Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning
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Space object observation requirements and the avoidance of specific attitudes produce pointing constraints that increase the complexity of the attitude maneuver path-planning problem. To deal with this issue, a feasible attitude trajectory generation method is proposed that utilizes a multiresolution technique and local attitude node adjustment to obtain sufficient time and quaternion nodes to satisfy the pointing constraints. These nodes are further used to calculate the continuous attitude trajectory based on quaternion polynomial interpolation and the inverse dynamics method. Then, the characteristic parameters of these nodes are extracted to transform the path-planning problem into a parameter optimization problem aimed at minimizing energy consumption. This problem is solved by an improved hierarchical optimization algorithm, in which an adaptive parameter-tuning mechanism is introduced to improve the performance of the original algorithm. A numerical simulation is performed, and the results confirm the feasibility and effectiveness of the proposed method.
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Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning
),
Abstract
Space object observation requirements and the avoidance of specific attitudes produce pointing constraints that increase the complexity of the attitude maneuver path-planning problem. To deal with this issue, a feasible attitude trajectory generation method is proposed that utilizes a multiresolution technique and local attitude node adjustment to obtain sufficient time and quaternion nodes to satisfy the pointing constraints. These nodes are further used to calculate the continuous attitude trajectory based on quaternion polynomial interpolation and the inverse dynamics method. Then, the characteristic parameters of these nodes are extracted to transform the path-planning problem into a parameter optimization problem aimed at minimizing energy consumption. This problem is solved by an improved hierarchical optimization algorithm, in which an adaptive parameter-tuning mechanism is introduced to improve the performance of the original algorithm. A numerical simulation is performed, and the results confirm the feasibility and effectiveness of the proposed method.
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (No. 11572019).
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