Transition control of a tail-sitter unmanned aerial vehicle with L1 neural network adaptive control

The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV) during the transition process. Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions, large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process, which leads to control precision degradation. Meanwhile, the PID controller’s tuning method relies on engineering experiences to a certain extent and the controller parameters need to be retuned under different working conditions, which limits the rapid deployment and preliminary validation. Based on the above issues, a novel control architecture of L1 neural network adaptive control associated with PID control is proposed to improve the compensation ability during the transition process and guarantee the security transition. The L1 neural network adaptive control is revised to solve the multi-input and multi-output problem of the tail-sitter UAV system in this study. Finally, the transition characteristics of the time setting difference between the desired transition speed and the desired transition pitch angle are analyzed.