Abstract

This work presents an Active Sliding Mode Control (ASMC) framework for a UAV-manipulator (UAM) system subject to external disturbances and parametric uncertainties. The manipulator dynamics are modeled as disturbance inputs acting on the aerial vehicle. The proposed controller ensures accurate trajectory tracking while improving transient response and robustness. Its effectiveness is evaluated under fixed-arm and moving-arm configurations. Simulation results demonstrate that the ASMC achieves faster convergence, with minimum settling times of 3.995 s and 3.755 s for the fixed-arm and moving-arm configurations, respectively. To further validate the controller's practical applicability and reliability in manipulation tasks, a pick-and-place case study is considered. Throughout the mission sequence, the UAM maintains stable trajectory tracking despite disturbances. These results demonstrate the robustness of the proposed ASMC framework and its potential to support real-world aerial manipulation tasks.

About the Speaker

Ms. Eman Mahmoud is currently pursuing her Ph.D. in Systems and Control Engineering in the Control and Instrumentation Engineering Department at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia. She received her M.Sc. in Aerospace Engineering from KFUPM and her B.Sc. in Mechatronics Engineering from Misr University for Science and Technology (MUST), Egypt.

Her research interests include nonlinear control, adaptive control, robust control, robotics, artificial intelligence, and intelligent autonomous systems.

Her pervious research focuses on developing robust, efficient, and intelligent control solutions for complex robotic systems, with particular emphasis on advanced control strategies, system stability, trajectory tracking, disturbance rejection in unmanned aerial vehicles (UAVs) and aerial robotic manipulators, and the application of deep learning and computer vision techniques to space debris detection and classification in low Earth orbit.