title: A Computationally Inexpensive Optimal Guidance via Radial-Basis-Function Neural Network for Autonomous Soft Landing on Asteroids.
abstract: Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.
authors.affiliation: Department of Control Science and Engineering, Jilin University, Changchun, Jilin, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Department of Control Engineering, Changchun University of Technology, Changchun, Jilin, China.
authors.forename: Keping
authors.initials: K
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Department of Control Science and Engineering, Jilin University, Changchun, Jilin, China; Department of Control Engineering, Changchun University of Technology, Changchun, Jilin, China.
authors.forename: Yuanchun
authors.initials: Y
authors.lastname: Li
mesh_terms: D003198:Computer Simulation; D018543:Minor Planets; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013026:Space Flight