In this paper, a real-time reentry guidance law for hypersonic vehicles is presented to accomplish rapid, high-precision, robust, and reliable reentry flights by leveraging the Time to Vector (Time2vec) and transformer networks. First, referring to the traditional predictor–corrector algorithm and quasi-equilibrium glide condition (QEGC), the reentry guidance issue is described as a univariate root-finding problem based on bank angle. Second, considering that reentry guidance is a sequential decision-making process, and its data has inherent characteristics in time series, so the Time2vec and transformer networks are trained to obtain the mapping relation between the flight states and bank angles, and the inputs and outputs are specially designed to guarantee that the constraints can be well satisfied. Based on the Time2vec and transformer-based bank angle predictor, an efficient and precise reentry guidance approach is proposed to realize on-line trajectory planning. Simulations and analysis are carried out through comparison with the traditional predictor-corrector algorithm, and the results manifest that the developed Time2vec and transformer-based reentry guidance algorithm has remarkable improvements in accuracy and efficiency under initial state errors and aerodynamic parameter perturbations.

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Time cooperation guidance is a key technology which can greatly increase the success rate of flight missions. However, it is difficult to satisfy all the strict constraints when designing the guidance system for multiple hypersonic vehicles. To solve these problems, a time cooperation framework is proposed. In this paper, the longitudinal predictor–corrector guidance law is firstly applied to meet the terminal and path constraints simultaneously. To settle the inaccurate estimation problem of residual flight time, a long short-term memory network (LSTM network) is trained and adopted in a time decision module, whose inputs are selected as six-dimensional feature vectors combined with the features of the sequential ballistics. In the time control module, the traditional artificial potential field method is modified to handle the no-fly zone constraints problem. Furthermore, the time potential field as a new type of potential field is added to indirectly control the flight time of hypersonic vehicles. The final simulation results show that the novel time potential field is compatible with the traditional potential field, which can satisfy the no-fly zone and flight time constraints at the same time. Meanwhile, compared with other time cooperative guidance, the algorithm proposed in this paper performs better in terms of time adjustable range.