论文标题
基于多项式混乱的扩展以进行轨迹优化的强大约束多目标进化算法
Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization
论文作者
论文摘要
提出了一种基于约束多目标进化算法（MOEA）和非侵入性多项式混乱扩展（PCE）的集成优化方法，该方法提出了解决时间序列动力学下强大的多目标优化问题。这些问题中的约束很难解决，不仅是因为动态约束的数量乘以离散的时间步骤，而且还因为每个步骤都是概率的。所提出的方法通过PCE将强大的公式重写为确定性问题，然后顺序处理MOEA中人群生成，轨迹产生和评估中生成的约束。作为一个案例研究，优化了具有风不确定性的超音速运输（SST）的着陆轨迹设计。结果证明了约束值比优化的解决方案集和相应的轨迹的定量影响，提出了强大的飞行控制。
An integrated optimization method based on the constrained multi-objective evolutionary algorithm (MOEA) and non-intrusive polynomial chaos expansion (PCE) is proposed, which solves robust multi-objective optimization problems under time-series dynamics. The constraints in such problems are difficult to handle, not only because the number of the dynamic constraints is multiplied by the discretized time steps but also because each of them is probabilistic. The proposed method rewrites a robust formulation into a deterministic problem via the PCE, and then sequentially processes the generated constraints in population generation, trajectory generation, and evaluation by the MOEA. As a case study, the landing trajectory design of supersonic transport (SST) with wind uncertainty is optimized. Results demonstrate the quantitative influence of the constraint values over the optimized solution sets and corresponding trajectories, proposing robust flight controls.
