Formation consensus for discrete-time heterogeneous multi-agent systems with link failures and actuator/sensor faults

Yan, Bing; Wu, Chengfu; Shi, Peng

doi:10.1016/j.jfranklin.2019.03.028

Cited by 69 publications

(25 citation statements)

References 31 publications

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“…A heterogeneous MAS is considered with two Qball‐X4 UAVs, 53 two Qball2 UAVs, 19 and one Qbot2 UGV 9 . Based on the MAS models, 9 the latitudinal dynamics of UAVs and dynamics of UGV can be simplified to the following linear fourth‐order system and second‐order system, respectively.…”

Section: Verificationmentioning

confidence: 99%

\begin{array}{lllll} ([], \begin{matrix} centermatrix \\ \dot{p_{i}^{x}} \\ \dot{v_{i}^{x}} \\ \dot{θ_{i}^{x}} \\ \dot{q_{i}} \end{matrix}) & = ([], \begin{matrix} left left left leftmatrix \\ 0 & 1 & 0 & 0 \\ 0 & 0 & g & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 \end{matrix}) ([], \begin{matrix} leftmatrix \\ p_{i}^{x} \\ v_{i}^{x} \\ θ_{i}^{x} \\ q_{i} \end{matrix}) + ([], \begin{matrix} centermatrix \\ 0 \\ 0 \\ 0 \\ \frac{k_{i}^{m} L_{i}}{I_{i}^{x}} \end{matrix}) u_{i}^{x} + ([], \begin{matrix} centermatrix \\ 1 \\ 1 \\ 1 \\ 1 \end{matrix}) ω^{x}, & ([], \begin{matrix} leftmatrix \\ \dot{p_{i}^{y}} \\ \dot{v_{i}^{y}} \\ \dot{ϕ_{i}^{y}} \\ \dot{p_{i}} \end{matrix}) & = ([], \begin{matrix} left left left leftmatrix \\ 0 & 1 & 0 & 0 \\ 0 \end{matrix}) \end{array}

…”

Section: Verificationmentioning

confidence: 99%

“…Heterogeneous systems can involve different types of agents, such as unmanned aerial vehicles‐unmanned ground vehicles (UAVs‐UGVs), and are required to be more flexible in allocating tasks compared with homogeneous agents. One of the basic heterogeneous systems, mixed‐order MASs were studied on consensus problems 8 and formation control 9,10 . For unified heterogeneous MASs with different state orders and dynamics, their common interest variables can be regarded as the outputs with the same dimensions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal robust formation control for heterogeneous multi‐agent systems based on reinforcement learning

Yan

Shi

Lim

et al. 2021

Intl J Robust & Nonlinear

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In this article, a reinforcement learning (RL)‐based robust control strategy is proposed for uncertain heterogeneous multi‐agent systems to achieve optimal collision‐free time‐varying formations. Without using any global information, a fully distributed adaptive observer is developed to estimate both dynamics and states of the reference and disturbance systems. The observer parameters are found by an observed model‐based or a model‐free off‐policy RL algorithm. Using the internal model principle, a novel optimal robust formation control strategy is developed based on another proposed off‐policy RL algorithm. The algorithm addresses the nonquadratic optimization problem when the system model is completely unknown. Taking the bushfire edge tracking and patrolling task for an unmanned aerial vehicle‐unmanned ground vehicle heterogeneous system as an example, the effectiveness and robustness of the developed control strategy are verified by simulations.

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Section: Verificationmentioning

confidence: 99%

\begin{array}{lllll} ([], \begin{matrix} centermatrix \\ \dot{p_{i}^{x}} \\ \dot{v_{i}^{x}} \\ \dot{θ_{i}^{x}} \\ \dot{q_{i}} \end{matrix}) & = ([], \begin{matrix} left left left leftmatrix \\ 0 & 1 & 0 & 0 \\ 0 & 0 & g & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 \end{matrix}) ([], \begin{matrix} leftmatrix \\ p_{i}^{x} \\ v_{i}^{x} \\ θ_{i}^{x} \\ q_{i} \end{matrix}) + ([], \begin{matrix} centermatrix \\ 0 \\ 0 \\ 0 \\ \frac{k_{i}^{m} L_{i}}{I_{i}^{x}} \end{matrix}) u_{i}^{x} + ([], \begin{matrix} centermatrix \\ 1 \\ 1 \\ 1 \\ 1 \end{matrix}) ω^{x}, & ([], \begin{matrix} leftmatrix \\ \dot{p_{i}^{y}} \\ \dot{v_{i}^{y}} \\ \dot{ϕ_{i}^{y}} \\ \dot{p_{i}} \end{matrix}) & = ([], \begin{matrix} left left left leftmatrix \\ 0 & 1 & 0 & 0 \\ 0 \end{matrix}) \end{array}

