Enhanced geometry control Powered by AI for UAVS with a robotic arm for compensating for disturbances

Unmanned Aerial Vehicles (UAVs) equipped with robotic manipulators have emerged as a powerful paradigm for advanced aerial manipulation tasks, including infrastructure inspection, emergency response, and precise object handling in hazardous or inaccessible environments. Despite their potential, integrating robotic arms introduces significant challenges due to strong dynamic coupling, time-varying payloads, and external disturbances such as wind and aerodynamic turbulence, which can severely compromise flight stability and control performance. Conventional linear and nonlinear control strategies often struggle to cope with these highly nonlinear dynamics, particularly during aggressive manipulator motions. This paper proposes an AI-enhanced geometric control framework for stabilising UAV–manipulator systems under high-manoeuvrability conditions. By operating directly on the nonlinear configuration space, geometric control provides robust attitude and position stabilization while avoiding singularities and ensuring global stability. To overcome the limited adaptability of purely geometric controllers, artificial intelligence algorithms are integrated to predict and compensate for manipulator-induced disturbances, optimize thrust distribution, and dynamically adjust BLDC motor currents in real time. A Long Short-Term Memory (LSTM) network predicts disturbances that will next occur based on previous values and early information about the commanded trajectory. A Kalman Filter–Neural Fusion layer enforces probabilistic behaviours on the outputs of the LSTM to preserve stability in the control loop, combine filtered and prediction information, and minimise uncertainty on the overall estimation process. A quadrotor UAV equipped with a Cartesian robotic arm employs the proposed framework for compensation of disturbances affecting both position and orientation. Simulation results obtained using a high-fidelity CoppeliaSim–MATLAB environment demonstrate the effectiveness of the proposed approach. The UAV maintains stable flight and accurate attitude regulation while carrying relatively high payloads and executing rapid robotic arm maneuvers. Notably, the proposed controller successfully preserves the UAV’s tilt angle within safe limits during aggressive manipulation scenarios, significantly reducing attitude oscillations and stabilization time compared to conventional PID and standard geometric control schemes. These results confirm that the proposed (AI)-driven geometric control strategy achieves robust dynamic balance and enhanced disturbance rejection, enabling reliable and intelligent aerial manipulation in complex and disturbance-rich operational environments.