The Experts below are selected from a list of 33840 Experts worldwide ranked by ideXlab platform

Wei Ren - One of the best experts on this subject based on the ideXlab platform.

  • finite time consensus for multi agent networks with unknown inherent nonlinear dynamics
    Automatica, 2014
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    The objective of this paper is to analyze the finite-time convergence of a nonlinear but continuous consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel comparison based tool. By using this tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching Interaction Graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching Interaction Graph can even be guaranteed by the stability and the finite-time convergence of some well-designed nonlinear closed-loop system under some special directed switching Interaction Graph. This provides a stimulating example for the potential applications of the proposed comparison based tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems.

  • finite time consensus for multi agent networks with unknown inherent nonlinear dynamics
    arXiv: Optimization and Control, 2013
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    This paper focuses on analyzing the finite-time convergence of a nonlinear consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis of the closed-loop system under the proposed consensus algorithm are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel stability tool based on a generalized comparison lemma. With the aid of the novel stability tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching Interaction Graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching Interaction Graph can even be guaranteed by the stability and the finite-time convergence of some special well-designed nonlinear closed-loop system under some special directed switching Interaction Graph, where each agent has at most one neighbor whose state is either the maximum of those states that are smaller than its own state or the minimum of those states that are larger than its own state. This provides a stimulating example for the potential applications of the proposed novel stability tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems. For illustration of the theoretical result, we provide a simulation example.

  • distributed tracking control for linear multiagent systems with a leader of bounded unknown input
    IEEE Transactions on Automatic Control, 2013
    Co-Authors: Xiangdong Liu, Wei Ren, Lihua Xie
    Abstract:

    This technical note considers the distributed tracking control problem of multiagent systems with general linear dynamics and a leader whose control input is nonzero and not available to any follower. Based on the relative states of neighboring agents, two distributed discontinuous controllers with, respectively, static and adaptive coupling gains, are designed for each follower to ensure that the states of the followers converge to the state of the leader, if the Interaction Graph among the followers is undirected, the leader has directed paths to all followers, and the leader's control input is bounded. A sufficient condition for the existence of the distributed controllers is that each agent is stabilizable. Simulation examples are given to illustrate the theoretical results.

  • finite time consensus of multi agent networks with inherent nonlinear dynamics under an undirected Interaction Graph
    American Control Conference, 2011
    Co-Authors: Yongcan Cao, Wei Ren, Fei Chen, Guangdeng Zong
    Abstract:

    This paper studies finite-time consensus of multi-agent networks with inherent nonlinear dynamics where each agent is driven by a nonlinear term based on its state under an undirected Interaction Graph. We propose two distributed nonlinear algorithms to guarantee finite-time consensus. To facilitate the stability analysis of the closed-loop system using the proposed nonlinear algorithms, we present a general comparison lemma. The general comparison lemma provides an important tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems. With the aid of the general comparison lemma, the two nonlinear algorithms are shown to guarantee finite-time consensus by comparing the original closed-loop systems with one or more predesigned closed-loop systems that can guarantee finite-time consensus.

  • sampled data discrete time coordination algorithms for double integrator dynamics under dynamic directed Interaction
    International Journal of Control, 2010
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    In this article, we study two sampled-data-based discrete-time coordination algorithms for multi-vehicle systems with double-integrator dynamics under dynamic directed Interaction. For both algorithms, we derive sufficient conditions on the Interaction Graph, the damping gain and the sampling period to guarantee coordination by using the property of infinity products of stochastic matrices. When the conditions on the damping gain and the sampling period are satisfied, the first algorithm guarantees coordination on positions with a zero final velocity if the Interaction Graph has a directed spanning tree jointly while the second algorithm guarantees coordination on positions with a constant final velocity if the Interaction Graph has a directed spanning tree at each time interval. Simulation results are presented to show the effectiveness of the theoretical results.

