Solving Trajectory Optimization Problems in the Presence of Probabilistic Constraints

Runqi Chai, Al Savvaris, Antonios Tsourdos, Senchun Chai, Yuanqing Xia, Shuo Wang

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33 引用 (Scopus)

摘要

The objective of this paper is to present an approximation-based strategy for solving the problem of nonlinear trajectory optimization with the consideration of probabilistic constraints. The proposed method defines a smooth and differentiable function to replace probabilistic constraints by the deterministic ones, thereby converting the chance-constrained trajectory optimization model into a parametric nonlinear programming model. In addition, it is proved that the approximation function and the corresponding approximation set will converge to that of the original problem. Furthermore, the optimal solution of the approximated model is ensured to converge to the optimal solution of the original problem. Numerical results, obtained from a new chance-constrained space vehicle trajectory optimization model and a 3-D unmanned vehicle trajectory smoothing problem, verify the feasibility and effectiveness of the proposed approach. Comparative studies were also carried out to show the proposed design can yield good performance and outperform other typical chance-constrained optimization techniques investigated in this paper.

源语言英语
文章编号8637163
页(从-至)4332-4345
页数14
期刊IEEE Transactions on Cybernetics
50
10
DOI
出版状态已出版 - 10月 2020
已对外发布

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