Pengcheng Liu

TL;DR: Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances.

TL;DR: Li et al. as discussed by the authors proposed a weakly supervised learning approach which is able to learn a deep convolutional neural network from unlabeled RGBD videos while requiring very few annotations.

TL;DR: This paper studies the issue of adaptive trajectory tracking control for an underactuated vibro-driven capsule system and presents a novel motion generation framework that defines an exogenous state variable whose dynamics is employed as a control input and the tracking performance and system stability are investigated through rigorous theoretic analysis.

TL;DR: The results demonstrate that the proposed B-CSP method can classify EEG-based MI tasks effectively, and this study provides practical and theoretical approaches to BCI applications.

TL;DR: This paper aims to strengthen the links between two research communities of robotics and control by presenting a systematic survey work in underactuated robotic systems, in which both key challenges and notable successes in bio-inspiration, trajectory planning and control are highlighted and discussed.