Trajectory planning for satellite cluster reconfigurations with sequential convex programming method

Wang, Lixiang; Ye, Dong; Xiao, Yan; Kong, Xiangbin

doi:10.1016/j.ast.2023.108216

Cited by 7 publications

(1 citation statement)

References 48 publications

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“…Moreover, the primal-dual interior point method can be adopted to solve the convex problem without the initial guess value. Motivated by these preponderances, the convex-optimization-based methods have been applied to different aerospace problems, such as planetary reentry trajectory optimization [9], ascent trajectory optimization [10,11], Mars-landing trajectory planning [12,13], low-thrust orbit transfer [14], spacecraft rendezvous and proximity operations [15,16], and trajectory planning for satellite cluster reconfigurations [17].…”

Section: Introductionmentioning

confidence: 99%

A Reentry Trajectory Planning Algorithm via Pseudo-Spectral Convexification and Method of Multipliers

Liang,

Luo,

Che

et al. 2024

Mathematics

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The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem, and it constitutes a critical challenge within the field of aerospace engineering. In this paper, an improved sequential convexification algorithm is proposed to solve it and achieve online trajectory planning. In the proposed algorithm, the Chebyshev pseudo-spectral method with high-accuracy approximation performance is first employed to discretize the continuous dynamic equations. Subsequently, based on the multipliers and linearization methods, the original nonconvex trajectory planning problem is transformed into a series of relaxed convex subproblems in the form of an augmented Lagrange function. Then, the interior point method is utilized to iteratively solve the relaxed convex subproblem until the expected convergence precision is achieved. The convex-optimization-based and multipliers methods guarantee the promotion of fast convergence precision, making it suitable for online trajectory planning applications. Finally, numerical simulations are conducted to verify the performance of the proposed algorithm. The simulation results show that the algorithm possesses better convergence performance, and the solution time can reach the level of seconds, which is more than 97% less than nonlinear programming algorithms, such as the sequential quadratic programming algorithm.

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