A Computationally Inexpensive Optimal Guidance via Radial-Basis-Function Neural Network for Autonomous Soft Landing on Asteroids | Zendy

Peng Zhang | Zendy; Keping Liu | Zendy; Bo Zhao | Zendy; Yuanchun Li | Zendy

Open Access

A Computationally Inexpensive Optimal Guidance via Radial-Basis-Function Neural Network for Autonomous Soft Landing on Asteroids

Author(s) -

Peng Zhang,

Keping Liu,

Bo Zhao,

Yuanchun Li

Publication year - 2015

Publication title -

plos one

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.99

H-Index - 332

ISSN - 1932-6203

DOI - 10.1371/journal.pone.0137792

Subject(s) - soft landing , trajectory , computer science , artificial neural network , trajectory optimization , monte carlo method , function (biology) , radial basis function , task (project management) , artificial intelligence , mathematical optimization , mathematics , physics , engineering , statistics , systems engineering , astronomy , evolutionary biology , biology , economics , macroeconomics

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.

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