology and wireless network communication technology, the mix platoon of connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs) is becoming inevitable. The abnormal V2V communication and uncertain vehicle dynamics may affect the CAV control performance. Considering time-varying communication delays and random packet loss, an event-trigger-based robust control strategy is proposed based on the stability theory of stochastic time-delay systems. The Bernoulli packet loss model is constructed to solve the random packet loss problem. The robust controller with the event-triggered mechanism designed using the Lyapunov functional stability theory of stochastic systems ensures the stability of the mix platoon with vehicle heterogeneity and the stability of CAV. The simulation comparison has verified that the proposed control strategy can better suppress the fluctuation of the preceding vehicle and more quickly reach the desired value when both time delay and packet loss are present. © 2024 IEEE.

关键词： Stochastic systems

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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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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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Microgrid Economic Dispatch Problem Based on Distributed Algorithm in V2G Background

Microgrid Economic Dispatch Problem Based on Distributed Alg...

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Data Driven control and Learning Systems (DDCLS)

作者： Lina Ren Shuai Li Zhihan Hu Key Lab of Industrial Computer Control of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the emergence of increasingly complex modern energy networks, there is a need for flexible and reliable methods to solve economic dispatch problems in smart grids. For this purpose, a broadcast gossip algorithm is proposed for solving the economic dispatch problem in a smart grid environment populated with distributed generators and vehicle to grid. First, with the objective of minimizing the system cost, the cost model of each part of the microgrid is developed under various constraints. At the same time, considering the peak-to-valley difference in the process of electric energy dispatching, this paper proposes an electric vehicle incentive and compensation cost model to achieve the purpose of making the grid peak and valley reduction. Then, an event triggering mechanism is introduced to save communication resources while economical scheduling. Finally, the proposed model and algorithm are simulated and analyzed by using the microgrid structure of IEEE 11 - bus standard, and the effectiveness of the proposed model and algorithm are verified.

关键词： Photovoltaic systems Vehicle-to-grid Analytical models Costs Microgrids Wind power generation Thermal loading

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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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EAVFormer: an end-to-end audio and visual emotion recognition network based on transformers

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Multimedia Systems 2025年第3期31卷 1-22页

作者： Liu, Haoran Sun, Zijian Li, Haibin Li, Yaqian Zhang, Wenming Song, Tao School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Shenzhen Research Institute of Yanshan University Shenzhen518057 China Key Lab of Measurement Technology & Instrumentation of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Audio-visual emotion recognition, integrating information from various sources, enhances the comprehensive understanding and analysis of human emotions. However, existing methods in this field primarily focus on feature fusion, often overlooking the equally crucial stage of feature extraction. Typically, these methods employ pre-trained models to extract features during the initial phase, complicating modality-specific feature extraction and increasing system complexity and coordination challenges, which hinder effective audio-visual feature fusion. To address these issues, we propose an end-to-end audio-visual emotion recognition model, EAVFormer. For the audio modality, we input raw audio signals along with a mix of mel spectrograms and Mel Frequency Cepstral Coefficients (MFCCs), employing a hierarchical transformer technique that facilitates both shallow local and deep global feature extraction to fully harness complementary acoustic information, significantly enhancing the acoustic modality’s contribution. For the visual modality, we input raw video frames and employ a hierarchical transformer feature extraction method that integrates three-dimensional convolution and spatio-temporal self-attention mechanisms to address spatial and temporal redundancies and dependencies, thereby enhancing the expression of dynamic visual information. In the modal fusion stage, we construct dual-modality representations and encode inter-modal information while preserving modality-specific and meaningful intra-modal features. Testing on three popular datasets RAVDESS, CREMA-D, and CMU-MOSEI demonstrates that EAVFormer surpasses existing state-of-the-art methods in audio-visual emotion recognition accuracy, achieving rates of 95.17%, 86.13%, and 78.22%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image coding

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A Novel A-ESO Composited Non-recursive control Methodology for Complex High-order Disturbed Nonlinear Systems

A Novel A-ESO Composited Non-recursive Control Methodology f...

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第42届中国控制会议

作者： Cong Zhang Shiji Liu Shuang Liu School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

This paper mainly focuses on a disturbance rejection control methodology for a series of mismatched and matched disturbed complex ***,a novel finite-time arbitrary-order extended state observer(A-ESO) is proposed to accurately estimate the system states,disturbances,and their corresponding order *** then,a novel concise non-recursive control ideology utilizing all the estimations of A-ESO is proposed to strictly guarantee finite-time stability and convergence of the whole closed-loop ***,compared with the existing non-recursive control methods,the essential characteristic is that all the estimation information is utilized to construct the composite *** will be tremendously conducive to cost-saving and promoting practical implementations.

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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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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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