The Experts below are selected from a list of 13911 Experts worldwide ranked by ideXlab platform
Weidong Zhang - One of the best experts on this subject based on the ideXlab platform.
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consensus tracking for multi agent systems with directed graph via distributed adaptive protocol
Neurocomputing, 2015Co-Authors: Hongjun Chu, Yunze Cai, Weidong ZhangAbstract:The consensus tracking problem is investigated for multi-agent systems with directed graph. To avoid using any global information, a novel adaptive protocol is proposed based only on the relative state information. A monotonically increasing function for each agent is inserted into the protocol to provide extra freedom for design. By using matrix theory and appropriate Lyapunov techniques, it is shown that the consensus tracking can be achieved in a fully distributed fashion if agents? dynamics are stabilizable and the topological graph contains a directed spanning tree with the leader as the Root Node. A simulation example shows the effectiveness of the design method.
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observer based adaptive consensus tracking for linear multi agent systems with input saturation
Iet Control Theory and Applications, 2015Co-Authors: Hongjun Chu, Jingqi Yuan, Weidong ZhangAbstract:This study considers the observer-based consensus tracking problem of linear multi-agent systems with input saturation. Existing observer-based consensus protocols are designed based on an undirected graph, and need global information, such as the network size or eigenvalue information of the Laplacian matrix. In this study, based on only the agent dynamics and the relative outputs of neighbouring agents, an adaptive consensus protocol is proposed by assigning a time-varying coupling weight to each Node, and this protocol is independent of any global information and hence is fully distributed. Under the assumptions that each agent is asymptotically null controllable with bounded controls and detectable, and the topological graph has a directed spanning tree with the leader as the Root Node, semi-global observer-based consensus tracking of multi-agent systems can be reached despite actuator saturation occurring. The results are applied to consensus tracking control of two-mass-spring systems, which are well-known models for vibration in many mechanical systems.
Zhisheng Duan - One of the best experts on this subject based on the ideXlab platform.
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novel distributed robust adaptive consensus protocols for linear multi agent systems with directed graphs and external disturbances
International Journal of Control, 2017Co-Authors: Zhisheng Duan, Gang FengAbstract:ABSTRACTThis paper addresses the distributed consensus protocol design problem for linear multi-agent systems with directed graphs and external unmatched disturbances. Novel distributed adaptive consensus protocols are proposed to achieve leader–follower consensus for any directed graph containing a directed spanning tree with the leader as the Root Node and leaderless consensus for strongly connected directed graphs. It is pointed out that the adaptive protocols involve undesirable parameter drift phenomenon when bounded external disturbances exist. By using the σ modification technique, distributed robust adaptive consensus protocols are designed to guarantee the ultimate boundedness of both the consensus error and the adaptive coupling weights in the presence of external disturbances. All the adaptive protocols in this paper are fully distributed, relying on only the agent dynamics and the relative states of neighbouring agents.
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Novel Distributed Robust Adaptive Consensus Protocols for Linear Multi-agent Systems with Directed Graphs and External Disturbances
arXiv: Systems and Control, 2015Co-Authors: Zhisheng Duan, Gang FengAbstract:This paper addresses the distributed consensus protocol design problem for linear multi-agent systems with directed graphs and external unmatched disturbances. A novel distributed adaptive consensus protocol is proposed to achieve leader-follower consensus for any directed graph containing a directed spanning tree with the leader as the Root Node. It is noted that the adaptive protocol might suffer from a problem of undesirable parameter drift phenomenon when bounded external disturbances exist. To deal with this issue, a distributed robust adaptive consensus protocol is designed to guarantee the ultimate boundedness of both the consensus error and the adaptive coupling weights in the presence of external disturbances. Both adaptive protocols are fully distributed, relying on only the agent dynamics and the relative states of neighboring agents.
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Designing Fully Distributed Consensus Protocols for Linear Multi-Agent Systems With Directed Graphs
IEEE Transactions on Automatic Control, 2015Co-Authors: Zhongkui Li, Zhisheng DuanAbstract:This technical note addresses the distributed consensus protocol design problem for multi-agent systems with general linear dynamics and directed communication graphs. Existing works usually design consensus protocols using the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph, which however is global information. In this technical note, based on only the agent dynamics and the relative states of neighboring agents, a distributed adaptive consensus protocol is designed to achieve leader-follower consensus in the presence of a leader with a zero input for any communication graph containing a directed spanning tree with the leader as the Root Node. The proposed adaptive protocol is independent of any global information of the communication graph and thereby is fully distributed. Extensions to the case with multiple leaders are further studied.
