Recently, sequential convex programming (SCP) has become a potential approach in trajectory optimization because of its high efficiency. To improve stability and discretization accuracy, a three-stage SCP approach based on the -adaptive Radau pseudospectral discretization is proposed in this paper. In most instances, the initial subproblem may risk infeasibility due to the undesignated initial guess. Therefore, we design a constraint relaxation stage for the SCP to enhance the feasibility of the subproblem as much as possible. Once the subproblem can be directly solved, the iteration enters the second stage, during which a mesh refinement algorithm based on discretization error analysis is utilized to decrease the discretization error to the tolerance. In the final stage, the damping term is introduced into the objective of the subproblem to suppress the oscillation of the solution and accelerate the convergence. A dual-channel control reentry trajectory optimization and an ascent trajectory optimization are taken as examples, and the simulation results show that the proposed approach outperforms conventional SCP approaches in terms of accuracy and efficiency.

中文翻译：

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用于轨迹优化的三阶段顺序凸规划方法


最近，顺序凸规划（SCP）因其高效率而成为轨迹优化的潜在方法。为了提高稳定性和离散化精度，本文提出了一种基于自适应Radau伪谱离散化的三阶段SCP方法。在大多数情况下，由于未指定的初始猜测，初始子问题可能面临不可行的风险。因此，我们为SCP设计了一个约束松弛阶段，以尽可能增强子问题的可行性。一旦子问题可以直接求解，迭代就进入第二阶段，在此阶段利用基于离散误差分析的网格细化算法将离散误差减小到容许范围内。最后阶段，在子问题的目标中引入阻尼项，以抑制解的振荡并加速收敛。以双通道控制再入轨迹优化和上升轨迹优化为例，仿真结果表明，该方法在精度和效率方面优于传统的SCP方法。