VTOL Aircraft Optimal Gain Prediction via Parameterized Log-Sum-Exp Networks

An optimal gain prediction method is proposed for VTOL aircraft controllers. The proposed method exploits parameterized convexity to quickly and reliably predict the optimal gain for a given initial state. The parameterized convexity is secured by using the parameterized log-sum-exp (PLSE) network, which is a shape-preserving universal approximator for parameterized convex continuous functions. A PLSE network is trained in the sense of supervised learning to approximate the cost function of initial state and gain. For a given initial state, the optimal gain is predicted by convex optimization. The result of numerical simulation demonstrates that the proposed method can perform real-time optimal gain prediction and outperforms compared to other network or adhoc approaches for the attitude and altitude control of VTOL aircraft.