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designing fully distributed consensus protocols for linear multi agent systems with directed graphs
arXiv: Optimization and Control, 2013Co-Authors: Guanghui Wen, Zhisheng Duan, Wei RenAbstract:This paper addresses the distributed consensus protocol design problem for multi-agent systems with general linear dynamics and directed communication graphs. Existing works usually design consensus protocols using the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph, which however is global information. In this paper, based on only the agent dynamics and the relative states of neighboring agents, a distributed adaptive consensus protocol is designed to achieve leader-follower consensus for any communication graph containing a directed spanning tree with the leader as the Root Node. The proposed adaptive protocol is independent of any global information of the communication graph and thereby is fully distributed. Extensions to the case with multiple leaders are further studied.
David Heckerman - One of the best experts on this subject based on the ideXlab platform.
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an experimental comparison of model based clustering methods
Machine Learning, 2001Co-Authors: Marina Meilă, David HeckermanAbstract:We compare the three basic algorithms for model-based clustering on high-dimensional discrete-variable datasets. All three algorithms use the same underlying model: a naive-Bayes model with a hidden Root Node, also known as a multinomial-mixture model. In the first part of the paper, we perform an experimental comparison between three batch algorithms that learn the parameters of this model: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based agglomerative clustering. We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization methods on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of agglomerative clustering. Although the methods are substantially different, they lead to learned models that are similar in quality.
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an experimental comparison of model based clustering methods
Uncertainty in Artificial Intelligence, 2001Co-Authors: Marina Meilă, David HeckermanAbstract:We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a "winner take all" version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden Root Node, using high-dimensional discrete-variable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality.
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an experimental comparison of several clustering and initialization methods
Uncertainty in Artificial Intelligence, 1998Co-Authors: Marina Meilă, David HeckermanAbstract:We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a "winner take all" version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden Root Node, using high-dimensional discrete-variable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality.
Hongjun Chu - One of the best experts on this subject based on the ideXlab platform.
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consensus tracking for multi agent systems with directed graph via distributed adaptive protocol
Neurocomputing, 2015Co-Authors: Hongjun Chu, Yunze Cai, Weidong ZhangAbstract:The consensus tracking problem is investigated for multi-agent systems with directed graph. To avoid using any global information, a novel adaptive protocol is proposed based only on the relative state information. A monotonically increasing function for each agent is inserted into the protocol to provide extra freedom for design. By using matrix theory and appropriate Lyapunov techniques, it is shown that the consensus tracking can be achieved in a fully distributed fashion if agents? dynamics are stabilizable and the topological graph contains a directed spanning tree with the leader as the Root Node. A simulation example shows the effectiveness of the design method.
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observer based adaptive consensus tracking for linear multi agent systems with input saturation
Iet Control Theory and Applications, 2015Co-Authors: Hongjun Chu, Jingqi Yuan, Weidong ZhangAbstract:This study considers the observer-based consensus tracking problem of linear multi-agent systems with input saturation. Existing observer-based consensus protocols are designed based on an undirected graph, and need global information, such as the network size or eigenvalue information of the Laplacian matrix. In this study, based on only the agent dynamics and the relative outputs of neighbouring agents, an adaptive consensus protocol is proposed by assigning a time-varying coupling weight to each Node, and this protocol is independent of any global information and hence is fully distributed. Under the assumptions that each agent is asymptotically null controllable with bounded controls and detectable, and the topological graph has a directed spanning tree with the leader as the Root Node, semi-global observer-based consensus tracking of multi-agent systems can be reached despite actuator saturation occurring. The results are applied to consensus tracking control of two-mass-spring systems, which are well-known models for vibration in many mechanical systems.
Marina Meilă - One of the best experts on this subject based on the ideXlab platform.
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an experimental comparison of model based clustering methods
Machine Learning, 2001Co-Authors: Marina Meilă, David HeckermanAbstract:We compare the three basic algorithms for model-based clustering on high-dimensional discrete-variable datasets. All three algorithms use the same underlying model: a naive-Bayes model with a hidden Root Node, also known as a multinomial-mixture model. In the first part of the paper, we perform an experimental comparison between three batch algorithms that learn the parameters of this model: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based agglomerative clustering. We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization methods on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of agglomerative clustering. Although the methods are substantially different, they lead to learned models that are similar in quality.
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an experimental comparison of model based clustering methods
Uncertainty in Artificial Intelligence, 2001Co-Authors: Marina Meilă, David HeckermanAbstract:We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a "winner take all" version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden Root Node, using high-dimensional discrete-variable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality.
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an experimental comparison of several clustering and initialization methods
Uncertainty in Artificial Intelligence, 1998Co-Authors: Marina Meilă, David HeckermanAbstract:We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a "winner take all" version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden Root Node, using high-dimensional discrete-variable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality.
