Trust-Driven Hybrid Control Integrating MPC and LSTM for VTOL Flight Transitions

This paper introduces a hybrid control strategy that combines Model Predictive Control (MPC) and Long Short-Term Memory (LSTM) networks. The method is designed to improve stability and adaptability during Vertical Take-off and Landing (VTOL) transitions between multi-copter and fixed-wing modes. A trust-based switching mechanism based on an exponential moving average dynamically allocates control between MPC and LSTM based on their recent performance. This ensures reliable operation under a wide range of flight conditions. The framework balances stability and computational efficiency by using MPC to handle constraints and LSTM to approximate control behavior in steady-state conditions. A VTOL transition scenario is used as a case study to test the controller’s performance under model uncertainties and external disturbances. Simulations show that the trust mechanism enables smooth switching between MPC and LSTM under various noise levels. A Monte Carlo sensitivity analysis indicates that a trust threshold of around 0.7 provides the best trade-off between accuracy and control effort. The hybrid controller achieves almost identical performance to a standalone robust MPC while reducing average computation time by about 40%. Eigenvalue and Lyapunov analysis further confirm closed-loop stability, validating the robustness of the hybrid control design. Overall, the proposed framework offers a scalable, reliable control solution for autonomous systems, enabling adaptive selection between model-based and learning-based strategies.