I am a Ph.D. student at the School of Automation Science and Electrical Engineering and Shenyuan Honors College, Beihang University, China. I am supervised by Prof. Lei Guo, Prof. Xiang Yu, and Prof. Kexin Guo. Robots deployed in the real world inevitably face uncertainties caused by internal model mismatch and external unmeasured disturbances. The topic of my Ph.D. research is learning and predicting uncertainties for robotic control and planning.
We present two converged uncertainty prediction frameworks, enabling accurate prediction of two general kinds of uncertainties, respectively.
We present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural ordinary differential equations (neural ODEs), leading to a prominent generalization improvement.
We present a framework, namely EVOLVER, to mimic the bio-behavior for robotics to achieve rapid transient reaction ability and high precision steady-state performance simultaneously.
Different from standard approaches that achieve precise tracking by feedforward compensating the estimated drag, this work presents a scheme to appropriately utilize drag.
A scheme of anti-disturbance agile flight control is developed for a maneuverable quadrotor unmanned aerial vehicle, subject to the aerodynamic drag, dynamic shift of center of gravity, and motor dynamics.
This paper presents a multiple observers-based anti-disturbance control scheme against multiple disturbances for a quadrotor unmanned aerial vehicle.
A safety control strategy based on a novel nonlinear disturbance observer and geometric control is developed for a quadrotor unmanned aerial vehicle, subject to the disturbances caused by center-of-gravity shift and loss of motor efficiency.
This paper presents an embedded micro loop to enhance the anti-disturbance performance for unmanned aerial vehicles.