Robust Nonlinear Trajectory Controllers for a Single-Rotor UAV with Particle Swarm Optimization Tuning

Abstract

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.

Optimization-Based Control for a Large-Scale Electrical Vertical Take-Off and Landing during an Aircraft’s Vertical Take-Off and Landing Phase with Variable-Pitch Propellers

“…Unlike tiny quadrotors which are able to sacrifice some targets in order to elegantly track some aggressive trajectories, large eVTOLs should consider many more aspects of the flight, including the robustness against external disturbances and measurement noise, and energy consumption analysis. Portillo et al [13] discussed a method to eliminate unmodeled dynamics or external disturbances at the control level, which provides a feasible solution to overcome these problems. Meanwhile, Bianchi et al [14] addressed the energy consumption of quadrotors when tracking a trajectory in an analytical way.…”