Quadrotor system design for a 3 DOF platform based on iterative learning control

Research into autonomous control and behaviour of mobile vehicles has become more and more widespread. Unmanned aerial vehicles (UAVs) have seen an upsurge of interest and of the many UAVs available, the quadrotor has shown significant potential in monitoring and surveillance tasks. This paper examines the performance of iterative learning control (ILC) in gradient-based control that enhances a quadrotor's controllability and stability during attitude control. It describes the development of the learning algorithms which exploit the repeated nature of the fault-finding task. Iterative learning control algorithms are derived and implemented on a quadrotor in a test bench. The proposed ILC algorithms on the quadrotor model are evaluated for system stability, convergence speed, and trajectory tracking error. Finally, the performance of the proposed algorithms is compared against a baseline performance of the PID control schemes