2021
DOI: 10.1002/rnc.5766
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Abstract: This article proposes a neural-network-based adaptive finite-time output constraint control scheme for attitude stabilization of rigid spacecrafts. First, a novel singularity-free terminal sliding mode variable is constructed and an auxiliary function is developed in the controller design to avoid the singularity problem.Then, an adaptive neural control law is designed to approximate the lumped uncertainty of spacecraft system including inertia uncertainties and external disturbances. Furthermore, a novel fini… Show more

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Cited by 14 publications
(8 citation statements)
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References 33 publications
(28 reference statements)
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“…Theorem 1 Considering the robotic manipulator system (1) under Assumptions 1-3, we construct the controller in ( 38) and (44) with auxiliary system (43) and adaptive law (45), if the parameters k 1 , k 2 , k 3 , κ 1 , Γ, δ > 0 are appropriately chosen, then all signals of the closed-loop system are bounded and the transient performance of tracking error e 1 (t) is maintained within the predefined boundary (20).…”
Section: Stability Analysismentioning
confidence: 99%
See 3 more Smart Citations
“…Theorem 1 Considering the robotic manipulator system (1) under Assumptions 1-3, we construct the controller in ( 38) and (44) with auxiliary system (43) and adaptive law (45), if the parameters k 1 , k 2 , k 3 , κ 1 , Γ, δ > 0 are appropriately chosen, then all signals of the closed-loop system are bounded and the transient performance of tracking error e 1 (t) is maintained within the predefined boundary (20).…”
Section: Stability Analysismentioning
confidence: 99%
“…In [22], for the small unmanned aerial vehicles, the output tracking errors converged to prescribed regions within a predefined time. Although results [20][21][22] can achieve prescribed small region within a predefined time, the exact initial condition of the tracking error must be known in advance. How to realize this property globally is still open.…”
Section: Introductionmentioning
confidence: 99%
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“…[1][2][3] Over the past few decades, adaptive control has been greatly advanced in both theories and practical applications. It can be divided into two categories according to the way of parameter updating, that is, the directed method 4,5 and the indirected method, 6,7 and also has been combined with other advanced tools to fulfill different control objectives, such as fuzzy rules, [8][9][10] neural networks, 11,12 and optimization algorithms 13 and so forth. Adaptive control has been widely applied to many areas, including but not limited to aerospaces, 14 smart grids, 15 and robot manipulators.…”
Section: Introductionmentioning
confidence: 99%