Safe Learning-based Tracking Control for Quadrotors under Wind Disturbances

TL;DR

This paper introduces a learning-based safety-preserving control method for quadrotors that ensures safe trajectory tracking under wind disturbances by combining Gaussian Processes, control barrier functions, and cascaded quadratic programming.

Contribution

It proposes a novel cascaded quadratic programming control framework integrating Gaussian Process-based disturbance estimation and safety constraints for robust quadrotor navigation.

Findings

Effective in tracking trajectories under varying wind conditions

Successfully avoids obstacles in cluttered environments

Demonstrates improved safety and robustness in numerical simulations

Abstract

Enforcing safety on precise trajectory tracking is critical for aerial robotics subject to wind disturbances. In this paper, we present a learning-based safety-preserving cascaded quadratic programming control (SPQC) for safe trajectory tracking under wind disturbances. The SPQC controller consists of a position-level controller and an attitude-level controller. Gaussian Processes (GPs) are utilized to estimate the uncertainties caused by wind disturbances, and then a nominal Lyapunov-based cascaded quadratic program (QP) controller is designed to track the reference trajectory. To avoid unexpected obstacles when tracking, safety constraints represented by control barrier functions (CBFs) are enforced on each nominal QP controller in a way of minimal modification. The performance of the proposed SPQC controller is illustrated through numerical validations of (a) trajectory tracking under different wind disturbances, and (b) trajectory tracking in a cluttered environment with a dense time-varying obstacle field under wind disturbances.