Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.