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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced control Applications

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Quadruped Stanford Pupper Type Robot Modeling and Simulation

Quadruped Stanford Pupper Type Robot Modeling and Simulation

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Nelu Mija Monica Leba Cosmin Rus Emanuel Muntean Department of System Control and Computer Engineering University of Petrosani UPET Petrosani Romania

Nowadays there are many research efforts in the field of artificial intelligence applied in all the fields of robotics. There are developed and trained new models both supervised and unsupervised learning. ln order to be able to get a reliable final product, a very important step is modeling and simulation. This paper focuses on the analysis of a quadruped robot, Stanford Pupper type, for which the mathematical model for the locomotor system was developed in order to be then implemented in a simulator that allows the development of machine learning algorithms for training it. There can be implemented in the simulator various use cases, like: learning to walk both supervised and unsupervised scenarios, learning to navigate on a zone with uneven surface and with small obstacles, or learning to climb stairs or other big obstacles.

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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Applied Research in Electrical Engineering (AREE), National and International Conference on

作者： Mohammad Hossein Mousavi Hassan Moradi Kumars Rouzbehi Department of Computer and Electrical Engineering Razi University Kermanshah Iran Department of System Engineering and Automatic Control University of Seville Seville Spain

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment.

关键词： Renewable energy sources Supply and demand Protocols HVDC transmission Wind farms Power system stability Stability analysis computer security Protection Resilience

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Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field 11

Path Planning Algorithm for Multiple UAVs Based on Artificia...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ren, Chongde Chen, Jinchao Du, Chenglie Yu, Wenquan School of Software Northwestern Polytechnical University Xi'an China Northwestern Polytechnical University Department of Computer Science Xi'an China North Automatic Control Technology Research Institute Department of Land Unmanned System Research and Development Taiyuan China

ISBN: (纸本)9798350333664

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy. © 2023 IEEE.

关键词： autonomous navigation deep reinforcement learning multi-agent systems path planning

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Feature Selection Techniques Analysis for Identification of Cognitive and Resting States Based on EEG Data 8

Feature Selection Techniques Analysis for Identification of ...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Murtazina, Marina Avdeenko, Tatiana Novosibirsk State Technical University Automated Control System Department Novosibirsk Russia Novosibirsk State Technical University Theoretical and Applied Computer Science Department Novosibirsk Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper investigates the application of feature selection methods included in the Weka software package to solve the problem of classifying cognitive states and resting states by EEG data. For the experiment, the data set 'EEG During Mental Arithmetic Tasks' was used. From the EEG records for 19 scalp electrodes, Hjort parameters and power in the delta, theta, alpha, and beta frequency bands were extracted. At the first stage of the experiment, classifying models were trained on the obtained feature space. At the second stage of the experiment, a study was made of the change in the accuracy of classifiers trained on the same set of algorithms on a subset of features obtained using feature selection methods. To obtain a subset of features, the attribute evaluators CfsSubsetEval and WrapperSubsetEval were used. We selected 25 machine learning algorithms included in the Weka software package, for which the classification accuracy exceeded 75% when trained on the original feature space. Feature selection using the CfsSubsetEval attribute estimator and the GreedyStepwise search method was successful for 13 algorithms. Feature selection using the WrapperSubsetEval estimator with the Logistic classifier and the GreedyStepwise search method was successful for12 algorithms. © 2022 IEEE.

关键词： Classification (of information)

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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The Secondary Circuit Insulation online Monitoring system based on Distributed Current Sampling 4

The Secondary Circuit Insulation online Monitoring System ba...

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4th International Conference on New Energy and Power Engineering, ICNEPE 2024

作者： Shi, Hengchu Chen, Xiaofan Li, Yinyin You, Hao Xu, Shoudong Liang, Yong Yunnan Electric Power Dispatching and Control Center China Southern Power Grid Kunming China Scientific Research Institute of Yunnan Power Grid System Analysis Department Kunming China Beijing Jiaotong University School of Computer Science and Technology Beijing China

ISBN: (纸本)9798331516086

The secondary system is very important for the reliable power supply and safe production of the power system. It includes secondary equipment and secondary circuits. The connection status of the secondary circuits will directly affect the correct operation of protection and automation devices. To solve these devices misoperation and refuse to operate problems caused by the multipoint grounding of PT and CT secondary circuits in substations, this paper proposes an online monitoring system for the insulation status of secondary circuits based on distributed zero sequence current sampling method. This system could greatly reduce the workload of maintenance on the secondary circuits, improves operation efficiency, and ensures the reliable operation of the secondary circuit in substations. © 2024 IEEE.

关键词： CT Distributed sampling Insulation online monitoring PT Secondary circuit zero sequence current

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Quantized Zero Dynamics Attacks against Sampled-data control systems

arXiv

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arXiv 2023年

作者： Kimura, Kosuke Ishii, Hideaki The Department of System Control The Department of Computer Science The Tokyo Institute of Technology Yokohama226-8502 Japan

For networked control systems, cyber-security issues have gained much attention in recent years. In this paper, we consider the so-called zero dynamics attacks, which form an important class of false data injection attacks, with a special focus on the effects of quantization in a sampled-data control setting. When the attack signals must be quantized, some error will be necessarily introduced, potentially increasing the chance of detection through the output of the system. In this paper, we show however that the attacker may reduce such errors by avoiding to directly quantize the attack signal. We look at two approaches for generating quantized attacks which can keep the error in the output smaller than a specified level by using the knowledge of the system dynamics. The methods are based on a dynamic quantization technique and a modified version of zero dynamics attacks. Numerical examples are provided to verify the effectiveness of the proposed methods. © 2023, CC BY-NC-ND.

关键词： Networked control systems

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