Affiliation:
1. School of Engineering University of Southampton Southampton UK
2. Rolls‐Royce UTC in Manufacturing and On‐wing Technology University of Nottingham Nottingham UK
3. Department of Control Science and Engineering Harbin Institute of Technology Harbin China
Abstract
SummaryThis paper proposes a reinforcement learning‐based guidance law for Mars powered descent phase, which is an effective online calculation method that handles the nonlinearity caused by the mass variation and avoids collisions. The reinforcement learning method is designed to solve the constrained nonlinear optimization problem by using a critic neural network. Specifically, to cope with the position constraint (i.e., glide‐slope constraint) and the thrust force limit constraint, a modified cost function is proposed, and the associated Hamilton‐Jacobi‐Bellman equation is solved online without using an actor neural network, which significantly reduces the computational burden. The convergence of the critic neural network is proven. Simulation results show the effectiveness of the proposed method.
Funder
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Aerospace Engineering,Biomedical Engineering,General Chemical Engineering,Control and Systems Engineering
Cited by
1 articles.
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1. Editorial;International Journal of Robust and Nonlinear Control;2023-09-20