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

作者： Xuelin Liu Weimin Wang Zhixin Liu School of control science and engineering Dalian University of Technology Dalian China School of electrical engineering Yanshan University Qinhuangdao China Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao Hebei Province China

Femtocell network has a great development space in increasing coverage and capacity of wireless network. In order to meet the quality of service (QoS) requirements of MUEs in MBS coverage, we propose a seed channel incentive mechanism in the two-tier femtocell network, where the MBS provides sub-channels to FBSs to motivate them to serve for the MUEs within the MBS coverage. To maximize the energy efficiency of femtocell layer after hybrid access, the optimization problem is modeled. The combined method of worst-case, Dinkelbach and continuous convex approximation is developed to make the optimization problem solvable. In addition, in order to realize the practical application of the proposed scheme, an optimization algorithm combining sub-channel and power allocation is developed. The numerical results show that the proposed algorithm is effective in improving the energy efficiency of the femtocell layer.

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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

Trajectory Optimization of Manipulator for Loading and Unloa...

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

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

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.

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Dynamic Multi-Objective Optimization Algorithm for Classification of Decision Variables Based on Objective Monotonicity

SSRN

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SSRN 2023年

作者： Wei, Lixin Yang, Qingshuai Guo, Zeyin Institute of Electrical Engineering Yanshan University Hebei Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066004 China

Dynamic multi-objective optimization problems (DMOPs) are prevalent in daily life, and the aim of dealing with DMOPs is to track the moving Pareto Front(PF) and find a series of Pareto Sets (PS) at different moments. Strategies based on decision variable classification improve the performance of multi-objective evolutionary algorithms. However, it has been a challenge to ensure fast and accurate classification. In this study, a decision variable classification method based on objective monotonicity(CDMO) is proposed to reduce the negative effects of inaccurate classification. In addition, a multi-step convergence prediction method and a diversity maintenance strategy are proposed to improve the algorithm performance. The effectiveness of the CDMO algorithm is verified by comparing it with three state-of-the-art DMOEAs on 16 benchmark test problems. The experimental results show that the CDMO algorithm is able to obtain high-quality populations with good convergence and distributivity in a dynamic environment. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Optimal Time and Power Allocation in Full-Duplex Wireless-Powered Cognitive Radio Networks

Optimal Time and Power Allocation in Full-Duplex Wireless-Po...

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

作者： Zhenyu Feng Ming Su Meihua Zhou Zhixin Liu School of Electrical Engineering Yanshan University Qinhuangdao China Training Center of Liren College Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao Hebei Province China

In this paper, we integrate wireless powered communication networks (WPCNs) with cognitive radio networks (CRNs) in order to develop wireless powered CRNs (WP-CRNs). The hybrid access point (HAP) is used to broadcast radio-frequency signals to the devices at the downlink stage, and to receive information from these devices. The HAP has two antennas to achieve full-duplex: one is for downlink wireless power transfer and the other is for receiving uplink information transmission from the users simultaneously in the uplink. In the underlay paradigms. The SUs can harvest energy to transmit data when the interference to primary users (PUs) does not exceed a tolerable threshold. To tackle the above network issues, an optimal resource allocation method is proposed to maximize the secondary system uplink throughput. The optimization problem with respect to time and power allocation is formulated including the constraint of energy causality and interference level. The non-convex problem is transformed into an equivalent convex optimization problem, then the joint optimal power and time allocation (JOPTA) algorithm is presented to determine the optimal solutions. The effectiveness of the proposed algorithm is verified in the simulations.

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

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

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第43届中国控制会议

作者： Fengfei Si Yiming Fang Hao Ju Dongliang Zhao Jingliang Xin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Tangshan Branch HBIS Company Limited

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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 YOLOv5 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.

关键词： Target board detection Ladle nozzle positioning YOLOv5 lightweight GhostNet thin neck structure channel pruning

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Event-Triggered Robust Model Predictive control for Multi-Agent Systems Under Hybrid Attacks

Event-Triggered Robust Model Predictive Control for Multi-Ag...

