Iterative Learning Based Feedforward Control for Transition of a Biplane-Quadrotor Tailsitter UAS

Abstract

This paper provides a real time on-board algorithm for a biplane-quadrotor to iteratively learn a forward transition maneuver via repeated flight trials. The maneuver is controlled by regulating the pitch angle and propeller thrust according to feedforward control laws that are parameterized by polynomials. Based on a nominal model with simplified aerodynamics, the optimal coefficients of the polynomials are chosen through simulation such that the maneuver is completed with specified terminal conditions on altitude and air speed. In order to compensate for modeling errors, repeated flight trials are performed by updating the feedforward control paramters according to an iterative learning algorithm until the maneuver is perfected. A geometric attitude controller, valid for all flight modes is employed in order to track the pitch angle according to the feedforward law. Further, a high-fidelity thrust model of the propeller for varying advance-ratio and orientation angle is obtained from wind tunnel data which is captured using a neural network model. This facilitates accurate application of feedforward thrust for varying flow conditions during transition. Experimental flight trials are performed to demonstrate the robustness and rapid convergence of the proposed learning algorithm.