2017
DOI: 10.1109/taes.2017.2671118
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Predictive Smooth Variable Structure Filter for Attitude Synchronization Estimation During Satellite Formation Flying

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Cited by 52 publications
(26 citation statements)
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“…In [4], a simplified linear motion was used to describe satellite dynamics, and a linear system control algorithm for satellite formation flying was introduced. In [5], a filter based on variable structure control concept was presented for satellite attitude synchronization. In [6], a relative position keeping problem in satellite formation flying was studied, and a composite nonlinear feedback controller was established by using an algebraic Riccati inequality and a low-and-high gain control strategy.…”
Section: Introductionmentioning
confidence: 99%
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“…In [4], a simplified linear motion was used to describe satellite dynamics, and a linear system control algorithm for satellite formation flying was introduced. In [5], a filter based on variable structure control concept was presented for satellite attitude synchronization. In [6], a relative position keeping problem in satellite formation flying was studied, and a composite nonlinear feedback controller was established by using an algebraic Riccati inequality and a low-and-high gain control strategy.…”
Section: Introductionmentioning
confidence: 99%
“…First, simplified dynamic models in [4] were used to describe the satellite motion, but it is not accurate enough in complex aerospace. The trajectory formation and attitude coordination problems were discussed separately in [5], [19]. Second, uncertainty rejection problems were ignored in satellite formation control problems reported in [11], [20], and limited types of uncertainties were discussed in stability analysis of the constructed global closed-loop control systems as shown in [12], [20].…”
Section: Introductionmentioning
confidence: 99%
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“…Whereas, a new range of uncertainties, parameter variations or noises with sufficient conditions and a lagrange multiplier made the idea successful for formation flying of satellite. [8] made the use of a novel predictive smooth variable structure filter (PSVSF) to accommodate the enhanced tracking.…”
Section: Introductionmentioning
confidence: 99%
“…According to Professor P. M. Frank , an authority in the field of fault diagnosis, fault diagnosis methods can be divided into three categories: analytical‐based, signal‐based, and knowledge‐based. The analytical‐based method is the oldest, most in‐depth, and most mature method, which applies mathematical models of the system, such as an observer‐based or filter‐based method . This method is also termed the model‐based method, and it can overcome the limitations of additional costs and weights that result from hardware redundancy .…”
Section: Introductionmentioning
confidence: 99%