Improved sequential convex programming based on pseudospectral discretization for entry trajectory optimization
•The improved sequential convex programming algorithm was established based on the pseudospectral discretization technique.•The combined use of trust region strategy and regularisation technique substantially improves the solution efficiency of the algorithm.•The proposed algorithm requires only 5 %...
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| Veröffentlicht in: | Aerospace science and technology Jg. 152; S. 109349 |
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| Sprache: | Englisch |
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Elsevier Masson SAS
01.09.2024
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| ISSN: | 1270-9638 |
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| Abstract | •The improved sequential convex programming algorithm was established based on the pseudospectral discretization technique.•The combined use of trust region strategy and regularisation technique substantially improves the solution efficiency of the algorithm.•The proposed algorithm requires only 5 % of the GPOPS in CPU time when solving the general entry trajectory optimization problem.
Sequential convex programming (SCP) is widely used to solve entry trajectory optimization problems. However, challenges persist in scenarios with strict constraints, such as unsolvable convex subproblems, iterative solution oscillations, and slow convergence rates. This study introduces an enhanced SCP algorithm designed to address these limitations. First, hp Radau pseudospectral discretization is used instead of trapezoidal discretization to improve the efficiency in solving subproblems while maintaining discretization accuracy. Second, the trust region is adaptively updated on the basis of trajectory information during iterations. Additionally, constraint relaxation and virtual control are introduced to facilitate smooth iteration at the initial stage. The regularization technique is also utilized to improve the convergence rates. Finally, the proposed algorithm is validated through two examples: maximum-terminal-velocity entry and maximum-terminal-longitude entry. The results show that these two problems cannot be effectively solved using the basic SCP algorithm. However, the proposed algorithm, along with the trust-region SCP algorithm and GPOPS, can solve them efficiently. With comparable accuracy in the obtained solutions, the algorithm proposed in this paper requires only half the CPU time of the trust-region SCP algorithm and just 5 % of the computing time of the general-purpose open-source solver GPOPS. |
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| AbstractList | •The improved sequential convex programming algorithm was established based on the pseudospectral discretization technique.•The combined use of trust region strategy and regularisation technique substantially improves the solution efficiency of the algorithm.•The proposed algorithm requires only 5 % of the GPOPS in CPU time when solving the general entry trajectory optimization problem.
Sequential convex programming (SCP) is widely used to solve entry trajectory optimization problems. However, challenges persist in scenarios with strict constraints, such as unsolvable convex subproblems, iterative solution oscillations, and slow convergence rates. This study introduces an enhanced SCP algorithm designed to address these limitations. First, hp Radau pseudospectral discretization is used instead of trapezoidal discretization to improve the efficiency in solving subproblems while maintaining discretization accuracy. Second, the trust region is adaptively updated on the basis of trajectory information during iterations. Additionally, constraint relaxation and virtual control are introduced to facilitate smooth iteration at the initial stage. The regularization technique is also utilized to improve the convergence rates. Finally, the proposed algorithm is validated through two examples: maximum-terminal-velocity entry and maximum-terminal-longitude entry. The results show that these two problems cannot be effectively solved using the basic SCP algorithm. However, the proposed algorithm, along with the trust-region SCP algorithm and GPOPS, can solve them efficiently. With comparable accuracy in the obtained solutions, the algorithm proposed in this paper requires only half the CPU time of the trust-region SCP algorithm and just 5 % of the computing time of the general-purpose open-source solver GPOPS. |
| ArticleNumber | 109349 |
| Author | Ma, Shoudong Wu, Changju Chen, Weifang Yang, Yuxin Tong, Zheyu Yang, Hua |
| Author_xml | – sequence: 1 givenname: Shoudong orcidid: 0000-0002-8374-9943 surname: Ma fullname: Ma, Shoudong – sequence: 2 givenname: Yuxin surname: Yang fullname: Yang, Yuxin – sequence: 3 givenname: Zheyu surname: Tong fullname: Tong, Zheyu – sequence: 4 givenname: Hua surname: Yang fullname: Yang, Hua email: yhsaa@zju.edu.cn – sequence: 5 givenname: Changju surname: Wu fullname: Wu, Changju – sequence: 6 givenname: Weifang surname: Chen fullname: Chen, Weifang |
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| Keywords | Optimization efficiency Sequential convex programming Entry Trajectory optimization |
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| Title | Improved sequential convex programming based on pseudospectral discretization for entry trajectory optimization |
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