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Convergence Acceleration of Direct Trajectory Optimization Using Novel Hessian Calculation Methods

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Listed:
  • N. Yokoyama

    (National Defence Academy of Japan)

  • S. Suzuki

    (University of Tokyo)

  • T. Tsuchiya

    (University of Tokyo)

Abstract

Sparse sequential quadratic programming (SQP) has offered fast and robust convergence of trajectory optimization based on direct collocation. However, the conventional approach of calculating the Hessian of the Lagrangian is sometimes inefficient in view of the computational time. Therefore, this paper proposes two novel Hessian calculation methods that exploit the doubly-bordered block diagonal structure of the Hessian. Through applications to the constrained brachistochrone problem and the space shuttle reentry problem, the proposed methods were demonstrated to show faster convergence speeds as compared with the conventional methods.

Suggested Citation

  • N. Yokoyama & S. Suzuki & T. Tsuchiya, 2008. "Convergence Acceleration of Direct Trajectory Optimization Using Novel Hessian Calculation Methods," Journal of Optimization Theory and Applications, Springer, vol. 136(3), pages 297-319, March.
    • Handle: RePEc:spr:joptap:v:136:y:2008:i:3:d:10.1007_s10957-008-9351-0
    DOI: 10.1007/s10957-008-9351-0
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    References listed on IDEAS

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    1. A. Miele & T. Wang, 2003. "Multiple-Subarc Gradient-Restoration Algorithm, Part 2: Application to a Multistage Launch Vehicle Design," Journal of Optimization Theory and Applications, Springer, vol. 116(1), pages 19-39, January.
    2. A. Miele & T. Wang, 2003. "Multiple-Subarc Gradient-Restoration Algorithm, Part 1: Algorithm Structure," Journal of Optimization Theory and Applications, Springer, vol. 116(1), pages 1-17, January.
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