A Digital Twin Model for UAV Control to Lift Irregular-Shaped Payloads Using Robust Model Predictive Control

This paper presents an innovative digital twin (DT) model integrated with robust model predictive control (MPC) to enhance the performance of an unmanned air vehicle (UAV) tasked with lifting and transporting irregular-shaped payloads. Traditional UAV control systems face complex challenges in stability and accuracy when dealing with asymmetrical payloads, as such payloads cause continuous shifts in the center of gravity (CoG) and variable inertial forces, which lead to unpredictable flight dynamics. The proposed DT framework enables the creation of a real-time replica of the UAV payload system. It creates an adaptive control environment that anticipates and mitigates disturbances before they impact the stability of the UAV during a mission. By combining a DT with MPC, the control system dynamically adjusts to variations in payload characteristics, namely (a) changes in mass distribution and (b) aerodynamic drag force. As a result, a stable flight path is ensured even under challenging environmental conditions. The DT model continuously forecasts potential destabilizing events and modifies MPC constraints to accommodate complex shifting dynamics, achieving improved control accuracy and energy efficiency. Extensive simulations across various hanging payload configurations and environmental disturbance scenarios validate the effectiveness of the proposed model. The simulation results show that the DT-MPC strategy significantly improves stability, control precision, and energy conservation, outperforming conventional methods. A comparative analysis is also carried out with a conventional control scheme to validate the robustness of the proposed framework. This research work advances the development of intelligent, autonomous UAV systems capable of reliably managing complex and irregularly shaped payloads with varying mass distributions in real-world scenarios, thereby broadening their potential applications in logistics, emergency response, and industrial transportation.