Robust finite-time consensus control for multi-agent systems with disturbances and unknown velocities

Tian, Xuehong; Liu, Huanlao; Liu, Haitao

doi:10.1016/j.isatra.2018.07.032

Cited by 44 publications

(21 citation statements)

References 35 publications

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“…By using RBFNN expressed in Eq. (11) and Eq. (12),F i (t) can approximate the unknown function F i (t) precisely:…”

Section: Design Of a Consensus Controllermentioning

confidence: 99%

“…The fundamental issue of the multi-agent system is the consensus problem [6]- [8], generally speaking, the purpose of consensus control of a multi-agent system is that all agents that will be synchronized to reach a common state by a control protocol based on the neighbour agents' information [9], [10], which can be divided into two classes that are leaderless consensus and leader-following consensus [11]- [13]. The process of leader-following consensus is that all agents reach the synchronous state by following a common reference…”

Section: Introductionmentioning

confidence: 99%

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Leader-Following Consensus Control for Multiple Fixed-Wing UAVs’ Attitude System With Time Delays and External Disturbances

et al. 2019

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This paper proposes an algorithm of leader-following consensus control of multiple fixed-wing unmanned aerial vehicles'(UAVs) attitudes with time delays and unknown external disturbances. Firstly, a distributed controller based on undirected graph for leader-following consensus control of multi-UAVs' attitudes is proposed. Then, the effects of time delays are compensated with the help of Lyapunov-Krasovskii function, Cauchy and Young's inequality. Secondly, RBF neural network is introduced to estimate and reduce the negative effects of external disturbances. Finally, the effectiveness of the proposed distributed controller is demonstrated by Lyapunov theory and simulations. INDEX TERMS Leader-following consensus, multi-UAVs, time delays, unknown external disturbances, RBF neural network.

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“…By using RBFNN expressed in Eq. (11) and Eq. (12),F i (t) can approximate the unknown function F i (t) precisely:…”

Section: Design Of a Consensus Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leader-Following Consensus Control for Multiple Fixed-Wing UAVs’ Attitude System With Time Delays and External Disturbances

et al. 2019

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“…Hence, it is significant to study the consensus tracking problem for MASs subject to mismatched disturbances. However, Tian et al [28] only proposed a robust finite-time control for MASs with matched disturbances by using backstepping method, and most of the existing results also only considered matched disturbances in the finite-time consensus [22][23][24][25]. The consensus problems of MASs subject to mismatched disturbances were solved, but convergence approaches were asymptotic [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Finite‐time leader–follower consensus control of multiagent systems with mismatched disturbances

Zhao

Sun

et al. 2021

Asian Journal of Control

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We study the robust finite‐time leader–follower output consensus problems for second‐order multiagent systems (MASs) with mismatched disturbances. For mismatched disturbance, the mismatched disturbance observer is used for each follower to estimate the mismatched disturbance in finite time. Then a backstepping control protocol is constructed via Lyapunov finite‐time stability theorem, and we can prove that the output consensus tracking with strong robustness can be reached for the considered MAS in finite time if the communication topology includes a directed spanning tree, where the leader is the root. Finally, we use two simulation examples to illustrate the correctness of the theoretical results.

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“…In [22], adaptive finitetime consensus control problems were addressed for the firstorder MAS, in which the linearly parameterized method was used to estimate the unknown nonlinear dynamics. The works [23] and [24] discussed the finite-time consensus control of second-order and high-order MASs with disturbances, respectively. However, the results presented in those papers did not consider full state constraints.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Distributed Finite-Time Tracking Control of Uncertain Multi-Agent Systems With Full State Constraints

Chen

2020

IEEE Access

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This paper addresses the distributed tracking control problem of pure-feedback multi-agent systems with full state constraints under a directed graph in finite time. By introducing the nonlinear mapping technique, the system with full state constraints is converted into the form without state constraints. Furthermore, by combining fractional dynamic surface and radial basis function neural networks, a novel finite-time adaptive tracking controller is conducted recursively. In light of Lyapunov stability theory, it is proven that all signals of multi-agent systems are semi-globally uniformly ultimately bounded in finite time and the full states satisfy the constraints. Lastly, numerical simulations are supplied to demonstrate the effectiveness of the proposed control strategy. INDEX TERMS Multi-agent systems, finite-time tracking control, full state constraints, pure-feedback form.

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Robust finite-time consensus control for multi-agent systems with disturbances and unknown velocities

Cited by 44 publications

References 35 publications

Leader-Following Consensus Control for Multiple Fixed-Wing UAVs’ Attitude System With Time Delays and External Disturbances

Leader-Following Consensus Control for Multiple Fixed-Wing UAVs’ Attitude System With Time Delays and External Disturbances

Finite‐time leader–follower consensus control of multiagent systems with mismatched disturbances

Neural Network-Based Distributed Finite-Time Tracking Control of Uncertain Multi-Agent Systems With Full State Constraints

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