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Chinese control Conference (CCC)

作者： Yongxin Yin Jianxiong Li Chongyi Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Electromechanical Engineering Tangshan University Tangshan China

In this paper, the consensus control problem for multi-agent systems (MASs) suffering hybrid attacks is discussed, in which the hybrid attacks include randomly occurring deception attacks on system dynamic behavior and denial-of-service attacks (DoS). An event-triggered robust model predictive control (RMPC) method is presented to achieve the consensus of MASs under hybrid attacks. It can be proved that the consensus errors can be asymptotic with prescribed H ∞ performance, the sufficient condition for stability can be given in terms of linear matrix inequalities (LMIs), and the controller gains are obtained by solving an optimization problem involving some LMIs. Employing the event-triggered mechanism with maximum trigger interval, not only can not only save computation time and facilitate the efficiency of the shared resources but also can effectively be responsible for the negative impact of cyber-attacks on system performance. Finally, simulation results are given to illustrate the validity and effectiveness of the proposed method.

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An Improved Artificial Potential Field Method for Formation control and Obstacle Avoidance of the Multi-Agents Systems

An Improved Artificial Potential Field Method for Formation ...

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Chinese Automation Congress (CAC)

作者： Yubiao Yan Jianxiong Li Haibo Dong Chongyi Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Electromechanical Engineering Tangshan University Tangshan China

Obstacle avoidance is a significant research content in multi-agents formation control. The obstacle avoidance of multi-agents systems is investigated in this paper, and an improved artificial potential field method (IAPF) is proposed to avoid unknown obstacles in multi-agent formation. Aiming at improving the efficiency of formation avoidance and solving the non-reachable and local minima problems, the dynamic sub-target algorithm is proposed. The proposed method enables the multi-agent system (MASs) to avoid obstacles smoothly and quickly and complete formation tasks in complex environments by tracking dynamic sub-target as well, in which the position and motion direction of adjacent agents are taken into account in the selection of a sub-target for the formation efficiency. In addition, a variable potential field range is defined to ensure the safety of the formation. Finally, several simulation results verify the superiority and effectiveness of the proposed approach.

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Rollover Prevention control for Autonomous Vehicles with High Centers of Gravity Considering Input Saturation

Rollover Prevention Control for Autonomous Vehicles with Hig...

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第43届中国控制会议

作者： Linying Lu Ting Zhang Lin Qi Jiayi Fang Xiaohong Jiao School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University School of Vehicle and Mobility Tsinghua University School of Mechanical Engineering Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

A fixed-time terminal sliding mode control(FTSMC) strategy based on extended state observer for cooperative control of rollover prevention and trajectory tracking is proposed to enable the high-center-of-gravity autonomous vehicles to ensure tracking performance and prevent rollover. The FTSMC allows the errors to converge globally to a predefined arbitrarily small set centered at zero in a fixed time and then exponentially converge to zero while weakening the influence of input saturation on the controller by compensating the auxiliary system of input saturation. Simulation comparisons with existing results verify the effectiveness and advantages of the designed strategy under the MATlab/Simulink environment.

关键词： Autonomous Vehicles Fixed-time Rollover Prevention Input Saturation

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An Improved Model Predictive control of Wave Energy Converters Based on Weighted Optimization of Objective Function

SSRN

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SSRN 2023年

作者： Wang, Zhenchun Wang, Nianguo School of Electrical Engineering Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

This paper investigates the model predictive control (MPC) of a point-absorption sea wave energy converter (WEC). The dynamic model and information about upcoming waves serve as the foundation for MPC implementation, but building the plant model and forecasting waves will invariably introduce errors. As a result of this fact and the multi-step predictive optimization feature of MPC, a novel objective function based on the weighting factor is adopted in the MPC design, which has the following benefits: First, the weighting factor penalizes the objective function with errors at various steps in time, which can limit the adverse effects of uncertainties on the controller. Second, the weighting factor can better constrain the float displacement, ensuring the device’s safe operation. In addition, the weighting factor is optimized offline using a genetic algorithm, and its value changes depending on the sea state. The simulation results show the effectiveness of the proposed MPC strategy. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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