4 papers
Learning-based Observer for Coupled Disturbance
Jindou Jia, Meng Wang, Zihan Yang +4
Achieving high-precision control for robotic systems is hindered by the low-fidelity dynamical model and external disturbances. Especially, the intricate coupling between internal…
Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation
Zihan Yang, Jindou Jia, Meng Wang +3
Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representa…
Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
Kexin Guo, Zihan Yang, Yuhang Liu +2
Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately…
Accurate Control under Voltage Drop for Rotor Drones
Yuhang Liu, Jindou Jia, Zihan Yang +1
This letter proposes an anti-disturbance control scheme for rotor drones to counteract voltage drop (VD) disturbance caused by voltage drop of the battery, which is a common case f…