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High-efficiency reduced space sequential convex programming using low-complexity matrix inversion
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DOI：10.1016/j.ast.2025.109958.png)
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In response to the increasing demand for real-time optimization in aerospace systems, reduced space sequential convex programming (rSCP) has been developed to improve the computational efficiency of conventional sequential convex programming by explicitly eliminating state variables and state equations from the optimization formulation. However, as the number of discretization nodes increases, this computational benefit diminishes due to the significantly increased computational burden of matrix inversions. To address this challenge, this study proposes three efficiency-enhanced rSCP methods utilizing low-complexity matrix inversion techniques. The first method employs an explicit discretization scheme to achieve closed-form matrix inversions. The second leverages inexact Jacobian information to circumvent the repeated computation of time-consuming matrix inversions in each iteration. The third transforms a single large-scale matrix inversion into multiple smaller-scale ones and their multiplications to reduce computational complexity. Numerical experiments of a minimum-fuel rocket landing problem and a maximum-velocity launch ascent problem are conducted to validate the performance and superiority of the proposed methods.
Keyword:
Trajectory optimization
Reduced space sequential convex programming
Low-complexity matrix inversion
期刊
IF:
5.8
论文数:
1.0W
被引数:
3.0W
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