Multi-arm Spacecraft Model Predictive Control Method Based on Mixture of Gaussian Processes, Equipment, and Medium

The present disclosure provides a multi-arm spacecraft model predictive control method based on the mixture of Gaussian processes, equipment, and a medium. Model predictive control has excellent performance in dealing with complex nonlinear systems such as multi-arm spacecrafts with various constraints, and is widely applied to ground robots, unmanned aerial vehicles, autonomous driving and other practical scenarios. Therefore, a task space controller is designed based on the model predictive control in the present disclosure. Besides, in order to enhance the anti-interference capability of the present disclosure, an interference model is established and compensation is carried out in the model predictive control by utilizing the characteristics of small training data volume and high training speed in the mixture of Gaussian processes. Finally, a thrust distribution method is designed to complete platform control. The method provided by the present disclosure is convenient and intuitive in design and has relatively high practicability.