Adaptive Model Prediction Control Framework With Game Theory for Brain-Controlled Air-Ground Collaborative Autonomous System

Abstract

Brain-machine interfaces (BMIs) can enable humans to bypass the peripheral nervous system and directly control devices through the central nervous system. In this way, operators' hands are freed up, allowing them to interact with other devices, thus enabling multitasking operations. In this letter, to improve the performance of air-ground collaborative systems, we propose an adaptive 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, and an adaptive model-predictive-control shared controller based on game theory. We establish a human-in-the-loop 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.