Scalable formation control in stealth with limited sensing range

Yu, Hongjun; Shi, Peng; Lim, Cheng‐Chew

doi:10.1002/rnc.3579

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…The displacement of any two agents can always find two points (

d^{'}

and

d^{″}

) that belong to F , and these two displacements should be equal when time tends to infinity. This kind of expression has been used in previous formation control work [6, 7] to represent points belong to the reference formation, which is given in detail in Section 3.1. When the final formation is reached, UAVs would hover and their velocities, attitude angles and angular rates in x and y axes would go to zero.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The scalable 2D formation can be defined as follows: Definition 3 For any

d_{i}, d_{j} \in F

and

d_{i}, d_{j} \in R^{2 \times 1}

, if there exists

a \in Z^{t \times 1}

, such that

d_{i} - d_{j} = D a

where

D = false[d_{1}, \dots, d_{i}, \dots, d_{t} false], D \in R^{2 \times t}

is the relative distance matrix of a desired formation [6], then we call

F = (][, . . ., d_{- 1}, d_{0}, d_{1}, \dots)

a scalable 2D reference formation based on relative distance D . Assumption 1 The formation of the quadrotor is on a plane with a fixed height.…”

Section: Optimal Formation Controlmentioning

confidence: 99%

“…

M false(P false)

is the mapping matrix to transform the reference formation F to the map formation

d false(P false)

, we have

d false(P false) = M false(P false) F

. According to [6], we introduce the coordination protocol of the formation mapping. Protocol 1 For MAS (4), (7) is used to map P to

d false(P false)

based on reference formation F

\begin{matrix} left1em4pt \\ d (g (P_{1})) = \begin{matrix} arg & \min_{d (g (P_{1})) \in F} \end{matrix} L^{F} (g (P_{1})) \\ d (g (P_{2})) = \begin{matrix} arg & \min_{d (g (P_{2})) \in F} \end{matrix} L_{d (g (P_{1}))}^{F} (g (P_{2})) \\ \dots \\ d (g (P_{n})) = \begin{matrix} arg & \min_{d g (p_{1}) \in F} \end{matrix} L_{d (g (p_{1})) \oplus . . . \oplus d (g (p_{n} ...} \end{matrix}

…”

Section: Optimal Formation Controlmentioning

confidence: 99%

“…In this paper, we extend the distributed formation control methods in [5, 6] and non‐grid avoidance methods to a class of MOMASs consisting of high‐order UAV models and first‐order robot models under directed switching topologies. Besides investigating the system stability, this paper also studies the optimisation for both formation control and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimally distributed formation control with obstacle avoidance for mixed‐order multi‐agent systems under switching topologies

Yan

Shi

Lim

et al. 2018

IET Control Theory & Applications

Self Cite

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“…The displacement of any two agents can always find two points (

d^{'}

and

d^{″}

Section: Problem Formulationmentioning

confidence: 99%

“…The scalable 2D formation can be defined as follows: Definition 3 For any

d_{i}, d_{j} \in F

and

d_{i}, d_{j} \in R^{2 \times 1}

, if there exists

a \in Z^{t \times 1}

, such that

d_{i} - d_{j} = D a

where

D = false[d_{1}, \dots, d_{i}, \dots, d_{t} false], D \in R^{2 \times t}

is the relative distance matrix of a desired formation [6], then we call

F = (][, . . ., d_{- 1}, d_{0}, d_{1}, \dots)

a scalable 2D reference formation based on relative distance D . Assumption 1 The formation of the quadrotor is on a plane with a fixed height.…”

Section: Optimal Formation Controlmentioning

confidence: 99%

“…

M false(P false)

is the mapping matrix to transform the reference formation F to the map formation

d false(P false)

, we have

d false(P false) = M false(P false) F

. According to [6], we introduce the coordination protocol of the formation mapping. Protocol 1 For MAS (4), (7) is used to map P to

d false(P false)

based on reference formation F

\begin{matrix} left1em4pt \\ d (g (P_{1})) = \begin{matrix} arg & \min_{d (g (P_{1})) \in F} \end{matrix} L^{F} (g (P_{1})) \\ d (g (P_{2})) = \begin{matrix} arg & \min_{d (g (P_{2})) \in F} \end{matrix} L_{d (g (P_{1}))}^{F} (g (P_{2})) \\ \dots \\ d (g (P_{n})) = \begin{matrix} arg & \min_{d g (p_{1}) \in F} \end{matrix} L_{d (g (p_{1})) \oplus . . . \oplus d (g (p_{n} ...} \end{matrix}

…”

Section: Optimal Formation Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimally distributed formation control with obstacle avoidance for mixed‐order multi‐agent systems under switching topologies

Yan

Shi

Lim

et al. 2018

IET Control Theory & Applications

Self Cite

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“…Intensive study of multiagent system cooperation in recent years has witnessed its value in numerous applications including robotic surveillance, underwater exploration, and target tracking and surrounding . As a basic phenomena of distributed cooperation, consensus has been a focus for a long time.…”

Section: Introductionmentioning

confidence: 99%

Distributed control of uncertain multiagent systems for tracking a leader with unknown fractional‐order dynamics

Shi

Qin

Liang

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper proposes distributed adaptive cooperative control algorithms for second‐order agents to track a leader with unknown dynamics. The models of the followers and the leader are composed of uncertain nonlinear components. The order of the leader's dynamics is unknown and can be fractional. Only the single output information is shared among neighbored agents. To simplify the control design, linearly parameterized neural networks are used to approximate the unknown functions. We first present an adaptive control for leaderless consensus and then extend the method to the tracking problem. Thorough theoretical proofs as well as numerical simulation are included to verify the results. Compared with relevant literature, the new approach applies to a larger variety of systems because (i) knowledge about the structure of leader's model is unnecessary; (ii) the unknown functions in different agents' dynamics can be diverse and arbitrary, in other words, the algorithms apply to heterogeneous agents; (iii) the results can be simply used without parameter calculations.

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Robust cooperative tracking of multiagent systems with asymmetric saturation actuator via output feedback

Qian,

Wang,

et al. 2023

Intl J Robust & Nonlinear

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The robust tracking control problem of the leader‐follower multiagent systems affected by asymmetric input saturation and external disturbances is addressed in this article, by following three steps. First, an radial basis function neural network (RBFNN) is developed to estimate the external disturbances and supersaturation, where the supersaturation is induced by asymmetric saturation actuator. Second, combining with the developed radial basis function neural network, a reduced‐order observer‐based static protocol and a reduced‐order observer‐based adaptive one are designed for leader‐follower systems with un‐directed communication topology as well as those with directed communication topology, respectively. Third, some mild premises are given to guarantee the semiglobal robust leader‐follower consensus for the above mentioned two kinds of multiagent systems. Finally, the theoretical results are verified by several numerical simulations.

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Scalable formation control in stealth with limited sensing range

Cited by 16 publications

References 27 publications

Optimally distributed formation control with obstacle avoidance for mixed‐order multi‐agent systems under switching topologies

Optimally distributed formation control with obstacle avoidance for mixed‐order multi‐agent systems under switching topologies

Distributed control of uncertain multiagent systems for tracking a leader with unknown fractional‐order dynamics

Robust cooperative tracking of multiagent systems with asymmetric saturation actuator via output feedback

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