Uncertainty Aware Model predictive control for free-floating space manipulator based on probabilistic ensembles neural network

Precise control of a free-floating space manipulator (FFSM) is of a great challenge due to the strong dynamic and kinematic coupling between its arms and base. This paper presents a model-based reinforcement learning framework for precise control of FFSMs with dynamics unknown. Dynamic behavior of an FFSM is predicted by a probabilistic ensembles neural network (PENN) trained offline. The PENN employs probabilistic neural networks to handle aleatoric uncertainty, which is further combined with ensemble method to capture epistemic uncertainty, and used to plan action sequences on-line under the model predictive control framework. Unlike modelfree methods which train a particular policy to pursue maximum reward for the corresponding task, this framework allows the same trained PENN to be applied to various tasks with task-specified reward function. Results of numerical experiments demonstrate the fast and robust performance of the proposed framework for both angular and end-effector position control. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar