Efficient Convex Optimization of Reentry Trajectory via the Chebyshev Pseudospectral Method

Abstract

A novel sequential convex (SCvx) optimization scheme via the Chebyshev pseudospectral method is proposed for efficiently solving the hypersonic reentry trajectory optimization problem which is highly constrained by heat flux, dynamic pressure, normal load, and multiple no-fly zones. The Chebyshev-Gauss Legend (CGL) node points are used to transcribe the original dynamic constraint into algebraic equality constraint; therefore, a nonlinear programming (NLP) problem is concave and time-consuming to be solved. The iterative linearization and convexification techniques are proposed to convert the concave constraints into convex constraints; therefore, a sequential convex programming problem can be efficiently solved by convex algorithms. Numerical results and a comparison study reveal that the proposed method is efficient and effective to solve the problem of reentry trajectory optimization with multiple constraints.