Research/ SPACE AI & AUTONOMY
Asteroid landing via onboard optimal guidance based on bidirectional extreme learning machine
Peng Zhang, Xiaosong Liu, Bo Zhao, Mujun Xie, Keping Liu, Yuanchun Li
Guidance, Dynamics & ControlOpenAlex topic: Guidance and Control Systems · Learning-based MethodsOpenAlex keyword: single hidden layer neural network
OpenAlexSpacecraft Dynamics and Control · Machine Learning and ELM · Guidance and Control Systems
In order to autonomously design the optimal descending trajectory for spacecraft soft landing on an asteroid, an onboard guidance based on the bidirectional extreme learning machine (B-ELM) is proposed. The optimization problem is formulated and transformed into a two-point boundary value problem (TPBVP). And then, based on the sample trajectories obtained off-line, a single-hidden layer feed-forward neural network (SLFN) trained by B-ELM is employed to design the optimal descending trajectory onboard. Finally, Monte Carlo simulations are performed to verify the effectiveness of the proposed guidance. Also, the learning process of the B-ELM is compared with traditional algorithms in simulations. Simulation results show that the guidance via the B-ELM trained SLFN meets the requirement of the soft landing in a lower learning and implementation cost.