2023 American Control Conference (ACC)最新文献_第2页

H∞ Control of a Heated/Cooled Rod Under Point Actuation and Point Sensing 点驱动和点传感下的热/冷杆H∞控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156276

A. Krener

引用次数: 0

Adaptive alternatives in the velocity control of Mean-Field Kuramoto Models 平均场Kuramoto模型速度控制中的自适应方案

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156040

M. Demetriou

引用次数: 0

Competitive Information Provision Among Internet Routing Nodes 互联网路由节点间的竞争性信息供给

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156591

Joshua H. Seaton, Sena Hounsinou, Gedare Bloom, Philip N. Brown

{"title":"Competitive Information Provision Among Internet Routing Nodes","authors":"Joshua H. Seaton, Sena Hounsinou, Gedare Bloom, Philip N. Brown","doi":"10.23919/ACC55779.2023.10156591","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156591","url":null,"abstract":"In proposed path-aware designs for the Internet, end hosts can select which path their packets use. What criteria should the end hosts use to select paths? Recent work has proposed path-aware access control frameworks in which routing nodes publicly report their knowledge of the security postures of other nodes; end hosts can then base their routing choices on these reports. However, nothing is known regarding the nodes’ incentives to report their knowledge truthfully. In this paper, we consider the case in which each network node is strategic, and seeks to craft its public reports to manipulate traffic patterns in its own favor. In the context of a simple selfish routing problem with two strategic nodes, we show that for a wide swath of the parameter space, each node has a dominant reporting strategy, meaning that its individually optimal strategy does not depend on the strategy of the other node. These dominant strategies are generally not truthful. At the resulting dominant-strategy Nash equilibrium, we show that the expected social cost is (often considerably) higher than that achieved when both nodes are completely truthful. Nonetheless, we prove that these equilibrium reporting strategies are never perverse, meaning that their resulting social cost is never worse than if traffic were uninformed as to network state.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129544391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximate Confidence Region of State Prediction in Stochastic Dynamical Discrete-Time Systems * 随机动力离散系统状态预测的近似置信区域*

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156110

Xun Shen, Tinghui Ouyang, Yuhu Wu

引用次数: 0

Event-Triggered Multi-Mode MPC with Application to Modular Aerial Vehicles 事件触发多模式MPC及其在模块化飞行器中的应用

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155794

Pedro Casau, Bruno J. Guerreiro

引用次数: 0

Distributed H2 Controller Synthesis for Multi-Agent Systems with Stochastic Packet Loss 随机丢包多智能体系统的分布式H2控制器综合

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156194

Christian Hespe, Adwait Datar, Daniel Schneider, Hamideh Saadabadi, Herbert Werner, Hannes Frey

引用次数: 3

HEV Energy Management Strategy Based on TD3 with Prioritized Exploration and Experience Replay 基于TD3优先探索与体验重播的HEV能量管理策略

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156220

Yu He, Youngki Kim

{"title":"HEV Energy Management Strategy Based on TD3 with Prioritized Exploration and Experience Replay","authors":"Yu He, Youngki Kim","doi":"10.23919/ACC55779.2023.10156220","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156220","url":null,"abstract":"This paper presents a novel energy management strategy for hybrid electric vehicles (HEVs) that is based on an expert twin-delayed deep deterministic policy gradient with prioritized exploration and experience replay (TD3-PEER). State-of-the-art TD3 requires critic networks to generate predicted Q value for state-action pairs to update a policy network. However, the critic networks may struggle with predicting Q values for certain states when the Q values of these states are sensitive to action selection. To address this issue, this paper proposes a prioritized exploration technique that encourages the agent to visit action-sensitive states more frequently in the application of HEV energy management. The proposed algorithm is tested and validated on a P0+P4 HEV model. To simplify the control design, a motor activation threshold is introduced into the final layer of the agent’s actor. In addition, dynamic programming results are incorporated into the training of the TD3, helping the agent avoid inefficient operations. Simulation results demonstrate that with expert knowledge considered for all learning-based methods, the proposed TD3-PEER outperforms other RL-based energy management strategies, including DDPG-PER and deep Q-network, by an average of 2.3% and 3.74% over the training and validation cycles, respectively.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128487109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Two-Layer Human-in-the-Loop Optimization Framework for Customizing Lower-Limb Exoskeleton Assistance 一种用于定制下肢外骨骼辅助的双层人在环优化框架

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155858

Siqi Zheng, Ge Lv

{"title":"A Two-Layer Human-in-the-Loop Optimization Framework for Customizing Lower-Limb Exoskeleton Assistance","authors":"Siqi Zheng, Ge Lv","doi":"10.23919/ACC55779.2023.10155858","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155858","url":null,"abstract":"Task-invariant control paradigms can enable lower-limb exoskeletons to provide assistance for their users across various locomotor tasks without prescribing to specific joint kinematics. As an energetic control method, energy shaping can alter a human’s body energetics in the closed-loop to provide gait benefits. To obtain the energy shaping law for underactuated systems, a set of nonlinear partial differential equations, called the matching condition, needs to be solved to determine the achievable closed-loop dynamics. However, solving matching conditions for high-dimensional nonlinear systems is generally difficult. In addition, how to define parameters for the closed-loop dynamics that render the optimal exoskeleton assistance remains unclear. In this paper, we proposed a two-layer, human-in-the-loop optimization framework for lower-limb exoskeletons to customize their assistance to human users. The inner-layer optimization finds solutions to the matching condition, meanwhile following the energy trajectories of a virtual reference model defined based on the self-selected gaits of humans and a scaled version of their anatomical parameters. The outer-layer incorporates human-in-the-loop Bayesian Optimization to update reference energy’s parameters for reducing metabolic costs. Simulation results on two biped models demonstrate that the proposed framework can solve matching conditions numerically at the selected timestamps and the associated energy shaping strategies can reduce human metabolic cost. Moreover, exoskeletons torques calculated using an able-bodied subject’s kinematic data well match human biological torques.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Robust Tracking Control for First-Order Linear Systems with Input Delay and Lipschitz Nonlinear Disturbance 具有输入延迟和Lipschitz非线性扰动的一阶线性系统自适应鲁棒跟踪控制

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10155836

H. Lai, Yang Zhu, Zheng Chen, B. Yao

引用次数: 0

Fixed-Time Consensus for Cooperative and Antagonistic Multi-Agent Systems With External Disturbances and Directed Topologies 具有外部干扰和有向拓扑的合作与对抗多智能体系统的定时一致性

2023 American Control Conference (ACC) Pub Date : 2023-05-31 DOI: 10.23919/ACC55779.2023.10156248

Mingjun Du, Hui Yao, Baicheng Chen, Zhiguo Yan

引用次数: 0