Robust Trajectory Tracking Control of an Uncertain Quadrotor via a Novel Adaptive Nonsingular Sliding Mode Control

Finite‐time control for a quadcopter UAV in the application of wildfire monitoring

“…In an effort to enhance the trajectory tracking accuracy of a quadcopter that is subject to modeling uncertainties, measurement noise, and external disturbances, a Proportional Derivative (PD)‐NFTSM control scheme is proposed in [18]. The control scheme consists of an adaptive method and a NFTSS that incorporates PD control.…”

“…Drawing inspiration from the previous discussions, this research article proposes a finite‐time control scheme for wildfire tracking of a quadcopter UAV subjected to external disturbances and parameter uncertainties. The key contributions of this article are outlined below: Unlike previous wildfire monitoring strategies in [9–11] that rely on asymptotic convergence‐based control schemes for quadcopter UAVs, this article proposes a finite‐time control scheme for a quadcopter UAV tasked with wildfire monitoring amidst external disturbances and parameter uncertainties. In contrast with the previous research presented in [17–20] that utilize adaptive techniques to estimate the upper limits of external disturbances and uncertainties affecting quadcopter UAVs, assuming the availability of all system states, this article adopts a Higher‐Order Sliding Mode Observer (HOSMO) to estimate external perturbations, parameter uncertainties, and unknown system states in both the position and attitude dynamics of the quadcopter UAV. Unlike the NFTSS presented in [15–20], this article introduces an improved NFTSM control scheme aimed at enhancing tracking accuracy and speed. The enhanced convergence speed is achieved by incorporating an exponential term into the NFTSS. A novel finite‐time control scheme based on the improved NFTSM and HOSMO is developed and applied to a quadcopter UAV tasked with monitoring a wildfire.…”