An Adaptive SINDy‐Lyapunov Model Predictive Control Framework for Dual‐System VTOL UAVs

ABSTRACT This paper presents an adaptive control framework for dual‐system VTOL UAVs capable of operating in both rotary‐wing and fixed‐wing modes. These aerial vehicles present considerable control challenges due to their nonlinear, time‐varying dynamics and inherent instability during flight‐mode transitions. The proposed approach addresses these issues by leveraging nonlinear system identification via Adaptive Sparse Identification of Nonlinear Dynamics (ASINDy) with a Lyapunov‐based Model Predictive Control (LMPC) scheme. This integrated framework facilitates continuous model updating and guarantees stable trajectory tracking and robust performance. Compared to the GA‐PID, the ASINDy–LMPC approach reduced tracking error by approximately 65%, maximum deviation by 67%, average deviation by 79%, and power consumption by 73% in simulation, while nearly halving the control effort. Preliminary hardware trials on a VTOL UAV prototype corroborate these trends, demonstrating consistent improvements during hovering and outdoor flights.