Vol.33, No.1, 2022, pp.291-303, doi:10.32604/iasc.2022.024252
OPEN ACCESS
ARTICLE
The Intelligent Trajectory Optimization of Multistage Rocket with Gauss Pseudo-Spectral Method
  • Lihua Zhu1,*, Yu Wang1, Zhiqiang Wu1, Cheire Cheng2
1 School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
2 Department of Electrical and Electronic Engineering, Colorado State University, Colorado, United States
* Corresponding Author: Lihua Zhu. Email:
Received 11 October 2021; Accepted 23 November 2021; Issue published 05 January 2022
Abstract
The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. In this paper, the trajectory optimization of a three-stage launch vehicle in the powering phase subject to the sun-synchronous orbit is considered. To solve the optimal control problem, the Gauss pseudo-spectral method (GPM) is used to transform the optimization model to a nonlinear programming (NLP) problem and sequential quadratic programming is applied to find the optimal solution. However, the sensitivity of the initial guess may cost the solver significant time to do the Newton iteration or even lead to the local minimum. Aiming at this issue, a good initial guess generated by combination of harmony search and GPM is introduced to help the optimizer to faster and better solve the sun-synchronous orbit (SSO) trajectory optimization. Comparative simulation tests are conducted with the proposed algorithm and popular approaches, the results indicate that with the optimized initial guess, the proposed method could achieve better performance in terms of convergence ability and convergence rate.
Keywords
Trajectory optimization; sun-synchronous orbit; gauss pseudo-spectral method; harmony search; initial guess optimization
Cite This Article
L. Zhu, Y. Wang, Z. Wu and C. Cheng, "The intelligent trajectory optimization of multistage rocket with gauss pseudo-spectral method," Intelligent Automation & Soft Computing, vol. 33, no.1, pp. 291–303, 2022.
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