Brain-Controlled Multi-Task Air-Ground Collaboration Based on Model Prediction Control and Fuzzy Logic

Brain-machine interfaces (BMIs) can assist healthy persons in completing tasks in multi-task scenarios. In this paper, to improve the performance of air-ground collaborative systems, we propose a model prediction control framework of brain-controlled air-ground collaboration systems, which consists of a BMI with a probabilistic output model, an interface model based on fuzzy logic. We establish a human-in-the-loop simulation experimental platform to validate the proposed method by trajectory tracking and obstacle avoidance scenarios. The experimental results show the effectiveness of the proposed method in improving performance and decreasing operators' workload. This work can contribute to the research and development of air-ground collaboration and provide new insights into the study of human-machine integration.