The Experts below are selected from a list of 147597 Experts worldwide ranked by ideXlab platform
Yingmin Jia - One of the best experts on this subject based on the ideXlab platform.
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adaptive consensus protocol for networks of Multiple Agents with nonlinear dynamics using neural networks
Asian Journal of Control, 2012Co-Authors: Yang Liu, Yingmin JiaAbstract:In this paper, an adaptive protocol is proposed to solve the consensus problem of multi-agent systems with high-order nonlinear dynamics by using neural networks (NNs) to approximate the unknown nonlinear system functions. It is derived that all Agents achieve consensus if the undirected interaction graph is connected, and the transient performance of the multi-agent system is also investigated. It shows that the adaptive protocol and the consensus analysis can be easily extended to switching networks by the existing LaSalle's Invariance Principle of switched systems. A numerical simulation illustrates the effectiveness of the proposed consensus protocol. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
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an iterative learning approach to formation control of multi agent systems
Systems & Control Letters, 2012Co-Authors: Yang Liu, Yingmin JiaAbstract:Abstract In this paper, an efficient framework is proposed to the formation control problem of Multiple Agents with unknown nonlinear dynamics, by means of the iterative learning approach. In particular, a distributed D-type iterative learning scheme is developed for the multi-agent system with switching topology, whose switching time and sequence are allowed to be varied at different iterations according to the actual trajectories of Agents, and a sufficient condition is derived to ensure that the desired formation can be always preserved from the initial starting location to the final one after some iterations. Simulation results are provided to verify the effectiveness of the proposed approach.
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h consensus control of multi agent systems with switching topology a dynamic output feedback protocol
International Journal of Control, 2010Co-Authors: Yang Liu, Yingmin JiaAbstract:This article is devoted to the consensus control for switching networks of Multiple Agents with linear coupling dynamics and subject to external disturbances, which is transformed into an H ∞ control problem by defining an appropriate controlled output. On this basis, a distributed dynamic output feedback protocol is proposed with an undetermined system matrix, and a condition in terms of linear matrix inequalities (LMIs) is derived to ensure consensus of the multi-agent system with a prescribed H ∞ level. Furthermore, system matrix of the protocol is designed by solving two LMIs. A numerical example is included to illustrate the effectiveness of the proposed consensus protocol.
Wei Xing Zheng - One of the best experts on this subject based on the ideXlab platform.
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on constructing Multiple lyapunov functions for tracking control of Multiple Agents with switching topologies
IEEE Transactions on Automatic Control, 2019Co-Authors: Guanghui Wen, Wei Xing ZhengAbstract:Distributed consensus tracking for linear multiagent systems (MASs) with directed switching topologies and a dynamic leader is investigated in this paper. By fully considering the special feature of Laplacian matrices for topology candidates, several new classes of Multiple Lyapunov functions (MLFs) are constructed in this paper for leader-following MASs with, respectively, an autonomous leader and a nonautonomous leader. Under the condition that each possible topology graph contains a spanning tree rooted at the leader node, some efficient criteria for achieving consensus tracking in the considered MASs are provided. Specifically, it is proven that consensus tracking in the closed-loop MASs can be ensured if the average dwell time for switching among different topologies is larger than a derived positive quantity and the control parameters in tracking protocols are appropriately designed. It is further theoretically shown that the present Lyapunov inequality based criteria for consensus tracking with an autonomous leader are much less conservative than the existing ones derived by the $M$ -matrix theory. The results are then extended to the case where the topology graph only frequently contains a directed spanning tree as the MASs evolve over time. At last, numerical simulations are performed to illustrate the effectiveness of the analytical analysis and the advantages of the proposed MLFs.
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coordination of Multiple Agents with double integrator dynamics under generalized interaction topologies
Systems Man and Cybernetics, 2012Co-Authors: Jiahu Qin, Wei Xing Zheng, Huijun GaoAbstract:The problem of the convergence of the consensus strategies for Multiple Agents with double-integrator dynamics is studied in this paper. The investigation covers two kinds of different settings. In the setting with the interaction topologies for the position and velocity information flows being modeled by different graphs, some sufficient conditions on the fixed interaction topologies are derived for the Agents to reach consensus. In the setting with the interaction topologies for the position and velocity information flows being modeled by the same graph, we systematically investigate the consensus algorithm for the Agents under both fixed and dynamically changing directed interaction topologies. Specifically, for the fixed case, a necessary and sufficient condition on the interaction topology is established for the Agents to reach (average) consensus under certain assumptions. For the dynamically changing case, some sufficient conditions are obtained for the Agents to reach consensus, where the condition imposed on the dynamical topologies is shown to be more relaxed than that required in the existing literature. Finally, we demonstrate the usefulness of the theoretical findings through some numerical examples.
Tongqiang Jiang - One of the best experts on this subject based on the ideXlab platform.
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consensus of first order multi agent systems with intermittent interaction
Neurocomputing, 2014Co-Authors: Yanping Gao, Bo Liu, Tongqiang JiangAbstract:This paper studies the consensus problem of Multiple Agents with continuous-time first-order dynamics, where each agent can only obtain its states relative to its neighbors at sampling instants. It is assumed that the sampling period is different from the period of zero-order hold. Some sufficient and necessary conditions for consensus, which reveal the relationship among the interaction topology, controller gains, and the periods of sampler and zero-order hold, are provided. Moreover, the convergence rate is compared with that in the case when the period of zero-order hold is the same as the sampling period. Simulations are performed to validate the theoretical results.