Liqiang Nie - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
    IEEE Transactions on Image Processing, 2021
    Co-Authors: Zongmeng Zhang, Xianjing Han, Xuemeng Song, Yan Yan, Liqiang Nie
    Abstract:

    This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video. However, it is non-trivial since it requires not only the comprehensive understanding of the video and sentence query, but also the accurate semantic correspondence capture between them. Existing efforts are mainly centered on exploring the sequential relation among video clips and query words to reason the video and sentence query, neglecting the other intra-modal relations (e.g., semantic similarity among video clips and syntactic dependency among the query words). Towards this end, in this work, we propose a Multi-modal Interaction Graph Convolutional Network (MIGCN), which jointly explores the complex intra-modal relations and inter-modal Interactions residing in the video and sentence query to facilitate the understanding and semantic correspondence capture of the video and sentence query. In addition, we devise an adaptive context-aware localization method, where the context information is taken into the candidate moments and the multi-scale fully connected layers are designed to rank and adjust the boundary of the generated coarse candidate moments with different lengths. Extensive experiments on Charades-STA and ActivityNet datasets demonstrate the promising performance and superior efficiency of our model.

Yongcan Cao - One of the best experts on this subject based on the ideXlab platform.

  • finite time consensus for multi agent networks with unknown inherent nonlinear dynamics
    Automatica, 2014
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    The objective of this paper is to analyze the finite-time convergence of a nonlinear but continuous consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel comparison based tool. By using this tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching Interaction Graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching Interaction Graph can even be guaranteed by the stability and the finite-time convergence of some well-designed nonlinear closed-loop system under some special directed switching Interaction Graph. This provides a stimulating example for the potential applications of the proposed comparison based tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems.

  • finite time consensus for multi agent networks with unknown inherent nonlinear dynamics
    arXiv: Optimization and Control, 2013
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    This paper focuses on analyzing the finite-time convergence of a nonlinear consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis of the closed-loop system under the proposed consensus algorithm are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel stability tool based on a generalized comparison lemma. With the aid of the novel stability tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching Interaction Graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching Interaction Graph can even be guaranteed by the stability and the finite-time convergence of some special well-designed nonlinear closed-loop system under some special directed switching Interaction Graph, where each agent has at most one neighbor whose state is either the maximum of those states that are smaller than its own state or the minimum of those states that are larger than its own state. This provides a stimulating example for the potential applications of the proposed novel stability tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems. For illustration of the theoretical result, we provide a simulation example.

  • finite time consensus of multi agent networks with inherent nonlinear dynamics under an undirected Interaction Graph
    American Control Conference, 2011
    Co-Authors: Yongcan Cao, Wei Ren, Fei Chen, Guangdeng Zong
    Abstract:

    This paper studies finite-time consensus of multi-agent networks with inherent nonlinear dynamics where each agent is driven by a nonlinear term based on its state under an undirected Interaction Graph. We propose two distributed nonlinear algorithms to guarantee finite-time consensus. To facilitate the stability analysis of the closed-loop system using the proposed nonlinear algorithms, we present a general comparison lemma. The general comparison lemma provides an important tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems. With the aid of the general comparison lemma, the two nonlinear algorithms are shown to guarantee finite-time consensus by comparing the original closed-loop systems with one or more predesigned closed-loop systems that can guarantee finite-time consensus.

  • sampled data discrete time coordination algorithms for double integrator dynamics under dynamic directed Interaction
    International Journal of Control, 2010
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    In this article, we study two sampled-data-based discrete-time coordination algorithms for multi-vehicle systems with double-integrator dynamics under dynamic directed Interaction. For both algorithms, we derive sufficient conditions on the Interaction Graph, the damping gain and the sampling period to guarantee coordination by using the property of infinity products of stochastic matrices. When the conditions on the damping gain and the sampling period are satisfied, the first algorithm guarantees coordination on positions with a zero final velocity if the Interaction Graph has a directed spanning tree jointly while the second algorithm guarantees coordination on positions with a constant final velocity if the Interaction Graph has a directed spanning tree at each time interval. Simulation results are presented to show the effectiveness of the theoretical results.

