This paper presents an improved two-phase sequential convex programming (SCP) algorithm for reentry trajectory optimization. The continuous-time optimal control problem is first discretized using the Chebyshev pseudospectral method (CPM). A conformal mapping is introduced to redistribute the Chebyshev–Gauss–Lobatto (CGL) nodes, which effectively mitigates the ill-conditioning of the differentiation matrix and enhances numerical stability. The influence of the trust-region radius on the descent behavior of the penalty function is then analyzed, leading to a two-phase optimization strategy. In phase Ⅰ, line-search is adopted to rapidly obtain a feasible solution, thereby avoiding convergence delays caused by prematurely large penalty coefficients. In phase Ⅱ, the step size is finely tuned using trust-region constraints, ensuring both convergence accuracy and improved robustness. Numerical simulations demonstrate that the proposed algorithm achieves superior performance in terms of convergence speed and solution accuracy, while effectively eliminating the numerical oscillations typically induced by excessive penalty coefficients in conventional penalty methods. The framework provides a reliable and efficient numerical approach for reentry trajectory optimization.

Aimed at improving the real-time performance of guidance instruction generation, an analytical hypersonic reentry guidance framework is presented. The key steps of the novel guidance framework are the parameterization of reentry guidance problems and the optimization of parameters. First, a quintic polynomial function of energy was designed to describe the altitude profile. Then, according to the altitude-energy profile, the altitude, velocity, flight path angle, and bank angle were obtained analytically, which naturally met the terminal constraints. In addition, the angle of the attack profile was determined using the velocity parameter. The swarm intelligent optimization algorithms were used to optimize the parameters. The path constraints were enforced by the penalty function method. Finally, extensive simulations were carried out in both nominal and dispersed cases, and the simulation results showed that the proposed guidance framework was effective, high-precision, and robust in different scenarios.

In view of the guidance problem of the hypersonic vehicles in the glide-reentry segment, an online guidance strategy based on improved particle swarm optimization and whale algorithm was proposed. By considering its constraint model and shift conditions, a process constraint and terminal constraint model were established. To shorten the trajectory generation time and reduce the calculation amount of the algorithm, a height-range flight profile that could automatically meet the constraints of terminal height, path angle, and range was designed. Based on the premise that the drag coefficient is a constant value, a numerical solution of velocity that satisfied the range constraint was derived. Based on the above model, a three-parameter optimization model was designed to realize the control of the terminal conditions. To optimize the process constraints, an improved whale algorithm combining the whale algorithm and particle swarm optimization algorithm was proposed to overcome the shortcoming of precocious convergence of the particle swarm optimization algorithm, improve the solution efficiency, and minimize the heat flow rate during the flight. Then, through the method of segmenting the trajectory, an online guidance method based on the mid-point was proposed to update the longitudinal profile online to meet the terminal constraints. The simulation results show that the proposed method can efficiently solve the optimal flight trajectory.