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consensus of discrete time second order Agents with time varying topology and time varying delays
Journal of The Franklin Institute-engineering and Applied Mathematics, 2012Co-Authors: Tongqiang Jiang, Junping DuAbstract:Abstract This paper studies the consensus problem of Multiple Agents with discrete-time second-order dynamics. It is assumed that the information obtained by each agent is with time-varying delays and the interaction topology is time-varying, where the associated direct graphs may not have spanning trees. Under the condition that the union graph is strongly connected and balanced, it is shown that there exist controller gains such that consensus can be reached for any bounded time-delays. Moreover, a method is provided to design controller gains. Simulations are performed to validate the theoretical results.
Yang Liu - One of the best experts on this subject based on the ideXlab platform.
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adaptive consensus protocol for networks of Multiple Agents with nonlinear dynamics using neural networks
Asian Journal of Control, 2012Co-Authors: Yang Liu, Yingmin JiaAbstract:In this paper, an adaptive protocol is proposed to solve the consensus problem of multi-agent systems with high-order nonlinear dynamics by using neural networks (NNs) to approximate the unknown nonlinear system functions. It is derived that all Agents achieve consensus if the undirected interaction graph is connected, and the transient performance of the multi-agent system is also investigated. It shows that the adaptive protocol and the consensus analysis can be easily extended to switching networks by the existing LaSalle's Invariance Principle of switched systems. A numerical simulation illustrates the effectiveness of the proposed consensus protocol. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
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an iterative learning approach to formation control of multi agent systems
Systems & Control Letters, 2012Co-Authors: Yang Liu, Yingmin JiaAbstract:Abstract In this paper, an efficient framework is proposed to the formation control problem of Multiple Agents with unknown nonlinear dynamics, by means of the iterative learning approach. In particular, a distributed D-type iterative learning scheme is developed for the multi-agent system with switching topology, whose switching time and sequence are allowed to be varied at different iterations according to the actual trajectories of Agents, and a sufficient condition is derived to ensure that the desired formation can be always preserved from the initial starting location to the final one after some iterations. Simulation results are provided to verify the effectiveness of the proposed approach.
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h consensus control of multi agent systems with switching topology a dynamic output feedback protocol
International Journal of Control, 2010Co-Authors: Yang Liu, Yingmin JiaAbstract:This article is devoted to the consensus control for switching networks of Multiple Agents with linear coupling dynamics and subject to external disturbances, which is transformed into an H ∞ control problem by defining an appropriate controlled output. On this basis, a distributed dynamic output feedback protocol is proposed with an undetermined system matrix, and a condition in terms of linear matrix inequalities (LMIs) is derived to ensure consensus of the multi-agent system with a prescribed H ∞ level. Furthermore, system matrix of the protocol is designed by solving two LMIs. A numerical example is included to illustrate the effectiveness of the proposed consensus protocol.
Angelia Nedic - One of the best experts on this subject based on the ideXlab platform.
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distributed random projection algorithm for convex optimization
IEEE Journal of Selected Topics in Signal Processing, 2013Co-Authors: Soomin Lee, Angelia NedicAbstract:Random projection algorithm is of interest for constrained optimization when the constraint set is not known in advance or the projection operation on the whole constraint set is computationally prohibitive. This paper presents a distributed random projection algorithm for constrained convex optimization problems that can be used by Multiple Agents connected over a time-varying network, where each agent has its own objective function and its own constrained set. We prove that the iterates of all Agents converge to the same point in the optimal set almost surely. Experiments on distributed support vector machines demonstrate good performance of the algorithm.
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constrained consensus and optimization in multi agent networks
IEEE Transactions on Automatic Control, 2010Co-Authors: Angelia Nedic, Asuman Ozdaglar, Pablo A ParriloAbstract:We present distributed algorithms that can be used by Multiple Agents to align their estimates with a particular value over a network with time-varying connectivity. Our framework is general in that this value can represent a consensus value among Multiple Agents or an optimal solution of an optimization problem, where the global objective function is a combination of local agent objective functions. Our main focus is on constrained problems where the estimates of each agent are restricted to lie in different convex sets. To highlight the effects of constraints, we first consider a constrained consensus problem and present a distributed "projected consensus algorithm" in which Agents combine their local averaging operation with projection on their individual constraint sets. This algorithm can be viewed as a version of an alternating projection method with weights that are varying over time and across Agents. We establish convergence and convergence rate results for the projected consensus algorithm. We next study a constrained optimization problem for optimizing the sum of local objective functions of the Agents subject to the intersection of their local constraint sets. We present a distributed "projected subgradient algorithm" which involves each agent performing a local averaging operation, taking a subgradient step to minimize its own objective function, and projecting on its constraint set. We show that, with an appropriately selected stepsize rule, the agent estimates generated by this algorithm converge to the same optimal solution for the cases when the weights are constant and equal, and when the weights are time-varying but all Agents have the same constraint set.
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distributed subgradient methods for multi agent optimization
IEEE Transactions on Automatic Control, 2009Co-Authors: Angelia Nedic, Asuman OzdaglarAbstract:We study a distributed computation model for optimizing a sum of convex objective functions corresponding to Multiple Agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the Agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other Agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.
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on the rate of convergence of distributed subgradient methods for multi agent optimization
Conference on Decision and Control, 2007Co-Authors: Angelia Nedic, Asuman OzdaglarAbstract:We study a distributed computation model for optimizing the sum of convex (nonsmooth) objective functions of Multiple Agents. We provide convergence results and estimates for convergence rate. Our analysis explicitly characterizes the tradeoff between the accuracy of the approximate optimal solutions generated and the number of iterations needed to achieve the given accuracy.
