Robust Predictive Control for EEG-Based Brain-Robot Teleoperation

Brain-teleoperation robot control ensures that human beings interact with telepresence mobile systems through the brain neural signals. In this study, a hierarchical robust predictive control framework consisting of a two-loop control scheme is developed to simultaneously enhance the safety, navigation, and robustness performance of electroencephalography (EEG)-based robotic systems and minimize the loss of control by the end-user. The outer loop is a model-based predictive controller to guarantee the optimal velocity evolution under various constraints. The inner loop is the integral sliding mode controller constructed by a novel integral sliding manifold and enables the velocity tracking properties under uncertainty compensation. Human-in-the-loop driving experiments are performed under different disturbances, and the results show that the proposed system offers advantages of safety, enhanced navigation performance, and stronger robustness over those conventional direct control of EEG-based robots. Therefore, brain-robot teleoperation is improved in terms of robust motion control and velocity modulation, providing insights into similar brain-controlled dynamic systems.