Secure model predictive static programming with initial value generator for online computational guidance of near-space vehicles

•Compared to the traditional initial value generator, a variable coefficient near-optimal initial value generator is proposed to obtain better initial values for trajectory optimization problems. The primary objective of initial value generator is to swiftly provide an initial control guess that closely approximates the optimal trajectory, thereby preventing trajectory divergence in subsequent iterations.•Compared to the conventional and small-step local Euler discretization method for MPSP, a large-step global flipped-Radau pseudospectral method is developed for high efficiency discretization, and the original problem is directly optimized by performance index which improves online optimization efficiency.•Compared to the conventional MPSP method, an improved TR-MPSP method is proposed to ensure that the problem is solved within the feasible domain and constraints. Meanwhile, this approach guarantees the updated trajectory approximates reference trajectory, thereby significantly enhancing solution stability and optimization accuracy with the same number of iterations. For online trajectory programming of near-space vehicles with limited computation resources, conventional model predictive static programming approaches have two main challenges. Firstly, an inadequate initial control guess can lead to trajectory divergence or slow convergence, resulting in mission failure. Secondly, Euler discretization is a small-step local algorithm by point-to-point recursion. To ensure high precision solution, more discretization points are required, leading to low computation efficiency and accuracy; meanwhile conventional methods cannot guarantee that the problems are solved within the constraints and feasible domains, potentially affecting the solution stability. To solve the first problem, a variable coefficient near-optimal initial value generator is developed to provide an initial control guess that approximates the optimal trajectory, preventing divergence in subsequent iterations. To address the second problem, the trust-region constrained model predictive static programming is proposed with flipped-Radau pseudospectrum. This method reduces the number of discretization points and optimizes the performance index directly, thereby enhancing efficiency; meanwhile the trust region improves accuracy and ensures the updated trajectory remains close to the reference trajectory. Finally, the combination of above approaches enhances the calculating efficiency and precisi