2022
DOI: 10.1049/cth2.12342
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Finite‐time attitude consensus control for multiple rigid spacecraft based on distributed observers

Abstract: The problem of attitude consensus control is addressed for multiple rigid spacecraft under a fixed interaction network where only a subset of followers can obtain the leader's state information. To estimate the attitude and angular velocity of the leader, a group of distributed finite-time observers is constructed for each follower. Then fast nonsingular terminal sliding mode attitude controllers are proposed for followers based on the observed information. Rigorous proofs are presented to show the finite-time… Show more

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Cited by 4 publications
(2 citation statements)
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“…Mansourinasab [21] proposes distributed optimal attitude consensus control for single-integrator multi-rigid bodies with an undirected network, while simultaneously guaranteeing the connectivity preservation property for agents using a descent gradient algorithm. Qi [22] addresses the problem of attitude consensus control for multiple rigid spacecraft where only a subset of followers can obtain the leader's state information. In fact, with the increasing complexity of space missions, the attitude control of spacecraft formation system inevitably faces some optimization problems, such as fuel consumption, energy cost, and time optimization.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Mansourinasab [21] proposes distributed optimal attitude consensus control for single-integrator multi-rigid bodies with an undirected network, while simultaneously guaranteeing the connectivity preservation property for agents using a descent gradient algorithm. Qi [22] addresses the problem of attitude consensus control for multiple rigid spacecraft where only a subset of followers can obtain the leader's state information. In fact, with the increasing complexity of space missions, the attitude control of spacecraft formation system inevitably faces some optimization problems, such as fuel consumption, energy cost, and time optimization.…”
Section: Introductionmentioning
confidence: 99%
“…J 2 = diag(22,16,12) kg ⋅ m 2J 3 = diag (17, 14, 1.2) kg ⋅ m 2 J 4 = diag (15, 13, 8.0) kg ⋅ m 2FIGURE 2…”
mentioning
confidence: 99%