Abstract:

Under complete terminal state constraints, the reentry gliding trajectory planning of suborbital reusable aircraft is short of convergence and accuracy. A high-precision solution algorithm combining sequential convex optimization and adaptive mesh refinement based homotopic constrained model predictive convex programming is proposed to achieve high-precision and reliable convergence in the reentry trajectory planning process under the conditions of weak initial value dependency. Firstly, several terminal constraints are relaxed, and the trust region is utilized as a soft constraint during the convex optimization process. An augmented index function is defined to reflect the linearization error and the degree of constraint violation. A multi-dimensional parameter search method is proposed to update the state and control variables separately, avoiding the situation of no solution in the basic line search. Further, constraint relaxation terms are selected as homotopy parameters, the discrete mesh is adjusted adaptively based on evaluation of model prediction deviation, and a static convex programming problem model is constructed considering multi-constraints. Then, the control adjustment quantity in the entire process is homotopically optimized to achieve high-precision correction of all-element trajectory state variables and improve algorithm convergence. Finally, the effectiveness of the proposed method is verified through numerical simulation.

Key words: reentry trajectory planning, complete constraints, convex optimization, model prediction correction, adaptive mesh refinement