this paper presents a distributed continuous-time optimization framework aimed at overcoming the challenges posed by time-varying cost functions and constraints in multi-agent systems, particularly those subject to di...
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ISBN:
(纸本)9798331517519;9798331517526
this paper presents a distributed continuous-time optimization framework aimed at overcoming the challenges posed by time-varying cost functions and constraints in multi-agent systems, particularly those subject to disturbances. By incorporating tools such as log-barrier penalty functions to address inequality constraints, an integral sliding mode control for disturbance mitigation is proposed. the algorithm ensures asymptotic tracking of the optimal solution, achieving a tracking error of zero. the convergence of the introduced algorithms is demonstrated through Lyapunov analysis and nonsmooth techniques. Furthermore, the framework's effectiveness is validated through numerical simulations considering two scenarios for the communication networks.
the growing interest in the collaborative achievement of diverse tasks in complex environments by multiple unmanned aerial vehicles (UAVs) has garnered significant attention. this paper is dedicated to the study of co...
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ISBN:
(纸本)9798331517519;9798331517526
the growing interest in the collaborative achievement of diverse tasks in complex environments by multiple unmanned aerial vehicles (UAVs) has garnered significant attention. this paper is dedicated to the study of cooperative control and obstacle avoidance for UAVs, presenting a novel, integrated solution based on model predictive control that encompasses trajectory tracking and obstacle avoidance. the paper adopts the UAV's complete dynamic model instead of a particle model, and integrates obstacle avoidance at the control layer, ensuring the feasibility of UAV obstacle avoidance. the effectiveness of the proposed algorithm is substantiated through numerous numerical simulations that consider both static and dynamic obstacles.
the brain computer interface field draws inspirations from neuroscience, but is also deeply influenced by the emerging trends in the deep learning field and the broader machine learning community. In my talk I will pr...
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As more distributed energy resources (DERs) are integrated into the power system, there is a growing need to manage them effectively. Virtual Power Plants (VPPs) have emerged to address this need by aggregating divers...
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ISBN:
(纸本)9798350377385;9798350377378
As more distributed energy resources (DERs) are integrated into the power system, there is a growing need to manage them effectively. Virtual Power Plants (VPPs) have emerged to address this need by aggregating diverse energy resources such as photovoltaic systems (PVs), Wind turbines, energy storage systems (ESS), and electric vehicles (EVs). VPPs help balance the grid supply and demand, sell excess power, enhance system reliability, supporting renewable energy and improving overall efficiency. this paper provides a comprehensive overview of VPP technologies, focusing on control methods and energy management applications. the paper presents an overview of the core components of VPPs along withthe global market growth forecasts for DERs until 2030 and discusses the importance of hierarchical control for ancillary services, including recent contributions at the primary, secondary, and tertiary control techniques. Additionally, the paper examines five real world VPP projects in North America and identifies exploring future research directions and opportunities in VPP, emphasizing the need for ongoing innovation to address new challenges and optimize renewable energy integration into the grid.
Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are swiftly revolutionizing industrial and logistics operations by automating the tasks related to material handling and transportation. While both ...
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Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are swiftly revolutionizing industrial and logistics operations by automating the tasks related to material handling and transportation. While both technologies share the goal of automation, they differ in terms of control, navigation, flexibility, and interaction with humans. this research article briefly discusses the differences and the navigation principles of AGVs and AMRs. It explores the control and navigation systems, flexibility and adaptability. By understanding these differences and navigation principles, decision-makers can make informed choices to optimize material handling processes and maximize operational efficiency. this article serves as a valuable resource for industry professionals, researchers, seeking to leverage AGVs and AMRs in their operations and stay at the forefront of industrial automation.
In order to solve the problems of low efficiency and difficult practical application of the centralized method in the coordinated control of urban road network signals, this paper proposes a game-based multi-intersect...
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ISBN:
(纸本)9798350379860;9798350379877
In order to solve the problems of low efficiency and difficult practical application of the centralized method in the coordinated control of urban road network signals, this paper proposes a game-based multi-intersection cooperative control method. By constructing a distributed game model, the signal control between intersections is regarded as a game relationship, and the Nash equilibrium solution is solved by using a mixed strategy game, and the signal control strategies of each intersection are obtained. At the same time, combined withthe multi-agent reinforcement learning framework, Nash Q-learning is used to update the benefit matrix to realize the learning strategy of the agent. Simulation results show that the proposed method can significantly reduce the average delay and waiting time under low traffic demand[1], and outperforms single-agent control under high traffic demand, effectively avoiding traffic congestion and reducing network delay.
this paper presents ZePoP, a leader election protocol for distributedsystems, optimizing a delay-based closeness centrality. We design the protocol specifically for the Peer to Peer(P2P) applications, where the leade...
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ISBN:
(纸本)9798350313062;9798350313079
this paper presents ZePoP, a leader election protocol for distributedsystems, optimizing a delay-based closeness centrality. We design the protocol specifically for the Peer to Peer(P2P) applications, where the leader peer (node) is responsible for collecting, processing, and redistributing data or control signals satisfying some timing constraints. the protocol elects an optimal leader node in the dynamically changing network and constructs a Data Collection and Distribution Tree (DCDT) rooted at the leader node. the elected optimal leader is closest to all nodes in the system compared to other nodes. We validate the proposed protocol through theoretical proofs as well as experimental results.
Modern ML applications increasingly rely on complex deep learning models and large datasets. there has been an exponential growth in the amount of computation needed to train the largest models. therefore, to scale co...
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the proceedings contain 148 papers. the topics discussed include: AI-enhanced velocity prediction for efficient EV energy management with hybrid storage;distributed reinforcement learning framework for autonomous opti...
ISBN:
(纸本)9798350361612
the proceedings contain 148 papers. the topics discussed include: AI-enhanced velocity prediction for efficient EV energy management with hybrid storage;distributed reinforcement learning framework for autonomous optimization of grid-scale energy storage systems in renewable energy integration;synergistic multi-service operation of hybrid energy storage systems;grid stability enhancement through machine learning-driven control strategies in renewable energy integration;high gain voltage SEPIC converter for PV system;towards a blockchain-enabled transactive renewable energy trading market;intelligent energy management system for microgrids using reinforcement learning;power management strategy of AC/DC hybrid microgrids based on integrating battery SoC conditions to state logic control algorithm;and AI-driven energy forecasting for electric vehicle charging stations powered by solar and wind energy.
As wind turbine (WT) power fluctuates due to the intermittent nature of the wind, batteries can help smooth out this variation. Traditionally, multiple conversion stages are used to manage the power flow between the w...
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ISBN:
(纸本)9798350361612;9798350361629
As wind turbine (WT) power fluctuates due to the intermittent nature of the wind, batteries can help smooth out this variation. Traditionally, multiple conversion stages are used to manage the power flow between the wind source and the storage device. this paper investigates the feasibility of a dual-port inverter (DPI) as single conversion stage to connect a battery and a micro-WT and shows how the DPI can be controlled to ensure that the most power possible is harvested from the wind turbine. Numerical simulations demonstrate that the DPI has the capability to deliver power to the grid with a unity power factor, even under conditions of highly variable speed, while always extracting maximum power from the WT. the simple DPI-based configuration enables smoothing wind power variations withthe battery without employing multiple dc-dc and dc-ac conversion stages.
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