Guaranteed cost control of mobile sensor networks with Markov switching topologies

Zhao, Yuan; Guo, Ge; Ding, Lei

doi:10.1016/j.isatra.2015.05.013

Cited by 32 publications

(35 citation statements)

References 34 publications

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“…The two relationships intrinsically reflect the structure characteristics of multiagent networks; that is, the average of the initial states of all agents determines the whole motion for leaderless structures, but the whole motion only depends on the leader for leader-following structures. Remark 6: The LMI criteria for guaranteed-cost synchronization are dependent on the Laplacian matrices of interaction topologies in [31]- [33]. In this case, the dimensions of the variables are identical with the number of agents, so it is timecost to check those criteria when multiagent networks consist of a large number of agents.…”

Section: Then the Dynamics Of Multiagent Networkmentioning

confidence: 99%

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Dynamic Output Feedback Guaranteed-Cost Synchronization for Multiagent Networks With Given Cost Budgets

Wang

Liu

et al. 2018

IEEE Access

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The current paper addresses the distributed guaranteed-cost synchronization problems for general high-order linear multiagent networks. Existing works on the guaranteedcost synchronization usually require all state information of neighboring agents and cannot give the cost budget previously. For both leaderless and leader-following interaction topologies, the current paper firstly proposes a dynamic output feedback synchronization protocol with guaranteed-cost constraints, which can realize the tradeoff design between the energy consumption and the synchronization regulation performance with the given cost budget. Then, according to different structure features of interaction topologies, leaderless and leader-following guaranteedcost synchronization analysis and design criteria are presented, respectively, and an algorithm is proposed to deal with the impacts of nonlinear terms by using both synchronization analysis and design criteria. Especially, an explicit expression of the synchronization function is shown for leaderless cases, which is independent of protocol states and the given cost budget. Finally, numerical examples are presented to demonstrate theoretical results.

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Section: Then the Dynamics Of Multiagent Networkmentioning

confidence: 99%

“…It is well-known that output feedback synchronization control is more complex and challengeable than state feedback synchronization control. In [31]- [37], all state information of neighboring agents is required to realize the guaranteed-cost synchronization control.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Output Feedback Guaranteed-Cost Synchronization for Multiagent Networks With Given Cost Budgets

Wang

Liu

et al. 2018

IEEE Access

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“…For high‐order multiagent networks, optimal consensus is difficult to be achieved due to complex structures, and guaranteed‐cost consensus is proposed to realize suboptimal control. Linear matrix inequality (LMI) criteria for guaranteed‐cost consensualization were proposed in the works of Zhao et al and Zhou et al, where the dimensions of all the variables are associated with the number of agents, so the computation complexity greatly increases as the number of agents increases. This shortcoming was overcome in the work of Xi et al, where the dimensions of all the variables in LMI guaranteed‐cost consensus criteria are equal to the one of the dynamics of each agent.…”

Section: Introductionmentioning

confidence: 99%

“…Second, this paper determines an explicit expression of the consensus dynamics and its initial states. No approach was given to determine the consensus function in the works of Zhao et al and Zhou et al, and the method in the work of Xi et al cannot be used to investigate nonlinear cases. Third, this paper proposes an approach to linearize the nonlinear factor on disagreement dynamics, whereas the methods in the aforementioned works are no longer valid to deal with nonlinear dynamics.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed‐cost consensus for multiagent networks with Lipschitz nonlinear dynamics and switching topologies

Zhang

Liu

et al. 2018

Intl J Robust & Nonlinear

119

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Guaranteed-cost consensus for high-order nonlinear multiagent networks with switching topologies is investigated. By constructing a time-varying nonsingular matrix with a specific structure, the whole dynamics of multiagent networks is decomposed into the consensus and disagreement parts with nonlinear terms, which is the key challenge to be dealt with. An explicit expression of the consensus dynamics, which contains the nonlinear term, is given and its initial state is determined. Furthermore, by the structure property of the time-varying nonsingular transformation matrix and the Lipschitz condition, the impacts of the nonlinear term on the disagreement dynamics are linearized, and the gain matrix of the consensus protocol is determined on the basis of the Riccati equation. Moreover, an approach to minimize the guaranteed cost is given in terms of linear matrix inequalities. Finally, the numerical simulation is shown to demonstrate the effectiveness of theoretical results. KEYWORDS guaranteed-cost consensus, Lipschitz nonlinearity, multiagent network, Riccati equation, switching topology INTRODUCTIONConsensus is a typical collection behavior of multiagent networks consisting of a number of autonomous dynamic agents and has been extensively investigated recently due to its wide applications in different fields such as formation and containment control for unmanned aerial vehicles, 1-4 synchronization control for sensor networks, 5,6 and reconfiguration for spacecraft clusters. 7,8 For leadless multiagent networks, the whole dynamics contains 2 parts: the consensus dynamics and the disagreement dynamics, which describe the macroscopical and microcosmic behaviors of multiagent networks, respectively. The consensus dynamics is often described by the consensus function, and the disagreement dynamics is used to determine the consensus and consensualization criteria.According to the dynamics of each agent, multiagent networks can be classified 3 types: first-order ones, second-order ones, and high-order ones. The dynamics of each agent in first-order and second-order multiagent networks is usually described as the first-order and second-order integrators, respectively, whose consensus analysis and design problems can be simplified by these structure features (see other works 9-14 and references therein). Each agent in high-order multiagent networks is often modeled by a general high-order linear system, whose consensus and consensualization criteria are more difficult to be determined since each agent does not have specific structure features compared with first-order Int J Robust Nonlinear Control. 2018;28:2841-2852.wileyonlinelibrary.com/journal/rnc

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“…Leader-following consensus problem was studied for data-sampled multiagent systems under the Markovian switching topologies [24] and a more interesting case with multiple dynamic leaders was considered in [25]. In [26], under a switching topology governed by Markov chains, the consensus seeking problem was solved through a guaranteed cost control method. It was unnecessary for the Markov chain to be ergodic, since each topology had a spanning tree.…”

Section: Introductionmentioning

confidence: 99%

Distributed Tracking Control for Discrete-Time Multiagent Systems with Novel Markovian Switching Topologies

Wang

Zhou

2017

Discrete Dynamics in Nature and Society

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Distributed discrete-time coordinated tracking control problem is investigated for multiagent systems in the ideal case, where agents with a fixed graph combine with a leader-following group, aiming to expand the function of the traditional one in some scenes. The modified union switching topology is derived from a set of Markov chains to the edges by introducing a novel mapping. The issue on how to guarantee all the agents tracking the leader is solved through a PD-like consensus algorithm. The available sampling period and the feasible control gain are calculated in terms of the trigonometric function theory, and the mean-square bound of tracking errors is provided finally. Simulation example is presented to demonstrate the validity of the theoretical results.

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Guaranteed cost control of mobile sensor networks with Markov switching topologies

Cited by 32 publications

References 34 publications

Dynamic Output Feedback Guaranteed-Cost Synchronization for Multiagent Networks With Given Cost Budgets

Dynamic Output Feedback Guaranteed-Cost Synchronization for Multiagent Networks With Given Cost Budgets

Guaranteed‐cost consensus for multiagent networks with Lipschitz nonlinear dynamics and switching topologies

Distributed Tracking Control for Discrete-Time Multiagent Systems with Novel Markovian Switching Topologies

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