  • containment control with multiple stationary or dynamic leaders under a directed Interaction Graph
    Conference on Decision and Control, 2009
    Co-Authors: Yongcan Cao, Wei Ren
    Abstract:

    In this paper, we study the problem of distributed containment control of a group of mobile autonomous agents with multiple stationary or dynamic leaders under fixed and switching directed network topologies. In the case of stationary leaders, we show necessary and sufficient conditions on the network topology such that all followers will ultimately converge to the stationary convex hull formed by the stationary leaders for arbitrary initial states in both continuous-time and discrete-time settings. In particular, when the network topology is fixed, the final states of the followers are constant. When the network topology is switching, the final states of the followers might be changing depending on the switching Graphs. In the case of dynamic leaders, we propose a distributed tracking control algorithm without velocity measurements and derive conditions on the network topology and the control gain to guarantee that all followers will ultimately converge to the dynamic convex hull formed by the dynamic leaders for arbitrary initial states.

Zongmeng Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
    IEEE Transactions on Image Processing, 2021
    Co-Authors: Zongmeng Zhang, Xianjing Han, Xuemeng Song, Yan Yan, Liqiang Nie
    Abstract:

    This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video. However, it is non-trivial since it requires not only the comprehensive understanding of the video and sentence query, but also the accurate semantic correspondence capture between them. Existing efforts are mainly centered on exploring the sequential relation among video clips and query words to reason the video and sentence query, neglecting the other intra-modal relations (e.g., semantic similarity among video clips and syntactic dependency among the query words). Towards this end, in this work, we propose a Multi-modal Interaction Graph Convolutional Network (MIGCN), which jointly explores the complex intra-modal relations and inter-modal Interactions residing in the video and sentence query to facilitate the understanding and semantic correspondence capture of the video and sentence query. In addition, we devise an adaptive context-aware localization method, where the context information is taken into the candidate moments and the multi-scale fully connected layers are designed to rank and adjust the boundary of the generated coarse candidate moments with different lengths. Extensive experiments on Charades-STA and ActivityNet datasets demonstrate the promising performance and superior efficiency of our model.

Yan Yan - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
    IEEE Transactions on Image Processing, 2021
    Co-Authors: Zongmeng Zhang, Xianjing Han, Xuemeng Song, Yan Yan, Liqiang Nie
    Abstract:

    This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video. However, it is non-trivial since it requires not only the comprehensive understanding of the video and sentence query, but also the accurate semantic correspondence capture between them. Existing efforts are mainly centered on exploring the sequential relation among video clips and query words to reason the video and sentence query, neglecting the other intra-modal relations (e.g., semantic similarity among video clips and syntactic dependency among the query words). Towards this end, in this work, we propose a Multi-modal Interaction Graph Convolutional Network (MIGCN), which jointly explores the complex intra-modal relations and inter-modal Interactions residing in the video and sentence query to facilitate the understanding and semantic correspondence capture of the video and sentence query. In addition, we devise an adaptive context-aware localization method, where the context information is taken into the candidate moments and the multi-scale fully connected layers are designed to rank and adjust the boundary of the generated coarse candidate moments with different lengths. Extensive experiments on Charades-STA and ActivityNet datasets demonstrate the promising performance and superior efficiency of our model.

  • Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
    'Institute of Electrical and Electronics Engineers (IEEE)', 2021
    Co-Authors: Zhang Zongmeng, Yan Yan, Han Xianjing, Song Xuemeng, Nie Liqiang
    Abstract:

    This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language sentence in an untrimmed video. However, it is non-trivial since it requires not only the comprehensive understanding of the video and sentence query, but also the accurate semantic correspondence capture between them. Existing efforts are mainly centered on exploring the sequential relation among video clips and query words to reason the video and sentence query, neglecting the other intra-modal relations (e.g., semantic similarity among video clips and syntactic dependency among the query words). Towards this end, in this work, we propose a Multi-modal Interaction Graph Convolutional Network (MIGCN), which jointly explores the complex intra-modal relations and inter-modal Interactions residing in the video and sentence query to facilitate the understanding and semantic correspondence capture of the video and sentence query. In addition, we devise an adaptive context-aware localization method, where the context information is taken into the candidate moments and the multi-scale fully connected layers are designed to rank and adjust the boundary of the generated coarse candidate moments with different lengths. Extensive experiments on Charades-STA and ActivityNet datasets demonstrate the promising performance and superior efficiency of our model.Comment: Accepted by IEEE Transactions on Image Processin