A fast matrix generation method based on correlations is proposed for the pseudospectral method (PSM) to solve the entry trajectory optimization problem. First, the optimization problem is analyzed to obtain some assumptions of performance indicators and constraints. Then, the chain rule and correlations among the variables are used to simplify the gradient calculations, resulting in a fast generation method for the gradient vector. Meanwhile, based on the previous assumptions, a fast generation method for the Jacobi matrix is derived semi-analytically using the correlation information among the variables. Finally, the trajectory optimization problem is studied using a typical aircraft. The results are verified via comparisons with the well-known GPOPS II software. Furthermore, the superiority of the proposed method in terms of the computational efficiency is demonstrated by comparing the relevant time indexes. The performance of the proposed method in calculating the Jacobian matrix and gradient matches that of the GPOPS, combined with a relatively short solution time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A fast matrix generation method for solving entry trajectory optimization problems under the pseudospectral method


    Contributors:


    Publication date :

    2022-09-01


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Rapid trajectory optimization for hypersonic entry using convex optimization and pseudospectral method

    Wang, Jinbo / Cui, Naigang / Wei, Changzhu | Emerald Group Publishing | 2019



    Re-entry trajectory optimization using an hp-adaptive Radau pseudospectral method

    Han, Peng / Shan, Jiayuan / Meng, Xiuyun | SAGE Publications | 2013


    Improved Gauss Pseudospectral Method for Mars Entry Trajectory Planning

    Li, Shuang / Liu, Xu / Jiang, Xiu-qiang et al. | Springer Verlag | 2024