Quadrotor UAVs benefit from control strategies that can deliver rapid convergence and strong robustness in order to fully exploit their high agility. Finite-time control based on terminal sliding modes has been recognized as an effective alternative to classical sliding mode control, which only guarantees asymptotic convergence. Its enhanced variant, nonsingular fast terminal sliding mode control, eliminates singularities and achieves accelerated convergence; however, chattering-induced high-frequency oscillations remain a major concern. To address this issue, this study introduces a hybrid control framework that combines the super-twisting algorithm with L-1 adaptive control. The super-twisting component preserves the robustness of sliding mode control while mitigating chattering, whereas L-1 adaptive control provides rapid online estimation and compensation of model uncertainties and unknown disturbances. The resulting scheme is implemented in a quadrotor flight-control architecture and evaluated through numerical simulations. The results show that the proposed controller offers faster convergence and enhanced robustness relative to existing approaches, particularly in the presence of wind perturbations, periodic obstacle-avoidance maneuvers, and abrupt partial loss of propeller thrust.

This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from the model under control at each time step. However, MPC cannot guarantee control performance under disturbances such as modeling errors and wind gusts because it predicts future states of the control objects using a nominal model. To solve this problem, we propose a composite control method that uses Adaptive Super-Twisting Sliding Mode Disturbance Observer (ASTSMDO), which constrains the system to follow the MPC’s nominal model. The effectiveness of the proposed method is confirmed through numerical simulation. Compared to conventional MPC, the proposed controller achieves superior robustness and trajectory tracking performance under modeling error and wind disturbance. © 2025 by the authors.

This paper describes obstacle avoidance control for planetary exploration rover based on a novel potential function method. Planetary exploration rovers are crucial for investigating environments that are inaccessible to humans, such as the surfaces of Mars or the Moon. These rovers must autonomously navigate unstructured terrain filled with rocks and other obstacles. The potential function (PF) method has been widely adopted for real-time path planning due to its algorithmic simplicity and low computational cost. However, its inherent limitation, specifically the tendency to become trapped in local minima, remains a major challenge, especially in complex environments. In this study, we propose the PF with an adaptive rotated velocity field (ARVF) to enhance rover navigation. Our PF with ARVF improves upon the conventional PF with a rotated VF by adaptively determining the direction of repulsive velocity field rotation, based on the angular relationship among the rover, target, and obstacles. Furthermore, it introduces virtual obstacles by grouping clusters of obstacles, enabling the rover to avoid dense and trap-like regions. Numerical simulations compare the PF with ARVF against traditional PF and PF with rotated VF. We confirm that the proposed method allows the rover to reach the target position while avoiding local minima and suppressing abrupt changes in control inputs. ©2025 IEEE.