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SSRN 2022年

作者： Liu, Shuang Meng, Qingfu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Mr Hebei066004 China

Aiming at the vibration behavior of the main driving system of rolling mill when the lubrication condition changes, the Hopf bifurcation phenomenon of the system under unsteady lubrication condition is analyzed, and an improved hybrid control strategy is proposed to suppress the vibration of rolling mill. Firstly, according to the variation law of lubrication conditions in rolling process, the dynamic rolling moment of the main drive system under unsteady lubrication was given, and the torsional vibration mechanical model of the main drive system was established, and the Hopf bifurcation behavior of the system under boundary lubrication was analyzed. It is found that when the subcritical Hopf bifurcation occurs in the system, the main transmission system of the linker will experience sudden and destructive instability oscillations. Therefore, this paper proposes an improved hybrid control strategy to control the Hopf bifurcation behavior of the system. The traditional hybrid control strategy uses a single control parameter to control both parameter disturbance and state feedback. Here, two control parameters are used to control parameter disturbance and state feedback respectively. In the state feedback part, nonlinear feedback is used to replace the traditional linear feedback. After the control is added, the critical value of Hopf bifurcation can be changed and the stability region of the system can be increased by adjusting the control parameters of the disturbance part of the parameters. Here, direct Normal form calculation method is introduced to calculate the Hopf bifurcation specification of the system. By adjusting the control parameters of the state feedback part by the canonical coefficient, the system can be changed from subcritical bifurcation to supercritical bifurcation, the phase trajectory of the system can be changed from unstable limit cycle to stable limit cycle, and the amplitude of limit cycle can be controlled. The effectiveness of the proposed

关键词： Lubrication

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A Heterogeneous Reinforcement Learning Approach for Joint Relay Selection and Power Allocation in Time-Varying Uasns with Energy Harvesting

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SSRN 2024年

作者： Han, Song Wang, Ziyuan Li, Aijia Li, Xinbin Liu, Zhixin Yan, Lei Zhang, Tong Wei Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University Hebei Province Qinhuangdao066004 China School of Computer and Communication Engineering Northeastern University Hebei Province Qinhuangdao066004 China Department of Technology Northeastern University Shandong Province Qingdao266237 China

In this study, we investigate the joint relay selection and power allocation problem for underwater acoustic communication networks with energy harvesting to maximize the cumulative capacity. We specially formulate a heterogeneous reinforcement learning-based framework. In the different decision layers, the Upper Confidence Bound 1 and enhanced Deep Deterministic Policy Gradient algorithms are employed to efficiently explore the discrete and continuous action spaces, respectively. Therefore, the solving difficulty of the complex hybrid discrete-continuous optimization problem can be significantly mitigated. Furthermore, a circular interaction structure is constructed to facilitate the exchange of decision-making information between the two decision layers. The strongly interdependent relationship of the different substrategies can be comprehensively considered, thereby improving the entire performance of thejoint strategy. Moreover, we propose an adaptive reward function, which can guide the user to balance the instantaneous capacity and interruptions, thereby effectively utilizing the green energy harvesting technology and enhancing long-term cumulative performance. Simulation results verify the effectiveness of our proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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

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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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A Ship Power Sharing Strategy Based on Shapley Value Method and Cartel Mechanism

A Ship Power Sharing Strategy Based on Shapley Value Method ...

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Complex Systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Zhengxu Zhang Jie Yang Kai Ma Shiliang Guo Hui Li School of Electrical Engineering Yanshan University Qinhuangdao China Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Intelligent Control and Neural Information Processing of the Ministry of Education Yanshan University Qinhuangdao China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

With the development of the maritime industry, the number and capacity of existing port cold-ironing (CI) equipment can no longer meet the power demand of ships during berthing, resulting in significant carbon emissions from ships within the port area. Therefore, a ship power sharing strategy based on Shapley value method and Cartel mechanism is proposed. Firstly, the rolling optimization model of ship power scheduling is established to adjust the ship power scheduling strategy according to the real-time information. Secondly, the cooperative game model of ship power sharing is established, and the cost sharing among cooperative ships is realized by Shapley value method. Finally, in order to reduce the situation that ships increase their own profits by deviating from cooperation and affect the overall profits, this paper introduces Cartel mechanism to punish the behavior of deviating from cooperation. The simulation results show that the power sharing strategy proposed in this paper can reduce the total ship cost and carbon emission by 5.28% and 3.78%, respectively. The introduction of Cartel mechanism can increase the cost of non-cooperative behavior, thus ensuring that all ships take cooperative behavior. The power sharing strategy based on Shapley method and Cartel mechanism proposed in this paper is helpful to the construction of green ports.

关键词： Costs Job shop scheduling Optimization models Simulation Carbon dioxide Microgrids Games Seaports Real-time systems Marine vehicles

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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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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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Fully Distributed Adaptive Finite Time Fault-Tolerant control for Multi-Agent Systems with Actuator Faults

Fully Distributed Adaptive Finite Time Fault-Tolerant Contro...

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

作者： Xiaoqi Wang Yiming Fang Miao Xu Zhichao Lei Key Labpratory 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

For a class of second-order nonlinear leader-following multi-agent systems with actuator faults and integral quadratic constraints (IQCs) of followers, a fully distributed adaptive consensus control algorithm based on improved PID sliding mode is designed. The algorithm only requires local topology information but no global information. According to Lyapunov stability theory, it is demonstrated that multi-agent systems can converge to the vicinity near equilibrium points within a finite time. Finally, the feasibility of the control algorithm proposed in this paper is verified by numerical simulation of multi-agent systems.

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Energy Efficiency Maximization for Hybrid Access Femtocell Networks via Incentive Mechanism and Power control Strategy

Energy Efficiency Maximization for Hybrid Access Femtocell N...

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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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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly labeled and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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