Adaptive Second-order Sliding Mode Control of UAVs for Civil Applications

TL;DR

This paper presents an adaptive second-order sliding mode control scheme for UAVs, enhancing robustness and tracking performance in civil applications like infrastructure monitoring amidst disturbances and uncertainties.

Contribution

It introduces a novel adaptive 2-QCSM control method with stability analysis for robust UAV attitude control under uncertainties and external disturbances.

Findings

Achieves robustness against disturbances and parameter variations

Demonstrates improved tracking performance over existing methods

Validated through extensive simulations and real-time monitoring experiments

Abstract

Quadcopters, as unmanned aerial vehicles (UAVs), have great potential in civil applications such as surveying, building monitoring, and infrastructure condition assessment. Quadcopters, however, are relatively sensitive to noises and disturbances so that their performance may be quickly downgraded in the case of inadequate control, system uncertainties and/or external disturbances. In this study, we deal with the quadrotor low-level control by proposing a robust scheme named the adaptive second-order quasi-continuous sliding mode control (adaptive 2-QCSM). The ultimate objective is for robust attitude control of the UAV in monitoring and inspection of built infrastructure. First, the mathematical model of the quadcopter is derived considering nonlinearity, strong coupling, uncertain dynamics and external disturbances. The control design includes the selection of the sliding manifold and the development of quasi-continuous second-order sliding mode controller with an adaptive gain. Stability of the overall control system is analysed by using a global Lyapunov function for convergence of both the sliding dynamics and adaptation scheme. Extensive simulations have been carried out for evaluation. Results show that the proposed controller can achieve robustness against disturbances or parameter variations and has better tracking performance in comparison with experimental responses of a UAV in a real-time monitoring task.