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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

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2024 International Conference on Machine Learning, Pattern Recognition and automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Target Capturing in an Ellipsoidal Region for a Swarm of Double Integrator Agents

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 801-811页

作者： Antonio Bono Luigi D'Alfonso Giuseppe Fedele Veysel Gazi IEEE Department of Informatics ModelingElectronics and Systems EngineeringUniversity of CalabriaRende 87036Italy Dept.Electrical and Electronics Eng. Faculty of EngineeringMarmara UniversityIstanbul 34722 Department of Control and Automation Engineering Faculty of Electrical and Electronics EngineeringYildiz Technical UniversityEsenler 34220Turkey

In this paper we focus on the target capturing problem for a swarm of agents modelled as double integrators in any finite space *** agent knows the relative position of the target and has only an estimation of its velocity and *** that the estimation errors are bounded by some known values,it is possible to design a control law that ensures that agents enter a user-defined ellipsoidal ring around the moving *** know the relative position of the other members whose distance is smaller than a common detection ***,in the case of no uncertainty about target data and homogeneous agents,we show how the swarm can reach a static configuration around the moving *** simulations are reported to show the effectiveness of the proposed strategy.

关键词： Agents-based systems cooperative control swarms target-capturing

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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Polyphonic sound event localization and detection based on Multiple Attention Fusion ResNet

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Mathematical Biosciences and engineering 2024年第2期21卷 2004-2023页

作者： Zhang, Shouming Zhang, Yaling Liao, Yixiao Pang, Kunkun Wan, Zhiyong Zhou, Songbin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Institute of Intelligent Manufacturing Guangdong Academy of Science Guangdong Key Laboratory of Modern Control Technology Guangzhou510030 China

Sound event localization and detection have been applied in various fields. Due to the polyphony and noise interference, it becomes challenging to accurately predict the sound event and their occurrence locations. Aiming at this problem, we propose a Multiple Attention Fusion ResNet, which uses ResNet34 as the base network. Given the situation that the sound duration is not fixed, and there are multiple polyphonic and noise, we introduce the Gated Channel Transform to enhance the residual basic block. This enables the model to capture contextual information, evaluate channel weights, and reduce the interference caused by polyphony and noise. Furthermore, Split Attention is introduced to the model for capturing cross-channel information, which enhances the ability to distinguish the polyphony. Finally, Coordinate Attention is introduced to the model so that the model can focus on both the channel information and spatial location information of sound events. Experiments were conducted on two different datasets, TAU-NIGENS Spatial Sound Events 2020, and TAU-NIGENS Spatial Sound Events 2021. The results demonstrate that the proposed model significantly outperforms state-of-the-art methods under multiple polyphonic and noise-directional interference environments and it achieves competitive performance under a single polyphonic environment. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Deep learning

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Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-Driven Predictive control

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IEEE Transactions on Automatic control 2024年第6期70卷 3920-3935页

作者： Zheng, Kaikai Shi, Dawei Shi, Yang Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Victoria Department of Mechanical Engineering Faculty of Engineering VictoriaBCV8N 3P6 Canada

Safety and data efficiency are important concerns in data-driven control, especially for nonlinear systems with unknown dynamics and subject to disturbances. In this work, we consider a class of control-affine nonlinear systems with partially unknown dynamics and aim to introduce an event-triggered learning-based control approach with guaranteed safety and improved data utilization efficiency. Specifically, a randomized learning approach is employed to evaluate the safety of state trajectories by defining and estimating its confidence interval, with data from a multisample of randomly generated state trajectories. Using the proposed randomized learning algorithm, a nominal trajectory with a high probability safety guarantee is designed, thus ensuring the disturbed system states to remain within a pre-specified range around the nominal trajectory with a sufficiently high probability. Through removing irrelevant data, a local prediction model around the nominal trajectory is learned with satisfactory precision, and is updated online using an event-triggered learning strategy. Based on the learned model, an efficient data-driven predictive controller is designed to force the system states to evolve within the vicinity of the designed safety nominal trajectory. The effectiveness of the proposed event-triggered learning and data-driven control approaches is validated through comprehensive simulation studies. © 1963-2012 IEEE.

关键词： Predictive control systems

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Optimizing Maximum Profit for Electric Vehicle Users in a Bipolar DC Distribution Grid with Probabilistic EV Load Demand and V2G Integration 12

Optimizing Maximum Profit for Electric Vehicle Users in a Bi...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Pathumwan Institute Of Technology Industrial Technology And Innovation Management Faculty Of Science And Technology Bangkok Thailand Bansomdejchaopraya Rajabhat University Electrical Engineering And Automation Control Systems Faculty Of Engineering And Industrial Technology Bangkok Thailand Rajamangala University Of Technology Thanyaburi Faculty Of Engineering Department Of Electrical Engineering Pathumthani Thailand

ISBN: (纸本)9798350383591

This article delves into the synergistic potential of integrating Bipolar DC distribution grids, Probabilistic EV load demand forecasting, and Vehicle-to-Grid (V2G) technology. It offers a blueprint for electric vehicle (EV) users to smartly improve their financial results. Through the amalgamation of these components, individuals who possess electric vehicles have the potential to mitigate their energy expenditures while concurrently generating additional income. The study reveals that the integration of EV into the distribution system can lead to a substantial increase in daily energy loss, reaching up to 25.91%. However, when V2G technology is employed within the distribution grid, it has the remarkable capability to mitigate this increase in daily energy loss significantly, achieving a reduction of up to 64.12%. © 2024 IEEE.

关键词： Integration

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Design of Supervisory Model Predictive control for HVAC Systems and Two Zones with Consideration of Energy Efficiency and Thermal Comfort 23

Design of Supervisory Model Predictive Control for HVAC Syst...

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23rd International Conference on control, automation and Systems, ICCAS 2023

作者： Srisurapanon, Panithan Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation for Process Systems Faculty of Engineering Department of Electrical Engineering Pathumwan Bangkok10330 Thailand

ISBN: (纸本)9788993215274

This paper presents the design of supervisory model predictive control for HVAC systems and two zones. The supervisory control (SC) is designed to find the optimal reference temperature of each zone. The design criteria consist of the total operating cost (TOC) and the thermal comfort cost (TCC). Then, the model predictive control (MPC) is designed to track the optimal reference signals. When SC and MPC are designed for two zones simultaneously, they are referred to centralized SC and centralized MPC. On the other hand, when SC and MPC are designed for two zones separately, they referred to decentralized SC and decentralized MPC. We then compare the results between the decentralized control and the centralized control. The trade-off curve shows that the performance of centralized control is better than that of decentralized control. In particular, centralized MPC improves the performance in tracking reference signals. Furthermore, TOC of centralized control is lower than that of decentralized control. It can be concluded that the centralized control is appropriate for two zones in terms of the tracking of reference and the electrical charge. © 2023 ICROS.

关键词： Model predictive control

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Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

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engineering 2024年第2期33卷 90-107页

作者： Xinwei Wang Zirui Li Javier Alonso-Mora Meng Wang School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK Department of Cognitive Robotics Delft University of TechnologyDelft 2628 CDNetherlands School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Chair of Traffic Process Automation “Friedrich List”Faculty of Transport and Traffic SciencesTU DresdenDresden 01069Germany

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent ***-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle ***,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world *** this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway *** this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time ***,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical *** construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction *** experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving *** efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway *** proposed risk assessment framework is promising for real-world applications.

关键词： Probabilistic collision detection Confidence awareness Probabilistic acceleration prediction Reachability analysis Risk assessment

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