Interventional consensus for high-order multi-agent systems with unknown disturbances on coopetition networks

Wu, Yanzhi; Hu, Jiangping; Zhang, Yuping; Zeng, Yong

doi:10.1016/j.neucom.2016.01.070

Cited by 42 publications

(23 citation statements)

References 27 publications

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“…, N). It follows the from (16) (14) and 15, we have A + cλ i BK H P + P A + cλ i BK � A T P + PA (27) which implies that A + cλ i BK (i � 2, 3, . .…”

Section: Proof Take Variable Transformation ξmentioning

confidence: 84%

“…In [26], the authors discussed the effects of measurement noises on bipartite consensus problem with first-order multiagent systems under undirected signed graphs. An adaptive bipartite consensus was proposed for high-order multiagent systems with unknown disturbances in [27]. An adaptive protocol was proposed in [28] to solve the bipartite output consensus problem over signed graphs.…”

Section: Introductionmentioning

confidence: 99%

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Weighted Consensus Problem for Multiagent Systems with Edge- and Node-Weighted Directed Graphs

et al. 2019

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In this paper, the concept of consensus is generalized to weighted consensus, by which the conventional consensus, the bipartite consensus, and the cluster consensus problems can be unified in the proposed weighted consensus frame. The dynamics of agents are modeled by the general linear time-invariant systems. The interaction topology is modeled by edge- and node-weighted directed graphs. Under both state feedback and output feedback control strategies, the weighted consensus problems are transformed into the equivalent conventional consensus problems. Then, some distributed state feedback and output feedback protocols are proposed to solve the weighted consensus problems. For output feedback case, a unified frame to construct the state-observer-based weighted consensus protocols is proposed, and different design approaches are discussed. As special cases, some related results for bipartite consensus and cluster consensus can be obtained directly. Finally, a simple example is given to illustrate the effectiveness of our proposed approaches.

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“…, N). It follows the from (16) (14) and 15, we have A + cλ i BK H P + P A + cλ i BK � A T P + PA (27) which implies that A + cλ i BK (i � 2, 3, . .…”

Section: Proof Take Variable Transformation ξmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Weighted Consensus Problem for Multiagent Systems with Edge- and Node-Weighted Directed Graphs

et al. 2019

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“…Contrary to consensus control, bipartite consensus control enables all agents to reach a final state with identical magnitude but opposite sign through cooperative and competitive interactions. [6][7][8][9][10] It is an extremely effective control strategy in those scenarios where cooperation and competition coexist among agents.…”

Section: Introductionmentioning

confidence: 99%

“…For cooperative-competitive multiagent systems with high-order agent dynamics, a static state-feedback controller was proposed in the work of Valcher and Misra, 7 and a dynamic output feedback controller was given in the work of Zhang and Chen 20 to guarantee bipartite consensus. A state-feedback controller was designed for a high-order multiagent system with input saturation in the work of Qin et al 21 An iterative learning control algorithm was provided for all agents to reach bipartite consensus on a reference trajectory in the work of Meng et al 9 Adaptive laws of time-varying coupling weights were proposed to solve a sign-consensus problem of linear multiagent systems in the work of Jiang et al 22 Some connectivity conditions were provided for bipartite consensus of discrete-time multiagent systems with switching networks in the work of Meng et al 23 An adaptive control protocol was designed to realize an interventional bipartite consensus for high-order multiagent systems with unknown disturbances in the work of Wu et al 10 Although many advances have been reported on the bipartite consensus problem, some challenging issues remain unresolved. For instance, the existing bipartite consensus strategies generally rely on some global information of the interaction topologies associated with multiagent systems, and thus, they are essentially not fully distributed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive bipartite consensus control of high‐order multiagent systems on coopetition networks

Liu

et al. 2018

Intl J Robust & Nonlinear

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In this paper, a bipartite consensus problem is considered for a high-order multiagent system with cooperative-competitive interactions and unknown time-varying disturbances. A signed graph is used to describe the interaction network associated with the multiagent system. The unknown disturbances are expressed by linearly parameterized models, and distributed adaptive laws are designed to estimate the unknown parameters in the models. For the case that there is no exogenous reference system, a fully distributed adaptive control law is proposed to ensure that all the agents reach a bipartite consensus. For the other case that there exists an exogenous reference system, another fully distributed adaptive control law is also developed to ensure that all the agents achieve bipartite consensus on the state of the exogenous system. The stability of the closed-loop multiagent systems with the 2 proposed adaptive control laws are analyzed under an assumption that the interaction network is structurally balanced. Moreover, the convergence of the parameter estimation errors is guaranteed with a persistent excitation condition. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed adaptive bipartite consensus control laws for the concerned multiagent system. KEYWORDSbipartite consensus, coopetition networks, distributed adaptive control, high-order multiagent systems 2868

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“…The fixed communication topology in [8] is extended in [12] to time-varying signed digraphs. Moreover, bipartite consensus for high-order MASs with unknown disturbances is investigated [15][16][17]. In [15], an adaptive consensus protocol is designed for second-order MASs.…”

Section: Introductionmentioning

confidence: 99%

Bipartite consensus of integrator multi‐agent systems with measurement noise

Qin

Zhao

2017

IET Control Theory & Applications

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The bipartite consensus problem for integrator multi-agent systems over signed fixed digraphs is investigated in the presence of measurement noise. A time-varying consensus gain is introduced and then a stochastic type protocol is proposed, whose performance is analysed using the state transition matrix of the closed-loop system. Necessary and sufficient conditions for ensuring a mean square bipartite consensus protocol are obtained in the presence of noise. Furthermore, in the absence of noise it is shown that these conditions are also necessary and sufficient for ensuring the bipartite consensus except for the quadratic integrability of the consensus gain. It is found that the signed digraph being structurally balanced and having a spanning tree are the weakest assumptions on connectivity for achieving bipartite consensus regardless of the measurement noise. In particular, if the signed digraph is structurally unbalanced, then under some mild conditions, the states of the closedloop system converge to zero in mean square, regardless of the initial states.

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Interventional consensus for high-order multi-agent systems with unknown disturbances on coopetition networks

Cited by 42 publications

References 27 publications

Weighted Consensus Problem for Multiagent Systems with Edge- and Node-Weighted Directed Graphs

Weighted Consensus Problem for Multiagent Systems with Edge- and Node-Weighted Directed Graphs

Adaptive bipartite consensus control of high‐order multiagent systems on coopetition networks

Bipartite consensus of integrator multi‐agent systems with measurement noise

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