An autonomous approach and landing（A＆L） guidance law is presented in this paper for landing an unpowered reusable launch vehicle（RLV） at the designated runway touchdown. Considering the full nonlinear point-mass ...An autonomous approach and landing（A＆L） guidance law is presented in this paper for landing an unpowered reusable launch vehicle（RLV） at the designated runway touchdown. Considering the full nonlinear point-mass dynamics, a guidance scheme is developed in threedimensional space. In order to guarantee a successful A＆L movement, the multiple sliding surfaces guidance（MSSG） technique is applied to derive the closed-loop guidance law, which stems from higher order sliding mode control theory and has advantage in the finite time reaching property.The global stability of the proposed guidance approach is proved by the Lyapunov-based method.The designed guidance law can generate new trajectories on-line without any specific requirement on off-line analysis except for the information on the boundary conditions of the A＆L phase and instantaneous states of the RLV. Therefore, the designed guidance law is flexible enough to target different touchdown points on the runway and is capable of dealing with large initial condition errors resulted from the previous flight phase. Finally, simulation results show the effectiveness of the proposed guidance law in different scenarios.展开更多
Real-time guidance is critical for the vertical recovery of rockets.However,traditional sequential convex optimization algorithms suffer from shortcomings in terms of their poor real-time performance.This work focuses...Real-time guidance is critical for the vertical recovery of rockets.However,traditional sequential convex optimization algorithms suffer from shortcomings in terms of their poor real-time performance.This work focuses on applying the deep learning-based closedloop guidance algorithm and error propagation analysis for powered landing,thereby significantly improving the real-time performance.First,a controller consisting of two deep neural networks is constructed to map the thrust direction and magnitude of the rocket according to the state variables.Thereafter,the analytical transition relationships between different uncertainty sources and the state propagation error in a single guidance period are analyzed by adopting linear covariance analysis.Finally,the accuracy of the proposed methods is verified via a comparison with the indirect method and Monte Carlo simulations.Compared with the traditional sequential convex optimization algorithm,our method reduces the computation time from 75 ms to less than 1 ms.Therefore,it shows potential for online applications.展开更多
基金co-supported by the National Natural Science Foundation of China (Nos. 51407011, 11372034, 11572035)
文摘An autonomous approach and landing（A＆L） guidance law is presented in this paper for landing an unpowered reusable launch vehicle（RLV） at the designated runway touchdown. Considering the full nonlinear point-mass dynamics, a guidance scheme is developed in threedimensional space. In order to guarantee a successful A＆L movement, the multiple sliding surfaces guidance（MSSG） technique is applied to derive the closed-loop guidance law, which stems from higher order sliding mode control theory and has advantage in the finite time reaching property.The global stability of the proposed guidance approach is proved by the Lyapunov-based method.The designed guidance law can generate new trajectories on-line without any specific requirement on off-line analysis except for the information on the boundary conditions of the A＆L phase and instantaneous states of the RLV. Therefore, the designed guidance law is flexible enough to target different touchdown points on the runway and is capable of dealing with large initial condition errors resulted from the previous flight phase. Finally, simulation results show the effectiveness of the proposed guidance law in different scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.11822205 and 11772167).
文摘Real-time guidance is critical for the vertical recovery of rockets.However,traditional sequential convex optimization algorithms suffer from shortcomings in terms of their poor real-time performance.This work focuses on applying the deep learning-based closedloop guidance algorithm and error propagation analysis for powered landing,thereby significantly improving the real-time performance.First,a controller consisting of two deep neural networks is constructed to map the thrust direction and magnitude of the rocket according to the state variables.Thereafter,the analytical transition relationships between different uncertainty sources and the state propagation error in a single guidance period are analyzed by adopting linear covariance analysis.Finally,the accuracy of the proposed methods is verified via a comparison with the indirect method and Monte Carlo simulations.Compared with the traditional sequential convex optimization algorithm,our method reduces the computation time from 75 ms to less than 1 ms.Therefore,it shows potential for online applications.
