learning

A learning-based safety-stability-driven control algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties.

Designed efficient incremental Gaussian Processes accounting for [airflow uncertainties](ttps://youtu.be/KJyqZyzD4gc). The wind disturbance caused by the external environment is estimated to improve flight safety and control stability in cluttered environments. Following that, the estimated wind disturbance is used to compensate for the associated control error.