Improved Sequential Convex Programming Algorithms for Entry Trajectory Optimization

Entry trajectory optimization for hypersonic vehicles has been formulated as constrained optimal control problems, which are difficult to solve because of the existing high nonlinearity and nonconvexity. In recent years, convex optimization has shown promise for real-time onboard applications with the state-of-the-art interior-point methods. In this paper, line-search and trust-region techniques are introduced to fundamentally improve the performance of the sequential convex programming method for entry trajectory optimization. Two improved algorithms are developed: line-search sequential convex programming and trust-region sequential convex programming. In addition, a new trajectory generation method is proposed by taking advantage of the predictor–corrector method to find an initial 3-D trajectory for the developed successive algorithms. As such, the convergence of the solution process is improved. To demonstrate the effectiveness and performance of the newly proposed algorithms, numerical simulations are presented for maximum-terminal-velocity and minimum-heat-load entry problems. Results show that these two problems are not well solved using the basic sequential convex programming algorithm. However, they can be efficiently solved by the two improved algorithms in a few seconds in MATLAB, showing a significant improvement over general-purpose solvers, such as GPOPS.