…”

Section: Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal robust formation control for heterogeneous multi‐agent systems based on reinforcement learning

Yan

Shi

Lim

et al. 2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

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“…An extensional Lyapunov function discussed by Corollary 2 in [25] is always used to explore the consensus for heterogeneous multi‐agent systems, so an extensional Lyapunov function or an extensional HPLF also could be applied in stability analysis or stabilisation of heterogeneous switched systems. Lemma 1 ((extensional Lyapunov function in continuous‐time version)) Consider heterogeneous continuous‐time switched systems

{\dot{x}}_{i} = G_{i} x_{i}

, where

G_{i}

is a given matrix and satisfies the Hurwitz stability, if there exists an extensionally symmetric and positive definite matrix

P^{+} \in {double-struckR}^{\sum_{N}^{i} n_{i} \times \sum_{N}^{i} n_{i}}

, such that

({, \begin{matrix} left1em4pt \\ P^{+} > 0 \\ he ()(, P^{+} G^{+}) < 0 \end{matrix})

where

i = 1, \dots, N

and

G^{+} \in {double-struckR}^{\sum_{N}^{i} n_{i} \times \sum_{N}^{i} n_{i}}

could be expressed as

G^{+} = (][, \begin{matrix} 1em4pt \\ G_{1} & 0 & 0 & 0 \\ 0 & G_{2} & 0 & 0 \\ 0 & 0 & ⋱ & 0 \\ 0 & 0 & 0 & G_{N} \end{matrix})

Then, the heterogeneous continuous‐time switched system is GUAS. Proof The stability of the heterogeneous switched system is centred on the activated subsystem, other subsystems do not influence global stability during the activated period of time.…”

Section: Preliminariesmentioning

confidence: 99%

Stability and stabilisation of heterogeneous switched systems with arbitrary switching constraints

et al. 2020

IET Control Theory & Appl

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“…During the past decades, the consensus problems of multiagent system have drawn considerable attention in various fields due to its broad potential applications such as sensor network [1][2][3][4], formation control [5][6][7], synchronization control [8][9][10], flocking [11][12][13], persistent monitoring [14,15], and target tracking [16][17][18][19]. The consensus of multiagents means that all agents can reach the same state through a proper control protocol.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed‐performance consensus for nonlinear singular multiagent systems with directed topologies

Wang

Niu

2022

Asian Journal of Control

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The guaranteed-performance consensus problems for nonlinear singular multiagent systems with directed topologies in both leader-following case and leaderless case are investigated, where the directed interaction topologies contain a directed spanning tree. For situations with and without the leader, two different quadratic performance functions are proposed based on state errors. The state errors in the leader-following case are the state differences between the followers and the leader while in the leaderless case are the state differences between agents with adjacent numbers. First, by exploiting state transformation, the consensus problems are transformed into the stability problems of the corresponding error systems. Second, on the basis of singular systems theory, Laplacian matrix properties, and linear matrix inequality techniques, sufficient conditions for guaranteed-performance consensus are derived for both leaderfollowing and leaderless cases. Third, the explicit expressions of the guaranteed-performance cost are obtained. Finally, two numerical examples are presented to illustrate the effectiveness of the theoretical results.

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Formation consensus for discrete-time heterogeneous multi-agent systems with link failures and actuator/sensor faults

Cited by 69 publications

References 31 publications

Optimal robust formation control for heterogeneous multi‐agent systems based on reinforcement learning

Optimal robust formation control for heterogeneous multi‐agent systems based on reinforcement learning

Stability and stabilisation of heterogeneous switched systems with arbitrary switching constraints

Guaranteed‐performance consensus for nonlinear singular multiagent systems with directed topologies

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