Entry trajectory optimization for cross-domain morphing vehicles using oscillation-avoidance-based multistage trust-region sequential convex programming: Trajectory optimization for morphing vehicles by convex programming

This paper develops an Oscillation-avoidance-based Multistage Trust-region Sequential Convex Programming (OMTSCP) method for the highly nonlinear entry trajectory optimization problem of Cross-Domain Morphing Vehicles (CDMVs). The decoupling of states and controls for complex nonlinear dynamics is achieved by defining new control and state variables. A series of sub convex problems is formulated by successive linearization and discretization of the constraints. The proposed Trust-region Sequential Convex Programming (TSCP) scheme consists of three stages: an initial guess generation stage, a basic solution stage, and an optimal solution stage. An approach to penalize the dynamic relaxation is firstly developed to obtain an initial guess with considerable accuracy and significantly improve the robustness of the algorithm by overcoming the drawbacks of potential artificial infeasibility. The oscillation phenomenon of the TSCP method under rectangular trust region is then investigated, and a novel N-shape-based oscillation identification method is proposed to identify the oscillation accurately. Finally, an oscillation-avoidance method based on the sort trust-region is proposed to improve the convergence of the TSCP algorithm. Numerical comparisons of the proposed method and a typical TSCP method, as well as the morphing and fixed-morphing vehicles are provided to demonstrate the effectiveness and efficiency of the proposed method and the performance advantages of the morphing vehicle. The robustness of the method is further verified by Monte Carlo simulation.