Constraint-handling techniques for reusable launch vehicle reentry trajectory optimization using marine predator whale optimizer

Abstract

Reentry trajectory optimization for reusable launch vehicles (RLVs) is a class of optimal control problems with multiple highly nonlinear constraints. Nature-inspired algorithms (NIAs), which can somehow reduce the reliance on initial points, function differentiability, and convexity, with gradient-free nature and ease of implementation, have been actively applied in RLV reentry trajectory optimization problems. As NIAs are primarily designed for unconstrained optimization, constraint-handling techniques (CHTs), which play a crucial role in addressing RLV trajectory optimization issues and significantly impact the overall quality of the solutions, are necessary to guide the search towards feasible regions. However, the existing literature has not yet, or at least not systematically, investigated how well the current CHTs perform. Additionally, an in-depth analysis of parametric approaches and a performance evaluation framework is not yet available. To bridge this gap, this research constructs a benchmark model based on Space Shuttle reentry scenarios, investigates the effects of collocation type and interpolation method, and compares the performance of eight CHTs. An improved marine predator whale optimization algorithm is developed as a direct search engine, with results analyzed using the Wilcoxon signed rank and the Friedman tests. The results show that the ε-constrained technique and multi-objective-based CHTs with the algorithm, are somewhat superior in overall performance, and can produce relatively high-quality solutions over other competitors, while the optimization framework facilitates algorithm integration and CHTs for RLV reentry trajectory optimization problems.