Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization

Takubo, Yuji; Kanazaki, Masahiro

doi:10.1109/cec55065.2022.9870365

Cited by 1 publication

(1 citation statement)

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“…Instead of using NLP-based methods that process constraints simultaneously, the constraints are processed sequentially, and the violations are evaluated by the Pareto ranking-based evolutionary algorithm. Additionally, extending the previous work by the authors [33], the benefit of the proposed method in the SST landing when encountering unexpected gusts of wind is demonstrated. The MC simulation validates the advantageous flight profile of the obtained robust control history against wind uncertainty, compared to the nominal solution.…”

Section: Introductionsupporting

confidence: 56%

Robust Constrained Multi-Objective Guidance of Supersonic Transport Landing Using Evolutionary Algorithm and Polynomial Chaos

Takubo,

Kanazaki

2023

Aerospace

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Landing of supersonic transport (SST) suffers from a large uncertainty due to its highly sensitive aerodynamic properties in the subsonic domain, as well as the wind gusts around runways. At the vehicle design stage, a landing trajectory optimization under wind uncertainty in a multi-objective solution space is desired to explore the possible trade-off in its key flight performance metrics. The proposed algorithm solves this robust constrained multi-objective optimal control problem by integrating non-intrusive polynomial chaos expansion into a constrained evolutionary algorithm. The computationally tractable optimization is made possible through the conversion of a probabilistic problem into an equivalent deterministic representation while maintaining a form of the multi-objective problem. The generated guidance trajectories achieve a significant reduction of the uncertainty in their terminal states with a marginal modification in the control history of the deterministic solutions, validating the importance of the consideration of robustness in trajectory optimization.

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Section: Introductionsupporting

confidence: 56%